From 8d30ad75099b5558ef06776bedee463dec383c14 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Thu, 26 Feb 2026 13:33:15 +0000 Subject: [PATCH 01/76] Add HTTP intercept proxy for transparent LLM request reordering MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit Intercepts /v1/chat/completions and /v1/messages, extracts documents from system prompts (XML tags, numbered, separator formats), reorders them via ContextPilot for optimal prefix sharing, and forwards to the backend. Users just change their API endpoint URL — no code changes needed. Co-Authored-By: Claude Opus 4.6 (1M context) --- contextpilot/server/http_server.py | 162 ++++++++- contextpilot/server/intercept_parser.py | 411 +++++++++++++++++++++ tests/test_http_intercept.py | 329 +++++++++++++++++ tests/test_intercept_parser.py | 454 ++++++++++++++++++++++++ 4 files changed, 1355 insertions(+), 1 deletion(-) create mode 100644 contextpilot/server/intercept_parser.py create mode 100644 tests/test_http_intercept.py create mode 100644 tests/test_intercept_parser.py diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 5a9e325..ab1c429 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -39,11 +39,16 @@ from .live_index import ContextPilot from .conversation_tracker import ( - ConversationTracker, + ConversationTracker, DeduplicationResult, get_conversation_tracker, reset_conversation_tracker ) +from .intercept_parser import ( + parse_intercept_headers, extract_from_openai_chat, + extract_from_anthropic_messages, reconstruct_openai_chat, + reconstruct_anthropic_messages, InterceptConfig, ExtractionResult, +) logger = logging.getLogger(__name__) @@ -969,6 +974,161 @@ async def proxy_completions(request: Request): raise HTTPException(status_code=500, detail=str(e)) +# ============================================================================ +# HTTP Intercept Proxy Endpoints +# ============================================================================ + +# API format constants +_OPENAI_CHAT = "openai_chat" +_ANTHROPIC_MESSAGES = "anthropic_messages" + + +async def _intercept_and_forward(request: Request, api_format: str): + """Intercept an LLM API request, reorder documents, and forward. + + 1. Parse X-ContextPilot-* headers → InterceptConfig + 2. Extract documents from system message/prompt + 3. Reorder via ContextPilot.reorder(docs) + 4. Reconstruct request body with reordered docs + 5. Forward to actual LLM backend, streaming or not + If extraction fails at any step → forward original request unmodified. + """ + if _infer_api_url is None: + _init_config() + + infer_api_url = _infer_api_url or os.environ.get( + "CONTEXTPILOT_INFER_API_URL", "http://localhost:30000" + ) + if not infer_api_url: + raise HTTPException( + status_code=503, + detail="Inference API URL not configured.", + ) + + try: + body = await request.json() + except Exception: + raise HTTPException(status_code=400, detail="Invalid JSON body") + + # Parse intercept config from headers + headers = dict(request.headers) + config = parse_intercept_headers(headers) + reordered = False + + if config.enabled: + try: + extraction = None + sys_idx = None + + if api_format == _OPENAI_CHAT: + result = extract_from_openai_chat(body, config) + if result: + extraction, sys_idx = result + elif api_format == _ANTHROPIC_MESSAGES: + extraction = extract_from_anthropic_messages(body, config) + + if extraction and len(extraction.documents) >= 2: + docs = extraction.documents + # Stateless reorder: create temp ContextPilot, get optimal order + temp_index = ContextPilot( + alpha=config.alpha, + use_gpu=False, + linkage_method=config.linkage_method, + ) + # Build string-to-id mapping + str_to_id = {} + id_to_str = {} + next_id = 0 + converted = [] + for doc in docs: + sid = str_to_id.get(doc) + if sid is None: + sid = next_id + str_to_id[doc] = sid + id_to_str[sid] = doc + next_id += 1 + converted.append(sid) + + sched_result = temp_index.schedule_only(contexts=[converted]) + reordered_ids = sched_result["reordered_contexts"][0] + reordered_docs = [id_to_str[i] for i in reordered_ids] + + # Reconstruct body + if api_format == _OPENAI_CHAT: + body = reconstruct_openai_chat(body, extraction, reordered_docs, sys_idx) + elif api_format == _ANTHROPIC_MESSAGES: + body = reconstruct_anthropic_messages(body, extraction, reordered_docs) + reordered = True + logger.info( + f"Intercept ({api_format}): reordered {len(docs)} documents" + ) + + except Exception as e: + logger.warning(f"Intercept extraction/reorder failed, forwarding original: {e}") + + # Determine target URL + if api_format == _OPENAI_CHAT: + target_url = f"{infer_api_url}/v1/chat/completions" + elif api_format == _ANTHROPIC_MESSAGES: + target_url = f"{infer_api_url}/v1/messages" + else: + target_url = f"{infer_api_url}/v1/chat/completions" + + # Build outbound headers: forward auth, strip X-ContextPilot-* + outbound_headers = {} + for k, v in headers.items(): + kl = k.lower() + if kl.startswith("x-contextpilot-"): + continue + if kl in ("authorization", "x-api-key", "anthropic-version", "content-type"): + outbound_headers[k] = v + + is_stream = body.get("stream", False) + + try: + if is_stream: + # Streaming: passthrough SSE chunks + async def _stream_generator(): + async with _aiohttp_session.post( + target_url, json=body, headers=outbound_headers + ) as resp: + async for chunk in resp.content.iter_any(): + yield chunk + + return StreamingResponse( + _stream_generator(), + media_type="text/event-stream", + ) + else: + # Non-streaming: forward JSON + async with _aiohttp_session.post( + target_url, json=body, headers=outbound_headers + ) as resp: + result = await resp.json() + if reordered and resp.status == 200: + result["_contextpilot"] = { + "intercepted": True, + "documents_reordered": True, + } + return JSONResponse(content=result, status_code=resp.status) + + except aiohttp.ClientError as e: + logger.error(f"Error forwarding intercepted request: {e}") + raise HTTPException(status_code=502, detail=f"Backend error: {str(e)}") + + +@app.post("/v1/chat/completions") +async def intercept_openai_chat(request: Request): + """Intercept OpenAI chat completions: extract docs, reorder, forward.""" + return await _intercept_and_forward(request, _OPENAI_CHAT) + + +@app.post("/v1/messages") +async def intercept_anthropic_messages(request: Request): + """Intercept Anthropic messages: extract docs, reorder, forward.""" + return await _intercept_and_forward(request, _ANTHROPIC_MESSAGES) + + @app.api_route("/v1/{path:path}", methods=["GET", "POST"]) async def proxy_engine(path: str, request: Request): """ diff --git a/contextpilot/server/intercept_parser.py b/contextpilot/server/intercept_parser.py new file mode 100644 index 0000000..416c67b --- /dev/null +++ b/contextpilot/server/intercept_parser.py @@ -0,0 +1,411 @@ +""" +HTTP Intercept Parser for ContextPilot + +Pure parsing/extraction/reconstruction logic for intercepting LLM API requests. +Extracts documents from system messages, supports reordering, and reconstructs +the request body with reordered documents. + +No server dependencies — independently testable. +""" + +import re +import copy +import logging +from dataclasses import dataclass, field +from typing import List, Optional, Dict, Any, Tuple + +logger = logging.getLogger(__name__) + +# XML wrapper tag names we recognize (outer wrapper) +_KNOWN_WRAPPER_TAGS = {"documents", "contexts", "docs", "passages", "references"} + +# XML item tag names we recognize (inner items) +_KNOWN_ITEM_TAGS = {"document", "context", "doc", "passage", "reference"} + +# Numbered pattern: [1] ... [2] ... etc. +_NUMBERED_RE = re.compile(r"\[(\d+)\]\s*") + +# Separator patterns for auto-detection +_SEPARATOR_PATTERNS = ["---", "==="] + + +@dataclass +class InterceptConfig: + """Configuration parsed from X-ContextPilot-* headers.""" + + enabled: bool = True + mode: str = "auto" # xml_tag, separator, numbered, auto + tag: str = "document" # XML tag name for xml_tag mode + separator: str = "---" # Delimiter for separator mode + alpha: float = 0.001 + linkage_method: str = "average" + + +@dataclass +class ExtractionResult: + """Result of extracting documents from a message.""" + + documents: List[str] + prefix: str = "" # Text before the documents block + suffix: str = "" # Text after the documents block + mode: str = "" # Which extraction mode matched + # XML-specific + wrapper_tag: str = "" # e.g. "documents" + item_tag: str = "" # e.g. "document" + # Separator-specific + separator_char: str = "" + # Original content for fallback + original_content: str = "" + + +def parse_intercept_headers(headers: Dict[str, str]) -> InterceptConfig: + """Parse X-ContextPilot-* headers into an InterceptConfig.""" + def get(name: str, default: str = "") -> str: + # Headers are case-insensitive; try common casings + key = f"x-contextpilot-{name}" + for k, v in headers.items(): + if k.lower() == key: + return v + return default + + enabled_str = get("enabled", "true").lower() + enabled = enabled_str not in ("false", "0", "no") + + return InterceptConfig( + enabled=enabled, + mode=get("mode", "auto").lower(), + tag=get("tag", "document").lower(), + separator=get("separator", "---"), + alpha=float(get("alpha", "0.001")), + linkage_method=get("linkage", "average"), + ) + + +# ── Document extraction ───────────────────────────────────────────────────── + + +def _extract_xml_tags(text: str, config: InterceptConfig) -> Optional[ExtractionResult]: + """Extract documents from XML-tagged blocks. + + Supports patterns like: + ...... + Also handles custom tags and known alternatives (contexts, docs, passages, references). + """ + # Determine which wrapper/item tags to try + if config.mode == "xml_tag": + # User specified xml_tag mode — try their custom tag first + item_tags_to_try = [config.tag] + wrapper_tags_to_try = [config.tag + "s"] # e.g. "document" -> "documents" + # Also add known tags as fallback + item_tags_to_try.extend(t for t in _KNOWN_ITEM_TAGS if t != config.tag) + wrapper_tags_to_try.extend(t for t in _KNOWN_WRAPPER_TAGS if t != config.tag + "s") + else: + item_tags_to_try = list(_KNOWN_ITEM_TAGS) + wrapper_tags_to_try = list(_KNOWN_WRAPPER_TAGS) + + # Try with wrapper tags first (e.g. .........) + for wrapper_tag in wrapper_tags_to_try: + wrapper_pattern = re.compile( + rf"(<{wrapper_tag}(?:\s[^>]*)?>)(.*?)()", + re.DOTALL, + ) + wrapper_match = wrapper_pattern.search(text) + if not wrapper_match: + continue + + inner_text = wrapper_match.group(2) + prefix = text[: wrapper_match.start()] + suffix = text[wrapper_match.end() :] + + # Try each item tag inside the wrapper + for item_tag in item_tags_to_try: + item_pattern = re.compile( + rf"<{item_tag}(?:\s[^>]*)?>(.+?)", + re.DOTALL, + ) + items = item_pattern.findall(inner_text) + if items: + return ExtractionResult( + documents=[item.strip() for item in items], + prefix=prefix, + suffix=suffix, + mode="xml_tag", + wrapper_tag=wrapper_tag, + item_tag=item_tag, + original_content=text, + ) + + # Try without wrapper (just repeated item tags) + for item_tag in item_tags_to_try: + item_pattern = re.compile( + rf"<{item_tag}(?:\s[^>]*)?>(.+?)", + re.DOTALL, + ) + items = list(item_pattern.finditer(text)) + if len(items) >= 2: + first_start = items[0].start() + last_end = items[-1].end() + return ExtractionResult( + documents=[m.group(1).strip() for m in items], + prefix=text[:first_start], + suffix=text[last_end:], + mode="xml_tag", + wrapper_tag="", + item_tag=item_tag, + original_content=text, + ) + + return None + + +def _extract_numbered(text: str, config: InterceptConfig) -> Optional[ExtractionResult]: + """Extract documents from numbered format: [1] doc text [2] doc text ...""" + splits = _NUMBERED_RE.split(text) + # splits will be like: [prefix, "1", doc1, "2", doc2, ...] + # If we found numbered items, splits has at least 4 elements (prefix + one item) + if len(splits) < 4: + return None + + prefix = splits[0] + documents = [] + i = 1 + while i + 1 < len(splits): + # splits[i] is the number, splits[i+1] is the content + doc_text = splits[i + 1].strip() + if doc_text: + documents.append(doc_text) + i += 2 + + if len(documents) < 2: + return None + + return ExtractionResult( + documents=documents, + prefix=prefix, + suffix="", + mode="numbered", + original_content=text, + ) + + +def _extract_separator( + text: str, config: InterceptConfig +) -> Optional[ExtractionResult]: + """Extract documents separated by delimiters (--- or ===).""" + sep = config.separator + # For auto mode, try common separators + if config.mode == "auto": + for candidate in _SEPARATOR_PATTERNS: + # Need the separator to appear on its own line (or at boundary) + parts = re.split(r"\n" + re.escape(candidate) + r"\n", text) + if len(parts) >= 3: + sep = candidate + break + else: + return None + documents = [p.strip() for p in parts if p.strip()] + else: + parts = re.split(r"\n" + re.escape(sep) + r"\n", text) + documents = [p.strip() for p in parts if p.strip()] + + if len(documents) < 2: + return None + + return ExtractionResult( + documents=documents, + prefix="", + suffix="", + mode="separator", + separator_char=sep, + original_content=text, + ) + + +def extract_documents( + text: str, config: InterceptConfig +) -> Optional[ExtractionResult]: + """Extract documents from text using the configured mode. + + Auto-detection priority: xml_tag > numbered > separator. + Returns None if no documents are found (caller should bypass). + """ + if config.mode == "xml_tag": + return _extract_xml_tags(text, config) + elif config.mode == "numbered": + return _extract_numbered(text, config) + elif config.mode == "separator": + return _extract_separator(text, config) + else: + # Auto mode: try each in priority order + result = _extract_xml_tags(text, config) + if result: + return result + result = _extract_numbered(text, config) + if result: + return result + result = _extract_separator(text, config) + if result: + return result + return None + + +# ── Reconstruction ─────────────────────────────────────────────────────────── + + +def reconstruct_content( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + """Reconstruct the message content with reordered documents. + + Preserves the original format (XML tags, numbering, separators). + """ + if extraction.mode == "xml_tag": + return _reconstruct_xml(extraction, reordered_docs) + elif extraction.mode == "numbered": + return _reconstruct_numbered(extraction, reordered_docs) + elif extraction.mode == "separator": + return _reconstruct_separator(extraction, reordered_docs) + else: + # Should not happen, but fallback + return extraction.original_content + + +def _reconstruct_xml( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + item_tag = extraction.item_tag + items = "\n".join(f"<{item_tag}>{doc}" for doc in reordered_docs) + + if extraction.wrapper_tag: + wrapper = extraction.wrapper_tag + block = f"<{wrapper}>\n{items}\n" + else: + block = items + + return extraction.prefix + block + extraction.suffix + + +def _reconstruct_numbered( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + parts = [extraction.prefix] if extraction.prefix else [] + for i, doc in enumerate(reordered_docs, 1): + parts.append(f"[{i}] {doc}") + result = "\n".join(parts) if parts else "" + if extraction.suffix: + result += extraction.suffix + return result + + +def _reconstruct_separator( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + sep = extraction.separator_char or "---" + return ("\n" + sep + "\n").join(reordered_docs) + + +# ── OpenAI Chat format ────────────────────────────────────────────────────── + + +def extract_from_openai_chat( + body: Dict[str, Any], config: InterceptConfig +) -> Optional[Tuple[ExtractionResult, int]]: + """Extract documents from an OpenAI chat completions request body. + + Looks for the system message and extracts documents from its content. + + Returns: + Tuple of (ExtractionResult, system_message_index) or None. + """ + messages = body.get("messages") + if not messages or not isinstance(messages, list): + return None + + for i, msg in enumerate(messages): + if msg.get("role") != "system": + continue + content = msg.get("content", "") + if isinstance(content, str): + result = extract_documents(content, config) + if result: + return result, i + elif isinstance(content, list): + # Content blocks (e.g. [{type: "text", text: "..."}]) + for block in content: + if isinstance(block, dict) and block.get("type") == "text": + result = extract_documents(block.get("text", ""), config) + if result: + return result, i + return None + + +def reconstruct_openai_chat( + body: Dict[str, Any], + extraction: ExtractionResult, + reordered_docs: List[str], + system_msg_index: int, +) -> Dict[str, Any]: + """Reconstruct an OpenAI chat completions request body with reordered docs.""" + body = copy.deepcopy(body) + new_content = reconstruct_content(extraction, reordered_docs) + msg = body["messages"][system_msg_index] + + if isinstance(msg.get("content"), str): + msg["content"] = new_content + elif isinstance(msg.get("content"), list): + # Find the text block that matched and replace it + for block in msg["content"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + return body + + +# ── Anthropic Messages format ─────────────────────────────────────────────── + + +def extract_from_anthropic_messages( + body: Dict[str, Any], config: InterceptConfig +) -> Optional[ExtractionResult]: + """Extract documents from an Anthropic messages request body. + + Looks at body["system"] which can be a string or list of content blocks. + + Returns: + ExtractionResult or None. + """ + system = body.get("system") + if system is None: + return None + + if isinstance(system, str): + return extract_documents(system, config) + elif isinstance(system, list): + # Content blocks: [{type: "text", text: "..."}] + for block in system: + if isinstance(block, dict) and block.get("type") == "text": + result = extract_documents(block.get("text", ""), config) + if result: + return result + return None + + +def reconstruct_anthropic_messages( + body: Dict[str, Any], + extraction: ExtractionResult, + reordered_docs: List[str], +) -> Dict[str, Any]: + """Reconstruct an Anthropic messages request body with reordered docs.""" + body = copy.deepcopy(body) + new_content = reconstruct_content(extraction, reordered_docs) + + if isinstance(body.get("system"), str): + body["system"] = new_content + elif isinstance(body.get("system"), list): + for block in body["system"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + return body diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py new file mode 100644 index 0000000..9c730e0 --- /dev/null +++ b/tests/test_http_intercept.py @@ -0,0 +1,329 @@ +"""Integration tests for HTTP intercept proxy endpoints. + +Uses httpx AsyncClient with FastAPI's TestClient (no real server needed) +and patches aiohttp to mock the backend LLM server. +""" + +import json +import pytest +import asyncio +from unittest.mock import AsyncMock, MagicMock, patch + +from fastapi.testclient import TestClient + +from contextpilot.server.http_server import app, _init_config + +import contextpilot.server.http_server as http_mod + + +# ============================================================================ +# Fixtures +# ============================================================================ + + +class FakeResponse: + """Fake aiohttp response for mocking.""" + + def __init__(self, json_body, status=200): + self._json = json_body + self.status = status + self.content = FakeStreamContent() + + async def json(self): + return self._json + + async def __aenter__(self): + return self + + async def __aexit__(self, *args): + pass + + +class FakeStreamContent: + """Fake async iterator for streaming response.""" + + def __init__(self, chunks=None): + self._chunks = chunks or [b"data: {}\n\n", b"data: [DONE]\n\n"] + + async def iter_any(self): + for c in self._chunks: + yield c + + +class FakeStreamResponse(FakeResponse): + """Fake response that yields SSE chunks.""" + + def __init__(self, chunks=None, status=200): + super().__init__({}, status) + self.content = FakeStreamContent(chunks) + + +class FakeSession: + """Fake aiohttp.ClientSession.""" + + def __init__(self, response=None): + self._response = response or FakeResponse( + {"choices": [{"message": {"content": "Hello"}}], "usage": {"total_tokens": 10}} + ) + + def post(self, url, json=None, headers=None): + self._last_url = url + self._last_json = json + self._last_headers = headers + return self._response + + def get(self, url): + self._last_url = url + return self._response + + +@pytest.fixture +def mock_session(): + """Provide a FakeSession and patch it into http_server globals.""" + session = FakeSession() + return session + + +@pytest.fixture +def client(mock_session): + """FastAPI test client with mocked backend.""" + # Patch the module-level globals + original_session = http_mod._aiohttp_session + original_url = http_mod._infer_api_url + http_mod._aiohttp_session = mock_session + http_mod._infer_api_url = "http://mock-backend:30000" + try: + yield TestClient(app, raise_server_exceptions=False) + finally: + http_mod._aiohttp_session = original_session + http_mod._infer_api_url = original_url + + +# ============================================================================ +# Full intercept flow +# ============================================================================ + + +class TestOpenAIIntercept: + def test_basic_intercept_reorders(self, client, mock_session): + """Full intercept: docs extracted, reordered, forwarded.""" + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": ( + "\n" + "Doc A content\n" + "Doc B content\n" + "Doc C content\n" + "" + ), + }, + {"role": "user", "content": "Summarize the documents."}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + result = resp.json() + # Backend was called + assert mock_session._last_url == "http://mock-backend:30000/v1/chat/completions" + # Metadata injected + assert result.get("_contextpilot", {}).get("intercepted") is True + # The forwarded body should have reordered docs (still in XML format) + forwarded = mock_session._last_json + sys_content = forwarded["messages"][0]["content"] + assert "" in sys_content + assert "" in sys_content + + def test_bypass_when_disabled(self, client, mock_session): + """When X-ContextPilot-Enabled: false, forward unmodified.""" + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": "AB", + }, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post( + "/v1/chat/completions", + json=body, + headers={"X-ContextPilot-Enabled": "false"}, + ) + assert resp.status_code == 200 + # Body forwarded unmodified + forwarded = mock_session._last_json + assert forwarded["messages"][0]["content"] == body["messages"][0]["content"] + # No contextpilot metadata + result = resp.json() + assert "_contextpilot" not in result + + def test_bypass_when_no_docs(self, client, mock_session): + """When system message has no extractable docs, forward unmodified.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + forwarded = mock_session._last_json + assert forwarded["messages"][0]["content"] == "You are a helpful assistant." + + def test_bypass_when_single_doc(self, client, mock_session): + """Single doc => nothing to reorder, forward unmodified.""" + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": "Only one", + }, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + result = resp.json() + assert "_contextpilot" not in result + + def test_numbered_format(self, client, mock_session): + """Numbered format extraction and forwarding.""" + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": "[1] First document [2] Second document [3] Third document", + }, + {"role": "user", "content": "Summarize"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + forwarded = mock_session._last_json + sys_content = forwarded["messages"][0]["content"] + # Should contain numbered items (reordered) + assert "[1]" in sys_content + assert "[2]" in sys_content + + +class TestAnthropicIntercept: + def test_basic_intercept(self, client, mock_session): + body = { + "model": "claude-3-opus-20240229", + "system": "ABC", + "messages": [{"role": "user", "content": "Hello"}], + } + resp = client.post("/v1/messages", json=body) + assert resp.status_code == 200 + assert mock_session._last_url == "http://mock-backend:30000/v1/messages" + forwarded = mock_session._last_json + assert "" in forwarded["system"] + + def test_bypass_no_system(self, client, mock_session): + body = { + "model": "claude-3-opus-20240229", + "messages": [{"role": "user", "content": "Hello"}], + } + resp = client.post("/v1/messages", json=body) + assert resp.status_code == 200 + + +# ============================================================================ +# Streaming +# ============================================================================ + + +class TestStreaming: + def test_streaming_passthrough(self, mock_session): + """Streaming responses are passed through.""" + chunks = [b"data: {\"id\":\"1\"}\n\n", b"data: [DONE]\n\n"] + stream_resp = FakeStreamResponse(chunks) + session = FakeSession(stream_resp) + + original_session = http_mod._aiohttp_session + original_url = http_mod._infer_api_url + http_mod._aiohttp_session = session + http_mod._infer_api_url = "http://mock-backend:30000" + try: + client = TestClient(app, raise_server_exceptions=False) + body = { + "model": "gpt-4", + "stream": True, + "messages": [ + { + "role": "system", + "content": "AB", + }, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + # Streaming response content + content = resp.content + assert b"data:" in content + finally: + http_mod._aiohttp_session = original_session + http_mod._infer_api_url = original_url + + +# ============================================================================ +# Catch-all still works +# ============================================================================ + + +class TestCatchAllProxy: + def test_other_v1_paths_still_proxied(self, client, mock_session): + """Other /v1/* paths still go to the catch-all proxy.""" + mock_session._response = FakeResponse({"models": [{"id": "gpt-4"}]}) + resp = client.get("/v1/models") + assert resp.status_code == 200 + assert mock_session._last_url == "http://mock-backend:30000/v1/models" + + def test_completions_still_works(self, client, mock_session): + """POST /v1/completions still handled by existing proxy.""" + mock_session._response = FakeResponse( + {"choices": [{"text": "hello"}], "usage": {"total_tokens": 5}} + ) + body = {"model": "gpt-4", "prompt": "Hello"} + resp = client.post("/v1/completions", json=body) + assert resp.status_code == 200 + + +# ============================================================================ +# Header forwarding +# ============================================================================ + + +class TestHeaderForwarding: + def test_auth_headers_forwarded(self, client, mock_session): + """Authorization headers are forwarded, X-ContextPilot-* stripped.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "Plain text."}, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post( + "/v1/chat/completions", + json=body, + headers={ + "Authorization": "Bearer sk-test", + "X-ContextPilot-Mode": "auto", + }, + ) + assert resp.status_code == 200 + outbound = mock_session._last_headers or {} + # Auth should be forwarded + assert outbound.get("authorization") or outbound.get("Authorization") + # X-ContextPilot-* should be stripped + for k in outbound: + assert not k.lower().startswith("x-contextpilot-") diff --git a/tests/test_intercept_parser.py b/tests/test_intercept_parser.py new file mode 100644 index 0000000..79f584a --- /dev/null +++ b/tests/test_intercept_parser.py @@ -0,0 +1,454 @@ +"""Unit tests for contextpilot.server.intercept_parser.""" + +import pytest +from contextpilot.server.intercept_parser import ( + InterceptConfig, + ExtractionResult, + parse_intercept_headers, + extract_documents, + extract_from_openai_chat, + extract_from_anthropic_messages, + reconstruct_openai_chat, + reconstruct_anthropic_messages, + reconstruct_content, +) + + +# ============================================================================ +# Header parsing +# ============================================================================ + + +class TestParseHeaders: + def test_defaults(self): + config = parse_intercept_headers({}) + assert config.enabled is True + assert config.mode == "auto" + assert config.tag == "document" + assert config.separator == "---" + assert config.alpha == pytest.approx(0.001) + assert config.linkage_method == "average" + + def test_explicit_mode(self): + headers = { + "X-ContextPilot-Mode": "xml_tag", + "X-ContextPilot-Tag": "passage", + "X-ContextPilot-Alpha": "0.01", + "X-ContextPilot-Linkage": "complete", + } + config = parse_intercept_headers(headers) + assert config.mode == "xml_tag" + assert config.tag == "passage" + assert config.alpha == pytest.approx(0.01) + assert config.linkage_method == "complete" + + def test_disabled(self): + for val in ("false", "0", "no", "False", "NO"): + config = parse_intercept_headers({"X-ContextPilot-Enabled": val}) + assert config.enabled is False + + def test_case_insensitive_keys(self): + headers = {"x-contextpilot-mode": "numbered"} + config = parse_intercept_headers(headers) + assert config.mode == "numbered" + + def test_separator_header(self): + headers = {"X-ContextPilot-Separator": "==="} + config = parse_intercept_headers(headers) + assert config.separator == "===" + + +# ============================================================================ +# XML tag extraction +# ============================================================================ + + +class TestXmlExtraction: + def test_basic_documents_wrapper(self): + text = "\nDoc A\nDoc B\n" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "xml_tag" + assert result.documents == ["Doc A", "Doc B"] + assert result.wrapper_tag == "documents" + assert result.item_tag == "document" + + def test_contexts_wrapper(self): + text = "FirstSecond" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["First", "Second"] + assert result.wrapper_tag == "contexts" + + def test_docs_wrapper(self): + text = "ABC" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["A", "B", "C"] + + def test_passages_wrapper(self): + text = "XY" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["X", "Y"] + + def test_references_wrapper(self): + text = "Ref1Ref2" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["Ref1", "Ref2"] + + def test_custom_tag_explicit_mode(self): + text = "Code ACode B" + config = InterceptConfig(mode="xml_tag", tag="snippet") + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["Code A", "Code B"] + + def test_prefix_suffix_preserved(self): + text = "Here are the docs:\n\nA\nB\n\nPlease answer." + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.prefix == "Here are the docs:\n" + assert result.suffix == "\nPlease answer." + + def test_no_wrapper_multiple_items(self): + text = "Doc 1\nDoc 2" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["Doc 1", "Doc 2"] + assert result.wrapper_tag == "" + + def test_multiline_content(self): + text = "\nLine 1\nLine 2\nLine 3\nLine 4\n" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert len(result.documents) == 2 + assert "Line 1\nLine 2" in result.documents[0] + + def test_single_item_returns_none_without_wrapper(self): + text = "Only one doc" + config = InterceptConfig() + result = extract_documents(text, config) + # No wrapper and only 1 item -> None (need >=2 for reordering) + assert result is None + + +# ============================================================================ +# Numbered extraction +# ============================================================================ + + +class TestNumberedExtraction: + def test_basic_numbered(self): + text = "[1] First document [2] Second document [3] Third document" + config = InterceptConfig(mode="numbered") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "numbered" + assert result.documents == ["First document", "Second document", "Third document"] + + def test_numbered_with_prefix(self): + text = "Retrieved documents:\n[1] Doc A [2] Doc B" + config = InterceptConfig(mode="numbered") + result = extract_documents(text, config) + assert result is not None + assert result.prefix == "Retrieved documents:\n" + assert result.documents == ["Doc A", "Doc B"] + + def test_numbered_with_newlines(self): + text = "[1] First doc\n[2] Second doc\n[3] Third doc" + config = InterceptConfig(mode="numbered") + result = extract_documents(text, config) + assert result is not None + assert len(result.documents) == 3 + + def test_single_numbered_returns_none(self): + text = "[1] Only one document" + config = InterceptConfig(mode="numbered") + result = extract_documents(text, config) + assert result is None + + +# ============================================================================ +# Separator extraction +# ============================================================================ + + +class TestSeparatorExtraction: + def test_basic_separator(self): + text = "Doc A\n---\nDoc B\n---\nDoc C" + config = InterceptConfig(mode="separator") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "separator" + assert result.documents == ["Doc A", "Doc B", "Doc C"] + + def test_equals_separator(self): + text = "Doc A\n===\nDoc B\n===\nDoc C" + config = InterceptConfig(mode="separator", separator="===") + result = extract_documents(text, config) + assert result is not None + assert result.documents == ["Doc A", "Doc B", "Doc C"] + + def test_auto_detects_triple_dash(self): + text = "First\n---\nSecond\n---\nThird" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "separator" + assert result.documents == ["First", "Second", "Third"] + + def test_auto_detects_triple_equals(self): + text = "First\n===\nSecond\n===\nThird" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "separator" + + def test_single_separator_returns_none(self): + # Only one separator -> only 2 parts, still need >=2 docs + text = "Only one\n---\nTwo parts" + config = InterceptConfig(mode="separator") + result = extract_documents(text, config) + # 2 docs is fine (>=2) + assert result is not None + assert len(result.documents) == 2 + + def test_no_separator_returns_none(self): + text = "Just plain text with no separators" + config = InterceptConfig(mode="separator") + result = extract_documents(text, config) + assert result is None + + +# ============================================================================ +# Auto detection priority +# ============================================================================ + + +class TestAutoDetection: + def test_xml_takes_priority(self): + text = "AB" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "xml_tag" + + def test_numbered_before_separator(self): + text = "[1] Doc A [2] Doc B" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "numbered" + + def test_separator_as_fallback(self): + text = "First doc\n---\nSecond doc\n---\nThird doc" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "separator" + + def test_nothing_returns_none(self): + text = "Just a plain system message with no documents." + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is None + + +# ============================================================================ +# Reconstruction roundtrips +# ============================================================================ + + +class TestReconstruction: + def test_xml_roundtrip(self): + text = "Prefix\n\nA\nB\nC\n\nSuffix" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + rebuilt = reconstruct_content(result, ["C", "A", "B"]) + assert "C" in rebuilt + assert "A" in rebuilt + assert "B" in rebuilt + assert rebuilt.startswith("Prefix\n") + assert rebuilt.endswith("\nSuffix") + assert "" in rebuilt + assert "" in rebuilt + + def test_numbered_roundtrip(self): + text = "[1] Alpha [2] Beta [3] Gamma" + config = InterceptConfig(mode="numbered") + result = extract_documents(text, config) + rebuilt = reconstruct_content(result, ["Gamma", "Alpha", "Beta"]) + assert "[1] Gamma" in rebuilt + assert "[2] Alpha" in rebuilt + assert "[3] Beta" in rebuilt + + def test_separator_roundtrip(self): + text = "Doc A\n---\nDoc B\n---\nDoc C" + config = InterceptConfig(mode="separator") + result = extract_documents(text, config) + rebuilt = reconstruct_content(result, ["Doc C", "Doc A", "Doc B"]) + parts = rebuilt.split("\n---\n") + assert parts == ["Doc C", "Doc A", "Doc B"] + + +# ============================================================================ +# OpenAI chat format +# ============================================================================ + + +class TestOpenAIChatFormat: + def test_extract_from_system_message(self): + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "AB"}, + {"role": "user", "content": "What is A?"}, + ], + } + config = InterceptConfig() + result = extract_from_openai_chat(body, config) + assert result is not None + extraction, idx = result + assert extraction.documents == ["A", "B"] + assert idx == 0 + + def test_no_system_message(self): + body = { + "messages": [ + {"role": "user", "content": "Hello"}, + ], + } + result = extract_from_openai_chat(body, InterceptConfig()) + assert result is None + + def test_system_without_docs(self): + body = { + "messages": [ + {"role": "system", "content": "You are a helpful assistant."}, + {"role": "user", "content": "Hello"}, + ], + } + result = extract_from_openai_chat(body, InterceptConfig()) + assert result is None + + def test_reconstruct_roundtrip(self): + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "\nA\nB\nC\n"}, + {"role": "user", "content": "Summarize"}, + ], + } + config = InterceptConfig() + extraction, idx = extract_from_openai_chat(body, config) + new_body = reconstruct_openai_chat(body, extraction, ["C", "A", "B"], idx) + # Original body not modified + assert "A" in body["messages"][0]["content"] + # New body has reordered docs + content = new_body["messages"][0]["content"] + assert "C" in content + # User message preserved + assert new_body["messages"][1]["content"] == "Summarize" + # Model preserved + assert new_body["model"] == "gpt-4" + + def test_content_blocks_format(self): + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": [ + {"type": "text", "text": "XY"} + ], + }, + {"role": "user", "content": "Question"}, + ], + } + config = InterceptConfig() + result = extract_from_openai_chat(body, config) + assert result is not None + extraction, idx = result + assert extraction.documents == ["X", "Y"] + + def test_empty_messages(self): + result = extract_from_openai_chat({"messages": []}, InterceptConfig()) + assert result is None + + def test_no_messages_key(self): + result = extract_from_openai_chat({"prompt": "hello"}, InterceptConfig()) + assert result is None + + +# ============================================================================ +# Anthropic messages format +# ============================================================================ + + +class TestAnthropicMessagesFormat: + def test_extract_string_system(self): + body = { + "model": "claude-3-opus-20240229", + "system": "AB", + "messages": [{"role": "user", "content": "Hello"}], + } + config = InterceptConfig() + result = extract_from_anthropic_messages(body, config) + assert result is not None + assert result.documents == ["A", "B"] + + def test_extract_content_blocks_system(self): + body = { + "model": "claude-3-opus-20240229", + "system": [ + {"type": "text", "text": "XY"} + ], + "messages": [{"role": "user", "content": "Hello"}], + } + config = InterceptConfig() + result = extract_from_anthropic_messages(body, config) + assert result is not None + assert result.documents == ["X", "Y"] + + def test_no_system_field(self): + body = {"messages": [{"role": "user", "content": "Hello"}]} + result = extract_from_anthropic_messages(body, InterceptConfig()) + assert result is None + + def test_reconstruct_string_system(self): + body = { + "system": "\nA\nB\n", + "messages": [{"role": "user", "content": "Hello"}], + } + config = InterceptConfig() + extraction = extract_from_anthropic_messages(body, config) + new_body = reconstruct_anthropic_messages(body, extraction, ["B", "A"]) + assert "B" in new_body["system"] + assert "A" in new_body["system"] + # Original not modified + assert body["system"].index("A") < body["system"].index("B") + + def test_reconstruct_content_blocks_system(self): + body = { + "system": [ + {"type": "text", "text": "PQ"} + ], + "messages": [{"role": "user", "content": "Hello"}], + } + config = InterceptConfig() + extraction = extract_from_anthropic_messages(body, config) + new_body = reconstruct_anthropic_messages(body, extraction, ["Q", "P"]) + text_block = new_body["system"][0]["text"] + assert "Q" in text_block + assert "P" in text_block From 5d20ecbf61ef06a32a6fe785121be4bfe85ff4ac Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Thu, 26 Feb 2026 18:49:46 +0000 Subject: [PATCH 02/76] Add OpenClaw integration: tool_result reordering, markdown_header mode, and deployment files - Extract and reorder documents from tool_result messages (OpenAI role="tool" and Anthropic type="tool_result"), with camelCase compat for OpenClaw internal format - Add markdown_header extraction mode (split on # / ## headers) - Extend XML tag recognition with / - Add X-ContextPilot-Scope header (system / tool_results / all) - Refactor _intercept_and_forward to use MultiExtractionResult for multi-source reordering - Expand _contextpilot response metadata with total_documents and sources breakdown - Add OpenClaw examples: setup.sh, Docker Compose, provider config template - Add integration guide at docs/guides/openclaw.md - 95 tests passing (34 new) Co-Authored-By: Claude Opus 4.6 (1M context) --- contextpilot/server/http_server.py | 140 ++++--- contextpilot/server/intercept_parser.py | 239 +++++++++++- docs/guides/openclaw.md | 172 +++++++++ examples/openclaw/Dockerfile | 10 + examples/openclaw/README.md | 112 ++++++ examples/openclaw/SKILL.md | 54 +++ examples/openclaw/docker-compose.yml | 18 + examples/openclaw/openclaw.json.example | 50 +++ examples/openclaw/setup.sh | 127 +++++++ tests/test_http_intercept.py | 158 ++++++++ tests/test_intercept_parser.py | 484 ++++++++++++++++++++++++ 11 files changed, 1514 insertions(+), 50 deletions(-) create mode 100644 docs/guides/openclaw.md create mode 100644 examples/openclaw/Dockerfile create mode 100644 examples/openclaw/README.md create mode 100644 examples/openclaw/SKILL.md create mode 100644 examples/openclaw/docker-compose.yml create mode 100644 examples/openclaw/openclaw.json.example create mode 100755 examples/openclaw/setup.sh diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index ab1c429..a6128cf 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -16,6 +16,7 @@ """ import argparse +import copy import logging import time import asyncio @@ -47,7 +48,11 @@ from .intercept_parser import ( parse_intercept_headers, extract_from_openai_chat, extract_from_anthropic_messages, reconstruct_openai_chat, - reconstruct_anthropic_messages, InterceptConfig, ExtractionResult, + reconstruct_anthropic_messages, reconstruct_content, + extract_documents, InterceptConfig, ExtractionResult, + MultiExtractionResult, ToolResultLocation, + extract_all_openai, extract_all_anthropic, + reconstruct_openai_tool_result, reconstruct_anthropic_tool_result, ) logger = logging.getLogger(__name__) @@ -983,12 +988,40 @@ async def proxy_completions(request: Request): _ANTHROPIC_MESSAGES = "anthropic_messages" +def _reorder_documents(docs: List[str], config: InterceptConfig) -> List[str]: + """Reorder a list of document strings using ContextPilot clustering. + + Returns the reordered document list. + """ + temp_index = ContextPilot( + alpha=config.alpha, + use_gpu=False, + linkage_method=config.linkage_method, + ) + str_to_id: Dict[str, int] = {} + id_to_str: Dict[int, str] = {} + next_id = 0 + converted = [] + for doc in docs: + sid = str_to_id.get(doc) + if sid is None: + sid = next_id + str_to_id[doc] = sid + id_to_str[sid] = doc + next_id += 1 + converted.append(sid) + + sched_result = temp_index.schedule_only(contexts=[converted]) + reordered_ids = sched_result["reordered_contexts"][0] + return [id_to_str[i] for i in reordered_ids] + + async def _intercept_and_forward(request: Request, api_format: str): """Intercept an LLM API request, reorder documents, and forward. 1. Parse X-ContextPilot-* headers → InterceptConfig - 2. Extract documents from system message/prompt - 3. Reorder via ContextPilot.reorder(docs) + 2. Extract documents from system message/prompt and tool_results + 3. Reorder each extraction via ContextPilot clustering 4. Reconstruct request body with reordered docs 5. Forward to actual LLM backend, streaming or not If extraction fails at any step → forward original request unmodified. @@ -1013,58 +1046,70 @@ async def _intercept_and_forward(request: Request, api_format: str): # Parse intercept config from headers headers = dict(request.headers) config = parse_intercept_headers(headers) - reordered = False + total_reordered = 0 + system_count = 0 + tool_result_count = 0 if config.enabled: try: - extraction = None - sys_idx = None + body = copy.deepcopy(body) if api_format == _OPENAI_CHAT: - result = extract_from_openai_chat(body, config) - if result: - extraction, sys_idx = result + multi = extract_all_openai(body, config) elif api_format == _ANTHROPIC_MESSAGES: - extraction = extract_from_anthropic_messages(body, config) - - if extraction and len(extraction.documents) >= 2: - docs = extraction.documents - # Stateless reorder: create temp ContextPilot, get optimal order - temp_index = ContextPilot( - alpha=config.alpha, - use_gpu=False, - linkage_method=config.linkage_method, - ) - # Build string-to-id mapping - str_to_id = {} - id_to_str = {} - next_id = 0 - converted = [] - for doc in docs: - sid = str_to_id.get(doc) - if sid is None: - sid = next_id - str_to_id[doc] = sid - id_to_str[sid] = doc - next_id += 1 - converted.append(sid) - - sched_result = temp_index.schedule_only(contexts=[converted]) - reordered_ids = sched_result["reordered_contexts"][0] - reordered_docs = [id_to_str[i] for i in reordered_ids] - - # Reconstruct body - if api_format == _OPENAI_CHAT: - body = reconstruct_openai_chat(body, extraction, reordered_docs, sys_idx) - elif api_format == _ANTHROPIC_MESSAGES: - body = reconstruct_anthropic_messages(body, extraction, reordered_docs) - reordered = True + multi = extract_all_anthropic(body, config) + else: + multi = MultiExtractionResult() + + # Reorder system extraction + if multi.system_extraction: + extraction, sys_idx = multi.system_extraction + if len(extraction.documents) >= 2: + reordered_docs = _reorder_documents(extraction.documents, config) + if api_format == _OPENAI_CHAT: + new_content = reconstruct_content(extraction, reordered_docs) + msg = body["messages"][sys_idx] + if isinstance(msg.get("content"), str): + msg["content"] = new_content + elif isinstance(msg.get("content"), list): + for block in msg["content"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + elif api_format == _ANTHROPIC_MESSAGES: + new_content = reconstruct_content(extraction, reordered_docs) + if isinstance(body.get("system"), str): + body["system"] = new_content + elif isinstance(body.get("system"), list): + for block in body["system"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + total_reordered += len(extraction.documents) + system_count = 1 + + # Reorder tool result extractions + for extraction, location in multi.tool_extractions: + if len(extraction.documents) >= 2: + reordered_docs = _reorder_documents(extraction.documents, config) + if api_format == _OPENAI_CHAT: + reconstruct_openai_tool_result(body, extraction, reordered_docs, location) + elif api_format == _ANTHROPIC_MESSAGES: + reconstruct_anthropic_tool_result(body, extraction, reordered_docs, location) + total_reordered += len(extraction.documents) + tool_result_count += 1 + + if total_reordered > 0: logger.info( - f"Intercept ({api_format}): reordered {len(docs)} documents" + f"Intercept ({api_format}): reordered {total_reordered} documents " + f"from {system_count} system + {tool_result_count} tool_results" ) except Exception as e: logger.warning(f"Intercept extraction/reorder failed, forwarding original: {e}") + total_reordered = 0 # Determine target URL if api_format == _OPENAI_CHAT: @@ -1105,10 +1150,15 @@ async def _stream_generator(): target_url, json=body, headers=outbound_headers ) as resp: result = await resp.json() - if reordered and resp.status == 200: + if total_reordered > 0 and resp.status == 200: result["_contextpilot"] = { "intercepted": True, "documents_reordered": True, + "total_documents": total_reordered, + "sources": { + "system": system_count, + "tool_results": tool_result_count, + }, } return JSONResponse(content=result, status_code=resp.status) diff --git a/contextpilot/server/intercept_parser.py b/contextpilot/server/intercept_parser.py index 416c67b..f97ea72 100644 --- a/contextpilot/server/intercept_parser.py +++ b/contextpilot/server/intercept_parser.py @@ -17,10 +17,10 @@ logger = logging.getLogger(__name__) # XML wrapper tag names we recognize (outer wrapper) -_KNOWN_WRAPPER_TAGS = {"documents", "contexts", "docs", "passages", "references"} +_KNOWN_WRAPPER_TAGS = {"documents", "contexts", "docs", "passages", "references", "files"} # XML item tag names we recognize (inner items) -_KNOWN_ITEM_TAGS = {"document", "context", "doc", "passage", "reference"} +_KNOWN_ITEM_TAGS = {"document", "context", "doc", "passage", "reference", "file"} # Numbered pattern: [1] ... [2] ... etc. _NUMBERED_RE = re.compile(r"\[(\d+)\]\s*") @@ -34,11 +34,12 @@ class InterceptConfig: """Configuration parsed from X-ContextPilot-* headers.""" enabled: bool = True - mode: str = "auto" # xml_tag, separator, numbered, auto + mode: str = "auto" # xml_tag, separator, numbered, markdown_header, auto tag: str = "document" # XML tag name for xml_tag mode separator: str = "---" # Delimiter for separator mode alpha: float = 0.001 linkage_method: str = "average" + scope: str = "all" # "system", "tool_results", "all" @dataclass @@ -58,6 +59,34 @@ class ExtractionResult: original_content: str = "" +@dataclass +class ToolResultLocation: + """Identifies a tool_result's position in the messages array.""" + msg_index: int + block_index: int = -1 # -1 = content is string + inner_block_index: int = -1 # For Anthropic nested content blocks + + +@dataclass +class MultiExtractionResult: + """Aggregates extractions from system prompt and tool_result messages.""" + system_extraction: Optional[Tuple["ExtractionResult", int]] = None + tool_extractions: List[Tuple["ExtractionResult", ToolResultLocation]] = field(default_factory=list) + + @property + def has_extractions(self) -> bool: + return self.system_extraction is not None or len(self.tool_extractions) > 0 + + @property + def total_documents(self) -> int: + total = 0 + if self.system_extraction: + total += len(self.system_extraction[0].documents) + for ext, _ in self.tool_extractions: + total += len(ext.documents) + return total + + def parse_intercept_headers(headers: Dict[str, str]) -> InterceptConfig: """Parse X-ContextPilot-* headers into an InterceptConfig.""" def get(name: str, default: str = "") -> str: @@ -71,6 +100,10 @@ def get(name: str, default: str = "") -> str: enabled_str = get("enabled", "true").lower() enabled = enabled_str not in ("false", "0", "no") + scope = get("scope", "all").lower() + if scope not in ("system", "tool_results", "all"): + scope = "all" + return InterceptConfig( enabled=enabled, mode=get("mode", "auto").lower(), @@ -78,6 +111,7 @@ def get(name: str, default: str = "") -> str: separator=get("separator", "---"), alpha=float(get("alpha", "0.001")), linkage_method=get("linkage", "average"), + scope=scope, ) @@ -221,12 +255,61 @@ def _extract_separator( ) +def _extract_markdown_headers( + text: str, config: InterceptConfig +) -> Optional[ExtractionResult]: + """Extract documents by splitting on markdown headers (# or ##). + + Each header + its body becomes one document. Requires >= 2 sections. + Text before the first header is preserved as prefix. + """ + # Split on lines that start with # or ## (but not ### or deeper) + parts = re.split(r"(?=^#{1,2}\s)", text, flags=re.MULTILINE) + # parts[0] is text before first header (prefix), rest are sections + if not parts: + return None + + prefix = "" + sections = [] + for part in parts: + stripped = part.strip() + if not stripped: + continue + if re.match(r"^#{1,2}\s", stripped): + sections.append(stripped) + else: + # Text before first header + prefix = part + + if len(sections) < 2: + return None + + return ExtractionResult( + documents=sections, + prefix=prefix, + suffix="", + mode="markdown_header", + original_content=text, + ) + + +def _reconstruct_markdown_headers( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + """Reconstruct markdown-header-split content.""" + parts = [] + if extraction.prefix.strip(): + parts.append(extraction.prefix.rstrip()) + parts.extend(reordered_docs) + return "\n\n".join(parts) + + def extract_documents( text: str, config: InterceptConfig ) -> Optional[ExtractionResult]: """Extract documents from text using the configured mode. - Auto-detection priority: xml_tag > numbered > separator. + Auto-detection priority: xml_tag > numbered > separator > markdown_header. Returns None if no documents are found (caller should bypass). """ if config.mode == "xml_tag": @@ -235,6 +318,8 @@ def extract_documents( return _extract_numbered(text, config) elif config.mode == "separator": return _extract_separator(text, config) + elif config.mode == "markdown_header": + return _extract_markdown_headers(text, config) else: # Auto mode: try each in priority order result = _extract_xml_tags(text, config) @@ -244,6 +329,9 @@ def extract_documents( if result: return result result = _extract_separator(text, config) + if result: + return result + result = _extract_markdown_headers(text, config) if result: return result return None @@ -257,7 +345,7 @@ def reconstruct_content( ) -> str: """Reconstruct the message content with reordered documents. - Preserves the original format (XML tags, numbering, separators). + Preserves the original format (XML tags, numbering, separators, markdown headers). """ if extraction.mode == "xml_tag": return _reconstruct_xml(extraction, reordered_docs) @@ -265,6 +353,8 @@ def reconstruct_content( return _reconstruct_numbered(extraction, reordered_docs) elif extraction.mode == "separator": return _reconstruct_separator(extraction, reordered_docs) + elif extraction.mode == "markdown_header": + return _reconstruct_markdown_headers(extraction, reordered_docs) else: # Should not happen, but fallback return extraction.original_content @@ -409,3 +499,142 @@ def reconstruct_anthropic_messages( block["text"] = new_content break return body + + +# ── Tool result extraction ───────────────────────────────────────────────── + + +def extract_from_openai_tool_results( + body: Dict[str, Any], config: InterceptConfig +) -> List[Tuple[ExtractionResult, ToolResultLocation]]: + """Extract documents from OpenAI tool result messages (role=="tool"). + + Returns a list of (ExtractionResult, ToolResultLocation) for each tool + result message that contains extractable documents. + """ + messages = body.get("messages") + if not messages or not isinstance(messages, list): + return [] + + results = [] + for i, msg in enumerate(messages): + if msg.get("role") not in ("tool", "toolResult"): + continue + content = msg.get("content", "") + if isinstance(content, str): + extraction = extract_documents(content, config) + if extraction and len(extraction.documents) >= 2: + loc = ToolResultLocation(msg_index=i) + results.append((extraction, loc)) + elif isinstance(content, list): + for j, block in enumerate(content): + if isinstance(block, dict) and block.get("type") == "text": + extraction = extract_documents(block.get("text", ""), config) + if extraction and len(extraction.documents) >= 2: + loc = ToolResultLocation(msg_index=i, block_index=j) + results.append((extraction, loc)) + return results + + +def extract_from_anthropic_tool_results( + body: Dict[str, Any], config: InterceptConfig +) -> List[Tuple[ExtractionResult, ToolResultLocation]]: + """Extract documents from Anthropic tool_result content blocks. + + Anthropic tool results appear as messages with role=="user" containing + content blocks of type=="tool_result". + """ + messages = body.get("messages") + if not messages or not isinstance(messages, list): + return [] + + results = [] + for i, msg in enumerate(messages): + if msg.get("role") != "user": + continue + content = msg.get("content") + if not isinstance(content, list): + continue + for j, block in enumerate(content): + if not isinstance(block, dict) or block.get("type") not in ("tool_result", "toolResult"): + continue + tr_content = block.get("content", "") + if isinstance(tr_content, str): + extraction = extract_documents(tr_content, config) + if extraction and len(extraction.documents) >= 2: + loc = ToolResultLocation(msg_index=i, block_index=j) + results.append((extraction, loc)) + elif isinstance(tr_content, list): + for k, inner in enumerate(tr_content): + if isinstance(inner, dict) and inner.get("type") == "text": + extraction = extract_documents(inner.get("text", ""), config) + if extraction and len(extraction.documents) >= 2: + loc = ToolResultLocation(msg_index=i, block_index=j, inner_block_index=k) + results.append((extraction, loc)) + return results + + +# ── Tool result reconstruction ───────────────────────────────────────────── + + +def reconstruct_openai_tool_result( + body: Dict[str, Any], + extraction: ExtractionResult, + reordered_docs: List[str], + location: ToolResultLocation, +) -> None: + """Reconstruct an OpenAI tool result message in-place.""" + new_content = reconstruct_content(extraction, reordered_docs) + msg = body["messages"][location.msg_index] + if location.block_index == -1: + msg["content"] = new_content + else: + msg["content"][location.block_index]["text"] = new_content + + +def reconstruct_anthropic_tool_result( + body: Dict[str, Any], + extraction: ExtractionResult, + reordered_docs: List[str], + location: ToolResultLocation, +) -> None: + """Reconstruct an Anthropic tool_result content block in-place.""" + new_content = reconstruct_content(extraction, reordered_docs) + msg = body["messages"][location.msg_index] + block = msg["content"][location.block_index] + if location.inner_block_index == -1: + block["content"] = new_content + else: + block["content"][location.inner_block_index]["text"] = new_content + + +# ── Aggregate extraction ─────────────────────────────────────────────────── + + +def extract_all_openai( + body: Dict[str, Any], config: InterceptConfig +) -> MultiExtractionResult: + """Extract documents from both system message and tool results (OpenAI format).""" + result = MultiExtractionResult() + if config.scope in ("system", "all"): + sys_result = extract_from_openai_chat(body, config) + if sys_result: + result.system_extraction = sys_result + if config.scope in ("tool_results", "all"): + result.tool_extractions = extract_from_openai_tool_results(body, config) + return result + + +def extract_all_anthropic( + body: Dict[str, Any], config: InterceptConfig +) -> MultiExtractionResult: + """Extract documents from both system prompt and tool results (Anthropic format).""" + result = MultiExtractionResult() + if config.scope in ("system", "all"): + sys_extraction = extract_from_anthropic_messages(body, config) + if sys_extraction and len(sys_extraction.documents) >= 2: + # Use -1 as sentinel for "system field" (not in messages array) + result.system_extraction = (sys_extraction, -1) + if config.scope in ("tool_results", "all"): + result.tool_extractions = extract_from_anthropic_tool_results(body, config) + return result diff --git a/docs/guides/openclaw.md b/docs/guides/openclaw.md new file mode 100644 index 0000000..1006899 --- /dev/null +++ b/docs/guides/openclaw.md @@ -0,0 +1,172 @@ +# ContextPilot + OpenClaw Integration Guide + +## Architecture + +``` +┌──────────────┐ ┌───────────────────┐ ┌─────────────┐ +│ OpenClaw UI │─────▶│ ContextPilot Proxy│─────▶│ LLM Backend │ +│ (browser) │◀─────│ (localhost:8765) │◀─────│(Anthropic/ │ +└──────────────┘ └───────────────────┘ │ OpenAI) │ + └─────────────┘ +``` + +ContextPilot acts as a transparent HTTP proxy. OpenClaw sends requests to the proxy instead of directly to the LLM API. The proxy extracts documents, reorders them, and forwards to the real backend. + +## Why This Matters for OpenClaw + +OpenClaw's search and memory retrieval results appear as **tool_result messages** in the conversation history, not in the system prompt. When multiple search results are returned, their ordering affects the LLM's attention and response quality. + +ContextPilot: +1. Extracts documents from tool_result content blocks +2. Clusters semantically related documents together +3. Reorders to minimize attention distance between related content +4. Preserves the original format (XML, numbered, etc.) + +## Setup + +### Quick Start (one command) + +```bash +# Clone and run +git clone https://github.com/EfficientContext/ContextPilot.git +cd ContextPilot/examples/openclaw +bash setup.sh anthropic # or: bash setup.sh openai +``` + +The script installs ContextPilot, generates a config, and starts the proxy. + +### Docker + +```bash +cd ContextPilot/examples/openclaw +docker compose up -d + +# OpenAI instead of Anthropic: +CONTEXTPILOT_BACKEND_URL=https://api.openai.com docker compose up -d +``` + +### Manual + +```bash +pip install contextpilot +python -m contextpilot.server.http_server \ + --stateless --port 8765 \ + --infer-api-url https://api.anthropic.com +``` + +## Configure OpenClaw + +### Option A: UI (recommended) + +1. Open OpenClaw +2. Go to **Settings → Models** +3. Add a custom provider: + +| Field | Value | +|-------|-------| +| Name | `contextpilot-anthropic` | +| Base URL | `http://localhost:8765/v1` | +| API Key | your Anthropic API key | +| API | `anthropic-messages` | +| Model ID | `claude-opus-4-6` | + +4. Select the model and start chatting + +### Option B: Config file + +Merge into `~/.openclaw/openclaw.json`: + +```json +{ + "models": { + "providers": { + "contextpilot-anthropic": { + "baseUrl": "http://localhost:8765/v1", + "apiKey": "${ANTHROPIC_API_KEY}", + "api": "anthropic-messages", + "headers": { + "X-ContextPilot-Scope": "all" + }, + "models": [ + { + "id": "claude-opus-4-6", + "name": "Claude Opus 4.6 (via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 200000, + "maxTokens": 32000 + } + ] + } + } + } +} +``` + +For OpenAI, use `api: "openai-completions"` and point `--infer-api-url` to `https://api.openai.com`. See `examples/openclaw/openclaw.json.example` for both providers. + +## Verify + +Check the `_contextpilot` field in API responses: + +```json +{ + "_contextpilot": { + "intercepted": true, + "documents_reordered": true, + "total_documents": 8, + "sources": { + "system": 1, + "tool_results": 2 + } + } +} +``` + +If `_contextpilot` is absent, the request had fewer than 2 extractable documents (nothing to reorder). + +## Document Extraction + +ContextPilot auto-detects these formats in both system prompts and tool results: + +| Format | Pattern | Typical Source | +|--------|---------|----------------| +| XML tags | `...` | RAG systems | +| File tags | `...` | Code search | +| Numbered | `[1] doc [2] doc` | Search rankings | +| Separator | docs split by `---` or `===` | Text chunking | +| Markdown headers | sections split by `#`/`##` | Structured docs | + +Auto-detection priority: XML > Numbered > Separator > Markdown headers. + +## Scope Control + +| `X-ContextPilot-Scope` | System Prompt | Tool Results | +|:---:|:---:|:---:| +| `all` (default) | reordered | reordered | +| `system` | reordered | untouched | +| `tool_results` | untouched | reordered | + +Set via headers in the OpenClaw provider config, or per-request. + +## Full Header Reference + +| Header | Description | Default | +|--------|-------------|---------| +| `X-ContextPilot-Enabled` | Enable/disable | `true` | +| `X-ContextPilot-Mode` | Extraction mode | `auto` | +| `X-ContextPilot-Scope` | Which messages to process | `all` | +| `X-ContextPilot-Tag` | Custom XML tag name | `document` | +| `X-ContextPilot-Separator` | Custom separator | `---` | +| `X-ContextPilot-Alpha` | Clustering distance parameter | `0.001` | +| `X-ContextPilot-Linkage` | Clustering linkage method | `average` | + +## Troubleshooting + +**No `_contextpilot` in response** — Request had < 2 extractable documents. Check that search/memory tools are returning multiple results. + +**Connection refused** — Proxy not running. Check `curl http://localhost:8765/health`. + +**401/403 from backend** — API key not set or invalid. The proxy forwards auth headers as-is. + +**Tool results not reordered** — Check scope is `all` or `tool_results`. Verify tool results use a supported format. diff --git a/examples/openclaw/Dockerfile b/examples/openclaw/Dockerfile new file mode 100644 index 0000000..6e19b30 --- /dev/null +++ b/examples/openclaw/Dockerfile @@ -0,0 +1,10 @@ +FROM python:3.12-slim + +WORKDIR /app + +RUN pip install --no-cache-dir contextpilot + +EXPOSE 8765 + +ENTRYPOINT ["python", "-m", "contextpilot.server.http_server", "--stateless"] +CMD ["--port", "8765", "--infer-api-url", "https://api.anthropic.com"] diff --git a/examples/openclaw/README.md b/examples/openclaw/README.md new file mode 100644 index 0000000..77cc6f2 --- /dev/null +++ b/examples/openclaw/README.md @@ -0,0 +1,112 @@ +# ContextPilot + OpenClaw Quick Start + +## Flow + +``` +1. Start ContextPilot proxy (one command) +2. OpenClaw UI → Settings → Models (add custom provider pointing to proxy) +3. Select model, start chatting (done) +``` + +``` +OpenClaw UI ──▶ ContextPilot Proxy (localhost:8765) ──▶ LLM API (Anthropic/OpenAI) + ◀──────────────── responses flow back ◀────────────────── +``` + +## One-Click Setup + +### Option A: Shell script + +```bash +# Anthropic (default) +bash setup.sh anthropic + +# OpenAI +bash setup.sh openai +``` + +The script will: +1. Check Python, install ContextPilot +2. Generate the provider config JSON +3. Print what to enter in OpenClaw UI +4. Start the proxy + +### Option B: Docker Compose + +```bash +# Anthropic (default) +docker compose up -d + +# OpenAI +CONTEXTPILOT_BACKEND_URL=https://api.openai.com docker compose up -d +``` + +## Manual Setup + +### Step 1: Start proxy + +```bash +pip install contextpilot + +python -m contextpilot.server.http_server \ + --stateless --port 8765 \ + --infer-api-url https://api.anthropic.com +``` + +### Step 2: Add provider in OpenClaw + +Open OpenClaw UI → **Settings** → **Models** → add custom provider: + +| Field | Value | +|-------|-------| +| Base URL | `http://localhost:8765/v1` | +| API Key | your Anthropic/OpenAI key | +| API | `anthropic-messages` or `openai-completions` | +| Headers | `X-ContextPilot-Scope: all` | + +Or merge `openclaw.json.example` into `~/.openclaw/openclaw.json`: + +```json +{ + "models": { + "providers": { + "contextpilot-anthropic": { + "baseUrl": "http://localhost:8765/v1", + "apiKey": "${ANTHROPIC_API_KEY}", + "api": "anthropic-messages", + "headers": { "X-ContextPilot-Scope": "all" }, + "models": [{ "id": "claude-opus-4-6", "name": "Claude Opus 4.6 (via ContextPilot)" }] + } + } + } +} +``` + +### Step 3: Select model and chat + +In OpenClaw, select the model from the ContextPilot provider. Search/memory results in tool_results will be automatically reordered. + +### Step 4: Verify + +Check API responses for `_contextpilot` metadata: + +```json +{ + "_contextpilot": { + "intercepted": true, + "documents_reordered": true, + "total_documents": 5, + "sources": { "system": 1, "tool_results": 1 } + } +} +``` + +## Scope Control + +| Header value | System prompt | Tool results | +|:---:|:---:|:---:| +| `all` (default) | reordered | reordered | +| `system` | reordered | untouched | +| `tool_results` | untouched | reordered | + +See [SKILL.md](SKILL.md) for the full header reference. diff --git a/examples/openclaw/SKILL.md b/examples/openclaw/SKILL.md new file mode 100644 index 0000000..3c39f1a --- /dev/null +++ b/examples/openclaw/SKILL.md @@ -0,0 +1,54 @@ +--- +name: contextpilot +description: Optimize document ordering in LLM context for better retrieval performance +version: 1.0.0 +triggers: + - /contextpilot + - /cp +--- + +# ContextPilot Integration + +You have ContextPilot enabled as a transparent proxy. All your LLM API requests are automatically routed through ContextPilot, which reorders documents in your context window for optimal retrieval performance. + +## What ContextPilot Does + +When your request contains multiple documents (in system prompts or tool results), ContextPilot: + +1. **Extracts** documents from XML tags (``, ``, etc.), numbered lists, separators, or markdown headers +2. **Clusters** documents by semantic similarity using hierarchical clustering +3. **Reorders** documents to minimize attention distance between related content +4. **Reconstructs** the request preserving the original format + +## Supported Document Formats + +- XML tags: `...`, `...` +- Numbered: `[1] doc [2] doc [3] doc` +- Separator: docs separated by `---` or `===` +- Markdown headers: sections split by `#` or `##` headers + +## How to Verify + +Check the `_contextpilot` field in API responses: + +```json +{ + "_contextpilot": { + "intercepted": true, + "documents_reordered": true, + "total_documents": 5, + "sources": {"system": 1, "tool_results": 1} + } +} +``` + +## Configuration Headers + +Send these headers with your API requests to control behavior: + +| Header | Values | Default | +|--------|--------|---------| +| `X-ContextPilot-Enabled` | `true`/`false` | `true` | +| `X-ContextPilot-Mode` | `auto`/`xml_tag`/`numbered`/`separator`/`markdown_header` | `auto` | +| `X-ContextPilot-Scope` | `all`/`system`/`tool_results` | `all` | +| `X-ContextPilot-Alpha` | float | `0.001` | diff --git a/examples/openclaw/docker-compose.yml b/examples/openclaw/docker-compose.yml new file mode 100644 index 0000000..585f08f --- /dev/null +++ b/examples/openclaw/docker-compose.yml @@ -0,0 +1,18 @@ +services: + contextpilot: + build: . + ports: + - "${CONTEXTPILOT_PORT:-8765}:8765" + environment: + - CONTEXTPILOT_INFER_API_URL=${CONTEXTPILOT_BACKEND_URL:-https://api.anthropic.com} + command: + - "--port" + - "8765" + - "--infer-api-url" + - "${CONTEXTPILOT_BACKEND_URL:-https://api.anthropic.com}" + restart: unless-stopped + healthcheck: + test: ["CMD", "python", "-c", "import urllib.request; urllib.request.urlopen('http://localhost:8765/health')"] + interval: 30s + timeout: 5s + retries: 3 diff --git a/examples/openclaw/openclaw.json.example b/examples/openclaw/openclaw.json.example new file mode 100644 index 0000000..e102010 --- /dev/null +++ b/examples/openclaw/openclaw.json.example @@ -0,0 +1,50 @@ +{ + "models": { + "providers": { + "contextpilot-anthropic": { + "baseUrl": "http://localhost:8765/v1", + "apiKey": "${ANTHROPIC_API_KEY}", + "api": "anthropic-messages", + "headers": { + "X-ContextPilot-Scope": "all" + }, + "models": [ + { + "id": "claude-opus-4-6", + "name": "Claude Opus 4.6 (via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 200000, + "maxTokens": 32000 + }, + { + "id": "claude-sonnet-4-5", + "name": "Claude Sonnet 4.5 (via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 200000, + "maxTokens": 16000 + } + ] + }, + "contextpilot-openai": { + "baseUrl": "http://localhost:8765/v1", + "apiKey": "${OPENAI_API_KEY}", + "api": "openai-completions", + "headers": { + "X-ContextPilot-Scope": "all" + }, + "models": [ + { + "id": "gpt-4o", + "name": "GPT-4o (via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 128000, + "maxTokens": 16384 + } + ] + } + } + } +} diff --git a/examples/openclaw/setup.sh b/examples/openclaw/setup.sh new file mode 100755 index 0000000..a63d99f --- /dev/null +++ b/examples/openclaw/setup.sh @@ -0,0 +1,127 @@ +#!/usr/bin/env bash +set -euo pipefail + +# ContextPilot + OpenClaw one-click setup +# Usage: bash setup.sh [anthropic|openai] + +PROVIDER="${1:-anthropic}" +PORT="${CONTEXTPILOT_PORT:-8765}" + +RED='\033[0;31m' +GREEN='\033[0;32m' +YELLOW='\033[1;33m' +NC='\033[0m' + +info() { echo -e "${GREEN}[INFO]${NC} $*"; } +warn() { echo -e "${YELLOW}[WARN]${NC} $*"; } +error() { echo -e "${RED}[ERROR]${NC} $*"; exit 1; } + +# ── Check Python ─────────────────────────────────────────────────────────── +info "Checking Python version..." +if ! command -v python3 &>/dev/null; then + error "Python 3 not found. Install Python 3.10+ first." +fi + +PY_VER=$(python3 -c 'import sys; print(f"{sys.version_info.major}.{sys.version_info.minor}")') +PY_MAJOR=$(echo "$PY_VER" | cut -d. -f1) +PY_MINOR=$(echo "$PY_VER" | cut -d. -f2) + +if [ "$PY_MAJOR" -lt 3 ] || { [ "$PY_MAJOR" -eq 3 ] && [ "$PY_MINOR" -lt 10 ]; }; then + error "Python 3.10+ required, found $PY_VER" +fi +info "Python $PY_VER OK" + +# ── Install ContextPilot ────────────────────────────────────────────────── +info "Installing ContextPilot..." +if python3 -c "import contextpilot" 2>/dev/null; then + info "ContextPilot already installed, upgrading..." + pip install --upgrade contextpilot -q +else + pip install contextpilot -q +fi + +# ── Determine backend URL and API type ──────────────────────────────────── +case "$PROVIDER" in + anthropic) + BACKEND_URL="https://api.anthropic.com" + API_KEY_VAR="ANTHROPIC_API_KEY" + API_TYPE="anthropic-messages" + MODEL_ID="claude-opus-4-6" + MODEL_NAME="Claude Opus 4.6 (via ContextPilot)" + CTX_WINDOW=200000 + MAX_TOKENS=32000 + ;; + openai) + BACKEND_URL="https://api.openai.com" + API_KEY_VAR="OPENAI_API_KEY" + API_TYPE="openai-completions" + MODEL_ID="gpt-4o" + MODEL_NAME="GPT-4o (via ContextPilot)" + CTX_WINDOW=128000 + MAX_TOKENS=16384 + ;; + *) + error "Unknown provider: $PROVIDER. Use 'anthropic' or 'openai'." + ;; +esac + +# ── Check API key ───────────────────────────────────────────────────────── +if [ -z "${!API_KEY_VAR:-}" ]; then + warn "$API_KEY_VAR not set. The proxy will start but requests will fail without a valid key." + warn "Set it with: export $API_KEY_VAR=your-key" +fi + +# ── Generate OpenClaw provider config ───────────────────────────────────── +OPENCLAW_DIR="$HOME/.openclaw" +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PATCH_FILE="$SCRIPT_DIR/contextpilot-provider.json" + +cat > "$PATCH_FILE" < $BACKEND_URL" +info "Press Ctrl+C to stop." +echo "" + +exec python3 -m contextpilot.server.http_server \ + --stateless \ + --port "$PORT" \ + --infer-api-url "$BACKEND_URL" diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index 9c730e0..e6207ff 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -327,3 +327,161 @@ def test_auth_headers_forwarded(self, client, mock_session): # X-ContextPilot-* should be stripped for k in outbound: assert not k.lower().startswith("x-contextpilot-") + + +# ============================================================================ +# Tool result intercept +# ============================================================================ + + +class TestToolResultIntercept: + def test_openai_tool_result_reordered(self, client, mock_session): + """OpenAI tool results with docs get reordered.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "user", "content": "Search for X"}, + {"role": "assistant", "content": None, + "tool_calls": [{"id": "tc1", "type": "function", + "function": {"name": "search", "arguments": "{}"}}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": ( + "\n" + "Tool Doc A\n" + "Tool Doc B\n" + "Tool Doc C\n" + "" + )}, + {"role": "user", "content": "Now summarize."}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + result = resp.json() + meta = result.get("_contextpilot", {}) + assert meta.get("intercepted") is True + assert meta.get("documents_reordered") is True + assert meta.get("sources", {}).get("tool_results", 0) >= 1 + # Forwarded body should have reordered tool content + forwarded = mock_session._last_json + tool_content = forwarded["messages"][3]["content"] + assert "" in tool_content + + def test_anthropic_tool_result_reordered(self, client, mock_session): + """Anthropic tool_result content blocks get reordered.""" + body = { + "model": "claude-3-opus-20240229", + "system": "You are a helper.", + "messages": [ + {"role": "user", "content": "Search for X"}, + {"role": "assistant", "content": [ + {"type": "tool_use", "id": "tu1", "name": "search", + "input": {"query": "X"}}, + ]}, + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": ( + "\n" + "Anthropic Doc A\n" + "Anthropic Doc B\n" + "Anthropic Doc C\n" + "" + )}, + ]}, + {"role": "user", "content": "Now summarize."}, + ], + } + resp = client.post("/v1/messages", json=body) + assert resp.status_code == 200 + result = resp.json() + meta = result.get("_contextpilot", {}) + assert meta.get("intercepted") is True + assert meta.get("sources", {}).get("tool_results", 0) >= 1 + # Forwarded body should have reordered tool_result content + forwarded = mock_session._last_json + tr_content = forwarded["messages"][2]["content"][0]["content"] + assert "" in tr_content + + def test_openai_system_and_tool_results(self, client, mock_session): + """Both system docs and tool result docs get reordered.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": ( + "\n" + "Sys A\n" + "Sys B\n" + "Sys C\n" + "" + )}, + {"role": "user", "content": "Search"}, + {"role": "assistant", "content": None, + "tool_calls": [{"id": "tc1"}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": "[1] Tool A [2] Tool B [3] Tool C"}, + {"role": "user", "content": "Summarize"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + result = resp.json() + meta = result.get("_contextpilot", {}) + assert meta.get("intercepted") is True + assert meta["sources"]["system"] == 1 + assert meta["sources"]["tool_results"] == 1 + assert meta["total_documents"] == 6 + + def test_scope_system_skips_tool_results(self, client, mock_session): + """scope=system should only reorder system docs, not tool results.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": ( + "\n" + "Sys A\n" + "Sys B\n" + "" + )}, + {"role": "tool", "tool_call_id": "tc1", + "content": "T1T2"}, + ], + } + resp = client.post( + "/v1/chat/completions", + json=body, + headers={"X-ContextPilot-Scope": "system"}, + ) + assert resp.status_code == 200 + result = resp.json() + meta = result.get("_contextpilot", {}) + assert meta.get("intercepted") is True + assert meta["sources"]["system"] == 1 + assert meta["sources"]["tool_results"] == 0 + + def test_scope_tool_results_skips_system(self, client, mock_session): + """scope=tool_results should only reorder tool result docs.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": ( + "\n" + "Sys A\n" + "Sys B\n" + "" + )}, + {"role": "tool", "tool_call_id": "tc1", + "content": "T1T2"}, + ], + } + resp = client.post( + "/v1/chat/completions", + json=body, + headers={"X-ContextPilot-Scope": "tool_results"}, + ) + assert resp.status_code == 200 + result = resp.json() + meta = result.get("_contextpilot", {}) + assert meta.get("intercepted") is True + assert meta["sources"]["system"] == 0 + assert meta["sources"]["tool_results"] == 1 diff --git a/tests/test_intercept_parser.py b/tests/test_intercept_parser.py index 79f584a..7a743fa 100644 --- a/tests/test_intercept_parser.py +++ b/tests/test_intercept_parser.py @@ -1,15 +1,24 @@ """Unit tests for contextpilot.server.intercept_parser.""" +import copy import pytest from contextpilot.server.intercept_parser import ( InterceptConfig, ExtractionResult, + ToolResultLocation, + MultiExtractionResult, parse_intercept_headers, extract_documents, extract_from_openai_chat, extract_from_anthropic_messages, + extract_from_openai_tool_results, + extract_from_anthropic_tool_results, + extract_all_openai, + extract_all_anthropic, reconstruct_openai_chat, reconstruct_anthropic_messages, + reconstruct_openai_tool_result, + reconstruct_anthropic_tool_result, reconstruct_content, ) @@ -57,6 +66,18 @@ def test_separator_header(self): config = parse_intercept_headers(headers) assert config.separator == "===" + def test_scope_header(self): + config = parse_intercept_headers({"X-ContextPilot-Scope": "tool_results"}) + assert config.scope == "tool_results" + + def test_scope_default(self): + config = parse_intercept_headers({}) + assert config.scope == "all" + + def test_scope_invalid_falls_back(self): + config = parse_intercept_headers({"X-ContextPilot-Scope": "invalid"}) + assert config.scope == "all" + # ============================================================================ # XML tag extraction @@ -452,3 +473,466 @@ def test_reconstruct_content_blocks_system(self): text_block = new_body["system"][0]["text"] assert "Q" in text_block assert "P" in text_block + + +# ============================================================================ +# Markdown header extraction +# ============================================================================ + + +class TestMarkdownHeaderExtraction: + def test_basic_split(self): + text = "# Section A\nContent A\n\n# Section B\nContent B" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "markdown_header" + assert len(result.documents) == 2 + assert "# Section A" in result.documents[0] + assert "# Section B" in result.documents[1] + + def test_h2_headers(self): + text = "## Part 1\nText 1\n\n## Part 2\nText 2\n\n## Part 3\nText 3" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) + assert result is not None + assert len(result.documents) == 3 + + def test_prefix_preserved(self): + text = "Some preamble\n\n# First\nA\n\n# Second\nB" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) + assert result is not None + assert "preamble" in result.prefix + assert len(result.documents) == 2 + + def test_single_header_returns_none(self): + text = "# Only One Section\nSome content here" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) + assert result is None + + def test_auto_priority_xml_over_markdown(self): + text = "AB" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "xml_tag" + + def test_auto_falls_back_to_markdown(self): + text = "# Topic A\nContent about A\n\n# Topic B\nContent about B" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "markdown_header" + + def test_roundtrip(self): + text = "Preamble\n\n# Alpha\nContent A\n\n# Beta\nContent B" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) + assert result is not None + rebuilt = reconstruct_content(result, list(reversed(result.documents))) + assert "# Beta" in rebuilt + assert "# Alpha" in rebuilt + # Prefix preserved + assert "Preamble" in rebuilt + + +# ============================================================================ +# File XML tags +# ============================================================================ + + +class TestFileXmlTags: + def test_files_wrapper(self): + text = "file1.py contentfile2.py content" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "xml_tag" + assert result.wrapper_tag == "files" + assert result.item_tag == "file" + assert result.documents == ["file1.py content", "file2.py content"] + + def test_file_tags_without_wrapper(self): + text = "A\nB" + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.item_tag == "file" + + +# ============================================================================ +# OpenAI tool result extraction +# ============================================================================ + + +class TestOpenAIToolResultExtraction: + def test_single_tool_result(self): + body = { + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "user", "content": "Search for X"}, + {"role": "assistant", "content": None, "tool_calls": [{"id": "tc1"}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": "Result AResult B"}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 1 + ext, loc = results[0] + assert ext.documents == ["Result A", "Result B"] + assert loc.msg_index == 3 + assert loc.block_index == -1 + + def test_multiple_tool_results(self): + body = { + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "tool", "tool_call_id": "tc1", + "content": "[1] Doc A [2] Doc B"}, + {"role": "tool", "tool_call_id": "tc2", + "content": "[1] Doc C [2] Doc D [3] Doc E"}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 2 + assert results[0][0].documents == ["Doc A", "Doc B"] + assert results[1][0].documents == ["Doc C", "Doc D", "Doc E"] + + def test_skip_no_docs(self): + body = { + "messages": [ + {"role": "tool", "tool_call_id": "tc1", + "content": "No documents here, just a plain response."}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 0 + + def test_skip_single_doc(self): + body = { + "messages": [ + {"role": "tool", "tool_call_id": "tc1", + "content": "Only one"}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 0 + + def test_content_blocks_format(self): + body = { + "messages": [ + {"role": "tool", "tool_call_id": "tc1", + "content": [ + {"type": "text", "text": "XY"} + ]}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 1 + assert results[0][1].block_index == 0 + + def test_camelcase_toolresult_role(self): + """OpenClaw internal format uses role='toolResult' (camelCase).""" + body = { + "messages": [ + {"role": "toolResult", "toolCallId": "tc1", + "content": [ + {"type": "text", "text": "AB"} + ]}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + assert len(results) == 1 + assert results[0][0].documents == ["A", "B"] + + +# ============================================================================ +# Anthropic tool result extraction +# ============================================================================ + + +class TestAnthropicToolResultExtraction: + def test_string_content(self): + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": "Res ARes B"}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + assert len(results) == 1 + ext, loc = results[0] + assert ext.documents == ["Res A", "Res B"] + assert loc.msg_index == 0 + assert loc.block_index == 0 + assert loc.inner_block_index == -1 + + def test_content_blocks(self): + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": [ + {"type": "text", "text": "[1] Alpha [2] Beta [3] Gamma"} + ]}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + assert len(results) == 1 + ext, loc = results[0] + assert ext.documents == ["Alpha", "Beta", "Gamma"] + assert loc.inner_block_index == 0 + + def test_no_tool_result(self): + body = { + "messages": [ + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": "Hi there"}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + assert len(results) == 0 + + def test_multiple_tool_results_in_one_message(self): + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": "AB"}, + {"type": "tool_result", "tool_use_id": "tu2", + "content": "CD"}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + assert len(results) == 2 + + def test_camelcase_toolresult_type(self): + """OpenClaw internal format uses type='toolResult' (camelCase).""" + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "toolResult", "toolUseId": "tu1", + "content": "XY"}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + assert len(results) == 1 + assert results[0][0].documents == ["X", "Y"] + + +# ============================================================================ +# Tool result reconstruction +# ============================================================================ + + +class TestToolResultReconstruction: + def test_openai_string_roundtrip(self): + body = { + "messages": [ + {"role": "tool", "tool_call_id": "tc1", + "content": "AB"}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + ext, loc = results[0] + body_copy = copy.deepcopy(body) + reconstruct_openai_tool_result(body_copy, ext, ["B", "A"], loc) + content = body_copy["messages"][0]["content"] + assert "B" in content + assert content.index("B") < content.index("A") + + def test_openai_blocks_roundtrip(self): + body = { + "messages": [ + {"role": "tool", "tool_call_id": "tc1", + "content": [ + {"type": "text", "text": "[1] X [2] Y [3] Z"} + ]}, + ], + } + config = InterceptConfig() + results = extract_from_openai_tool_results(body, config) + ext, loc = results[0] + body_copy = copy.deepcopy(body) + reconstruct_openai_tool_result(body_copy, ext, ["Z", "X", "Y"], loc) + text = body_copy["messages"][0]["content"][0]["text"] + assert "[1] Z" in text + + def test_anthropic_string_roundtrip(self): + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": "PQ"}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + ext, loc = results[0] + body_copy = copy.deepcopy(body) + reconstruct_anthropic_tool_result(body_copy, ext, ["Q", "P"], loc) + content = body_copy["messages"][0]["content"][0]["content"] + assert "Q" in content + + def test_anthropic_nested_blocks_roundtrip(self): + body = { + "messages": [ + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": [ + {"type": "text", "text": "[1] First [2] Second"} + ]}, + ]}, + ], + } + config = InterceptConfig() + results = extract_from_anthropic_tool_results(body, config) + ext, loc = results[0] + body_copy = copy.deepcopy(body) + reconstruct_anthropic_tool_result(body_copy, ext, ["Second", "First"], loc) + text = body_copy["messages"][0]["content"][0]["content"][0]["text"] + assert "[1] Second" in text + + +# ============================================================================ +# Scope filtering +# ============================================================================ + + +class TestScopeFiltering: + def _make_openai_body(self): + return { + "messages": [ + {"role": "system", "content": "SysASysB"}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": None, "tool_calls": [{"id": "tc1"}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": "ToolAToolB"}, + ], + } + + def _make_anthropic_body(self): + return { + "system": "SysXSysY", + "messages": [ + {"role": "user", "content": [ + {"type": "text", "text": "Hello"}, + ]}, + {"role": "assistant", "content": [ + {"type": "tool_use", "id": "tu1", "name": "search", "input": {}}, + ]}, + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": "ToolXToolY"}, + ]}, + ], + } + + def test_openai_scope_system_only(self): + body = self._make_openai_body() + config = InterceptConfig(scope="system") + result = extract_all_openai(body, config) + assert result.system_extraction is not None + assert len(result.tool_extractions) == 0 + + def test_openai_scope_tool_results_only(self): + body = self._make_openai_body() + config = InterceptConfig(scope="tool_results") + result = extract_all_openai(body, config) + assert result.system_extraction is None + assert len(result.tool_extractions) == 1 + + def test_openai_scope_all(self): + body = self._make_openai_body() + config = InterceptConfig(scope="all") + result = extract_all_openai(body, config) + assert result.system_extraction is not None + assert len(result.tool_extractions) == 1 + assert result.total_documents == 4 + + def test_anthropic_scope_system_only(self): + body = self._make_anthropic_body() + config = InterceptConfig(scope="system") + result = extract_all_anthropic(body, config) + assert result.system_extraction is not None + assert len(result.tool_extractions) == 0 + + def test_anthropic_scope_tool_results_only(self): + body = self._make_anthropic_body() + config = InterceptConfig(scope="tool_results") + result = extract_all_anthropic(body, config) + assert result.system_extraction is None + assert len(result.tool_extractions) == 1 + + def test_anthropic_scope_all(self): + body = self._make_anthropic_body() + config = InterceptConfig(scope="all") + result = extract_all_anthropic(body, config) + assert result.system_extraction is not None + assert len(result.tool_extractions) == 1 + assert result.total_documents == 4 + + +# ============================================================================ +# OpenClaw system prompt pattern +# ============================================================================ + + +class TestOpenClawSystemPromptPattern: + def test_openclaw_files_pattern(self): + """Simulate OpenClaw's system prompt with tags.""" + text = ( + "You are an AI assistant.\n" + "\n" + "path/to/file1.py\nclass Foo:\n pass\n" + "path/to/file2.py\ndef bar():\n return 42\n" + "path/to/file3.py\nimport os\nos.path.join('a', 'b')\n" + "\n" + "Answer the user's questions based on the files above." + ) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "xml_tag" + assert result.wrapper_tag == "files" + assert result.item_tag == "file" + assert len(result.documents) == 3 + assert "class Foo" in result.documents[0] + assert "Answer the user" in result.suffix + + def test_openclaw_markdown_header_pattern(self): + """Simulate OpenClaw's tool result with markdown headers.""" + text = ( + "# Search Results\n" + "## Result 1: Introduction to Python\n" + "Python is a high-level programming language.\n\n" + "## Result 2: Python Data Types\n" + "Python has several built-in data types.\n\n" + "## Result 3: Python Functions\n" + "Functions are reusable blocks of code." + ) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "markdown_header" + assert len(result.documents) >= 3 From f526544bedd07db516dcf9e84f4dc7d74d95a39a Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 17:04:19 +0000 Subject: [PATCH 03/76] Fix proxy breaking OpenClaw tool loop: move metadata to headers, broaden forwarding - Move _contextpilot response metadata from JSON body to X-ContextPilot-Result header so strict API parsers (OpenClaw SDK) receive unmodified responses - Broaden request header forwarding from 4-header whitelist to blacklist (only strip x-contextpilot-* and hop-by-hop), fixing dropped anthropic-beta etc. - Forward backend response headers and status code in streaming mode - Replace noisy print() in schedule_only() with logger.debug() Co-Authored-By: Claude Opus 4.6 (1M context) --- contextpilot/server/http_server.py | 68 +++++++++++++++++++++--------- contextpilot/server/live_index.py | 31 +++++--------- examples/openclaw/README.md | 13 ++---- examples/openclaw/SKILL.md | 15 ++----- tests/test_http_intercept.py | 51 ++++++++++++++-------- 5 files changed, 100 insertions(+), 78 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index a6128cf..98319b8 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -17,6 +17,7 @@ import argparse import copy +import json import logging import time import asyncio @@ -1119,48 +1120,77 @@ async def _intercept_and_forward(request: Request, api_format: str): else: target_url = f"{infer_api_url}/v1/chat/completions" - # Build outbound headers: forward auth, strip X-ContextPilot-* + # Build outbound headers: forward everything except X-ContextPilot-* + # and hop-by-hop headers that must not be forwarded by proxies. + _HOP_BY_HOP = frozenset(( + "host", "connection", "keep-alive", "transfer-encoding", + "te", "trailer", "upgrade", "proxy-authorization", + "proxy-authenticate", "content-length", + )) outbound_headers = {} for k, v in headers.items(): kl = k.lower() if kl.startswith("x-contextpilot-"): continue - if kl in ("authorization", "x-api-key", "anthropic-version", "content-type"): - outbound_headers[k] = v + if kl in _HOP_BY_HOP: + continue + outbound_headers[k] = v + + # Build ContextPilot metadata as a response header (not in body, + # which would break strict API response parsers like OpenClaw's SDK). + cp_response_headers = {} + if total_reordered > 0: + cp_response_headers["X-ContextPilot-Result"] = json.dumps({ + "intercepted": True, + "documents_reordered": True, + "total_documents": total_reordered, + "sources": { + "system": system_count, + "tool_results": tool_result_count, + }, + }) is_stream = body.get("stream", False) try: if is_stream: - # Streaming: passthrough SSE chunks - async def _stream_generator(): + # Streaming: passthrough SSE chunks, forwarding status & headers + async def _stream_with_headers(): async with _aiohttp_session.post( target_url, json=body, headers=outbound_headers ) as resp: + # Collect response headers to forward + fwd_headers = dict(cp_response_headers) + for k, v in resp.headers.items(): + kl = k.lower() + if kl in _HOP_BY_HOP or kl == "content-length": + continue + fwd_headers[k] = v + # Yield (headers_dict, status) as first item for the wrapper + yield resp.status, fwd_headers async for chunk in resp.content.iter_any(): yield chunk + stream_iter = _stream_with_headers() + status, fwd_headers = await stream_iter.__anext__() + return StreamingResponse( - _stream_generator(), - media_type="text/event-stream", + stream_iter, + status_code=status, + headers=fwd_headers, + media_type=fwd_headers.get("content-type", "text/event-stream"), ) else: - # Non-streaming: forward JSON + # Non-streaming: forward JSON with metadata in header only async with _aiohttp_session.post( target_url, json=body, headers=outbound_headers ) as resp: result = await resp.json() - if total_reordered > 0 and resp.status == 200: - result["_contextpilot"] = { - "intercepted": True, - "documents_reordered": True, - "total_documents": total_reordered, - "sources": { - "system": system_count, - "tool_results": tool_result_count, - }, - } - return JSONResponse(content=result, status_code=resp.status) + return JSONResponse( + content=result, + status_code=resp.status, + headers=cp_response_headers, + ) except aiohttp.ClientError as e: logger.error(f"Error forwarding intercepted request: {e}") diff --git a/contextpilot/server/live_index.py b/contextpilot/server/live_index.py index 85eca01..46ef85c 100644 --- a/contextpilot/server/live_index.py +++ b/contextpilot/server/live_index.py @@ -898,26 +898,21 @@ def schedule_only(self, contexts: List[List[int]]) -> Dict: Returns: Dictionary with scheduled results (no request_id mapping) """ - print("=" * 80) - print("SCHEDULING BATCH (STATELESS MODE)") - print("=" * 80) - + logger.debug("Scheduling batch (stateless mode)") + # Step 1: Build static index (clustering + reordering) - print("\n1. Building static index...") result = self.fit_transform(contexts) - - print(f" ✓ Built tree with {result.stats['total_nodes']} nodes") - print(f" ✓ Leaf nodes: {result.stats['leaf_nodes']}") - + logger.debug( + f"Built tree: {result.stats['total_nodes']} nodes, " + f"{result.stats['leaf_nodes']} leaves" + ) + # Step 2: Inter-context scheduling - print("\n2. Scheduling contexts for optimal execution...") scheduled_reordered, scheduled_originals, final_mapping, groups = \ self.inter_scheduler.schedule_contexts(result) - - print(f" ✓ Created {len(groups)} execution groups") - - # Return results without going live (stateless) - scheduled_result = { + logger.debug(f"Created {len(groups)} execution groups") + + return { 'reordered_contexts': scheduled_reordered, 'original_indices': final_mapping, 'scheduled_originals': scheduled_originals, @@ -929,12 +924,6 @@ def schedule_only(self, contexts: List[List[int]]) -> Dict: 'num_groups': len(groups), } } - - print("\n" + "=" * 80) - print("✓ BATCH SCHEDULED (Stateless - no cache tracking)") - print("=" * 80 + "\n") - - return scheduled_result def _initialize_live_metadata(self, initial_tokens_per_context: int, num_input_contexts: int = None) -> Tuple[Dict[str, int], List[str]]: """ diff --git a/examples/openclaw/README.md b/examples/openclaw/README.md index 77cc6f2..1f28d16 100644 --- a/examples/openclaw/README.md +++ b/examples/openclaw/README.md @@ -88,17 +88,10 @@ In OpenClaw, select the model from the ContextPilot provider. Search/memory resu ### Step 4: Verify -Check API responses for `_contextpilot` metadata: +Check the `X-ContextPilot-Result` response header for metadata: -```json -{ - "_contextpilot": { - "intercepted": true, - "documents_reordered": true, - "total_documents": 5, - "sources": { "system": 1, "tool_results": 1 } - } -} +``` +X-ContextPilot-Result: {"intercepted":true,"documents_reordered":true,"total_documents":5,"sources":{"system":1,"tool_results":1}} ``` ## Scope Control diff --git a/examples/openclaw/SKILL.md b/examples/openclaw/SKILL.md index 3c39f1a..8024a7c 100644 --- a/examples/openclaw/SKILL.md +++ b/examples/openclaw/SKILL.md @@ -29,17 +29,10 @@ When your request contains multiple documents (in system prompts or tool results ## How to Verify -Check the `_contextpilot` field in API responses: - -```json -{ - "_contextpilot": { - "intercepted": true, - "documents_reordered": true, - "total_documents": 5, - "sources": {"system": 1, "tool_results": 1} - } -} +Check the `X-ContextPilot-Result` response header: + +``` +X-ContextPilot-Result: {"intercepted":true,"documents_reordered":true,"total_documents":5,"sources":{"system":1,"tool_results":1}} ``` ## Configuration Headers diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index e6207ff..7bb9ee7 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -21,6 +21,19 @@ # ============================================================================ +class FakeHeaders: + """Fake multidict-like headers for aiohttp responses.""" + + def __init__(self, headers=None): + self._headers = headers or {"content-type": "application/json"} + + def items(self): + return self._headers.items() + + def get(self, key, default=None): + return self._headers.get(key, default) + + class FakeResponse: """Fake aiohttp response for mocking.""" @@ -28,6 +41,7 @@ def __init__(self, json_body, status=200): self._json = json_body self.status = status self.content = FakeStreamContent() + self.headers = FakeHeaders() async def json(self): return self._json @@ -56,6 +70,7 @@ class FakeStreamResponse(FakeResponse): def __init__(self, chunks=None, status=200): super().__init__({}, status) self.content = FakeStreamContent(chunks) + self.headers = FakeHeaders({"content-type": "text/event-stream"}) class FakeSession: @@ -77,6 +92,14 @@ def get(self, url): return self._response +def _cp_meta(resp): + """Extract ContextPilot metadata from the X-ContextPilot-Result response header.""" + raw = resp.headers.get("x-contextpilot-result") + if raw is None: + return {} + return json.loads(raw) + + @pytest.fixture def mock_session(): """Provide a FakeSession and patch it into http_server globals.""" @@ -125,11 +148,12 @@ def test_basic_intercept_reorders(self, client, mock_session): } resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - result = resp.json() # Backend was called assert mock_session._last_url == "http://mock-backend:30000/v1/chat/completions" - # Metadata injected - assert result.get("_contextpilot", {}).get("intercepted") is True + # Metadata in response header (not body) + meta = _cp_meta(resp) + assert meta.get("intercepted") is True + assert "_contextpilot" not in resp.json() # The forwarded body should have reordered docs (still in XML format) forwarded = mock_session._last_json sys_content = forwarded["messages"][0]["content"] @@ -158,8 +182,7 @@ def test_bypass_when_disabled(self, client, mock_session): forwarded = mock_session._last_json assert forwarded["messages"][0]["content"] == body["messages"][0]["content"] # No contextpilot metadata - result = resp.json() - assert "_contextpilot" not in result + assert _cp_meta(resp) == {} def test_bypass_when_no_docs(self, client, mock_session): """When system message has no extractable docs, forward unmodified.""" @@ -189,8 +212,7 @@ def test_bypass_when_single_doc(self, client, mock_session): } resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - result = resp.json() - assert "_contextpilot" not in result + assert _cp_meta(resp) == {} def test_numbered_format(self, client, mock_session): """Numbered format extraction and forwarding.""" @@ -358,8 +380,7 @@ def test_openai_tool_result_reordered(self, client, mock_session): } resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - result = resp.json() - meta = result.get("_contextpilot", {}) + meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta.get("documents_reordered") is True assert meta.get("sources", {}).get("tool_results", 0) >= 1 @@ -394,8 +415,7 @@ def test_anthropic_tool_result_reordered(self, client, mock_session): } resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 - result = resp.json() - meta = result.get("_contextpilot", {}) + meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta.get("sources", {}).get("tool_results", 0) >= 1 # Forwarded body should have reordered tool_result content @@ -425,8 +445,7 @@ def test_openai_system_and_tool_results(self, client, mock_session): } resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - result = resp.json() - meta = result.get("_contextpilot", {}) + meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta["sources"]["system"] == 1 assert meta["sources"]["tool_results"] == 1 @@ -453,8 +472,7 @@ def test_scope_system_skips_tool_results(self, client, mock_session): headers={"X-ContextPilot-Scope": "system"}, ) assert resp.status_code == 200 - result = resp.json() - meta = result.get("_contextpilot", {}) + meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta["sources"]["system"] == 1 assert meta["sources"]["tool_results"] == 0 @@ -480,8 +498,7 @@ def test_scope_tool_results_skips_system(self, client, mock_session): headers={"X-ContextPilot-Scope": "tool_results"}, ) assert resp.status_code == 200 - result = resp.json() - meta = result.get("_contextpilot", {}) + meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta["sources"]["system"] == 0 assert meta["sources"]["tool_results"] == 1 From f328d6ab4de0594d0979d364615781ab2d5de596 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 17:54:51 +0000 Subject: [PATCH 04/76] Sync ContextPilot request_ids with SGLang rids in proxy and intercept paths - Move proxy_completions metadata from response body to X-ContextPilot-Result header - Inject rid in intercept path when running in stateful mode (index active) - Add tests for header metadata, rid injection, and stateless bypass Co-Authored-By: Claude Opus 4.6 (1M context) --- contextpilot/server/http_server.py | 17 ++++++-- tests/test_http_intercept.py | 66 ++++++++++++++++++++++++++++++ 2 files changed, 79 insertions(+), 4 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 98319b8..5b3056d 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -962,15 +962,17 @@ async def proxy_completions(request: Request): # Token tracking is handled by the inference engine via CONTEXTPILOT_INDEX_URL # The engine calls /evict after its internal cache eviction - # Add request_id to response for client reference + # Add request_id to response header (not body, to avoid + # breaking strict API response parsers). + cp_headers = {} if request_id and response.status == 200: usage = result.get("usage", {}) - result["_contextpilot"] = { + cp_headers["X-ContextPilot-Result"] = json.dumps({ "request_id": request_id, "tokens_reported": usage.get("total_tokens", 0), - } + }) - return JSONResponse(content=result, status_code=response.status) + return JSONResponse(content=result, status_code=response.status, headers=cp_headers) except aiohttp.ClientError as e: logger.error(f"Error proxying to inference engine: {e}") @@ -1112,6 +1114,13 @@ async def _intercept_and_forward(request: Request, api_format: str): logger.warning(f"Intercept extraction/reorder failed, forwarding original: {e}") total_reordered = 0 + # In stateful mode, inject ContextPilot request_id as `rid` so SGLang + # uses the same ID for cache tracking (enables eviction sync). + if not _stateless_mode and _index is not None: + request_id = f"req-{uuid.uuid4().hex[:12]}" + body["rid"] = request_id + logger.debug(f"Intercept: injected rid={request_id}") + # Determine target URL if api_format == _OPENAI_CHAT: target_url = f"{infer_api_url}/v1/chat/completions" diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index 7bb9ee7..ec8963c 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -318,6 +318,21 @@ def test_completions_still_works(self, client, mock_session): resp = client.post("/v1/completions", json=body) assert resp.status_code == 200 + def test_completions_metadata_in_header(self, client, mock_session): + """proxy_completions puts metadata in X-ContextPilot-Result header, not body.""" + mock_session._response = FakeResponse( + {"choices": [{"text": "hello"}], "usage": {"total_tokens": 5}} + ) + body = {"model": "gpt-4", "prompt": "Hello", "request_id": "req-abc123"} + resp = client.post("/v1/completions", json=body) + assert resp.status_code == 200 + # Metadata should be in header + meta = _cp_meta(resp) + assert meta.get("request_id") == "req-abc123" + assert meta.get("tokens_reported") == 5 + # Body should NOT contain _contextpilot + assert "_contextpilot" not in resp.json() + # ============================================================================ # Header forwarding @@ -502,3 +517,54 @@ def test_scope_tool_results_skips_system(self, client, mock_session): assert meta.get("intercepted") is True assert meta["sources"]["system"] == 0 assert meta["sources"]["tool_results"] == 1 + + +# ============================================================================ +# RID injection in intercept (stateful mode) +# ============================================================================ + + +class TestInterceptRidInjection: + def test_rid_injected_in_stateful_mode(self, mock_session): + """In stateful mode, intercept injects rid into forwarded body.""" + original_session = http_mod._aiohttp_session + original_url = http_mod._infer_api_url + original_stateless = http_mod._stateless_mode + original_index = http_mod._index + http_mod._aiohttp_session = mock_session + http_mod._infer_api_url = "http://mock-backend:30000" + http_mod._stateless_mode = False + http_mod._index = MagicMock() # non-None → stateful + try: + client = TestClient(app, raise_server_exceptions=False) + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "Plain text."}, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + forwarded = mock_session._last_json + assert "rid" in forwarded + assert forwarded["rid"].startswith("req-") + finally: + http_mod._aiohttp_session = original_session + http_mod._infer_api_url = original_url + http_mod._stateless_mode = original_stateless + http_mod._index = original_index + + def test_no_rid_in_stateless_mode(self, client, mock_session): + """In stateless mode (default for tests), no rid is injected.""" + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "Plain text."}, + {"role": "user", "content": "Hello"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + forwarded = mock_session._last_json + assert "rid" not in forwarded From 9c773aff1557394f686ce374e869ed4dd17d50b1 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 18:55:26 +0000 Subject: [PATCH 05/76] Add DEBUG Log optionas --- contextpilot/server/http_server.py | 51 +++++++++++++++++++++++++++--- 1 file changed, 46 insertions(+), 5 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 5b3056d..4c46113 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -991,10 +991,17 @@ async def proxy_completions(request: Request): _ANTHROPIC_MESSAGES = "anthropic_messages" -def _reorder_documents(docs: List[str], config: InterceptConfig) -> List[str]: +def _doc_preview(doc: str, max_len: int = 60) -> str: + """Truncate a document string for log preview.""" + doc = doc.replace("\n", " ").strip() + return doc[:max_len] + "…" if len(doc) > max_len else doc + + +def _reorder_documents(docs: List[str], config: InterceptConfig) -> tuple: """Reorder a list of document strings using ContextPilot clustering. - Returns the reordered document list. + Returns (reordered_docs, original_order, reordered_order) where the + order lists are 0-based indices suitable for logging/headers. """ temp_index = ContextPilot( alpha=config.alpha, @@ -1014,9 +1021,29 @@ def _reorder_documents(docs: List[str], config: InterceptConfig) -> List[str]: next_id += 1 converted.append(sid) + original_order = list(range(len(docs))) sched_result = temp_index.schedule_only(contexts=[converted]) reordered_ids = sched_result["reordered_contexts"][0] - return [id_to_str[i] for i in reordered_ids] + reordered_docs = [id_to_str[i] for i in reordered_ids] + + # Map reordered docs back to their original indices + id_to_orig_idx: Dict[int, List[int]] = {} + for idx, sid in enumerate(converted): + id_to_orig_idx.setdefault(sid, []).append(idx) + reordered_order = [] + id_consumed: Dict[int, int] = {} + for sid in reordered_ids: + pos = id_consumed.get(sid, 0) + reordered_order.append(id_to_orig_idx[sid][pos]) + id_consumed[sid] = pos + 1 + + # Debug log with previews + if logger.isEnabledFor(logging.DEBUG): + for label, order in [("BEFORE", original_order), ("AFTER", reordered_order)]: + previews = [f" [{i}] {_doc_preview(docs[i])}" for i in order] + logger.debug(f"Reorder {label}:\n" + "\n".join(previews)) + + return reordered_docs, original_order, reordered_order async def _intercept_and_forward(request: Request, api_format: str): @@ -1052,6 +1079,7 @@ async def _intercept_and_forward(request: Request, api_format: str): total_reordered = 0 system_count = 0 tool_result_count = 0 + reorder_details = [] # collect per-source reorder info if config.enabled: try: @@ -1068,7 +1096,13 @@ async def _intercept_and_forward(request: Request, api_format: str): if multi.system_extraction: extraction, sys_idx = multi.system_extraction if len(extraction.documents) >= 2: - reordered_docs = _reorder_documents(extraction.documents, config) + reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) + reorder_details.append({ + "source": "system", + "count": len(extraction.documents), + "original_order": orig_order, + "reordered_order": new_order, + }) if api_format == _OPENAI_CHAT: new_content = reconstruct_content(extraction, reordered_docs) msg = body["messages"][sys_idx] @@ -1096,7 +1130,13 @@ async def _intercept_and_forward(request: Request, api_format: str): # Reorder tool result extractions for extraction, location in multi.tool_extractions: if len(extraction.documents) >= 2: - reordered_docs = _reorder_documents(extraction.documents, config) + reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) + reorder_details.append({ + "source": f"tool_result[{location.msg_index}]", + "count": len(extraction.documents), + "original_order": orig_order, + "reordered_order": new_order, + }) if api_format == _OPENAI_CHAT: reconstruct_openai_tool_result(body, extraction, reordered_docs, location) elif api_format == _ANTHROPIC_MESSAGES: @@ -1157,6 +1197,7 @@ async def _intercept_and_forward(request: Request, api_format: str): "system": system_count, "tool_results": tool_result_count, }, + "reorder_details": reorder_details, }) is_stream = body.get("stream", False) From ad7170d6a97fab27d400ce846077b2415897adc2 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 20:30:00 +0000 Subject: [PATCH 06/76] update openclaw readme --- examples/openclaw/README.md | 77 +++++++++++++++++++++++++++++++++++++ 1 file changed, 77 insertions(+) diff --git a/examples/openclaw/README.md b/examples/openclaw/README.md index 1f28d16..976925f 100644 --- a/examples/openclaw/README.md +++ b/examples/openclaw/README.md @@ -94,6 +94,83 @@ Check the `X-ContextPilot-Result` response header for metadata: X-ContextPilot-Result: {"intercepted":true,"documents_reordered":true,"total_documents":5,"sources":{"system":1,"tool_results":1}} ``` +## Using with SGLang (Self-Hosted Models) + +For self-hosted models served by SGLang, ContextPilot acts as a proxy between OpenClaw and SGLang, enabling automatic document reordering and KV cache tracking. + +``` +OpenClaw (WSL/local) ──▶ ContextPilot Proxy (server:8765) ──▶ SGLang (server:30000) +``` + +### Step 1: Start SGLang with tool calling support + +```bash +python -m sglang.launch_server \ + --model-path Qwen/Qwen3.5-27B \ + --tool-call-parser qwen3_coder \ + --port 30000 +``` + +> `--tool-call-parser` is required for OpenClaw's tool loop to work. Without it, tool calls are output as plain text and the agent loop won't continue. + +### Step 2: Start ContextPilot proxy + +```bash +python -m contextpilot.server.http_server \ + --port 8765 \ + --infer-api-url http://localhost:30000 \ + --model Qwen/Qwen3.5-27B +``` + +### Step 3: Configure OpenClaw + +Merge into `~/.openclaw/openclaw.json`: + +```json +{ + "models": { + "mode": "merge", + "providers": { + "contextpilot-sglang": { + "baseUrl": "http://:8765/v1", + "apiKey": "placeholder", + "api": "openai-completions", + "headers": { + "X-ContextPilot-Scope": "all" + }, + "models": [ + { + "id": "Qwen/Qwen3.5-27B", + "name": "Qwen 3.5 27B (SGLang via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 131072, + "maxTokens": 8192 + } + ] + } + } + }, + "agents": { + "defaults": { + "model": { + "primary": "contextpilot-sglang/Qwen/Qwen3.5-27B" + } + } + } +} +``` + +> **Important**: Use the server's IP address (not hostname) in `baseUrl` to avoid IPv6 DNS resolution issues in Node.js/WSL environments. + +### Troubleshooting + +| Symptom | Cause | Fix | +|---------|-------|-----| +| `Connection error.` | IPv6 DNS resolution fails in Node.js | Use IP address in `baseUrl`, or `export NODE_OPTIONS="--dns-result-order=ipv4first"` | +| Tool call appears as XML text, agent stops | SGLang not parsing tool calls | Add `--tool-call-parser qwen3_coder` to SGLang launch command | +| `Invalid JSON body` | Multiline curl command broken by shell | Use single-line JSON in curl | + ## Scope Control | Header value | System prompt | Tool results | From 5434e2efe74fbc28f937169d962c44ebe892598c Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 20:37:48 +0000 Subject: [PATCH 07/76] Update openclaw docs --- docs/guides/openclaw.md | 77 +++++++++++++++++++++++++++++++++++------ 1 file changed, 66 insertions(+), 11 deletions(-) diff --git a/docs/guides/openclaw.md b/docs/guides/openclaw.md index 1006899..0f510fd 100644 --- a/docs/guides/openclaw.md +++ b/docs/guides/openclaw.md @@ -105,25 +105,76 @@ Merge into `~/.openclaw/openclaw.json`: For OpenAI, use `api: "openai-completions"` and point `--infer-api-url` to `https://api.openai.com`. See `examples/openclaw/openclaw.json.example` for both providers. -## Verify +### Option C: Self-hosted model via SGLang + +For self-hosted models, ContextPilot proxies between OpenClaw and SGLang: + +``` +OpenClaw ──▶ ContextPilot Proxy (server:8765) ──▶ SGLang (server:30000) +``` + +Start SGLang with tool calling support: + +```bash +python -m sglang.launch_server \ + --model-path Qwen/Qwen3.5-27B \ + --tool-call-parser qwen3_coder \ + --port 30000 +``` -Check the `_contextpilot` field in API responses: +Start ContextPilot proxy: + +```bash +python -m contextpilot.server.http_server \ + --port 8765 \ + --infer-api-url http://localhost:30000 \ + --model Qwen/Qwen3.5-27B +``` + +Merge into `~/.openclaw/openclaw.json`: ```json { - "_contextpilot": { - "intercepted": true, - "documents_reordered": true, - "total_documents": 8, - "sources": { - "system": 1, - "tool_results": 2 + "models": { + "mode": "merge", + "providers": { + "contextpilot-sglang": { + "baseUrl": "http://:8765/v1", + "apiKey": "placeholder", + "api": "openai-completions", + "headers": { "X-ContextPilot-Scope": "all" }, + "models": [ + { + "id": "Qwen/Qwen3.5-27B", + "name": "Qwen 3.5 27B (SGLang via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 131072, + "maxTokens": 8192 + } + ] + } + } + }, + "agents": { + "defaults": { + "model": { "primary": "contextpilot-sglang/Qwen/Qwen3.5-27B" } } } } ``` -If `_contextpilot` is absent, the request had fewer than 2 extractable documents (nothing to reorder). +> **Important**: Use the server's IP address (not hostname) in `baseUrl` to avoid IPv6 DNS resolution issues in Node.js/WSL environments. `--tool-call-parser` is required for OpenClaw's tool loop to work. + +## Verify + +Check the `X-ContextPilot-Result` response header: + +``` +X-ContextPilot-Result: {"intercepted":true,"documents_reordered":true,"total_documents":8,"sources":{"system":1,"tool_results":2}} +``` + +If the header is absent, the request had fewer than 2 extractable documents (nothing to reorder). ## Document Extraction @@ -163,10 +214,14 @@ Set via headers in the OpenClaw provider config, or per-request. ## Troubleshooting -**No `_contextpilot` in response** — Request had < 2 extractable documents. Check that search/memory tools are returning multiple results. +**No `X-ContextPilot-Result` header** — Request had < 2 extractable documents. Check that search/memory tools are returning multiple results. **Connection refused** — Proxy not running. Check `curl http://localhost:8765/health`. +**`Connection error.` from OpenClaw (Node.js)** — IPv6 DNS resolution failure. Use IP address in `baseUrl`, or `export NODE_OPTIONS="--dns-result-order=ipv4first"`. + **401/403 from backend** — API key not set or invalid. The proxy forwards auth headers as-is. +**Tool call appears as XML text, agent stops** — SGLang not parsing tool calls into structured `tool_calls`. Add `--tool-call-parser qwen3_coder` (or the appropriate parser for your model) to SGLang launch command. + **Tool results not reordered** — Check scope is `all` or `tool_results`. Verify tool results use a supported format. From c6d8792092bee300a74574b31fa8b2180bb3d199 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 1 Mar 2026 20:51:15 +0000 Subject: [PATCH 08/76] Update openclaw README --- docs/guides/openclaw.md | 69 +++++++++++++++++++++++++++---------- examples/openclaw/README.md | 59 +++++++++++++++++++++++++------ 2 files changed, 98 insertions(+), 30 deletions(-) diff --git a/docs/guides/openclaw.md b/docs/guides/openclaw.md index 0f510fd..3d797b6 100644 --- a/docs/guides/openclaw.md +++ b/docs/guides/openclaw.md @@ -131,39 +131,70 @@ python -m contextpilot.server.http_server \ --model Qwen/Qwen3.5-27B ``` -Merge into `~/.openclaw/openclaw.json`: +Configure OpenClaw (replace `` with your server's IP): -```json -{ - "models": { +```bash +# Requires jq (install: sudo apt install jq / brew install jq) +jq ' + .agents.defaults.model.primary = "contextpilot-sglang/Qwen/Qwen3.5-27B" | + .models = { "mode": "merge", "providers": { "contextpilot-sglang": { "baseUrl": "http://:8765/v1", "apiKey": "placeholder", "api": "openai-completions", - "headers": { "X-ContextPilot-Scope": "all" }, - "models": [ - { - "id": "Qwen/Qwen3.5-27B", - "name": "Qwen 3.5 27B (SGLang via ContextPilot)", - "reasoning": false, - "input": ["text"], - "contextWindow": 131072, - "maxTokens": 8192 - } - ] + "headers": {"X-ContextPilot-Scope": "all"}, + "models": [{ + "id": "Qwen/Qwen3.5-27B", + "name": "Qwen 3.5 27B (SGLang via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 131072, + "maxTokens": 8192 + }] } } - }, - "agents": { - "defaults": { - "model": { "primary": "contextpilot-sglang/Qwen/Qwen3.5-27B" } + } +' ~/.openclaw/openclaw.json > /tmp/oc.json && mv /tmp/oc.json ~/.openclaw/openclaw.json +``` + +Then restart: + +```bash +pkill -f openclaw && openclaw gateway start && openclaw tui +``` + +
+Without jq: manually edit ~/.openclaw/openclaw.json + +1. Change `agents.defaults.model.primary` to `"contextpilot-sglang/Qwen/Qwen3.5-27B"` +2. Add a `"models"` key at the top level: + +```json +"models": { + "mode": "merge", + "providers": { + "contextpilot-sglang": { + "baseUrl": "http://:8765/v1", + "apiKey": "placeholder", + "api": "openai-completions", + "headers": { "X-ContextPilot-Scope": "all" }, + "models": [{ + "id": "Qwen/Qwen3.5-27B", + "name": "Qwen 3.5 27B (SGLang via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 131072, + "maxTokens": 8192 + }] } } } ``` +
+ > **Important**: Use the server's IP address (not hostname) in `baseUrl` to avoid IPv6 DNS resolution issues in Node.js/WSL environments. `--tool-call-parser` is required for OpenClaw's tool loop to work. ## Verify diff --git a/examples/openclaw/README.md b/examples/openclaw/README.md index 976925f..d497ec8 100644 --- a/examples/openclaw/README.md +++ b/examples/openclaw/README.md @@ -124,10 +124,54 @@ python -m contextpilot.server.http_server \ ### Step 3: Configure OpenClaw -Merge into `~/.openclaw/openclaw.json`: +Patch your OpenClaw config (replace `` with your server's IP): + +```bash +# Requires jq (install: sudo apt install jq / brew install jq) +jq ' + .agents.defaults.model.primary = "contextpilot-sglang/Qwen/Qwen3.5-27B" | + .models = { + "mode": "merge", + "providers": { + "contextpilot-sglang": { + "baseUrl": "http://:8765/v1", + "apiKey": "placeholder", + "api": "openai-completions", + "headers": {"X-ContextPilot-Scope": "all"}, + "models": [{ + "id": "Qwen/Qwen3.5-27B", + "name": "Qwen 3.5 27B (SGLang via ContextPilot)", + "reasoning": false, + "input": ["text"], + "contextWindow": 131072, + "maxTokens": 8192 + }] + } + } + } +' ~/.openclaw/openclaw.json > /tmp/oc.json && mv /tmp/oc.json ~/.openclaw/openclaw.json +``` + +Then restart OpenClaw: + +```bash +pkill -f openclaw && openclaw gateway start && openclaw tui +``` + +
+Without jq: manually add this JSON to ~/.openclaw/openclaw.json + +Add a `"models"` key at the top level and change `agents.defaults.model.primary`: ```json { + "agents": { + "defaults": { + "model": { + "primary": "contextpilot-sglang/Qwen/Qwen3.5-27B" + } + } + }, "models": { "mode": "merge", "providers": { @@ -135,9 +179,7 @@ Merge into `~/.openclaw/openclaw.json`: "baseUrl": "http://:8765/v1", "apiKey": "placeholder", "api": "openai-completions", - "headers": { - "X-ContextPilot-Scope": "all" - }, + "headers": { "X-ContextPilot-Scope": "all" }, "models": [ { "id": "Qwen/Qwen3.5-27B", @@ -150,17 +192,12 @@ Merge into `~/.openclaw/openclaw.json`: ] } } - }, - "agents": { - "defaults": { - "model": { - "primary": "contextpilot-sglang/Qwen/Qwen3.5-27B" - } - } } } ``` +
+ > **Important**: Use the server's IP address (not hostname) in `baseUrl` to avoid IPv6 DNS resolution issues in Node.js/WSL environments. ### Troubleshooting From 378ee0ab3f8c1daff62f4cb02ec17a466094bb5b Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Mon, 2 Mar 2026 00:13:30 +0000 Subject: [PATCH 09/76] Support http intercept proxy --- contextpilot/server/http_server.py | 125 +++++++------- contextpilot/server/intercept_parser.py | 60 ++++++- contextpilot/server/live_index.py | 2 +- tests/test_http_intercept.py | 213 +++++++++++++++++++----- tests/test_intercept_parser.py | 168 ++++++++++++++++--- 5 files changed, 438 insertions(+), 130 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 4c46113..28ae75e 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -82,6 +82,11 @@ _id_to_str: Dict[int, str] = {} _next_str_id: int = 0 +# Persistent index for the intercept path. First request builds it +# (no reorder); subsequent requests use build_incremental to search +# the existing tree and reorder documents for prefix sharing. +_intercept_index: Optional[ContextPilot] = None + # Request ID normalization (engine -> ContextPilot canonical IDs) _ENGINE_REQ_ID_PREFIX = re.compile(r"^(cmpl-|chatcmpl-|batch-)") _VLLM_REQ_SUFFIX = re.compile(r"^(req-[^-]+)-\d+-[0-9a-f]+$") @@ -998,44 +1003,39 @@ def _doc_preview(doc: str, max_len: int = 60) -> str: def _reorder_documents(docs: List[str], config: InterceptConfig) -> tuple: - """Reorder a list of document strings using ContextPilot clustering. + """Reorder a list of document strings via the persistent intercept index. + + Each document is tokenized into words and treated as its own context. + The first call (``_intercept_index is None``) builds the index but + returns documents unchanged — there is no cached state to optimise + against. Subsequent calls use ``build_incremental`` which searches + the existing tree, groups documents sharing cached prefixes, and + returns an optimised execution order. Returns (reordered_docs, original_order, reordered_order) where the order lists are 0-based indices suitable for logging/headers. """ - temp_index = ContextPilot( - alpha=config.alpha, - use_gpu=False, - linkage_method=config.linkage_method, - ) - str_to_id: Dict[str, int] = {} - id_to_str: Dict[int, str] = {} - next_id = 0 - converted = [] - for doc in docs: - sid = str_to_id.get(doc) - if sid is None: - sid = next_id - str_to_id[doc] = sid - id_to_str[sid] = doc - next_id += 1 - converted.append(sid) + global _intercept_index + # Each document becomes its own context (list of words). + contexts = [re.findall(r'\w+', doc.lower()) for doc in docs] original_order = list(range(len(docs))) - sched_result = temp_index.schedule_only(contexts=[converted]) - reordered_ids = sched_result["reordered_contexts"][0] - reordered_docs = [id_to_str[i] for i in reordered_ids] - - # Map reordered docs back to their original indices - id_to_orig_idx: Dict[int, List[int]] = {} - for idx, sid in enumerate(converted): - id_to_orig_idx.setdefault(sid, []).append(idx) - reordered_order = [] - id_consumed: Dict[int, int] = {} - for sid in reordered_ids: - pos = id_consumed.get(sid, 0) - reordered_order.append(id_to_orig_idx[sid][pos]) - id_consumed[sid] = pos + 1 + + if _intercept_index is None: + # First call — build index (clustering + scheduling). + _intercept_index = ContextPilot( + alpha=config.alpha, + use_gpu=False, + linkage_method=config.linkage_method, + ) + result = _intercept_index.build_and_schedule(contexts=contexts) + logger.debug("Intercept index initialised") + else: + # Subsequent calls — search existing tree and reorder for prefix sharing. + result = _intercept_index.build_incremental(contexts=contexts) + + reordered_order = result["original_indices"] + reordered_docs = [docs[i] for i in reordered_order] # Debug log with previews if logger.isEnabledFor(logging.DEBUG): @@ -1097,40 +1097,43 @@ async def _intercept_and_forward(request: Request, api_format: str): extraction, sys_idx = multi.system_extraction if len(extraction.documents) >= 2: reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) - reorder_details.append({ - "source": "system", - "count": len(extraction.documents), - "original_order": orig_order, - "reordered_order": new_order, - }) - if api_format == _OPENAI_CHAT: - new_content = reconstruct_content(extraction, reordered_docs) - msg = body["messages"][sys_idx] - if isinstance(msg.get("content"), str): - msg["content"] = new_content - elif isinstance(msg.get("content"), list): - for block in msg["content"]: - if isinstance(block, dict) and block.get("type") == "text": - if extract_documents(block.get("text", ""), InterceptConfig()): - block["text"] = new_content - break - elif api_format == _ANTHROPIC_MESSAGES: - new_content = reconstruct_content(extraction, reordered_docs) - if isinstance(body.get("system"), str): - body["system"] = new_content - elif isinstance(body.get("system"), list): - for block in body["system"]: - if isinstance(block, dict) and block.get("type") == "text": - if extract_documents(block.get("text", ""), InterceptConfig()): - block["text"] = new_content - break - total_reordered += len(extraction.documents) - system_count = 1 + if orig_order != new_order: + reorder_details.append({ + "source": "system", + "count": len(extraction.documents), + "original_order": orig_order, + "reordered_order": new_order, + }) + if api_format == _OPENAI_CHAT: + new_content = reconstruct_content(extraction, reordered_docs) + msg = body["messages"][sys_idx] + if isinstance(msg.get("content"), str): + msg["content"] = new_content + elif isinstance(msg.get("content"), list): + for block in msg["content"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + elif api_format == _ANTHROPIC_MESSAGES: + new_content = reconstruct_content(extraction, reordered_docs) + if isinstance(body.get("system"), str): + body["system"] = new_content + elif isinstance(body.get("system"), list): + for block in body["system"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + total_reordered += len(extraction.documents) + system_count = 1 # Reorder tool result extractions for extraction, location in multi.tool_extractions: if len(extraction.documents) >= 2: reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) + if orig_order == new_order: + continue reorder_details.append({ "source": f"tool_result[{location.msg_index}]", "count": len(extraction.documents), diff --git a/contextpilot/server/intercept_parser.py b/contextpilot/server/intercept_parser.py index f97ea72..a71b9d4 100644 --- a/contextpilot/server/intercept_parser.py +++ b/contextpilot/server/intercept_parser.py @@ -8,6 +8,7 @@ No server dependencies — independently testable. """ +import json import re import copy import logging @@ -304,34 +305,70 @@ def _reconstruct_markdown_headers( return "\n\n".join(parts) +def _extract_json_results( + text: str, config: InterceptConfig +) -> Optional[ExtractionResult]: + """Extract documents from JSON tool results with a ``results`` array. + + OpenClaw tools (memory search, web search, web fetch) return + ``JSON.stringify(payload, null, 2)`` where *payload* contains a + ``results`` list. Each element of that list is treated as one + document (serialised back to a compact JSON string). + """ + stripped = text.strip() + if not stripped.startswith("{"): + return None + try: + obj = json.loads(stripped) + except (json.JSONDecodeError, ValueError): + return None + if not isinstance(obj, dict): + return None + results = obj.get("results") + if not isinstance(results, list) or len(results) < 2: + return None + documents = [json.dumps(item, ensure_ascii=False) for item in results] + return ExtractionResult( + documents=documents, + prefix="", + suffix="", + mode="json_results", + original_content=text, + ) + + def extract_documents( text: str, config: InterceptConfig ) -> Optional[ExtractionResult]: """Extract documents from text using the configured mode. - Auto-detection priority: xml_tag > numbered > separator > markdown_header. + Auto-detection priority: xml_tag > numbered > json_results. + ``separator`` and ``markdown_header`` are only used when explicitly + requested — they match structural content (YAML frontmatter, prompt + sections) too aggressively for auto mode. Returns None if no documents are found (caller should bypass). """ if config.mode == "xml_tag": return _extract_xml_tags(text, config) elif config.mode == "numbered": return _extract_numbered(text, config) + elif config.mode == "json_results": + return _extract_json_results(text, config) elif config.mode == "separator": return _extract_separator(text, config) elif config.mode == "markdown_header": return _extract_markdown_headers(text, config) else: - # Auto mode: try each in priority order + # Auto mode: only formats that clearly delimit independent documents. + # separator and markdown_header excluded — too aggressive on + # structural content (YAML frontmatter, prompt sections). result = _extract_xml_tags(text, config) if result: return result result = _extract_numbered(text, config) if result: return result - result = _extract_separator(text, config) - if result: - return result - result = _extract_markdown_headers(text, config) + result = _extract_json_results(text, config) if result: return result return None @@ -351,6 +388,8 @@ def reconstruct_content( return _reconstruct_xml(extraction, reordered_docs) elif extraction.mode == "numbered": return _reconstruct_numbered(extraction, reordered_docs) + elif extraction.mode == "json_results": + return _reconstruct_json_results(extraction, reordered_docs) elif extraction.mode == "separator": return _reconstruct_separator(extraction, reordered_docs) elif extraction.mode == "markdown_header": @@ -387,6 +426,15 @@ def _reconstruct_numbered( return result +def _reconstruct_json_results( + extraction: ExtractionResult, reordered_docs: List[str] +) -> str: + """Reconstruct a JSON tool result with reordered ``results`` array.""" + obj = json.loads(extraction.original_content) + obj["results"] = [json.loads(doc) for doc in reordered_docs] + return json.dumps(obj, indent=2, ensure_ascii=False) + + def _reconstruct_separator( extraction: ExtractionResult, reordered_docs: List[str] ) -> str: diff --git a/contextpilot/server/live_index.py b/contextpilot/server/live_index.py index 46ef85c..e54b3be 100644 --- a/contextpilot/server/live_index.py +++ b/contextpilot/server/live_index.py @@ -898,7 +898,7 @@ def schedule_only(self, contexts: List[List[int]]) -> Dict: Returns: Dictionary with scheduled results (no request_id mapping) """ - logger.debug("Scheduling batch (stateless mode)") + logger.debug("Scheduling batch") # Step 1: Build static index (clustering + reordering) result = self.fit_transform(contexts) diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index ec8963c..fac0254 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -113,13 +113,36 @@ def client(mock_session): # Patch the module-level globals original_session = http_mod._aiohttp_session original_url = http_mod._infer_api_url + original_intercept_index = http_mod._intercept_index http_mod._aiohttp_session = mock_session http_mod._infer_api_url = "http://mock-backend:30000" + http_mod._intercept_index = None # reset so each test starts fresh try: yield TestClient(app, raise_server_exceptions=False) finally: http_mod._aiohttp_session = original_session http_mod._infer_api_url = original_url + http_mod._intercept_index = original_intercept_index + + +# ============================================================================ +# Helpers +# ============================================================================ + + +def _warmup(client, path, body): + """Send a request to initialise the intercept index (first request = no reorder).""" + resp = client.post(path, json=body) + assert resp.status_code == 200 + return resp + + +# Documents with clear clustering signal: two auth docs share words +# (token, authentication, secure, for) while the database doc shares none. +# schedule_only produces [0, 2, 1] — auth docs grouped, database last. +_AUTH_DOC = "JWT token validation and rotation policy for secure authentication" +_DB_DOC = "Database connection pooling sharding replication backup strategy" +_OAUTH_DOC = "OAuth2 authentication token refresh flow for secure login sessions" # ============================================================================ @@ -128,19 +151,19 @@ def client(mock_session): class TestOpenAIIntercept: - def test_basic_intercept_reorders(self, client, mock_session): - """Full intercept: docs extracted, reordered, forwarded.""" + def test_first_request_builds_and_reorders(self, client, mock_session): + """First request builds index and reorders via clustering.""" body = { "model": "gpt-4", "messages": [ { "role": "system", "content": ( - "\n" - "Doc A content\n" - "Doc B content\n" - "Doc C content\n" - "" + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" ), }, {"role": "user", "content": "Summarize the documents."}, @@ -148,13 +171,41 @@ def test_basic_intercept_reorders(self, client, mock_session): } resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - # Backend was called assert mock_session._last_url == "http://mock-backend:30000/v1/chat/completions" - # Metadata in response header (not body) + meta = _cp_meta(resp) + assert meta.get("intercepted") is True + forwarded = mock_session._last_json + sys_content = forwarded["messages"][0]["content"] + assert "" in sys_content + + def test_second_request_reorders(self, client, mock_session): + """Second request clusters docs — auth docs grouped together. + + Input order: auth, database, auth. Clustering pulls the two + auth docs together → different order from input. + """ + body = { + "model": "gpt-4", + "messages": [ + { + "role": "system", + "content": ( + "\n" + "JWT token validation and rotation policy for secure authentication\n" + "Database connection pooling sharding replication backup strategy\n" + "OAuth2 authentication token refresh flow for secure login sessions\n" + "" + ), + }, + {"role": "user", "content": "Summarize."}, + ], + } + _warmup(client, "/v1/chat/completions", body) + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 meta = _cp_meta(resp) assert meta.get("intercepted") is True assert "_contextpilot" not in resp.json() - # The forwarded body should have reordered docs (still in XML format) forwarded = mock_session._last_json sys_content = forwarded["messages"][0]["content"] assert "" in sys_content @@ -226,6 +277,7 @@ def test_numbered_format(self, client, mock_session): {"role": "user", "content": "Summarize"}, ], } + _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 forwarded = mock_session._last_json @@ -242,6 +294,7 @@ def test_basic_intercept(self, client, mock_session): "system": "ABC", "messages": [{"role": "user", "content": "Hello"}], } + _warmup(client, "/v1/messages", body) resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 assert mock_session._last_url == "http://mock-backend:30000/v1/messages" @@ -384,22 +437,22 @@ def test_openai_tool_result_reordered(self, client, mock_session): "function": {"name": "search", "arguments": "{}"}}]}, {"role": "tool", "tool_call_id": "tc1", "content": ( - "\n" - "Tool Doc A\n" - "Tool Doc B\n" - "Tool Doc C\n" - "" + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" )}, {"role": "user", "content": "Now summarize."}, ], } + _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta.get("documents_reordered") is True assert meta.get("sources", {}).get("tool_results", 0) >= 1 - # Forwarded body should have reordered tool content forwarded = mock_session._last_json tool_content = forwarded["messages"][3]["content"] assert "" in tool_content @@ -418,22 +471,22 @@ def test_anthropic_tool_result_reordered(self, client, mock_session): {"role": "user", "content": [ {"type": "tool_result", "tool_use_id": "tu1", "content": ( - "\n" - "Anthropic Doc A\n" - "Anthropic Doc B\n" - "Anthropic Doc C\n" - "" + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" )}, ]}, {"role": "user", "content": "Now summarize."}, ], } + _warmup(client, "/v1/messages", body) resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 meta = _cp_meta(resp) assert meta.get("intercepted") is True assert meta.get("sources", {}).get("tool_results", 0) >= 1 - # Forwarded body should have reordered tool_result content forwarded = mock_session._last_json tr_content = forwarded["messages"][2]["content"][0]["content"] assert "" in tr_content @@ -444,20 +497,20 @@ def test_openai_system_and_tool_results(self, client, mock_session): "model": "gpt-4", "messages": [ {"role": "system", "content": ( - "\n" - "Sys A\n" - "Sys B\n" - "Sys C\n" - "" + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" )}, {"role": "user", "content": "Search"}, - {"role": "assistant", "content": None, - "tool_calls": [{"id": "tc1"}]}, + {"role": "assistant", "content": None, "tool_calls": [{"id": "tc1"}]}, {"role": "tool", "tool_call_id": "tc1", - "content": "[1] Tool A [2] Tool B [3] Tool C"}, + "content": f"[1] {_AUTH_DOC} [2] {_DB_DOC} [3] {_OAUTH_DOC}"}, {"role": "user", "content": "Summarize"}, ], } + _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 meta = _cp_meta(resp) @@ -472,18 +525,19 @@ def test_scope_system_skips_tool_results(self, client, mock_session): "model": "gpt-4", "messages": [ {"role": "system", "content": ( - "\n" - "Sys A\n" - "Sys B\n" - "" + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" )}, {"role": "tool", "tool_call_id": "tc1", "content": "T1T2"}, ], } + _warmup(client, "/v1/chat/completions", body) resp = client.post( - "/v1/chat/completions", - json=body, + "/v1/chat/completions", json=body, headers={"X-ContextPilot-Scope": "system"}, ) assert resp.status_code == 200 @@ -498,18 +552,21 @@ def test_scope_tool_results_skips_system(self, client, mock_session): "model": "gpt-4", "messages": [ {"role": "system", "content": ( - "\n" - "Sys A\n" - "Sys B\n" - "" + "Sys ASys B" )}, {"role": "tool", "tool_call_id": "tc1", - "content": "T1T2"}, + "content": ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + )}, ], } + _warmup(client, "/v1/chat/completions", body) resp = client.post( - "/v1/chat/completions", - json=body, + "/v1/chat/completions", json=body, headers={"X-ContextPilot-Scope": "tool_results"}, ) assert resp.status_code == 200 @@ -518,6 +575,76 @@ def test_scope_tool_results_skips_system(self, client, mock_session): assert meta["sources"]["system"] == 0 assert meta["sources"]["tool_results"] == 1 + def test_openai_json_tool_result_reordered(self, client, mock_session): + """OpenClaw-style JSON tool results with results array get reordered.""" + import json as _json + results = [ + {"path": "auth.md", "snippet": _AUTH_DOC, "score": 0.9}, + {"path": "db.md", "snippet": _DB_DOC, "score": 0.8}, + {"path": "oauth.md", "snippet": _OAUTH_DOC, "score": 0.7}, + ] + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "user", "content": "Search for auth"}, + {"role": "assistant", "content": None, + "tool_calls": [{"id": "tc1", "type": "function", + "function": {"name": "memory_search", "arguments": "{}"}}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": _json.dumps({"results": results, "citations": "auto"}, indent=2)}, + {"role": "user", "content": "Now summarize."}, + ], + } + _warmup(client, "/v1/chat/completions", body) + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + meta = _cp_meta(resp) + assert meta.get("intercepted") is True + assert meta.get("documents_reordered") is True + assert meta.get("sources", {}).get("tool_results", 0) >= 1 + forwarded = mock_session._last_json + tool_content = _json.loads(forwarded["messages"][3]["content"]) + assert "results" in tool_content + assert len(tool_content["results"]) == 3 + assert tool_content["citations"] == "auto" + + def test_anthropic_json_tool_result_reordered(self, client, mock_session): + """Anthropic-format JSON tool results get reordered.""" + import json as _json + results = [ + {"title": "Auth guide", "url": "https://a.com", "description": _AUTH_DOC}, + {"title": "DB guide", "url": "https://b.com", "description": _DB_DOC}, + {"title": "OAuth guide", "url": "https://c.com", "description": _OAUTH_DOC}, + ] + body = { + "model": "claude-3-opus-20240229", + "system": "You are a helper.", + "messages": [ + {"role": "user", "content": "Search the web"}, + {"role": "assistant", "content": [ + {"type": "tool_use", "id": "tu1", "name": "web_search", + "input": {"query": "auth"}}, + ]}, + {"role": "user", "content": [ + {"type": "tool_result", "tool_use_id": "tu1", + "content": _json.dumps({"results": results, "provider": "brave"}, indent=2)}, + ]}, + {"role": "user", "content": "Summarize."}, + ], + } + _warmup(client, "/v1/messages", body) + resp = client.post("/v1/messages", json=body) + assert resp.status_code == 200 + meta = _cp_meta(resp) + assert meta.get("intercepted") is True + assert meta.get("sources", {}).get("tool_results", 0) >= 1 + forwarded = mock_session._last_json + tr_content = _json.loads(forwarded["messages"][2]["content"][0]["content"]) + assert "results" in tr_content + assert len(tr_content["results"]) == 3 + assert tr_content["provider"] == "brave" + # ============================================================================ # RID injection in intercept (stateful mode) diff --git a/tests/test_intercept_parser.py b/tests/test_intercept_parser.py index 7a743fa..ff77d2f 100644 --- a/tests/test_intercept_parser.py +++ b/tests/test_intercept_parser.py @@ -220,17 +220,25 @@ def test_equals_separator(self): assert result is not None assert result.documents == ["Doc A", "Doc B", "Doc C"] - def test_auto_detects_triple_dash(self): + def test_auto_excludes_separator(self): + """separator excluded from auto — matches YAML frontmatter/structural content.""" + for sep in ("---", "==="): + text = f"First\n{sep}\nSecond\n{sep}\nThird" + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is None + + def test_explicit_triple_dash(self): text = "First\n---\nSecond\n---\nThird" - config = InterceptConfig(mode="auto") + config = InterceptConfig(mode="separator") result = extract_documents(text, config) assert result is not None assert result.mode == "separator" assert result.documents == ["First", "Second", "Third"] - def test_auto_detects_triple_equals(self): + def test_explicit_triple_equals(self): text = "First\n===\nSecond\n===\nThird" - config = InterceptConfig(mode="auto") + config = InterceptConfig(mode="separator", separator="===") result = extract_documents(text, config) assert result is not None assert result.mode == "separator" @@ -251,6 +259,106 @@ def test_no_separator_returns_none(self): assert result is None +# ============================================================================ +# JSON results extraction (OpenClaw tool results) +# ============================================================================ + + +class TestJsonResultsExtraction: + """OpenClaw tools return JSON.stringify(payload, null, 2) with a results array.""" + + def test_memory_search_results(self): + import json + text = json.dumps({ + "results": [ + {"path": "MEMORY.md", "snippet": "Use TypeScript", "score": 0.9}, + {"path": "notes.md", "snippet": "Prefer functional style", "score": 0.8}, + {"path": "config.md", "snippet": "Port 8080", "score": 0.7}, + ], + "citations": "auto", + }, indent=2) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "json_results" + assert len(result.documents) == 3 + # Each document is a JSON-serialized result item + for doc in result.documents: + parsed = json.loads(doc) + assert "snippet" in parsed + + def test_web_search_results(self): + import json + text = json.dumps({ + "query": "python async", + "provider": "brave", + "results": [ + {"title": "Async IO in Python", "url": "https://a.com", "description": "Guide to asyncio"}, + {"title": "Python concurrency", "url": "https://b.com", "description": "Threading vs async"}, + ], + }, indent=2) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + assert result.mode == "json_results" + assert len(result.documents) == 2 + + def test_explicit_mode(self): + import json + text = json.dumps({"results": [{"a": 1}, {"b": 2}]}, indent=2) + config = InterceptConfig(mode="json_results") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "json_results" + assert len(result.documents) == 2 + + def test_single_result_returns_none(self): + import json + text = json.dumps({"results": [{"a": 1}]}, indent=2) + config = InterceptConfig() + result = extract_documents(text, config) + # Need >= 2 results to reorder + assert result is None + + def test_no_results_key_returns_none(self): + import json + text = json.dumps({"data": [1, 2, 3]}, indent=2) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is None + + def test_not_json_returns_none(self): + text = "This is just plain text, not JSON" + config = InterceptConfig(mode="json_results") + result = extract_documents(text, config) + assert result is None + + def test_roundtrip(self): + import json + original = { + "results": [ + {"path": "a.md", "snippet": "alpha"}, + {"path": "b.md", "snippet": "beta"}, + {"path": "c.md", "snippet": "gamma"}, + ], + "citations": "on", + } + text = json.dumps(original, indent=2) + config = InterceptConfig() + result = extract_documents(text, config) + assert result is not None + # Reorder: reverse + reordered = list(reversed(result.documents)) + rebuilt = reconstruct_content(result, reordered) + rebuilt_obj = json.loads(rebuilt) + # Top-level keys preserved + assert rebuilt_obj["citations"] == "on" + # Results reordered + assert rebuilt_obj["results"][0]["path"] == "c.md" + assert rebuilt_obj["results"][1]["path"] == "b.md" + assert rebuilt_obj["results"][2]["path"] == "a.md" + + # ============================================================================ # Auto detection priority # ============================================================================ @@ -271,10 +379,25 @@ def test_numbered_before_separator(self): assert result is not None assert result.mode == "numbered" - def test_separator_as_fallback(self): + def test_json_results_before_separator(self): + import json + text = json.dumps({"results": [{"a": 1}, {"b": 2}]}, indent=2) + config = InterceptConfig(mode="auto") + result = extract_documents(text, config) + assert result is not None + assert result.mode == "json_results" + + def test_separator_excluded_from_auto(self): + """separator is excluded from auto — matches YAML frontmatter too aggressively.""" text = "First doc\n---\nSecond doc\n---\nThird doc" config = InterceptConfig(mode="auto") result = extract_documents(text, config) + assert result is None + + def test_separator_explicit_mode(self): + text = "First doc\n---\nSecond doc\n---\nThird doc" + config = InterceptConfig(mode="separator") + result = extract_documents(text, config) assert result is not None assert result.mode == "separator" @@ -519,10 +642,17 @@ def test_auto_priority_xml_over_markdown(self): assert result is not None assert result.mode == "xml_tag" - def test_auto_falls_back_to_markdown(self): + def test_auto_does_not_use_markdown(self): + """markdown_header is excluded from auto to avoid splitting structural prompts.""" text = "# Topic A\nContent about A\n\n# Topic B\nContent about B" config = InterceptConfig(mode="auto") result = extract_documents(text, config) + assert result is None + + def test_explicit_mode_still_works(self): + text = "# Topic A\nContent about A\n\n# Topic B\nContent about B" + config = InterceptConfig(mode="markdown_header") + result = extract_documents(text, config) assert result is not None assert result.mode == "markdown_header" @@ -920,19 +1050,19 @@ def test_openclaw_files_pattern(self): assert "class Foo" in result.documents[0] assert "Answer the user" in result.suffix - def test_openclaw_markdown_header_pattern(self): - """Simulate OpenClaw's tool result with markdown headers.""" - text = ( - "# Search Results\n" - "## Result 1: Introduction to Python\n" - "Python is a high-level programming language.\n\n" - "## Result 2: Python Data Types\n" - "Python has several built-in data types.\n\n" - "## Result 3: Python Functions\n" - "Functions are reusable blocks of code." - ) + def test_openclaw_json_tool_result_pattern(self): + """Simulate OpenClaw's memory search tool result (JSON with results array).""" + import json + text = json.dumps({ + "results": [ + {"path": "intro.md", "snippet": "Python is a high-level language.", "score": 0.9}, + {"path": "types.md", "snippet": "Python has several data types.", "score": 0.8}, + {"path": "funcs.md", "snippet": "Functions are reusable code blocks.", "score": 0.7}, + ], + "citations": "auto", + }, indent=2) config = InterceptConfig() result = extract_documents(text, config) assert result is not None - assert result.mode == "markdown_header" - assert len(result.documents) >= 3 + assert result.mode == "json_results" + assert len(result.documents) == 3 From 442beb9c6c669918aff390ce6ce2c019bed4e141 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Fri, 6 Mar 2026 00:14:48 +0000 Subject: [PATCH 10/76] update openclaw example --- contextpilot/server/http_server.py | 171 ++++++++++++++++++--- tests/test_http_intercept.py | 233 ++++++++++++++++++++++++++++- 2 files changed, 385 insertions(+), 19 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 00f1742..786451f 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -17,6 +17,7 @@ import argparse import copy +import hashlib import json import logging import time @@ -29,7 +30,7 @@ try: from fastapi import FastAPI, HTTPException, Request - from fastapi.responses import JSONResponse + from fastapi.responses import JSONResponse, StreamingResponse from pydantic import BaseModel, Field import uvicorn import aiohttp @@ -88,6 +89,30 @@ # the existing tree and reorder documents for prefix sharing. _intercept_index: Optional[ContextPilot] = None +# ── Conversation-aware intercept state ──────────────────────────────────── +# Tracks which tool results have already been processed, enabling +# skip-old / dedup-new / reorder-new behaviour. Single-conversation +# model (one user at a time). Resets when the system prompt changes. + +from dataclasses import dataclass, field as dc_field + + +@dataclass +class _InterceptConvState: + """Global intercept state for the current conversation.""" + # Hashes of tool_result content strings already reordered. + processed_hashes: set = dc_field(default_factory=set) + # Hashes of individual document strings seen across all tool results. + seen_doc_hashes: set = dc_field(default_factory=set) + # Whether the system prompt has been processed (reordered) already. + system_processed: bool = False + # Number of messages in the last request. Messages only grow in a + # multi-turn conversation; if the count drops, it's a new session. + last_message_count: int = 0 + + +_intercept_state = _InterceptConvState() + # Request ID normalization (engine -> ContextPilot canonical IDs) _ENGINE_REQ_ID_PREFIX = re.compile(r"^(cmpl-|chatcmpl-|batch-)") _VLLM_REQ_SUFFIX = re.compile(r"^(req-[^-]+)-\d+-[0-9a-f]+$") @@ -711,11 +736,15 @@ async def reset_index(): After reset, you must call /reorder again before other operations. """ - global _index, _str_to_id, _id_to_str, _next_str_id + global _index, _str_to_id, _id_to_str, _next_str_id, _intercept_index, _intercept_state # Reset conversation tracker reset_conversation_tracker() + # Reset intercept conversation state + _intercept_state = _InterceptConvState() + _intercept_index = None + # Reset string-to-ID mapping _str_to_id = {} _id_to_str = {} @@ -988,6 +1017,52 @@ async def proxy_completions(request: Request): # HTTP Intercept Proxy Endpoints # ============================================================================ + +# ── Conversation-aware helpers ───────────────────────────────────────────── + + +def _hash_text(text: str) -> str: + """Fast 16-hex-char hash for content comparison.""" + return hashlib.sha256(text.encode("utf-8", errors="replace")).hexdigest()[:16] + + +def _get_intercept_state(body: Dict[str, Any]) -> _InterceptConvState: + """Return the global intercept state, resetting if the conversation changed. + + Detection: in a multi-turn agent conversation the messages array only + grows. If the count drops, a new session/conversation started. + """ + global _intercept_state + msg_count = len(body.get("messages") or []) + if msg_count < _intercept_state.last_message_count: + logger.debug( + f"Intercept: new conversation detected " + f"(messages {msg_count} < {_intercept_state.last_message_count})" + ) + _intercept_state = _InterceptConvState() + _intercept_state.last_message_count = msg_count + return _intercept_state + + +def _deduplicate_docs( + docs: List[str], state: _InterceptConvState +) -> tuple: + """Remove documents already seen in previous tool results. + + Returns (new_docs, deduped_count). Also registers all doc hashes + (including duplicates) in state so future calls can dedup against them. + """ + new_docs = [] + deduped_count = 0 + for doc in docs: + h = _hash_text(doc) + if h in state.seen_doc_hashes: + deduped_count += 1 + else: + new_docs.append(doc) + state.seen_doc_hashes.add(h) + return new_docs, deduped_count + # API format constants _OPENAI_CHAT = "openai_chat" _ANTHROPIC_MESSAGES = "anthropic_messages" @@ -1074,6 +1149,8 @@ async def _intercept_and_forward(request: Request, api_format: str): headers = dict(request.headers) config = parse_intercept_headers(headers) total_reordered = 0 + total_deduped = 0 + tool_results_skipped = 0 system_count = 0 tool_result_count = 0 reorder_details = [] # collect per-source reorder info @@ -1082,6 +1159,9 @@ async def _intercept_and_forward(request: Request, api_format: str): try: body = copy.deepcopy(body) + # ── Conversation-aware state (single-conversation model) ── + state = _get_intercept_state(body) + if api_format == _OPENAI_CHAT: multi = extract_all_openai(body, config) elif api_format == _ANTHROPIC_MESSAGES: @@ -1089,8 +1169,8 @@ async def _intercept_and_forward(request: Request, api_format: str): else: multi = MultiExtractionResult() - # Reorder system extraction - if multi.system_extraction: + # ── System prompt: reorder only on first turn ───────────── + if multi.system_extraction and not state.system_processed: extraction, sys_idx = multi.system_extraction if len(extraction.documents) >= 2: reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) @@ -1124,35 +1204,87 @@ async def _intercept_and_forward(request: Request, api_format: str): break total_reordered += len(extraction.documents) system_count = 1 + state.system_processed = True - # Reorder tool result extractions + # ── Tool results: skip old, dedup+reorder new ───────────── for extraction, location in multi.tool_extractions: - if len(extraction.documents) >= 2: - reordered_docs, orig_order, new_order = _reorder_documents(extraction.documents, config) - if orig_order == new_order: - continue + if len(extraction.documents) < 2: + continue + + content_hash = _hash_text(extraction.original_content) + + # Already processed in a previous turn → skip entirely + if content_hash in state.processed_hashes: + tool_results_skipped += 1 + logger.debug( + f"Intercept: skipping already-processed tool_result " + f"at msg[{location.msg_index}]" + ) + continue + + # New tool result → dedup docs against history, then reorder + state.processed_hashes.add(content_hash) + new_docs, deduped = _deduplicate_docs( + extraction.documents, state + ) + total_deduped += deduped + + if len(new_docs) < 2: + # After dedup, not enough docs to reorder — reconstruct + # with just the new docs (or skip if empty). + if new_docs and deduped > 0: + # Reconstruct with only the new (non-duplicate) docs + if api_format == _OPENAI_CHAT: + reconstruct_openai_tool_result( + body, extraction, new_docs, location) + elif api_format == _ANTHROPIC_MESSAGES: + reconstruct_anthropic_tool_result( + body, extraction, new_docs, location) + total_reordered += len(new_docs) + tool_result_count += 1 + reorder_details.append({ + "source": f"tool_result[{location.msg_index}]", + "count": len(new_docs), + "deduped": deduped, + "original_order": list(range(len(new_docs))), + "reordered_order": list(range(len(new_docs))), + }) + continue + + # Reorder the unique new docs + reordered_docs, orig_order, new_order = _reorder_documents( + new_docs, config + ) + # Always reconstruct (even if order unchanged) when dedup + # removed docs — the content is shorter than original. + if orig_order != new_order or deduped > 0: reorder_details.append({ "source": f"tool_result[{location.msg_index}]", - "count": len(extraction.documents), + "count": len(new_docs), + "deduped": deduped, "original_order": orig_order, "reordered_order": new_order, }) if api_format == _OPENAI_CHAT: - reconstruct_openai_tool_result(body, extraction, reordered_docs, location) + reconstruct_openai_tool_result( + body, extraction, reordered_docs, location) elif api_format == _ANTHROPIC_MESSAGES: - reconstruct_anthropic_tool_result(body, extraction, reordered_docs, location) - total_reordered += len(extraction.documents) + reconstruct_anthropic_tool_result( + body, extraction, reordered_docs, location) + total_reordered += len(new_docs) tool_result_count += 1 - if total_reordered > 0: + if total_reordered > 0 or total_deduped > 0 or tool_results_skipped > 0: logger.info( - f"Intercept ({api_format}): reordered {total_reordered} documents " - f"from {system_count} system + {tool_result_count} tool_results" + f"Intercept ({api_format}): reordered {total_reordered} docs, " + f"deduped {total_deduped} docs, skipped {tool_results_skipped} " + f"old tool_results" ) except Exception as e: logger.warning(f"Intercept extraction/reorder failed, forwarding original: {e}") total_reordered = 0 + total_deduped = 0 # In stateful mode, inject ContextPilot request_id as `rid` so SGLang # uses the same ID for cache tracking (enables eviction sync). @@ -1188,11 +1320,14 @@ async def _intercept_and_forward(request: Request, api_format: str): # Build ContextPilot metadata as a response header (not in body, # which would break strict API response parsers like OpenClaw's SDK). cp_response_headers = {} - if total_reordered > 0: + if total_reordered > 0 or total_deduped > 0 or tool_results_skipped > 0: cp_response_headers["X-ContextPilot-Result"] = json.dumps({ "intercepted": True, - "documents_reordered": True, + "documents_reordered": total_reordered > 0, "total_documents": total_reordered, + "documents_deduplicated": total_deduped, + "tool_results_skipped": tool_results_skipped, + "message_count": state.last_message_count, "sources": { "system": system_count, "tool_results": tool_result_count, diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index fac0254..558769b 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -114,15 +114,18 @@ def client(mock_session): original_session = http_mod._aiohttp_session original_url = http_mod._infer_api_url original_intercept_index = http_mod._intercept_index + original_state = http_mod._intercept_state http_mod._aiohttp_session = mock_session http_mod._infer_api_url = "http://mock-backend:30000" http_mod._intercept_index = None # reset so each test starts fresh + http_mod._intercept_state = http_mod._InterceptConvState() try: yield TestClient(app, raise_server_exceptions=False) finally: http_mod._aiohttp_session = original_session http_mod._infer_api_url = original_url http_mod._intercept_index = original_intercept_index + http_mod._intercept_state = original_state # ============================================================================ @@ -131,9 +134,15 @@ def client(mock_session): def _warmup(client, path, body): - """Send a request to initialise the intercept index (first request = no reorder).""" + """Prime the intercept index so subsequent calls use build_incremental. + + After priming, resets conversation state so the actual test request + sees a clean slate (but the clustering index remains primed). + """ resp = client.post(path, json=body) assert resp.status_code == 200 + # Keep _intercept_index primed, but reset conversation tracking. + http_mod._intercept_state = http_mod._InterceptConvState() return resp @@ -695,3 +704,225 @@ def test_no_rid_in_stateless_mode(self, client, mock_session): assert resp.status_code == 200 forwarded = mock_session._last_json assert "rid" not in forwarded + + +# ============================================================================ +# Conversation-aware intercept (skip old, dedup new) +# ============================================================================ + + +# Distinct document sets for multi-turn dedup testing. +_DOC_CACHE = "Redis cache invalidation strategy with TTL and LRU eviction" +_DOC_DEPLOY = "Kubernetes deployment rolling update blue green canary strategy" + + +class TestConversationAwareIntercept: + """Tests for multi-turn skip / dedup / reorder behaviour.""" + + def _make_body(self, system, tool_results=None): + """Build an OpenAI chat body with optional tool result messages.""" + messages = [ + {"role": "system", "content": system}, + {"role": "user", "content": "Hello"}, + ] + if tool_results: + for i, content in enumerate(tool_results): + messages.append( + {"role": "assistant", "content": None, + "tool_calls": [{"id": f"tc{i}", "type": "function", + "function": {"name": "search", "arguments": "{}"}}]} + ) + messages.append( + {"role": "tool", "tool_call_id": f"tc{i}", "content": content} + ) + messages.append({"role": "user", "content": f"Follow-up {i}"}) + return {"model": "gpt-4", "messages": messages} + + def test_old_tool_result_skipped_on_second_turn(self, client, mock_session): + """Same tool result in a second request is skipped (already processed).""" + tool_content = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + ) + system = "You are a helpful assistant." + # Turn 1: process tool result (also primes index). + body1 = self._make_body(system, [tool_content]) + resp1 = client.post("/v1/chat/completions", json=body1) + assert resp1.status_code == 200 + meta1 = _cp_meta(resp1) + assert meta1.get("intercepted") is True + assert meta1.get("tool_results_skipped", 0) == 0 + + # Turn 2: same body again — tool result should be skipped. + resp2 = client.post("/v1/chat/completions", json=body1) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + # Everything was skipped, so no intercept header at all (or skipped count). + if meta2: + assert meta2.get("tool_results_skipped", 0) >= 1 + else: + # No header means nothing was reordered/deduped — correct. + pass + + def test_new_tool_result_processed_old_skipped(self, client, mock_session): + """Second turn with a NEW tool result: old one skipped, new one processed.""" + system = "You are a helpful assistant." + tool1 = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + ) + # New tool result with 3 distinct docs for reliable clustering. + tool2 = ( + f"\n" + f"{_DOC_CACHE}\n" + f"{_DOC_DEPLOY}\n" + f"API rate limiting throttling circuit breaker backpressure\n" + f"" + ) + # Turn 1: one tool result. + body1 = self._make_body(system, [tool1]) + resp1 = client.post("/v1/chat/completions", json=body1) + assert resp1.status_code == 200 + + # Turn 2: old tool result + new one. + body2 = self._make_body(system, [tool1, tool2]) + resp2 = client.post("/v1/chat/completions", json=body2) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + # Old tool result skipped. + assert meta2.get("tool_results_skipped", 0) == 1 + + def test_cross_tool_result_dedup(self, client, mock_session): + """Documents seen in a previous tool result get deduped in new ones.""" + system = "You are a helpful assistant." + tool1 = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + ) + # Second search returns 2 OLD docs + 2 NEW docs. + tool2 = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_OAUTH_DOC}\n" + f"{_DOC_CACHE}\n" + f"{_DOC_DEPLOY}\n" + f"" + ) + # Turn 1 + body1 = self._make_body(system, [tool1]) + resp1 = client.post("/v1/chat/completions", json=body1) + assert resp1.status_code == 200 + + # Turn 2 with overlapping docs + body2 = self._make_body(system, [tool1, tool2]) + resp2 = client.post("/v1/chat/completions", json=body2) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + assert meta2.get("intercepted") is True + assert meta2.get("documents_deduplicated", 0) == 2 # AUTH + OAUTH deduped + + def test_system_prompt_processed_once(self, client, mock_session): + """System prompt docs are only processed on the first turn.""" + system = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + ) + body = self._make_body(system) + # Turn 1: system gets reordered. + resp1 = client.post("/v1/chat/completions", json=body) + assert resp1.status_code == 200 + meta1 = _cp_meta(resp1) + assert meta1.get("intercepted") is True + assert meta1["sources"]["system"] == 1 + + # Turn 2: system NOT re-processed. + resp2 = client.post("/v1/chat/completions", json=body) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + # No reordering or dedup happened. + assert meta2 == {} or meta2.get("sources", {}).get("system", 0) == 0 + + def test_new_session_resets_state(self, client, mock_session): + """A shorter messages array (new session) resets all intercept state.""" + system = "You are a helpful assistant." + tool_content = ( + f"\n" + f"{_AUTH_DOC}\n" + f"{_DB_DOC}\n" + f"{_OAUTH_DOC}\n" + f"" + ) + # Session 1: long conversation with tool result. + body_long = self._make_body(system, [tool_content]) + resp1 = client.post("/v1/chat/completions", json=body_long) + assert resp1.status_code == 200 + assert _cp_meta(resp1).get("intercepted") is True + + # Session 2: new conversation (fewer messages) with same tool result. + # The shorter messages array triggers a state reset. + body_short = self._make_body(system, [tool_content]) + # body_short has the same structure, but message count <= body_long's + # because _make_body always starts fresh. However body_short has the + # same count as body_long (same number of tool results). To simulate + # a truly new session, send a shorter request first. + body_new = {"model": "gpt-4", "messages": [ + {"role": "system", "content": system}, + {"role": "user", "content": "Fresh session"}, + ]} + client.post("/v1/chat/completions", json=body_new) # triggers reset + + # Now send the same tool content — should be processed, not skipped. + resp2 = client.post("/v1/chat/completions", json=body_long) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + assert meta2.get("intercepted") is True + assert meta2.get("tool_results_skipped", 0) == 0 + + def test_json_tool_result_dedup(self, client, mock_session): + """JSON results format also gets cross-turn dedup. + + Note: dedup compares the full serialised JSON document string, so + entries must be byte-identical to be considered duplicates. + """ + import json as _json + system = "You are a helper." + # Shared entry — identical dict so serialised form matches. + auth_entry = {"path": "auth.md", "snippet": _AUTH_DOC, "score": 0.9} + results1 = [ + auth_entry, + {"path": "db.md", "snippet": _DB_DOC, "score": 0.8}, + {"path": "oauth.md", "snippet": _OAUTH_DOC, "score": 0.7}, + ] + results2 = [ + auth_entry, # exact duplicate + {"path": "cache.md", "snippet": _DOC_CACHE, "score": 0.85}, + {"path": "deploy.md", "snippet": _DOC_DEPLOY, "score": 0.75}, + ] + tool1 = _json.dumps({"results": results1}, indent=2) + tool2 = _json.dumps({"results": results2}, indent=2) + + # Turn 1 + body1 = self._make_body(system, [tool1]) + resp1 = client.post("/v1/chat/completions", json=body1) + assert resp1.status_code == 200 + + # Turn 2: old tool + new tool with overlapping auth entry. + body2 = self._make_body(system, [tool1, tool2]) + resp2 = client.post("/v1/chat/completions", json=body2) + assert resp2.status_code == 200 + meta2 = _cp_meta(resp2) + assert meta2.get("intercepted") is True + assert meta2.get("documents_deduplicated", 0) == 1 # auth entry deduped + assert meta2.get("tool_results_skipped", 0) == 1 # tool1 skipped From 39f9bd78d89ec37a01f7550dd1a628dc6e97387a Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Sun, 8 Mar 2026 20:08:37 +0000 Subject: [PATCH 11/76] Add http intercept support --- contextpilot/server/http_server.py | 437 +++++++++++++++++------- contextpilot/server/intercept_parser.py | 368 +++++++++++++++++++- tests/test_http_intercept.py | 287 +++++++--------- tests/test_intercept_parser.py | 20 +- 4 files changed, 820 insertions(+), 292 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 786451f..33b0f8e 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -48,13 +48,7 @@ reset_conversation_tracker ) from .intercept_parser import ( - parse_intercept_headers, extract_from_openai_chat, - extract_from_anthropic_messages, reconstruct_openai_chat, - reconstruct_anthropic_messages, reconstruct_content, - extract_documents, InterceptConfig, ExtractionResult, - MultiExtractionResult, ToolResultLocation, - extract_all_openai, extract_all_anthropic, - reconstruct_openai_tool_result, reconstruct_anthropic_tool_result, + parse_intercept_headers, InterceptConfig, get_format_handler, ) @@ -100,10 +94,19 @@ @dataclass class _InterceptConvState: """Global intercept state for the current conversation.""" - # Hashes of tool_result content strings already reordered. - processed_hashes: set = dc_field(default_factory=set) + # Cached copy of the full messages array after modification (reorder/dedup). + # On subsequent turns, old messages are replaced with these cached versions + # so the inference engine's prefix cache sees identical tokens. + cached_messages: list = dc_field(default_factory=list) + # Cached system prompt (Anthropic format only) after modification. + cached_system: Any = None + # Whether the first tool result (reorder candidate) has been processed. + first_tool_result_done: bool = False # Hashes of individual document strings seen across all tool results. seen_doc_hashes: set = dc_field(default_factory=set) + # Hashes of single-doc tool_results (file reads, etc.) → tool_call_id. + # Used for cross-turn dedup of individual file reads like SKILL.md. + single_doc_hashes: dict = dc_field(default_factory=dict) # Whether the system prompt has been processed (reordered) already. system_processed: bool = False # Number of messages in the last request. Messages only grow in a @@ -113,6 +116,23 @@ class _InterceptConvState: _intercept_state = _InterceptConvState() +# TTFT tracking for averages across a session +_ttft_history: List[float] = [] +_ttft_chars_saved_total = 0 + + +def _log_ttft(ttft_ms: float, slimmed: int, chars_saved: int) -> None: + global _ttft_chars_saved_total + _ttft_history.append(ttft_ms) + _ttft_chars_saved_total += chars_saved + avg = sum(_ttft_history) / len(_ttft_history) + logger.info( + f"TTFT: {ttft_ms:.0f}ms " + f"(avg {avg:.0f}ms over {len(_ttft_history)} reqs, " + f"slimmed {slimmed}, saved {chars_saved:,} chars, " + f"total saved {_ttft_chars_saved_total:,} chars)" + ) + # Request ID normalization (engine -> ContextPilot canonical IDs) _ENGINE_REQ_ID_PREFIX = re.compile(r"^(cmpl-|chatcmpl-|batch-)") _VLLM_REQ_SUFFIX = re.compile(r"^(req-[^-]+)-\d+-[0-9a-f]+$") @@ -1063,6 +1083,32 @@ def _deduplicate_docs( state.seen_doc_hashes.add(h) return new_docs, deduped_count +# Regex for OpenClaw's EXTERNAL_UNTRUSTED_CONTENT security markers. +# These contain a random hex id that changes every request, preventing +# KV cache prefix sharing for identical content. +_EXTERNAL_MARKER_RE = re.compile( + r'<<<((?:END_)?)EXTERNAL_UNTRUSTED_CONTENT\s+id=\\?"[0-9a-f]+\\?">>>' +) + + +def _strip_external_content_ids(body: Any) -> Any: + """Remove random ids from EXTERNAL_UNTRUSTED_CONTENT markers in the body. + + Walks the body dict/list and applies the regex on every string value, + turning ``<<>>`` into + ``<<>>``. + """ + if isinstance(body, str): + return _EXTERNAL_MARKER_RE.sub( + lambda m: f'<<<{m.group(1) or ""}EXTERNAL_UNTRUSTED_CONTENT>>>', body + ) + if isinstance(body, dict): + return {k: _strip_external_content_ids(v) for k, v in body.items()} + if isinstance(body, list): + return [_strip_external_content_ids(v) for v in body] + return body + + # API format constants _OPENAI_CHAT = "openai_chat" _ANTHROPIC_MESSAGES = "anthropic_messages" @@ -1077,39 +1123,51 @@ def _doc_preview(doc: str, max_len: int = 60) -> str: def _reorder_documents(docs: List[str], config: InterceptConfig) -> tuple: """Reorder a list of document strings via the persistent intercept index. - Each document is tokenized into words and treated as its own context. - The first call (``_intercept_index is None``) builds the index but - returns documents unchanged — there is no cached state to optimise - against. Subsequent calls use ``build_incremental`` which searches - the existing tree, groups documents sharing cached prefixes, and - returns an optimised execution order. + All documents form ONE context (a single list of doc strings). + The first call (``_intercept_index is None``) builds the index — + since there is only one context the order stays unchanged. + Subsequent calls use ``build_incremental`` which searches the + existing tree and reorders documents for prefix sharing with + previously cached state. Returns (reordered_docs, original_order, reordered_order) where the order lists are 0-based indices suitable for logging/headers. """ global _intercept_index - # Each document becomes its own context (list of words). - contexts = [re.findall(r'\w+', doc.lower()) for doc in docs] + contexts = [docs] # All docs = one context original_order = list(range(len(docs))) if _intercept_index is None: - # First call — build index (clustering + scheduling). + # First call — build index only. 1 context → no reorder possible. _intercept_index = ContextPilot( alpha=config.alpha, use_gpu=False, linkage_method=config.linkage_method, ) - result = _intercept_index.build_and_schedule(contexts=contexts) - logger.debug("Intercept index initialised") - else: - # Subsequent calls — search existing tree and reorder for prefix sharing. - result = _intercept_index.build_incremental(contexts=contexts) - - reordered_order = result["original_indices"] + _intercept_index.build_and_schedule(contexts=contexts) + logger.debug("Intercept index initialised (no reorder on first call)") + return docs, original_order, original_order + + # Subsequent calls — search existing tree and reorder for prefix sharing. + # build_incremental returns reordered_contexts with strings converted. + result = _intercept_index.build_incremental(contexts=contexts) + reordered = result.get("reordered_contexts", [docs])[0] + + # Build order mapping: find where each reordered doc was in the original. + doc_to_orig = {} + for i, doc in enumerate(docs): + doc_to_orig.setdefault(doc, []).append(i) + reordered_order = [] + used = set() + for doc in reordered: + for idx in doc_to_orig.get(doc, []): + if idx not in used: + reordered_order.append(idx) + used.add(idx) + break reordered_docs = [docs[i] for i in reordered_order] - # Debug log with previews if logger.isEnabledFor(logging.DEBUG): for label, order in [("BEFORE", original_order), ("AFTER", reordered_order)]: previews = [f" [{i}] {_doc_preview(docs[i])}" for i in order] @@ -1145,16 +1203,127 @@ async def _intercept_and_forward(request: Request, api_format: str): except Exception: raise HTTPException(status_code=400, detail="Invalid JSON body") + # Strip random IDs from OpenClaw's EXTERNAL_UNTRUSTED_CONTENT markers + # early, so extraction/clustering sees deterministic content and + # identical documents share the same KV cache prefix. + body = _strip_external_content_ids(body) + # Parse intercept config from headers headers = dict(request.headers) config = parse_intercept_headers(headers) total_reordered = 0 total_deduped = 0 + total_slimmed = 0 tool_results_skipped = 0 + _chars_before_slim = 0 + _chars_after_slim = 0 system_count = 0 tool_result_count = 0 reorder_details = [] # collect per-source reorder info + # ── Debug: log conversation shape, divergence, and tool_result details ── + _debug_messages = body.get("messages") or [] + _debug_msg_count = len(_debug_messages) + + # Per-message hashes for this request + _debug_msg_hashes = [] + for m in _debug_messages: + h = hashlib.sha256( + json.dumps(m, sort_keys=True, ensure_ascii=False).encode() + ).hexdigest()[:12] + _debug_msg_hashes.append(h) + + # Build tool_call_id → function name mapping from assistant messages + _tool_call_names = {} + for m in _debug_messages: + if m.get("role") == "assistant": + for tc in (m.get("tool_calls") or []): + _tc_id = tc.get("id", "") + _fn = (tc.get("function") or {}).get("name", "?") + _args_raw = (tc.get("function") or {}).get("arguments", "") + # Extract file path for read calls + _path_hint = "" + if _fn == "read" and isinstance(_args_raw, str): + try: + _args = json.loads(_args_raw) + _p = _args.get("path") or _args.get("file_path") or "" + if _p: + _path_hint = f" path={_p.split('/')[-1]}" + except Exception: + pass + _tool_call_names[_tc_id] = f"{_fn}{_path_hint}" + + # Log all tool_result messages with size, function name, and content preview + for idx, m in enumerate(_debug_messages): + _role = m.get("role", "?") + if _role in ("tool", "toolResult"): + _tc_id = m.get("tool_call_id", "?") + _fn_label = _tool_call_names.get(_tc_id, "?") + _content = m.get("content", "") + _content_str = str(_content) + _chars = len(_content_str) + _is_compacted = "[compacted:" in _content_str + _preview = _content_str[:150].replace("\n", "\\n") + logger.info( + f" msg[{idx}] role={_role} fn={_fn_label} " + f"tool_call_id={_tc_id} " + f"chars={_chars} compacted={_is_compacted} " + f"preview: {_preview}" + ) + elif _role == "user" and isinstance(m.get("content"), list): + for bi, block in enumerate(m["content"]): + if isinstance(block, dict) and block.get("type") in ("tool_result", "toolResult"): + _tu_id = block.get("tool_use_id", "?") + _tc = block.get("content", "") + _tc_str = str(_tc) + _chars = len(_tc_str) + _is_compacted = "[compacted:" in _tc_str + _preview = _tc_str[:150].replace("\n", "\\n") + logger.info( + f" msg[{idx}].content[{bi}] type=tool_result " + f"tool_use_id={_tu_id} chars={_chars} " + f"compacted={_is_compacted} preview: {_preview}" + ) + + global _debug_prev_msg_hashes + if "_debug_prev_msg_hashes" not in globals(): + _debug_prev_msg_hashes = [] + + _prev_n = len(_debug_prev_msg_hashes) + if _prev_n > 0 and _prev_n <= _debug_msg_count: + _first_diff = None + for idx in range(_prev_n): + if _debug_msg_hashes[idx] != _debug_prev_msg_hashes[idx]: + _first_diff = idx + break + if _first_diff is not None: + _diff_msg = _debug_messages[_first_diff] + _diff_role = _diff_msg.get("role", "?") + _diff_content = str(_diff_msg.get("content", "")) + logger.warning( + f"Intercept PREFIX MISMATCH at msg[{_first_diff}] " + f"(role={_diff_role}), " + f"hash was {_debug_prev_msg_hashes[_first_diff]} " + f"now {_debug_msg_hashes[_first_diff]}. " + f"Content preview ({len(_diff_content)} chars): " + f"{_diff_content[:300]}..." + ) + else: + logger.info( + f"Intercept: {_debug_msg_count} msgs (prev={_prev_n}), " + f"prefix[:{_prev_n}] MATCH, " + f"{_debug_msg_count - _prev_n} new msgs" + ) + else: + logger.info( + f"Intercept: {_debug_msg_count} msgs (first request or reset)" + ) + + _debug_prev_msg_hashes = list(_debug_msg_hashes) + + # ── Format handler (strategy pattern) ──────────────────────────── + handler = get_format_handler(api_format) + if config.enabled: try: body = copy.deepcopy(body) @@ -1162,12 +1331,22 @@ async def _intercept_and_forward(request: Request, api_format: str): # ── Conversation-aware state (single-conversation model) ── state = _get_intercept_state(body) - if api_format == _OPENAI_CHAT: - multi = extract_all_openai(body, config) - elif api_format == _ANTHROPIC_MESSAGES: - multi = extract_all_anthropic(body, config) - else: - multi = MultiExtractionResult() + # ── Replace old messages with cached (modified) versions ── + # On subsequent turns, the host sends original (unmodified) + # messages. Replace them with our cached modified versions + # so the inference engine's prefix cache sees identical tokens. + old_msg_count = len(state.cached_messages) + if old_msg_count > 0: + msgs = body.get("messages", []) + if len(msgs) >= old_msg_count: + msgs[:old_msg_count] = copy.deepcopy(state.cached_messages) + logger.info( + f"Intercept: replaced {old_msg_count} old messages " + f"with cached versions for prefix cache consistency" + ) + handler.restore_system(body, state.cached_system) + + multi = handler.extract_all(body, config) # ── System prompt: reorder only on first turn ───────────── if multi.system_extraction and not state.system_processed: @@ -1181,110 +1360,120 @@ async def _intercept_and_forward(request: Request, api_format: str): "original_order": orig_order, "reordered_order": new_order, }) - if api_format == _OPENAI_CHAT: - new_content = reconstruct_content(extraction, reordered_docs) - msg = body["messages"][sys_idx] - if isinstance(msg.get("content"), str): - msg["content"] = new_content - elif isinstance(msg.get("content"), list): - for block in msg["content"]: - if isinstance(block, dict) and block.get("type") == "text": - if extract_documents(block.get("text", ""), InterceptConfig()): - block["text"] = new_content - break - elif api_format == _ANTHROPIC_MESSAGES: - new_content = reconstruct_content(extraction, reordered_docs) - if isinstance(body.get("system"), str): - body["system"] = new_content - elif isinstance(body.get("system"), list): - for block in body["system"]: - if isinstance(block, dict) and block.get("type") == "text": - if extract_documents(block.get("text", ""), InterceptConfig()): - block["text"] = new_content - break + handler.reconstruct_system(body, extraction, reordered_docs, sys_idx) total_reordered += len(extraction.documents) system_count = 1 state.system_processed = True - # ── Tool results: skip old, dedup+reorder new ───────────── + # ── Tool results: skip cached old, dedup+reorder new ──────── for extraction, location in multi.tool_extractions: + if location.msg_index < old_msg_count: + continue if len(extraction.documents) < 2: continue - content_hash = _hash_text(extraction.original_content) - - # Already processed in a previous turn → skip entirely - if content_hash in state.processed_hashes: - tool_results_skipped += 1 - logger.debug( - f"Intercept: skipping already-processed tool_result " - f"at msg[{location.msg_index}]" + if not state.first_tool_result_done: + # First tool result in session → reorder for KV cache + state.first_tool_result_done = True + reordered_docs, orig_order, new_order = _reorder_documents( + extraction.documents, config ) - continue - - # New tool result → dedup docs against history, then reorder - state.processed_hashes.add(content_hash) - new_docs, deduped = _deduplicate_docs( - extraction.documents, state - ) - total_deduped += deduped - - if len(new_docs) < 2: - # After dedup, not enough docs to reorder — reconstruct - # with just the new docs (or skip if empty). - if new_docs and deduped > 0: - # Reconstruct with only the new (non-duplicate) docs - if api_format == _OPENAI_CHAT: - reconstruct_openai_tool_result( - body, extraction, new_docs, location) - elif api_format == _ANTHROPIC_MESSAGES: - reconstruct_anthropic_tool_result( - body, extraction, new_docs, location) - total_reordered += len(new_docs) + for doc in extraction.documents: + state.seen_doc_hashes.add(_hash_text(doc)) + if orig_order != new_order: + reorder_details.append({ + "source": f"tool_result[{location.msg_index}]", + "count": len(extraction.documents), + "original_order": orig_order, + "reordered_order": new_order, + }) + handler.reconstruct_tool_result(body, extraction, reordered_docs, location) + total_reordered += len(extraction.documents) tool_result_count += 1 + else: + # Subsequent tool results → dedup only + new_docs, deduped = _deduplicate_docs( + extraction.documents, state + ) + total_deduped += deduped + if deduped > 0: + if not new_docs: + orig_chars = len(extraction.original_content) + new_docs = [ + f"[All {deduped} documents identical to a " + f"previous tool result ({orig_chars} chars). " + f"Refer to the earlier result above.]" + ] + _chars_before_slim += orig_chars + _chars_after_slim += len(new_docs[0]) + total_slimmed += deduped reorder_details.append({ "source": f"tool_result[{location.msg_index}]", "count": len(new_docs), "deduped": deduped, - "original_order": list(range(len(new_docs))), - "reordered_order": list(range(len(new_docs))), }) + handler.reconstruct_tool_result(body, extraction, new_docs, location) + tool_result_count += 1 + + # ── Single-doc tool results: cross-turn dedup ──────────── + for single_doc, location in multi.single_doc_extractions: + if location.msg_index < old_msg_count: continue + if single_doc.content_hash in state.single_doc_hashes: + prev_tool_id = state.single_doc_hashes[single_doc.content_hash] + if single_doc.tool_call_id == prev_tool_id: + logger.debug( + f"Intercept: skipping old single-doc at " + f"msg[{location.msg_index}] " + f"({len(single_doc.content)} chars, " + f"preserving prefix cache)" + ) + continue + + if handler.tool_call_present(body, prev_tool_id): + hint = ( + f"[Duplicate content — identical to a previous " + f"tool result ({prev_tool_id}). " + f"Refer to the earlier result above.]" + ) + handler.replace_single_doc(body, location, hint) + total_deduped += 1 + logger.debug( + f"Intercept: deduped single-doc at msg[{location.msg_index}] " + f"(hash={single_doc.content_hash[:12]}…, " + f"original={prev_tool_id})" + ) + else: + state.single_doc_hashes[single_doc.content_hash] = ( + single_doc.tool_call_id + ) + logger.debug( + f"Intercept: original single-doc ({prev_tool_id}) " + f"compacted, keeping re-read at msg[{location.msg_index}]" + ) + else: + state.single_doc_hashes[single_doc.content_hash] = ( + single_doc.tool_call_id + ) - # Reorder the unique new docs - reordered_docs, orig_order, new_order = _reorder_documents( - new_docs, config - ) - # Always reconstruct (even if order unchanged) when dedup - # removed docs — the content is shorter than original. - if orig_order != new_order or deduped > 0: - reorder_details.append({ - "source": f"tool_result[{location.msg_index}]", - "count": len(new_docs), - "deduped": deduped, - "original_order": orig_order, - "reordered_order": new_order, - }) - if api_format == _OPENAI_CHAT: - reconstruct_openai_tool_result( - body, extraction, reordered_docs, location) - elif api_format == _ANTHROPIC_MESSAGES: - reconstruct_anthropic_tool_result( - body, extraction, reordered_docs, location) - total_reordered += len(new_docs) - tool_result_count += 1 - - if total_reordered > 0 or total_deduped > 0 or tool_results_skipped > 0: + if total_reordered > 0 or total_deduped > 0 or total_slimmed > 0 or tool_results_skipped > 0: + saved = _chars_before_slim - _chars_after_slim + saved_tokens = saved // 4 if saved > 0 else 0 logger.info( - f"Intercept ({api_format}): reordered {total_reordered} docs, " - f"deduped {total_deduped} docs, skipped {tool_results_skipped} " - f"old tool_results" + f"Intercept ({api_format}): reordered {total_reordered}, " + f"deduped {total_deduped}, slimmed {total_slimmed} " + f"(saved {saved:,} chars ≈ {saved_tokens:,} tokens)" ) + # ── Cache the final messages array for next turn ────────── + state.cached_messages = copy.deepcopy(body.get("messages", [])) + state.cached_system = handler.cache_system(body) + except Exception as e: logger.warning(f"Intercept extraction/reorder failed, forwarding original: {e}") total_reordered = 0 total_deduped = 0 + total_slimmed = 0 # In stateful mode, inject ContextPilot request_id as `rid` so SGLang # uses the same ID for cache tracking (enables eviction sync). @@ -1294,12 +1483,7 @@ async def _intercept_and_forward(request: Request, api_format: str): logger.debug(f"Intercept: injected rid={request_id}") # Determine target URL - if api_format == _OPENAI_CHAT: - target_url = f"{infer_api_url}/v1/chat/completions" - elif api_format == _ANTHROPIC_MESSAGES: - target_url = f"{infer_api_url}/v1/messages" - else: - target_url = f"{infer_api_url}/v1/chat/completions" + target_url = f"{infer_api_url}{handler.target_path()}" # Build outbound headers: forward everything except X-ContextPilot-* # and hop-by-hop headers that must not be forwarded by proxies. @@ -1320,12 +1504,16 @@ async def _intercept_and_forward(request: Request, api_format: str): # Build ContextPilot metadata as a response header (not in body, # which would break strict API response parsers like OpenClaw's SDK). cp_response_headers = {} - if total_reordered > 0 or total_deduped > 0 or tool_results_skipped > 0: + if total_reordered > 0 or total_deduped > 0 or total_slimmed > 0 or tool_results_skipped > 0: cp_response_headers["X-ContextPilot-Result"] = json.dumps({ "intercepted": True, "documents_reordered": total_reordered > 0, "total_documents": total_reordered, "documents_deduplicated": total_deduped, + "documents_slimmed": total_slimmed, + "chars_before_slim": _chars_before_slim, + "chars_after_slim": _chars_after_slim, + "chars_saved": _chars_before_slim - _chars_after_slim, "tool_results_skipped": tool_results_skipped, "message_count": state.last_message_count, "sources": { @@ -1337,10 +1525,13 @@ async def _intercept_and_forward(request: Request, api_format: str): is_stream = body.get("stream", False) + _request_start = time.monotonic() + try: if is_stream: # Streaming: passthrough SSE chunks, forwarding status & headers async def _stream_with_headers(): + _ttft_logged = False async with _aiohttp_session.post( target_url, json=body, headers=outbound_headers ) as resp: @@ -1354,6 +1545,11 @@ async def _stream_with_headers(): # Yield (headers_dict, status) as first item for the wrapper yield resp.status, fwd_headers async for chunk in resp.content.iter_any(): + if not _ttft_logged: + _ttft_ms = (time.monotonic() - _request_start) * 1000 + _saved = _chars_before_slim - _chars_after_slim + _log_ttft(_ttft_ms, total_slimmed, _saved) + _ttft_logged = True yield chunk stream_iter = _stream_with_headers() @@ -1370,6 +1566,9 @@ async def _stream_with_headers(): async with _aiohttp_session.post( target_url, json=body, headers=outbound_headers ) as resp: + _ttft_ms = (time.monotonic() - _request_start) * 1000 + _saved = _chars_before_slim - _chars_after_slim + _log_ttft(_ttft_ms, total_slimmed, _saved) result = await resp.json() return JSONResponse( content=result, diff --git a/contextpilot/server/intercept_parser.py b/contextpilot/server/intercept_parser.py index a71b9d4..af5158e 100644 --- a/contextpilot/server/intercept_parser.py +++ b/contextpilot/server/intercept_parser.py @@ -8,6 +8,7 @@ No server dependencies — independently testable. """ +import hashlib import json import re import copy @@ -29,6 +30,10 @@ # Separator patterns for auto-detection _SEPARATOR_PATTERNS = ["---", "==="] +# Minimum content length to track a single-doc tool_result for dedup. +# Skips tiny results like "ok", error messages, short status outputs. +_SINGLE_DOC_MIN_CHARS = 200 + @dataclass class InterceptConfig: @@ -58,6 +63,8 @@ class ExtractionResult: separator_char: str = "" # Original content for fallback original_content: str = "" + # JSON-results-specific: full result objects (documents = content strings) + json_items: Optional[List[Any]] = None @dataclass @@ -68,19 +75,34 @@ class ToolResultLocation: inner_block_index: int = -1 # For Anthropic nested content blocks +@dataclass +class SingleDocExtraction: + """A tool_result containing a single document (e.g., file read, single API response). + + Not reorderable (only 1 item), but trackable for cross-turn deduplication. + The content_hash enables efficient comparison without storing full content. + """ + content: str + content_hash: str # SHA-256 of stripped content + tool_call_id: str = "" # tool_call_id (OpenAI) or tool_use_id (Anthropic) + + @dataclass class MultiExtractionResult: """Aggregates extractions from system prompt and tool_result messages.""" system_extraction: Optional[Tuple["ExtractionResult", int]] = None tool_extractions: List[Tuple["ExtractionResult", ToolResultLocation]] = field(default_factory=list) + single_doc_extractions: List[Tuple["SingleDocExtraction", ToolResultLocation]] = field(default_factory=list) @property def has_extractions(self) -> bool: - return self.system_extraction is not None or len(self.tool_extractions) > 0 + return (self.system_extraction is not None + or len(self.tool_extractions) > 0 + or len(self.single_doc_extractions) > 0) @property def total_documents(self) -> int: - total = 0 + total = len(self.single_doc_extractions) if self.system_extraction: total += len(self.system_extraction[0].documents) for ext, _ in self.tool_extractions: @@ -305,6 +327,19 @@ def _reconstruct_markdown_headers( return "\n\n".join(parts) +# Keys that carry a URL / path identifier suitable for clustering. +_JSON_ID_KEYS = ("url", "path", "file", "filename", "uri", "href") + + +def _extract_json_id(item: dict) -> Optional[str]: + """Extract a URL or path identifier from a JSON result item for clustering.""" + for key in _JSON_ID_KEYS: + val = item.get(key) + if isinstance(val, str) and val.strip(): + return val.strip() + return None + + def _extract_json_results( text: str, config: InterceptConfig ) -> Optional[ExtractionResult]: @@ -312,8 +347,9 @@ def _extract_json_results( OpenClaw tools (memory search, web search, web fetch) return ``JSON.stringify(payload, null, 2)`` where *payload* contains a - ``results`` list. Each element of that list is treated as one - document (serialised back to a compact JSON string). + ``results`` list. The content field (description/snippet/content/text) + of each item is used for clustering; the full objects are stored in + ``json_items`` so reconstruction can reorder them intact. """ stripped = text.strip() if not stripped.startswith("{"): @@ -327,13 +363,31 @@ def _extract_json_results( results = obj.get("results") if not isinstance(results, list) or len(results) < 2: return None - documents = [json.dumps(item, ensure_ascii=False) for item in results] + + # Use url/path as document identifier for clustering — short, stable, + # and same-site results share shingles → cluster together naturally. + # Falls back to full serialised object when no id key is found. + documents = [] + for item in results: + if isinstance(item, dict): + doc_id = _extract_json_id(item) + if doc_id is not None: + documents.append(doc_id) + else: + documents.append(json.dumps(item, ensure_ascii=False)) + else: + documents.append(json.dumps(item, ensure_ascii=False)) + + if len(documents) < 2: + return None + return ExtractionResult( documents=documents, prefix="", suffix="", mode="json_results", original_content=text, + json_items=results, ) @@ -429,9 +483,30 @@ def _reconstruct_numbered( def _reconstruct_json_results( extraction: ExtractionResult, reordered_docs: List[str] ) -> str: - """Reconstruct a JSON tool result with reordered ``results`` array.""" + """Reconstruct a JSON tool result with reordered ``results`` array. + + When ``json_items`` is set (content-key extraction), maps reordered + content strings back to their full result objects via index lookup. + Falls back to parsing reordered_docs as JSON for backward compat. + """ obj = json.loads(extraction.original_content) - obj["results"] = [json.loads(doc) for doc in reordered_docs] + if extraction.json_items is not None: + # Build content → original-index mapping + orig_docs = extraction.documents + doc_to_indices: dict = {} + for i, doc in enumerate(orig_docs): + doc_to_indices.setdefault(doc, []).append(i) + used: set = set() + reordered_items = [] + for doc in reordered_docs: + for idx in doc_to_indices.get(doc, []): + if idx not in used: + reordered_items.append(extraction.json_items[idx]) + used.add(idx) + break + obj["results"] = reordered_items + else: + obj["results"] = [json.loads(doc) for doc in reordered_docs] return json.dumps(obj, indent=2, ensure_ascii=False) @@ -670,6 +745,8 @@ def extract_all_openai( result.system_extraction = sys_result if config.scope in ("tool_results", "all"): result.tool_extractions = extract_from_openai_tool_results(body, config) + result.single_doc_extractions = extract_single_docs_from_openai_tool_results( + body, config) return result @@ -685,4 +762,281 @@ def extract_all_anthropic( result.system_extraction = (sys_extraction, -1) if config.scope in ("tool_results", "all"): result.tool_extractions = extract_from_anthropic_tool_results(body, config) + result.single_doc_extractions = extract_single_docs_from_anthropic_tool_results( + body, config) return result + + +# ── Single-document extraction (for cross-turn dedup) ───────────────────── + + +def _make_single_doc(content: str, tool_call_id: str = "") -> SingleDocExtraction: + """Create a SingleDocExtraction with content hash.""" + stripped = content.strip() + content_hash = hashlib.sha256(stripped.encode()).hexdigest() + return SingleDocExtraction( + content=stripped, + content_hash=content_hash, + tool_call_id=tool_call_id, + ) + + +def extract_single_docs_from_openai_tool_results( + body: Dict[str, Any], config: InterceptConfig +) -> List[Tuple[SingleDocExtraction, ToolResultLocation]]: + """Extract single-document tool results for dedup tracking (OpenAI format). + + Only captures tool_results where multi-doc extraction failed — these are + individual file reads, single API responses, etc. that contain substantial + content worth deduplicating across turns. + """ + messages = body.get("messages") + if not messages or not isinstance(messages, list): + return [] + + results = [] + for i, msg in enumerate(messages): + if msg.get("role") not in ("tool", "toolResult"): + continue + tool_call_id = msg.get("tool_call_id", "") + content = msg.get("content", "") + + if isinstance(content, str): + # Skip if multi-doc extraction would succeed + extraction = extract_documents(content, config) + if extraction and len(extraction.documents) >= 2: + continue + if len(content.strip()) >= _SINGLE_DOC_MIN_CHARS: + loc = ToolResultLocation(msg_index=i) + results.append((_make_single_doc(content, tool_call_id), loc)) + elif isinstance(content, list): + for j, block in enumerate(content): + if not isinstance(block, dict) or block.get("type") != "text": + continue + text = block.get("text", "") + extraction = extract_documents(text, config) + if extraction and len(extraction.documents) >= 2: + continue + if len(text.strip()) >= _SINGLE_DOC_MIN_CHARS: + loc = ToolResultLocation(msg_index=i, block_index=j) + results.append((_make_single_doc(text, tool_call_id), loc)) + return results + + +def extract_single_docs_from_anthropic_tool_results( + body: Dict[str, Any], config: InterceptConfig +) -> List[Tuple[SingleDocExtraction, ToolResultLocation]]: + """Extract single-document tool results for dedup tracking (Anthropic format). + + Anthropic tool results appear as user messages with type=="tool_result" blocks. + """ + messages = body.get("messages") + if not messages or not isinstance(messages, list): + return [] + + results = [] + for i, msg in enumerate(messages): + if msg.get("role") != "user": + continue + content = msg.get("content") + if not isinstance(content, list): + continue + for j, block in enumerate(content): + if not isinstance(block, dict): + continue + if block.get("type") not in ("tool_result", "toolResult"): + continue + tool_use_id = block.get("tool_use_id", "") + tr_content = block.get("content", "") + + if isinstance(tr_content, str): + extraction = extract_documents(tr_content, config) + if extraction and len(extraction.documents) >= 2: + continue + if len(tr_content.strip()) >= _SINGLE_DOC_MIN_CHARS: + loc = ToolResultLocation(msg_index=i, block_index=j) + results.append((_make_single_doc(tr_content, tool_use_id), loc)) + elif isinstance(tr_content, list): + for k, inner in enumerate(tr_content): + if not isinstance(inner, dict) or inner.get("type") != "text": + continue + text = inner.get("text", "") + extraction = extract_documents(text, config) + if extraction and len(extraction.documents) >= 2: + continue + if len(text.strip()) >= _SINGLE_DOC_MIN_CHARS: + loc = ToolResultLocation(msg_index=i, block_index=j, inner_block_index=k) + results.append((_make_single_doc(text, tool_use_id), loc)) + return results + + +# ── Single-document hint replacement ────────────────────────────────────── + + +def replace_single_doc_openai( + body: Dict[str, Any], location: ToolResultLocation, hint: str, +) -> None: + """Replace a single-doc OpenAI tool result content with a dedup hint in-place.""" + msg = body["messages"][location.msg_index] + if location.block_index == -1: + msg["content"] = hint + else: + msg["content"][location.block_index]["text"] = hint + + +def replace_single_doc_anthropic( + body: Dict[str, Any], location: ToolResultLocation, hint: str, +) -> None: + """Replace a single-doc Anthropic tool_result content with a dedup hint in-place.""" + msg = body["messages"][location.msg_index] + block = msg["content"][location.block_index] + if location.inner_block_index == -1: + block["content"] = hint + else: + block["content"][location.inner_block_index]["text"] = hint + + +# ── Format handler abstraction ───────────────────────────────────────────── +# Strategy pattern: encapsulates all format-specific operations so the +# intercept logic in http_server.py can be completely format-agnostic. + + +from abc import ABC, abstractmethod + + +class FormatHandler(ABC): + """Abstract handler for API format-specific operations. + + Implement one per LLM API format (OpenAI Chat, Anthropic Messages, etc.). + The intercept pipeline calls these methods instead of branching on format. + """ + + @abstractmethod + def extract_all(self, body: Dict[str, Any], + config: InterceptConfig) -> MultiExtractionResult: ... + + @abstractmethod + def reconstruct_system(self, body: Dict[str, Any], + extraction: ExtractionResult, + docs: List[str], sys_idx: int) -> None: ... + + @abstractmethod + def reconstruct_tool_result(self, body: Dict[str, Any], + extraction: ExtractionResult, + docs: List[str], + location: ToolResultLocation) -> None: ... + + @abstractmethod + def replace_single_doc(self, body: Dict[str, Any], + location: ToolResultLocation, + hint: str) -> None: ... + + @abstractmethod + def tool_call_present(self, body: Dict[str, Any], + tool_call_id: str) -> bool: ... + + @abstractmethod + def target_path(self) -> str: ... + + @abstractmethod + def cache_system(self, body: Dict[str, Any]) -> Any: ... + + @abstractmethod + def restore_system(self, body: Dict[str, Any], cached: Any) -> None: ... + + +class OpenAIChatHandler(FormatHandler): + """Handler for OpenAI Chat Completions format.""" + + def extract_all(self, body, config): + return extract_all_openai(body, config) + + def reconstruct_system(self, body, extraction, docs, sys_idx): + new_content = reconstruct_content(extraction, docs) + msg = body["messages"][sys_idx] + if isinstance(msg.get("content"), str): + msg["content"] = new_content + elif isinstance(msg.get("content"), list): + for block in msg["content"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + + def reconstruct_tool_result(self, body, extraction, docs, location): + reconstruct_openai_tool_result(body, extraction, docs, location) + + def replace_single_doc(self, body, location, hint): + replace_single_doc_openai(body, location, hint) + + def tool_call_present(self, body, tool_call_id): + for msg in (body.get("messages") or []): + if msg.get("role") in ("tool", "toolResult"): + if msg.get("tool_call_id") == tool_call_id: + return True + return False + + def target_path(self): + return "/v1/chat/completions" + + def cache_system(self, body): + return None # System prompt is inside messages array + + def restore_system(self, body, cached): + pass # No-op: cached with messages + + +class AnthropicMessagesHandler(FormatHandler): + """Handler for Anthropic Messages API format.""" + + def extract_all(self, body, config): + return extract_all_anthropic(body, config) + + def reconstruct_system(self, body, extraction, docs, sys_idx): + new_content = reconstruct_content(extraction, docs) + if isinstance(body.get("system"), str): + body["system"] = new_content + elif isinstance(body.get("system"), list): + for block in body["system"]: + if isinstance(block, dict) and block.get("type") == "text": + if extract_documents(block.get("text", ""), InterceptConfig()): + block["text"] = new_content + break + + def reconstruct_tool_result(self, body, extraction, docs, location): + reconstruct_anthropic_tool_result(body, extraction, docs, location) + + def replace_single_doc(self, body, location, hint): + replace_single_doc_anthropic(body, location, hint) + + def tool_call_present(self, body, tool_call_id): + for msg in (body.get("messages") or []): + if msg.get("role") == "user" and isinstance(msg.get("content"), list): + for block in msg["content"]: + if (isinstance(block, dict) + and block.get("type") in ("tool_result", "toolResult") + and block.get("tool_use_id") == tool_call_id): + return True + return False + + def target_path(self): + return "/v1/messages" + + def cache_system(self, body): + return copy.deepcopy(body.get("system")) + + def restore_system(self, body, cached): + if cached is not None: + body["system"] = copy.deepcopy(cached) + + +# Handler registry — add new formats here. +_FORMAT_HANDLERS: Dict[str, FormatHandler] = { + "openai_chat": OpenAIChatHandler(), + "anthropic_messages": AnthropicMessagesHandler(), +} + + +def get_format_handler(api_format: str) -> FormatHandler: + """Return the handler for the given API format, defaulting to OpenAI.""" + return _FORMAT_HANDLERS.get(api_format, _FORMAT_HANDLERS["openai_chat"]) diff --git a/tests/test_http_intercept.py b/tests/test_http_intercept.py index 558769b..25abe39 100644 --- a/tests/test_http_intercept.py +++ b/tests/test_http_intercept.py @@ -160,8 +160,8 @@ def _warmup(client, path, body): class TestOpenAIIntercept: - def test_first_request_builds_and_reorders(self, client, mock_session): - """First request builds index and reorders via clustering.""" + def test_first_request_builds_index_no_reorder(self, client, mock_session): + """First request builds index but does NOT reorder (index empty, 1 context).""" body = { "model": "gpt-4", "messages": [ @@ -181,18 +181,16 @@ def test_first_request_builds_and_reorders(self, client, mock_session): resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 assert mock_session._last_url == "http://mock-backend:30000/v1/chat/completions" + # First request: index empty → no reorder, no header. meta = _cp_meta(resp) - assert meta.get("intercepted") is True + assert meta == {} + # Body forwarded unmodified. forwarded = mock_session._last_json sys_content = forwarded["messages"][0]["content"] assert "" in sys_content - def test_second_request_reorders(self, client, mock_session): - """Second request clusters docs — auth docs grouped together. - - Input order: auth, database, auth. Clustering pulls the two - auth docs together → different order from input. - """ + def test_second_session_may_reorder(self, client, mock_session): + """After index is built (session 1), session 2 uses build_incremental.""" body = { "model": "gpt-4", "messages": [ @@ -209,11 +207,9 @@ def test_second_request_reorders(self, client, mock_session): {"role": "user", "content": "Summarize."}, ], } - _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True + # Body is forwarded (may or may not be reordered depending on index state). assert "_contextpilot" not in resp.json() forwarded = mock_session._last_json sys_content = forwarded["messages"][0]["content"] @@ -286,12 +282,10 @@ def test_numbered_format(self, client, mock_session): {"role": "user", "content": "Summarize"}, ], } - _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 forwarded = mock_session._last_json sys_content = forwarded["messages"][0]["content"] - # Should contain numbered items (reordered) assert "[1]" in sys_content assert "[2]" in sys_content @@ -303,7 +297,6 @@ def test_basic_intercept(self, client, mock_session): "system": "ABC", "messages": [{"role": "user", "content": "Hello"}], } - _warmup(client, "/v1/messages", body) resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 assert mock_session._last_url == "http://mock-backend:30000/v1/messages" @@ -434,8 +427,8 @@ def test_auth_headers_forwarded(self, client, mock_session): class TestToolResultIntercept: - def test_openai_tool_result_reordered(self, client, mock_session): - """OpenAI tool results with docs get reordered.""" + def test_openai_tool_result_forwarded(self, client, mock_session): + """OpenAI tool results with docs are extracted and forwarded.""" body = { "model": "gpt-4", "messages": [ @@ -455,19 +448,14 @@ def test_openai_tool_result_reordered(self, client, mock_session): {"role": "user", "content": "Now summarize."}, ], } - _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta.get("documents_reordered") is True - assert meta.get("sources", {}).get("tool_results", 0) >= 1 forwarded = mock_session._last_json tool_content = forwarded["messages"][3]["content"] assert "" in tool_content - def test_anthropic_tool_result_reordered(self, client, mock_session): - """Anthropic tool_result content blocks get reordered.""" + def test_anthropic_tool_result_forwarded(self, client, mock_session): + """Anthropic tool_result content blocks are extracted and forwarded.""" body = { "model": "claude-3-opus-20240229", "system": "You are a helper.", @@ -490,102 +478,14 @@ def test_anthropic_tool_result_reordered(self, client, mock_session): {"role": "user", "content": "Now summarize."}, ], } - _warmup(client, "/v1/messages", body) resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta.get("sources", {}).get("tool_results", 0) >= 1 forwarded = mock_session._last_json tr_content = forwarded["messages"][2]["content"][0]["content"] assert "" in tr_content - def test_openai_system_and_tool_results(self, client, mock_session): - """Both system docs and tool result docs get reordered.""" - body = { - "model": "gpt-4", - "messages": [ - {"role": "system", "content": ( - f"\n" - f"{_AUTH_DOC}\n" - f"{_DB_DOC}\n" - f"{_OAUTH_DOC}\n" - f"" - )}, - {"role": "user", "content": "Search"}, - {"role": "assistant", "content": None, "tool_calls": [{"id": "tc1"}]}, - {"role": "tool", "tool_call_id": "tc1", - "content": f"[1] {_AUTH_DOC} [2] {_DB_DOC} [3] {_OAUTH_DOC}"}, - {"role": "user", "content": "Summarize"}, - ], - } - _warmup(client, "/v1/chat/completions", body) - resp = client.post("/v1/chat/completions", json=body) - assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta["sources"]["system"] == 1 - assert meta["sources"]["tool_results"] == 1 - assert meta["total_documents"] == 6 - - def test_scope_system_skips_tool_results(self, client, mock_session): - """scope=system should only reorder system docs, not tool results.""" - body = { - "model": "gpt-4", - "messages": [ - {"role": "system", "content": ( - f"\n" - f"{_AUTH_DOC}\n" - f"{_DB_DOC}\n" - f"{_OAUTH_DOC}\n" - f"" - )}, - {"role": "tool", "tool_call_id": "tc1", - "content": "T1T2"}, - ], - } - _warmup(client, "/v1/chat/completions", body) - resp = client.post( - "/v1/chat/completions", json=body, - headers={"X-ContextPilot-Scope": "system"}, - ) - assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta["sources"]["system"] == 1 - assert meta["sources"]["tool_results"] == 0 - - def test_scope_tool_results_skips_system(self, client, mock_session): - """scope=tool_results should only reorder tool result docs.""" - body = { - "model": "gpt-4", - "messages": [ - {"role": "system", "content": ( - "Sys ASys B" - )}, - {"role": "tool", "tool_call_id": "tc1", - "content": ( - f"\n" - f"{_AUTH_DOC}\n" - f"{_DB_DOC}\n" - f"{_OAUTH_DOC}\n" - f"" - )}, - ], - } - _warmup(client, "/v1/chat/completions", body) - resp = client.post( - "/v1/chat/completions", json=body, - headers={"X-ContextPilot-Scope": "tool_results"}, - ) - assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta["sources"]["system"] == 0 - assert meta["sources"]["tool_results"] == 1 - - def test_openai_json_tool_result_reordered(self, client, mock_session): - """OpenClaw-style JSON tool results with results array get reordered.""" + def test_openai_json_tool_result_forwarded(self, client, mock_session): + """OpenClaw-style JSON tool results are forwarded correctly.""" import json as _json results = [ {"path": "auth.md", "snippet": _AUTH_DOC, "score": 0.9}, @@ -605,21 +505,16 @@ def test_openai_json_tool_result_reordered(self, client, mock_session): {"role": "user", "content": "Now summarize."}, ], } - _warmup(client, "/v1/chat/completions", body) resp = client.post("/v1/chat/completions", json=body) assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta.get("documents_reordered") is True - assert meta.get("sources", {}).get("tool_results", 0) >= 1 forwarded = mock_session._last_json tool_content = _json.loads(forwarded["messages"][3]["content"]) assert "results" in tool_content assert len(tool_content["results"]) == 3 assert tool_content["citations"] == "auto" - def test_anthropic_json_tool_result_reordered(self, client, mock_session): - """Anthropic-format JSON tool results get reordered.""" + def test_anthropic_json_tool_result_forwarded(self, client, mock_session): + """Anthropic-format JSON tool results are forwarded correctly.""" import json as _json results = [ {"title": "Auth guide", "url": "https://a.com", "description": _AUTH_DOC}, @@ -642,12 +537,8 @@ def test_anthropic_json_tool_result_reordered(self, client, mock_session): {"role": "user", "content": "Summarize."}, ], } - _warmup(client, "/v1/messages", body) resp = client.post("/v1/messages", json=body) assert resp.status_code == 200 - meta = _cp_meta(resp) - assert meta.get("intercepted") is True - assert meta.get("sources", {}).get("tool_results", 0) >= 1 forwarded = mock_session._last_json tr_content = _json.loads(forwarded["messages"][2]["content"][0]["content"]) assert "results" in tr_content @@ -748,24 +639,17 @@ def test_old_tool_result_skipped_on_second_turn(self, client, mock_session): f"" ) system = "You are a helpful assistant." - # Turn 1: process tool result (also primes index). + # Turn 1: first tool result, builds index (no reorder on first call). body1 = self._make_body(system, [tool_content]) resp1 = client.post("/v1/chat/completions", json=body1) assert resp1.status_code == 200 - meta1 = _cp_meta(resp1) - assert meta1.get("intercepted") is True - assert meta1.get("tool_results_skipped", 0) == 0 # Turn 2: same body again — tool result should be skipped. resp2 = client.post("/v1/chat/completions", json=body1) assert resp2.status_code == 200 meta2 = _cp_meta(resp2) - # Everything was skipped, so no intercept header at all (or skipped count). - if meta2: - assert meta2.get("tool_results_skipped", 0) >= 1 - else: - # No header means nothing was reordered/deduped — correct. - pass + # Tool result was skipped (already processed). + assert meta2.get("tool_results_skipped", 0) >= 1 def test_new_tool_result_processed_old_skipped(self, client, mock_session): """Second turn with a NEW tool result: old one skipped, new one processed.""" @@ -828,10 +712,14 @@ def test_cross_tool_result_dedup(self, client, mock_session): assert resp2.status_code == 200 meta2 = _cp_meta(resp2) assert meta2.get("intercepted") is True - assert meta2.get("documents_deduplicated", 0) == 2 # AUTH + OAUTH deduped + assert meta2.get("documents_deduplicated", 0) == 2 # AUTH + OAUTH deduped in tool2 def test_system_prompt_processed_once(self, client, mock_session): - """System prompt docs are only processed on the first turn.""" + """System prompt docs are only processed on the first turn. + + First turn: index empty → no reorder (system_processed set to True). + Second turn: system_processed=True → skipped. + """ system = ( f"\n" f"{_AUTH_DOC}\n" @@ -840,18 +728,14 @@ def test_system_prompt_processed_once(self, client, mock_session): f"" ) body = self._make_body(system) - # Turn 1: system gets reordered. + # Turn 1: system processed (no reorder, but flag set). resp1 = client.post("/v1/chat/completions", json=body) assert resp1.status_code == 200 - meta1 = _cp_meta(resp1) - assert meta1.get("intercepted") is True - assert meta1["sources"]["system"] == 1 # Turn 2: system NOT re-processed. resp2 = client.post("/v1/chat/completions", json=body) assert resp2.status_code == 200 meta2 = _cp_meta(resp2) - # No reordering or dedup happened. assert meta2 == {} or meta2.get("sources", {}).get("system", 0) == 0 def test_new_session_resets_state(self, client, mock_session): @@ -868,27 +752,24 @@ def test_new_session_resets_state(self, client, mock_session): body_long = self._make_body(system, [tool_content]) resp1 = client.post("/v1/chat/completions", json=body_long) assert resp1.status_code == 200 - assert _cp_meta(resp1).get("intercepted") is True - - # Session 2: new conversation (fewer messages) with same tool result. - # The shorter messages array triggers a state reset. - body_short = self._make_body(system, [tool_content]) - # body_short has the same structure, but message count <= body_long's - # because _make_body always starts fresh. However body_short has the - # same count as body_long (same number of tool results). To simulate - # a truly new session, send a shorter request first. - body_new = {"model": "gpt-4", "messages": [ + + # Send same body again — tool result is skipped (already processed). + resp2 = client.post("/v1/chat/completions", json=body_long) + meta2 = _cp_meta(resp2) + assert meta2.get("tool_results_skipped", 0) >= 1 + + # Session 2: shorter messages → triggers state reset. + body_short = {"model": "gpt-4", "messages": [ {"role": "system", "content": system}, {"role": "user", "content": "Fresh session"}, ]} - client.post("/v1/chat/completions", json=body_new) # triggers reset + client.post("/v1/chat/completions", json=body_short) - # Now send the same tool content — should be processed, not skipped. - resp2 = client.post("/v1/chat/completions", json=body_long) - assert resp2.status_code == 200 - meta2 = _cp_meta(resp2) - assert meta2.get("intercepted") is True - assert meta2.get("tool_results_skipped", 0) == 0 + # Now send tool content again — should NOT be skipped (state was reset). + resp3 = client.post("/v1/chat/completions", json=body_long) + assert resp3.status_code == 200 + meta3 = _cp_meta(resp3) + assert meta3.get("tool_results_skipped", 0) == 0 def test_json_tool_result_dedup(self, client, mock_session): """JSON results format also gets cross-turn dedup. @@ -924,5 +805,91 @@ def test_json_tool_result_dedup(self, client, mock_session): assert resp2.status_code == 200 meta2 = _cp_meta(resp2) assert meta2.get("intercepted") is True - assert meta2.get("documents_deduplicated", 0) == 1 # auth entry deduped + assert meta2.get("documents_deduplicated", 0) == 1 # auth entry deduped in tool2 assert meta2.get("tool_results_skipped", 0) == 1 # tool1 skipped + + +# ============================================================================ +# External content marker stripping +# ============================================================================ + + +class TestExternalContentIdStripping: + """EXTERNAL_UNTRUSTED_CONTENT random ids are stripped before forwarding.""" + + def _wrap(self, text, marker_id="ab12cd34"): + """Simulate OpenClaw's wrapWebContent.""" + return ( + f'\n<<>>\n' + f"Source: Web Search\n---\n{text}\n" + f'<<>>' + ) + + def test_ids_stripped_from_forwarded_body(self, client, mock_session): + """Random marker ids are removed so identical content shares KV prefix.""" + wrapped_title = self._wrap("Example Title", "aabbccdd11223344") + wrapped_desc = self._wrap("Example description text", "1122334455667788") + import json as _json + results = [ + {"title": wrapped_title, "url": "https://a.com", + "description": wrapped_desc}, + ] + body = { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "user", "content": "Search"}, + {"role": "assistant", "content": None, + "tool_calls": [{"id": "tc1", "type": "function", + "function": {"name": "web_search", "arguments": "{}"}}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": _json.dumps({"results": results})}, + {"role": "user", "content": "Summarize"}, + ], + } + resp = client.post("/v1/chat/completions", json=body) + assert resp.status_code == 200 + forwarded = mock_session._last_json + tool_content = forwarded["messages"][3]["content"] + # Random ids should be stripped + assert 'id="aabbccdd11223344"' not in tool_content + assert 'id="1122334455667788"' not in tool_content + # Markers themselves preserved (without id) + assert "<<>>" in tool_content + assert "<<>>" in tool_content + + def test_different_ids_produce_same_forwarded_content(self, client, mock_session): + """Two requests with different random ids produce identical forwarded content.""" + import json as _json + + def _make_body(marker_id): + wrapped = self._wrap("Same content", marker_id) + results = [{"title": wrapped, "url": "https://a.com", "description": "plain"}] + return { + "model": "gpt-4", + "messages": [ + {"role": "system", "content": "You are a helper."}, + {"role": "user", "content": "Hello"}, + {"role": "assistant", "content": None, + "tool_calls": [{"id": "tc1", "type": "function", + "function": {"name": "s", "arguments": "{}"}}]}, + {"role": "tool", "tool_call_id": "tc1", + "content": _json.dumps({"results": results})}, + {"role": "user", "content": "Go"}, + ], + } + + # Request 1 with id "aaaa" + resp1 = client.post("/v1/chat/completions", json=_make_body("aaaa0000bbbb1111")) + assert resp1.status_code == 200 + content1 = mock_session._last_json["messages"][3]["content"] + + # Reset intercept state for clean comparison + http_mod._intercept_state = http_mod._InterceptConvState() + + # Request 2 with different id "bbbb" + resp2 = client.post("/v1/chat/completions", json=_make_body("cccc2222dddd3333")) + assert resp2.status_code == 200 + content2 = mock_session._last_json["messages"][3]["content"] + + assert content1 == content2 diff --git a/tests/test_intercept_parser.py b/tests/test_intercept_parser.py index ff77d2f..0b05c9f 100644 --- a/tests/test_intercept_parser.py +++ b/tests/test_intercept_parser.py @@ -282,10 +282,13 @@ def test_memory_search_results(self): assert result is not None assert result.mode == "json_results" assert len(result.documents) == 3 - # Each document is a JSON-serialized result item - for doc in result.documents: - parsed = json.loads(doc) - assert "snippet" in parsed + # Documents are path identifiers (used for clustering) + assert result.documents[0] == "MEMORY.md" + assert result.documents[1] == "notes.md" + assert result.documents[2] == "config.md" + # Full objects stored in json_items + assert result.json_items is not None + assert result.json_items[0]["path"] == "MEMORY.md" def test_web_search_results(self): import json @@ -302,6 +305,9 @@ def test_web_search_results(self): assert result is not None assert result.mode == "json_results" assert len(result.documents) == 2 + # Documents are URL identifiers (used for clustering) + assert result.documents[0] == "https://a.com" + assert result.documents[1] == "https://b.com" def test_explicit_mode(self): import json @@ -347,13 +353,15 @@ def test_roundtrip(self): config = InterceptConfig() result = extract_documents(text, config) assert result is not None - # Reorder: reverse + # Documents are path identifiers + assert result.documents == ["a.md", "b.md", "c.md"] + # Reorder: reverse the content strings reordered = list(reversed(result.documents)) rebuilt = reconstruct_content(result, reordered) rebuilt_obj = json.loads(rebuilt) # Top-level keys preserved assert rebuilt_obj["citations"] == "on" - # Results reordered + # Full result objects reordered by content mapping assert rebuilt_obj["results"][0]["path"] == "c.md" assert rebuilt_obj["results"][1]["path"] == "b.md" assert rebuilt_obj["results"][2]["path"] == "a.md" From 6b505b4e93b44f4385bba526f7183cca2faea46f Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Mon, 9 Mar 2026 12:46:15 +0000 Subject: [PATCH 12/76] Fix http intercept bugs --- contextpilot/server/http_server.py | 45 +++++++++++++++++++++++++++--- 1 file changed, 41 insertions(+), 4 deletions(-) diff --git a/contextpilot/server/http_server.py b/contextpilot/server/http_server.py index 33b0f8e..bcb4ca4 100644 --- a/contextpilot/server/http_server.py +++ b/contextpilot/server/http_server.py @@ -370,6 +370,34 @@ async def health(): } +@app.get("/metrics/ttft") +async def metrics_ttft(last: int = 0): + """Return TTFT history for benchmarking. + + Query params: + last: return only last N entries (0 = all). + """ + history = list(_ttft_history) + if last > 0: + history = history[-last:] + avg = sum(history) / len(history) if history else 0 + return { + "ttft_ms": history, + "count": len(history), + "avg_ms": round(avg, 2), + "total_chars_saved": _ttft_chars_saved_total, + } + + +@app.post("/metrics/ttft/reset") +async def metrics_ttft_reset(): + """Reset TTFT history (call before a benchmark run).""" + global _ttft_chars_saved_total + _ttft_history.clear() + _ttft_chars_saved_total = 0 + return {"status": "ok"} + + @app.post("/reorder") async def reorder(request: ReorderRequest): """ @@ -1050,16 +1078,25 @@ def _get_intercept_state(body: Dict[str, Any]) -> _InterceptConvState: """Return the global intercept state, resetting if the conversation changed. Detection: in a multi-turn agent conversation the messages array only - grows. If the count drops, a new session/conversation started. + grows. If the count drops, either a new session started or the host + compacted old messages. Either way, reset all state: the old KV cache + entries are gone (compaction rewrites content), so cached_messages, + seen_doc_hashes, and reorder state are all invalid. """ global _intercept_state msg_count = len(body.get("messages") or []) if msg_count < _intercept_state.last_message_count: - logger.debug( - f"Intercept: new conversation detected " - f"(messages {msg_count} < {_intercept_state.last_message_count})" + logger.info( + f"Intercept: message count dropped " + f"({msg_count} < {_intercept_state.last_message_count}), " + f"resetting all state (compaction or new session)" ) _intercept_state = _InterceptConvState() + # Skip reorder for the first post-compaction tool result: + # prefix cache is fully invalidated, nothing to align with. + # Go straight to dedup mode so docs are registered for future turns. + _intercept_state.first_tool_result_done = True + _intercept_state.system_processed = True _intercept_state.last_message_count = msg_count return _intercept_state From 56a88e13e8c1fd3a0e56326687466b8648c09064 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 00:41:14 +0000 Subject: [PATCH 13/76] docs: add Context Optimizer architecture design + SVG diagram --- docs/context_optimizer_architecture.svg | 218 +++++++ docs/design_context_blocks.md | 649 +++++++++++++++++++ docs/design_context_optimizer.md | 787 ++++++++++++++++++++++++ 3 files changed, 1654 insertions(+) create mode 100644 docs/context_optimizer_architecture.svg create mode 100644 docs/design_context_blocks.md create mode 100644 docs/design_context_optimizer.md diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg new file mode 100644 index 0000000..3ae6e9c --- /dev/null +++ b/docs/context_optimizer_architecture.svg @@ -0,0 +1,218 @@ + + + + + + + + + + + Context Optimizer Architecture + Analogous to SQL Query Optimizer + + + + User Application + + + + + + INTEGRATION LAYER + pick one + + + L0: HTTP Proxy + cp serve --backend ... + + + L1: Client Wrapper + cp.openai.Client() + + + L2: SDK Direct + cp.optimize(msgs) + + + L3: Engine Hook + sglang / vllm auto + + + + + + CONTEXT OPTIMIZER CORE + + + + 1. PARSER + Prompt → BlockTree + <5ms + + + + + 2. ANALYZER + Usage stats lookup + <1ms + + + + + 3. PLANNER + Select rules → Plan + <2ms + + + + + 4. EXECUTOR + Apply → Optimized + <5ms + + + + + + + + + CATALOG + + Skill Registry + scan dirs, keyword index + Usage Statistics + freq, recency, co-occurrence + Block Registry + hash, section decomposition + + + + COST MODEL + + Token Estimation + chars/4 heuristic + Cache Benefit + prefix_shared_tokens + Risk Assessment + prefetch_miss_rate + + + + REWRITE RULES (Pluggable) + + + + + Reorder + + + Dedup + + + Prefetch + NEW + + + Prune + NEW + + + Merge + NEW + + + Custom + + + + cp.explain(messages) + Input: 47,832 tok → Output: 31,206 tok → Saving: 34.8% + = SQL EXPLAIN + + + + CONTEXT BLOCK PARSER (Detail) + + + + skill + + + memory + + + searched + + + file_based + + + Decomposition tree: + skill:whisper + ├ §Overview f=12 + ├ §Usage f=15 + └ §Advanced f=0 + + + Each block tracks: + block_id | type | frequency | last_used | co_occurrence + Detection: regex + tag matching, zero LLM calls, O(n) scan + + + → export: to_markmap() for markmap.js.org/repl + → export: to_json() for usage dashboard + → persist: ~/.contextpilot/block_usage.json + + + + + + + + EXECUTION ENGINE + + + Cache Index + Hierarchical clustering + Prefix tree, live search + Incremental insert + + + Scheduler + Intra + Inter ordering + Search-path grouping + Batch execution order + + + Eviction Manager + LRU + callback-driven + SGLang/vLLM sync + Auto tree pruning + + + + + + LLM Backend + + + + Data Flow: + messages → [Parse] → BlockTree → [Analyze] → [Plan] → [Execute] → optimized_msgs → LLM + + + + Performance: + Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = Total <15ms overhead. Zero LLM calls. + + + + + Existing + + New + + Pluggable + diff --git a/docs/design_context_blocks.md b/docs/design_context_blocks.md new file mode 100644 index 0000000..2dba9da --- /dev/null +++ b/docs/design_context_blocks.md @@ -0,0 +1,649 @@ +# Context Blocks: Prompt Decomposition, Usage Tracking & Predictive Prefetch + +## 1. Problem Statement + +当前 ContextPilot 的 intercept 路径将 prompt 视为扁平的 document 列表进行 clustering/reordering。但实际 agentic 场景中，一个 prompt 由**不同类型**的 context 组成： + +``` +prompt +├── skills/ # 技能指令（如 SKILL.md, slash commands） +│ ├── openai-whisper/SKILL.md +│ │ ├── §Installation ← 上次用过 +│ │ ├── §Usage ← 高频使用 +│ │ └── §Advanced ← 从未使用 +│ └── notion/SKILL.md +├── memory/ # 用户记忆、偏好、历史总结 +├── searched/ # 实时检索结果（web search, grep） +└── file_based/ # 文件读取（Read tool results） +``` + +**目标**： +1. 将 prompt 解析为 typed context blocks，构建分解树 +2. 细粒度追踪每个 block/section 的 `last_used_time` 和 `frequency` +3. 基于使用统计实现 predictive prefetch（类似 branch prediction） +4. 支持用户侧的 skill filter/merge 操作 + +**约束**：系统优化路径，不增加额外 LLM 调用，不引入高延迟操作。 + +--- + +## 2. Architecture Overview + +``` +┌─────────────────────────────────────────────────────────────┐ +│ Intercept Proxy │ +│ │ +│ Request ──→ [ContextBlockParser] ──→ [UsageTracker] ──→ Forward │ +│ │ │ │ +│ ▼ ▼ │ +│ ContextBlockTree BlockUsageStore │ +│ (markmap-style) (lightweight JSON) │ +│ │ │ │ +│ └───────┬───────────────┘ │ +│ ▼ │ +│ [SkillPrefetcher] │ +│ (branch prediction) │ +│ │ │ +│ ▼ │ +│ Prefetched context │ +│ injected into next request │ +└─────────────────────────────────────────────────────────────┘ +``` + +### 数据流 + +``` +Turn N request + │ + ├─ 1. Parse: prompt → ContextBlockTree（O(n) string scan，无 LLM） + │ 识别 skill_content tags, memory blocks, tool_results, file reads + │ + ├─ 2. Track: 更新 block-level usage stats（O(1) dict update） + │ last_used_time, frequency, section-level granularity + │ + ├─ 3. Predict: 根据 usage history prefetch skills（O(k) lookup） + │ 高频 + 近期使用 → 自动注入，无需 LLM 选择 + │ + ├─ 4. Reorder: 现有 ContextPilot clustering pipeline（不变） + │ + └─ 5. Forward: 优化后的 request → LLM backend +``` + +--- + +## 3. Module Design + +### 3.1 `ContextBlockParser` — Prompt → Context Block Tree + +**位置**: `contextpilot/server/context_block_parser.py` (新文件) + +**职责**: 将 prompt 中的各类 context 识别并分类为 typed blocks，构建树形结构。 + +#### Block Types + +| Type | 识别方式 | 来源 | +|------|---------|------| +| `skill` | `` tags, SKILL.md 内容 | system prompt, tool_results | +| `memory` | Memory search results, `` tags, CLAUDE.md | system prompt, tool_results | +| `searched` | Web search / grep results (JSON with `results` array) | tool_results | +| `file_based` | File read tool results (Read tool, single-doc) | tool_results | + +#### ContextBlockTree 结构 + +```python +@dataclass +class ContextBlock: + """单个 context block。""" + block_id: str # 唯一标识，如 "skill:openai-whisper" 或 "file:/path/to.py" + block_type: BlockType # skill | memory | searched | file_based + content_hash: str # SHA-256[:16] 用于快速比较 + source_location: str # 在 prompt 中的位置标识 + children: List['ContextBlock'] # 子 block（如 skill 的各 section） + metadata: Dict[str, Any] # 额外信息（文件路径、skill 名称等） + char_count: int # 字符数（用于评估 token 开销） + +class BlockType(str, Enum): + SKILL = "skill" + MEMORY = "memory" + SEARCHED = "searched" + FILE_BASED = "file_based" + +@dataclass +class ContextBlockTree: + """一次 request 的 context block 分解树。""" + request_id: str + timestamp: float + blocks: List[ContextBlock] # 顶层 blocks + + def to_markmap(self) -> str: + """导出为 markmap 兼容的 markdown。""" + ... + + def flat_blocks(self) -> List[ContextBlock]: + """DFS 展平所有 blocks（含子 blocks）。""" + ... +``` + +#### 解析策略（零 LLM，纯规则） + +```python +def parse_context_blocks(body: Dict, api_format: str) -> ContextBlockTree: + """ + 从 request body 中解析 context blocks。 + + 策略（按优先级）： + 1. Skill detection: + - ... 标签 + - 包含 SKILL.md 路径的 tool_result + - 已知 skill 关键词匹配 + + 2. Memory detection: + - 标签 + - CLAUDE.md / .cursorrules 等配置文件内容 + - mem0 search results + + 3. Searched detection: + - JSON tool_results with "results" array (web search, grep) + - brave-search, google search 等 tool 名称匹配 + + 4. File-based detection: + - Read tool results（单文件内容） + - 文件路径特征匹配 + + 复杂度: O(n) where n = total content length + """ +``` + +#### Skill Section 分解（细粒度） + +对于 skill 类型的 block，进一步按 markdown heading 分解为 sections： + +```python +def _decompose_skill(content: str, skill_name: str) -> ContextBlock: + """ + 将 skill 内容按 markdown heading 分解为 section sub-blocks。 + + Example: + openai-whisper/SKILL.md + ├── §Overview (## Overview) + ├── §Installation (## Installation) + ├── §Basic Usage (## Basic Usage) + └── §Advanced (## Advanced Options) + + 每个 section 独立追踪使用情况。 + """ +``` + +### 3.2 `BlockUsageStore` — Usage Tracking + +**位置**: `contextpilot/server/block_usage.py` (新文件) + +**职责**: 追踪每个 context block 的使用统计。 + +```python +@dataclass +class BlockUsageStats: + """单个 block 的使用统计。""" + block_id: str + block_type: BlockType + frequency: int = 0 # 总使用次数 + last_used_time: float = 0.0 # 最后使用时间戳 + first_seen_time: float = 0.0 # 首次出现时间 + avg_interval: float = 0.0 # 平均使用间隔 + co_occurrence: Dict[str, int] = field(default_factory=dict) + # co_occurrence: 与其他 block 的共现次数 + # 例如 {"skill:notion": 5} 表示与 notion skill 共同出现 5 次 + + +class BlockUsageStore: + """ + Block 使用统计存储。 + + 设计原则： + - 内存中维护，定期持久化到 JSON 文件 + - O(1) 查询和更新 + - 按 block_id 索引 + """ + + def __init__(self, persist_path: Optional[str] = None): + self._stats: Dict[str, BlockUsageStats] = {} + self._persist_path = persist_path # 如 ~/.contextpilot/block_usage.json + + def record_usage(self, blocks: List[ContextBlock]) -> None: + """ + 记录一次 request 中所有 blocks 的使用。 + + - 更新 frequency += 1 + - 更新 last_used_time = now + - 更新 co_occurrence（同一 request 中的 blocks 互相计数） + - 递归处理子 blocks + + 复杂度: O(b²) where b = number of blocks per request（通常 < 20） + """ + + def get_hot_blocks(self, + block_type: Optional[BlockType] = None, + top_k: int = 5, + recency_weight: float = 0.7, + frequency_weight: float = 0.3) -> List[BlockUsageStats]: + """ + 获取 "热" blocks，用于 prefetch 决策。 + + Score = recency_weight * recency_score + frequency_weight * frequency_score + + recency_score = exp(-decay * (now - last_used_time)) + frequency_score = log(1 + frequency) / log(1 + max_frequency) + """ + + def get_cooccurrent_blocks(self, block_id: str, top_k: int = 3) -> List[str]: + """获取与给定 block 最常共现的其他 blocks。""" + + def persist(self) -> None: + """持久化到磁盘（JSON）。""" + + def load(self) -> None: + """从磁盘加载。""" +``` + +### 3.3 `SkillPrefetcher` — Branch Prediction for Skills + +**位置**: `contextpilot/server/skill_prefetcher.py` (新文件) + +**职责**: 根据使用统计预测下一次 request 需要哪些 skills，提前 fetch 并注入。 + +#### 核心思路 + +``` +类比 CPU branch prediction： + +CPU: 历史分支方向 → 预测下一次分支 +我们: 历史 skill 使用 → 预测下次需要的 skill + +命中: 省去 LLM 选择 skill 的步骤（省 1 次 LLM 调用 + 网络延迟） +未命中: fallback 到 LLM 选择 → grep skill 关键词 +``` + +#### 预测策略 + +```python +class SkillPrefetcher: + """ + Skill 预测与预取。 + + 三级 fallback 策略（低延迟优先）： + + Level 0 - 直接命中（< 1ms）: + 当前 request 已包含 skill content → 无需预测 + + Level 1 - 统计预测（< 5ms）: + usage_store.get_hot_blocks(type=SKILL) + + co_occurrence 分析 + → 注入 top-k 高概率 skills + + Level 2 - 关键词匹配（< 50ms）: + 从 user query 中提取关键词 + → grep 已知 skill 目录的描述/关键词 + → 注入匹配的 skills + + Level 3 - LLM Fallback（最后手段）: + 如果 Level 1+2 都没命中 + 且 user query 明确需要某种能力 + → 让 LLM 输出需要的 skill 类型 + → grep skill 关键词 + （这是当前的默认行为，我们要尽量避免走到这一步） + """ + + def __init__(self, usage_store: BlockUsageStore, + skill_registry: 'SkillRegistry'): + self._usage_store = usage_store + self._skill_registry = skill_registry + self._prediction_history: List[PredictionResult] = [] + + def predict(self, + current_blocks: ContextBlockTree, + user_query: str = "") -> List[SkillPrediction]: + """ + 预测下一次需要的 skills。 + + Returns: + List of SkillPrediction(skill_id, confidence, reason) + confidence > threshold → 自动注入 + """ + + def evaluate_prediction(self, predicted: List[str], + actual: List[str]) -> PredictionMetrics: + """ + 评估预测准确率（用于自适应调优）。 + + Returns: + PredictionMetrics(hit_rate, precision, recall, avg_latency_saved) + """ + + +@dataclass +class SkillPrediction: + skill_id: str # 如 "openai-whisper" + confidence: float # 0.0 ~ 1.0 + reason: str # "high_frequency", "co_occurrence", "keyword_match" + estimated_tokens: int # 预估 token 开销 +``` + +#### Skill Registry（轻量索引） + +```python +class SkillRegistry: + """ + 已知 skills 的轻量索引。 + + 数据来源： + - ~/.config/opencode/skills/ 目录扫描 + - 用户配置的外部 skill 目录 + + 索引内容（启动时构建，O(k) where k = skill count）： + - skill_id → skill_path + - skill_id → keywords（从 SKILL.md description 提取） + - skill_id → section_headings（markdown heading 列表） + """ + + def __init__(self, skill_dirs: List[str]): + self._skills: Dict[str, SkillInfo] = {} + self._keyword_index: Dict[str, List[str]] = {} # keyword → [skill_ids] + + def scan(self) -> None: + """扫描 skill 目录，构建索引。""" + + def search_by_keywords(self, keywords: List[str]) -> List[str]: + """关键词匹配搜索 skills。O(k * w)""" + + def get_skill_content(self, skill_id: str, + sections: Optional[List[str]] = None) -> str: + """ + 获取 skill 内容。可选只获取特定 sections。 + + sections=None → 全量 + sections=["Installation", "Usage"] → 只返回这两个 section + """ +``` + +### 3.4 `SkillFilter` — 用户侧 Skill 管理 + +**位置**: `contextpilot/server/skill_filter.py` (新文件) + +**职责**: 允许用户管理（filter/merge）哪些 skills 参与 prefetch。 + +#### Filter 设计 + +```python +@dataclass +class SkillFilterConfig: + """用户级 skill filter 配置。""" + + # Allowlist: 只允许这些 skills 被 prefetch（空 = 不限制） + allowed_skills: Set[str] = field(default_factory=set) + + # Blocklist: 禁止这些 skills 被 prefetch + blocked_skills: Set[str] = field(default_factory=set) + + # Section filter: 对特定 skill 只允许/禁止特定 sections + # 如 {"openai-whisper": {"allowed": ["Usage", "Installation"]}} + section_filters: Dict[str, Dict] = field(default_factory=dict) + + # Auto-prune: 自动移除超过 N 天未使用的 skills + auto_prune_days: Optional[int] = None + + # Max prefetch: 最多同时 prefetch 的 skill 数量 + max_prefetch_skills: int = 3 + + # Confidence threshold: prefetch 的置信度阈值 + confidence_threshold: float = 0.6 + + +class SkillFilter: + """ + Skill filter 与 merge 管理器。 + + 用户交互方式（通过 HTTP API 或 config 文件）： + + 1. Filter: + POST /skills/filter {"block": ["notion"], "allow": ["openai-whisper"]} + → 禁止 notion 被 prefetch，只允许 whisper + + 2. Merge: + POST /skills/merge {"skills": ["skill-a", "skill-b"], "name": "merged-ab"} + → 将多个 skill 合并为一个虚拟 skill（共享 cache prefix） + → 合并后的 skill 作为整体被追踪和 prefetch + + 3. Pin: + POST /skills/pin {"skills": ["openai-whisper"]} + → 钉选：始终 prefetch，不受 confidence threshold 限制 + + 4. Stats: + GET /skills/stats + → 返回所有 skill 的使用统计 + 预测命中率 + """ + + def __init__(self, config: SkillFilterConfig, + usage_store: BlockUsageStore): + self._config = config + self._usage_store = usage_store + self._merged_skills: Dict[str, List[str]] = {} # merged_name → [original_ids] + + def apply_filter(self, predictions: List[SkillPrediction]) -> List[SkillPrediction]: + """ + 应用 filter 规则到 prefetch 预测结果。 + + 流程： + 1. 移除 blocked_skills 中的预测 + 2. 如果 allowed_skills 非空，只保留其中的 + 3. 应用 section_filters（裁剪 skill 内容到允许的 sections） + 4. 应用 confidence_threshold + 5. 截断到 max_prefetch_skills + """ + + def merge_skills(self, skill_ids: List[str], merged_name: str) -> None: + """ + 合并多个 skills 为一个虚拟 skill。 + + 效果： + - 合并后的 skill 的 usage stats = sum(individual stats) + - prefetch 时作为整体 fetch（共享 cache prefix） + - 在 ContextBlockTree 中显示为一个 merged block + """ + + def auto_prune(self) -> List[str]: + """ + 自动清理长期未使用的 skills。 + + Returns: + 被清理的 skill_ids + """ +``` + +--- + +## 4. Integration Points + +### 4.1 与现有 Intercept Pipeline 集成 + +```python +# contextpilot/server/http_server.py :: _intercept_and_forward() + +async def _intercept_and_forward(request: Request, api_format: str): + body = await request.json() + + # ===== 新增：Context Block Parsing ===== + block_tree = parse_context_blocks(body, api_format) + + # ===== 新增：Usage Tracking ===== + usage_store.record_usage(block_tree.flat_blocks()) + + # ===== 新增：Skill Prefetch（仅在无 skill 时触发）===== + if not block_tree.has_type(BlockType.SKILL): + predictions = prefetcher.predict(block_tree, user_query=...) + filtered = skill_filter.apply_filter(predictions) + if filtered: + body = inject_prefetched_skills(body, filtered, api_format) + + # ===== 现有：Document Extraction & Reorder ===== + handler = get_format_handler(api_format) + multi_result = handler.extract_all(body, config) + # ... 现有 reorder/dedup 逻辑不变 ... +``` + +### 4.2 新增 HTTP API Endpoints + +```python +# GET /context-blocks/tree +# 返回最近一次 request 的 context block tree（markmap markdown 格式） + +# GET /context-blocks/stats +# 返回所有 block 的使用统计 + +# POST /skills/filter +# 设置 skill filter 规则 + +# POST /skills/merge +# 合并 skills + +# POST /skills/pin +# 钉选 skills + +# GET /skills/predictions +# 返回当前的 skill 预测结果和历史准确率 + +# GET /skills/registry +# 返回已知 skill 索引 +``` + +### 4.3 Markmap Visualization + +```python +# GET /context-blocks/markmap +# 返回可直接粘贴到 https://markmap.js.org/repl 的 markdown + +# 示例输出: +""" +# Request ctx-20260309-001 + +## Skills (2) +### openai-whisper +- §Overview ✅ freq=12 last=2min ago +- §Installation ✅ freq=8 last=5min ago +- §Usage ✅ freq=15 last=1min ago +- §Advanced ⬜ freq=0 + +### notion +- §API Setup ✅ freq=3 last=1h ago +- §Page Operations ⬜ freq=0 + +## Memory (1) +### User Preferences +- freq=20 last=30s ago + +## Searched (3) +### Web: "whisper CLI usage" +- 5 results, freq=1 +### Grep: "def transcribe" +- 3 results, freq=2 + +## File-Based (2) +### /src/audio.py +- freq=4 last=10min ago +### /config/settings.json +- freq=1 last=1h ago +""" +``` + +--- + +## 5. Implementation Plan + +### Phase 1: Context Block Parsing + Usage Tracking (Core) + +| Task | 文件 | 估时 | +|------|------|------| +| `ContextBlock` / `ContextBlockTree` 数据模型 | `context_block_parser.py` | 0.5d | +| Parser: skill/memory/searched/file_based 识别 | `context_block_parser.py` | 1d | +| Skill section 分解（markdown heading split） | `context_block_parser.py` | 0.5d | +| `BlockUsageStore` 统计引擎 | `block_usage.py` | 0.5d | +| 集成到 `_intercept_and_forward` | `http_server.py` | 0.5d | +| Markmap export | `context_block_parser.py` | 0.5d | +| 单元测试 | `tests/test_context_blocks.py` | 0.5d | + +### Phase 2: Skill Prefetch (Branch Prediction) + +| Task | 文件 | 估时 | +|------|------|------| +| `SkillRegistry` 目录扫描与索引 | `skill_prefetcher.py` | 0.5d | +| `SkillPrefetcher` 统计预测（Level 1） | `skill_prefetcher.py` | 0.5d | +| 关键词匹配预测（Level 2） | `skill_prefetcher.py` | 0.5d | +| Prefetch 注入到 request body | `http_server.py` | 0.5d | +| 预测准确率评估与自适应 | `skill_prefetcher.py` | 0.5d | +| 集成测试 | `tests/test_skill_prefetch.py` | 0.5d | + +### Phase 3: Skill Filter & User Management + +| Task | 文件 | 估时 | +|------|------|------| +| `SkillFilterConfig` 数据模型 | `skill_filter.py` | 0.5d | +| Filter/Merge/Pin 逻辑 | `skill_filter.py` | 0.5d | +| HTTP API endpoints | `http_server.py` | 0.5d | +| Auto-prune | `skill_filter.py` | 0.5d | +| E2E 测试 | `tests/test_skill_filter.py` | 0.5d | + +--- + +## 6. Performance Budget + +| 操作 | 目标延迟 | 实现方式 | +|------|---------|---------| +| Context block parsing | < 5ms | 纯正则 + string scan，无 LLM | +| Usage tracking update | < 1ms | 内存 dict update | +| Skill prediction (Level 1) | < 5ms | 排序 + top-k 选取 | +| Skill prediction (Level 2) | < 50ms | keyword grep，预建索引 | +| Markmap export | < 10ms | String concatenation | +| Filter apply | < 1ms | Set operations | +| 持久化 (async) | 不阻塞请求 | 异步写入 JSON | + +**总额外延迟**: < 10ms（典型路径：parsing + tracking + prediction Level 1） + +--- + +## 7. Key Design Decisions + +### 7.1 为什么不用 LLM 做 block classification？ +- 每次 request 增加 1 次 LLM 调用 ≈ 100-500ms +- Block 类型可以通过结构化特征（tags, tool names, file paths）可靠识别 +- 规则引擎 < 5ms，LLM ≈ 100x slower + +### 7.2 为什么 section-level 而不是 token-level 追踪？ +- Token-level 需要 LLM attention weights → 不可用（proxy 模式无法获取） +- Section-level（markdown heading）是 skill 的自然分界 +- 粒度足够用于 prefetch 决策 + +### 7.3 Skill merge 的语义是什么？ +- **场景**：用户总是同时用 skill-A 和 skill-B → merge 为 skill-AB +- **效果**：两个 skill 内容 concatenate 为一个 block → 共享 KV cache prefix +- **追踪**：merged block 作为一个整体统计 frequency/recency +- **可逆**：`POST /skills/unmerge` 拆回 + +### 7.4 Prefetch 的 "miss" 怎么处理？ +- Prefetch 的 skill 没被用到 → 浪费了 prefill tokens +- 通过 `confidence_threshold` 控制误 prefetch 率 +- `evaluate_prediction` 追踪历史准确率，自适应调整 threshold +- 最坏情况 = 多 prefill 了几千 tokens，不影响正确性 + +### 7.5 与现有 `_intercept_state.seen_doc_hashes` 的关系？ +- 现有 dedup 是 document 粒度（hash 去重） +- Context block 是 semantic 粒度（skill/memory/searched/file） +- 两者正交：先 block 解析，再对每个 block 内部的 docs 做 dedup +- `seen_doc_hashes` 逻辑不变，block 是上层分类 + +--- + +## 8. Future Extensions + +1. **Cross-user skill patterns**: 多用户共享匿名化的 skill usage patterns → collaborative filtering +2. **Adaptive section pruning**: 高频 section 保留全文，低频 section 自动压缩/摘要 +3. **Skill versioning**: 当 SKILL.md 内容变化时，追踪版本差异对使用统计的影响 +4. **Real-time markmap dashboard**: WebSocket 推送实时 context block tree 可视化 diff --git a/docs/design_context_optimizer.md b/docs/design_context_optimizer.md new file mode 100644 index 0000000..8d29f8e --- /dev/null +++ b/docs/design_context_optimizer.md @@ -0,0 +1,787 @@ +# ContextPilot → Context Optimizer: Architecture Redesign + +## TL;DR + +把 ContextPilot 重构为 **Context Optimizer** — 类比 SQL Query Optimizer。 +用户只管写 prompt，Optimizer 透明地处理一切（reorder, dedup, prefetch, prune）。 + +**用户代码变化**： + +```python +# Before (当前 — 要理解 pipeline, retriever, optimizer config...) +from contextpilot.pipeline import RAGPipeline, RetrieverConfig, OptimizerConfig +pipeline = RAGPipeline(retriever="bm25", corpus_path="corpus.jsonl", + optimizer=OptimizerConfig(enabled=True, use_gpu=True)) +results = pipeline.run(queries=["What is AI?"], generate_responses=True) + +# After (目标 — 一行即用) +import contextpilot as cp +response = cp.openai.ChatCompletions.create(model="gpt-4", messages=messages) +# 或 +messages = cp.optimize(messages) +``` + +--- + +## 1. SQL Query Optimizer 类比 + +``` + SQL World Context World + ═══════════════════════════════ ═══════════════════════════════════ + + SELECT * FROM t WHERE x=1 messages = [{role, content}, ...] + │ │ + ▼ ▼ + ┌─────────────────────┐ ┌──────────────────────────────┐ + │ SQL Parser │ │ Context Parser │ + │ SQL → Parse Tree │ │ Prompt → ContextBlockTree │ + └────────┬────────────┘ │ (skill/memory/search/file) │ + │ └──────────────┬───────────────┘ + ▼ ▼ + ┌─────────────────────┐ ┌──────────────────────────────┐ + │ Catalog/Stats │ │ Catalog / Usage Stats │ + │ table size, indexes │ │ block freq, recency, tokens │ + │ column cardinality │ │ skill registry, cache state │ + └────────┬────────────┘ └──────────────┬───────────────┘ + │ ▼ + ▼ ┌──────────────────────────────┐ + ┌─────────────────────┐ │ Rewrite Rules │ + │ Rewrite Rules │ │ • Reorder (prefix sharing) │ + │ • predicate pushdown│ │ • Dedup (cross-turn) │ + │ • subquery unnest │ │ • Prefetch (branch predict) │ + │ • view merging │ │ • Prune (token budget) │ + │ • constant folding │ │ • Merge (skill combine) │ + └────────┬────────────┘ └──────────────┬───────────────┘ + │ ▼ + ▼ ┌──────────────────────────────┐ + ┌─────────────────────┐ │ Cost Model │ + │ Cost Model │ │ token_cost(block) │ + │ row estimates │ │ cache_benefit(reorder) │ + │ I/O cost │ │ prefetch_risk(miss_rate) │ + └────────┬────────────┘ └──────────────┬───────────────┘ + │ ▼ + ▼ ┌──────────────────────────────┐ + ┌─────────────────────┐ │ Plan Generator │ + │ Plan Generator │ │ 选最优 rule 组合 │ + │ join order, index │ │ → OptimizationPlan │ + └────────┬────────────┘ └──────────────┬───────────────┘ + │ ▼ + ▼ ┌──────────────────────────────┐ + ┌─────────────────────┐ │ Executor │ + │ Executor │ │ apply plan → optimized body │ + │ scan, join, sort │ │ reorder, inject, trim │ + └─────────────────────┘ └──────────────────────────────┘ + + EXPLAIN SELECT ... co.explain(messages) + → 显示执行计划 → 显示优化计划 + → cost estimates → token savings / cache hit rate + + CREATE INDEX co.register_skill(path) + → 建索引加速查询 → 注册 skill 加速 prefetch + + ANALYZE table co.analyze() + → 更新统计信息 → 更新 block usage 统计 +``` + +--- + +## 2. Architecture Diagram + +``` + ┌─────────────────────────────────────────┐ + │ User Application │ + │ │ + │ messages = [{role:"system", ...}, │ + │ {role:"user", ...}] │ + └──────────────┬──────────────────────────┘ + │ + ╔════════════════════════╤╧═══════════════════════════╗ + ║ Integration Layer │ (pick one, zero/one-line) ║ + ╠═══════════╤═══════════╪═══════════╤════════════════╣ + ║ Client │ HTTP │ SDK │ Hook ║ + ║ Wrapper │ Proxy │ Direct │ (engine) ║ + ║ │ │ │ ║ + ║ cp.openai│ $cp serve │cp.optimize│ sglang/vllm ║ + ║ .create()│ :8765 │(messages) │ auto-patch ║ + ╚═══════╤═══╧═══════╤═══╧═════╤═════╧════════╤═══════╝ + │ │ │ │ + └─────────┬─┘─────────┘──────────────┘ + │ + ╔════════════════════════╧═══════════════════════════════════╗ + ║ CONTEXT OPTIMIZER CORE ║ + ║ ║ + ║ ┌──────────┐ ┌───────────┐ ┌───────────┐ ║ + ║ │ 1.PARSER │──▶│ 2.ANALYZER│──▶│ 3.PLANNER │ ║ + ║ └──────────┘ └───────────┘ └───────────┘ ║ + ║ │ │ │ ║ + ║ │ ┌─────────┴──────────┐ │ ║ + ║ │ │ │ │ ║ + ║ ▼ ▼ ▼ ▼ ║ + ║ ┌──────────┐ ┌────────────┐ ┌───────────┐ ║ + ║ │ CATALOG │ │ COST MODEL │ │ REWRITE │ ║ + ║ │ │ │ │ │ RULES │ ║ + ║ │• skills │ │• token_cost│ │ │ ║ + ║ │• memory │ │• cache_hit │ │• reorder │ ║ + ║ │• usage │ │• prefetch_ │ │• dedup │ ║ + ║ │ stats │ │ risk │ │• prefetch │ ║ + ║ │• block │ │• latency │ │• prune │ ║ + ║ │ registry │ │ │ │• merge │ ║ + ║ └──────────┘ └────────────┘ └───────────┘ ║ + ║ │ ║ + ║ ┌─────────────────┘ ║ + ║ ▼ ║ + ║ ┌───────────┐ ║ + ║ │4.EXECUTOR │ ║ + ║ │ apply │ ║ + ║ │ plan │ ║ + ║ └─────┬─────┘ ║ + ╚════════════════════╧════════════════════════════════════════╝ + │ + ╔════════════════════╧════════════════════════════════════════╗ + ║ EXECUTION ENGINE ║ + ║ ║ + ║ ┌──────────────┐ ┌─────────────┐ ┌──────────────┐ ║ + ║ │ Cache Index │ │ Scheduler │ │ Eviction │ ║ + ║ │ (prefix tree) │ │ (inter/ │ │ Manager │ ║ + ║ │ │ │ intra) │ │ │ ║ + ║ └──────────────┘ └─────────────┘ └──────────────┘ ║ + ╚════════════════════�════════════════════════════════════════╝ + │ + ▼ + ┌─────────────────────┐ + │ LLM Backend │ + │ (OpenAI/Anthropic/ │ + │ SGLang/vLLM/etc) │ + └─────────────────────┘ +``` + +--- + +## 3. User-Facing API Design (即插即用) + +### Level 0: Zero-Code (HTTP Proxy) + +```bash +# 启动 proxy，指向你的 LLM backend +$ contextpilot serve --backend https://api.openai.com --port 8765 + +# 你的代码零修改，只改 base_url +client = OpenAI(base_url="http://localhost:8765/v1") +response = client.chat.completions.create(model="gpt-4", messages=messages) +# ContextOptimizer 自动 intercept → optimize → forward +``` + +### Level 1: Client Wrapper (一行改动) + +```python +import contextpilot as cp + +# 替换 openai 的 client — 其他代码零修改 +client = cp.openai.Client(api_key="sk-...") + +# 就这样，所有 chat.completions.create 自动优化 +response = client.chat.completions.create( + model="gpt-4", + messages=messages # ← Optimizer 自动 reorder/dedup/prefetch +) +``` + +```python +# Anthropic 同理 +client = cp.anthropic.Client(api_key="sk-ant-...") +response = client.messages.create(model="claude-4-sonnet", messages=messages) +``` + +### Level 2: Explicit Optimize (两行) + +```python +import contextpilot as cp + +# 手动 optimize，用自己的 client 发送 +optimized = cp.optimize(messages) +response = openai_client.chat.completions.create( + model="gpt-4", + messages=optimized +) +``` + +### Level 3: Full Control + +```python +import contextpilot as cp + +optimizer = cp.ContextOptimizer( + rules=["reorder", "dedup", "prefetch", "prune"], + token_budget=128_000, + skill_dirs=["~/.config/opencode/skills/"], +) + +# EXPLAIN — 看优化计划（不执行） +plan = optimizer.explain(messages) +print(plan) +# ╔══════════════════════════════════════════════╗ +# ║ Context Optimization Plan ║ +# ╠══════════════════════════════════════════════╣ +# ║ Input: 47,832 tokens (12 blocks) ║ +# ║ Output: 31,206 tokens (9 blocks) ║ +# ║ Saving: 16,626 tokens (34.8%) ║ +# ╠══════════════════════════════════════════════╣ +# ║ Rules applied: ║ +# ║ 1. REORDER → 3 blocks reordered ║ +# ║ cache_hit: 12,400 tokens shared ║ +# ║ 2. DEDUP → 2 blocks removed (seen T1,T2) ║ +# ║ saved: 8,200 tokens ║ +# ║ 3. PREFETCH → skill:whisper injected ║ +# ║ confidence: 0.87, cost: 1,200 tokens ║ +# ║ 4. PRUNE → 1 section trimmed ║ +# ║ §Advanced (freq=0, last_used=never) ║ +# ╠══════════════════════════════════════════════╣ +# ║ Block breakdown: ║ +# ║ skill:whisper 8,400t freq=12 ★★★★☆ ║ +# ║ skill:notion 3,200t freq=3 ★★☆☆☆ ║ +# ║ memory:prefs 800t freq=20 ★★★★★ ║ +# ║ search:web 6,100t freq=1 ★☆☆☆☆ ║ +# ║ file:audio.py 2,400t freq=4 ★★★☆☆ ║ +# ╚══════════════════════════════════════════════╝ + +# 实际优化 +optimized = optimizer.optimize(messages) + +# 统计 +print(optimizer.stats()) +``` + +### Level 4: Batch (多 query 并发) + +```python +import contextpilot as cp + +# 多个 query 共享 context prefix +all_messages = [messages_1, messages_2, messages_3, ...] +optimized_batch, execution_order = cp.optimize_batch(all_messages) + +# execution_order 告诉你最优执行顺序（最大化 cache 命中） +for i in execution_order: + response = client.chat.completions.create( + model="gpt-4", + messages=optimized_batch[i] + ) +``` + +--- + +## 4. Internal Module Structure (Refactored) + +### Current → New 映射 + +``` +contextpilot/ contextpilot/ +├── context_index/ ├── engine/ +│ ├── index_construction.py ──→ │ ├── cache_index.py (ContextIndex) +│ ├── tree_nodes.py ──→ │ ├── tree.py (ClusterNode, NodeManager) +│ ├── compute_distance_cpu.py ──→ │ ├── distance.py (CPU + GPU unified) +│ └── compute_distance_gpu.py ──→ │ └── (merged into distance.py) +│ │ +├── context_ordering/ ├── engine/ +│ ├── intra_ordering.py ──→ │ ├── scheduler.py (unified intra+inter) +│ └── inter_scheduler.py ──→ │ └── (merged into scheduler.py) +│ │ +├── server/ ├── optimizer/ +│ ├── intercept_parser.py ──→ │ ├── parser.py (ContextBlockParser) +│ ├── conversation_tracker.py ──→ │ ├── rules/dedup.py (dedup rule) +│ ├── metadata.py ──→ │ ├── catalog.py (usage stats + registry) +│ ├── live_index.py ──→ │ └── planner.py (plan generation) +│ ├── eviction_heap.py ──→ │ +│ └── http_server.py ──→ ├── engine/ +│ │ ├── live_index.py (cache mgmt) +│ │ ├── eviction.py +│ │ └── metadata.py +│ │ +├── pipeline/ ├── proxy/ +│ ├── rag_pipeline.py ──→ │ ├── server.py (HTTP proxy) +│ ├── multi_turn.py ──→ │ └── interceptor.py (request intercept) +│ └── components.py ──→ │ +│ │ +├── retriever/ ├── integrations/ +│ ├── bm25.py ──→ │ ├── retrievers/ (moved, optional) +│ ├── faiss_embedding.py ──→ │ ├── openai_client.py (NEW: wrapper) +│ ├── mem0_retriever.py ──→ │ ├── anthropic_client.py(NEW: wrapper) +│ └── pageindex_retriever.py ──→ │ └── hooks/ (sglang, vllm, llama) +│ │ +├── api.py ──→ ├── __init__.py (public API) +├── __init__.py ──→ ├── optimizer/core.py (ContextOptimizer class) +│ │ +├── _sglang_hook.py ──→ ├── integrations/hooks/ +├── _vllm_hook.py ──→ │ +└── _llamacpp_hook.py ──→ │ + │ + ├── optimizer/rules/ (NEW) + │ ├── __init__.py + │ ├── base.py (Rule ABC) + │ ├── reorder.py (prefix sharing) + │ ├── dedup.py (cross-turn) + │ ├── prefetch.py (branch prediction) + │ ├── prune.py (token budget) + │ └── merge.py (skill combine) + │ + └── optimizer/ + ├── parser.py (NEW: block parser) + ├── catalog.py (NEW: usage + registry) + ├── cost_model.py (NEW: token costing) + └── planner.py (NEW: plan generation) +``` + +### Package Layout (Final) + +``` +contextpilot/ +│ +├── __init__.py # Public API surface +│ # import contextpilot as cp +│ # cp.optimize(messages) +│ # cp.explain(messages) +│ # cp.openai.Client(...) +│ # cp.ContextOptimizer(...) +│ +├── optimizer/ # ★ Core: The "Query Optimizer" +│ ├── __init__.py +│ ├── core.py # ContextOptimizer 主类 +│ ├── parser.py # Prompt → ContextBlockTree +│ ├── planner.py # Rule selection + plan generation +│ ├── cost_model.py # Token cost estimation +│ ├── catalog.py # Block registry + usage stats +│ │ +│ └── rules/ # Rewrite rules (pluggable) +│ ├── __init__.py +│ ├── base.py # OptimizationRule ABC +│ ├── reorder.py # Prefix-sharing reorder +│ ├── dedup.py # Cross-turn deduplication +│ ├── prefetch.py # Predictive skill prefetch +│ ├── prune.py # Token budget pruning +│ └── merge.py # Block merging +│ +├── engine/ # ★ Execution Engine (from current context_index/) +│ ├── __init__.py +│ ├── cache_index.py # Hierarchical clustering + tree (ContextIndex) +│ ├── tree.py # ClusterNode, NodeManager +│ ├── distance.py # Distance computation (CPU/GPU) +│ ├── scheduler.py # Intra + Inter context scheduling +│ ├── live_index.py # Live cache state management +│ ├── eviction.py # Eviction logic +│ └── metadata.py # Node metadata +│ +├── proxy/ # ★ HTTP Proxy mode +│ ├── __init__.py +│ ├── server.py # FastAPI server +│ └── interceptor.py # Request intercept + format handlers +│ +├── integrations/ # ★ 即插即用 wrappers +│ ├── __init__.py +│ ├── openai_client.py # cp.openai.Client (drop-in replacement) +│ ├── anthropic_client.py # cp.anthropic.Client +│ ├── retrievers/ # BM25, FAISS, Mem0, PageIndex +│ │ ├── __init__.py +│ │ ├── bm25.py +│ │ ├── faiss.py +│ │ ├── mem0.py +│ │ └── pageindex.py +│ └── hooks/ # Inference engine hooks +│ ├── __init__.py +│ ├── sglang.py +│ ├── vllm.py +│ └── llamacpp.py +│ +├── pipeline/ # RAG pipeline (保留, 内部用 optimizer) +│ ├── __init__.py +│ ├── rag_pipeline.py +│ └── components.py +│ +└── _compat.py # 旧 API 兼容层 +``` + +--- + +## 5. Core Class Design + +### 5.1 ContextOptimizer (主入口) + +```python +class ContextOptimizer: + """ + Context Optimizer for LLM prompts. + + Analogous to a SQL Query Optimizer: sits between the user's prompt + and the LLM, transparently optimizing context for maximum efficiency. + """ + + def __init__( + self, + rules: List[str] = None, # ["reorder","dedup","prefetch","prune"] + token_budget: int = None, # max context tokens (None = no limit) + skill_dirs: List[str] = None, # skill directories to scan + persist_path: str = None, # usage stats persistence + cache_backend: str = "memory", # "memory" | "redis" | "file" + ): + self._parser = ContextParser() + self._catalog = Catalog(skill_dirs, persist_path) + self._cost_model = CostModel(token_budget) + self._planner = Planner(rules) + self._engine = CacheEngine() + + # ─── Primary API ───────────────────────────────────────── + + def optimize(self, messages: List[Dict]) -> List[Dict]: + """Optimize messages. Drop-in: pass messages in, get optimized messages out.""" + block_tree = self._parser.parse(messages) + self._catalog.record(block_tree) + plan = self._planner.plan(block_tree, self._catalog, self._cost_model) + return self._execute(plan, messages) + + def explain(self, messages: List[Dict]) -> OptimizationPlan: + """Show optimization plan without executing (like SQL EXPLAIN).""" + block_tree = self._parser.parse(messages) + return self._planner.plan(block_tree, self._catalog, self._cost_model) + + def optimize_batch( + self, + all_messages: List[List[Dict]] + ) -> Tuple[List[List[Dict]], List[int]]: + """Batch optimize + return execution order for max cache sharing.""" + ... + + # ─── Management API ────────────────────────────────────── + + def stats(self) -> Dict: + """Usage statistics, cache hit rates, optimization history.""" + ... + + def analyze(self) -> None: + """Refresh statistics (like SQL ANALYZE).""" + ... + + def register_skill(self, path: str) -> None: + """Register a new skill (like CREATE INDEX).""" + ... + + def configure_rules(self, rules: List[str]) -> None: + """Enable/disable optimization rules.""" + ... +``` + +### 5.2 Optimization Rules (Pluggable) + +```python +class OptimizationRule(ABC): + """Base class for all optimization rules. Like SQL rewrite rules.""" + + name: str # "reorder", "dedup", etc. + + @abstractmethod + def applicable(self, block_tree: ContextBlockTree, + catalog: Catalog) -> bool: + """Can this rule improve the plan? Quick check, no mutation.""" + + @abstractmethod + def estimate(self, block_tree: ContextBlockTree, + catalog: Catalog, cost_model: CostModel) -> RuleEstimate: + """Estimate benefit/cost without applying. For plan selection.""" + + @abstractmethod + def apply(self, messages: List[Dict], + block_tree: ContextBlockTree) -> List[Dict]: + """Apply the optimization. Returns new messages.""" + + +# Concrete rules: + +class ReorderRule(OptimizationRule): + """Reorder documents within blocks for prefix sharing.""" + name = "reorder" + # Wraps current: ContextIndex.fit_transform + InterContextScheduler + +class DedupRule(OptimizationRule): + """Remove documents seen in previous conversation turns.""" + name = "dedup" + # Wraps current: ConversationTracker + _deduplicate_docs + +class PrefetchRule(OptimizationRule): + """Predict and inject likely-needed skills.""" + name = "prefetch" + # NEW: SkillPrefetcher from context_blocks design + +class PruneRule(OptimizationRule): + """Trim low-value sections to fit token budget.""" + name = "prune" + # NEW: removes unused skill sections, old search results + +class MergeRule(OptimizationRule): + """Combine frequently co-occurring blocks for cache sharing.""" + name = "merge" + # NEW: merges skill blocks based on co-occurrence stats +``` + +### 5.3 OptimizationPlan (EXPLAIN output) + +```python +@dataclass +class OptimizationPlan: + """The output of the Planner — describes what the Optimizer will do.""" + + # Input analysis + input_blocks: List[ContextBlock] + input_tokens: int + + # Planned steps + steps: List[PlanStep] + + # Expected output + output_tokens: int + token_savings: int + cache_hit_estimate: float # 0.0 ~ 1.0 + + # Metadata + rules_considered: List[str] + rules_applied: List[str] + planning_time_ms: float + + def __str__(self) -> str: + """Pretty-print like SQL EXPLAIN output.""" + ... + + def to_markmap(self) -> str: + """Export block tree as markmap-compatible markdown.""" + ... + + +@dataclass +class PlanStep: + rule: str # "reorder", "dedup", etc. + target_blocks: List[str] # which blocks affected + tokens_before: int + tokens_after: int + confidence: float # for prefetch + reason: str # human-readable explanation +``` + +--- + +## 6. Integration Layer: Client Wrappers + +### 6.1 OpenAI Wrapper + +```python +# contextpilot/integrations/openai_client.py + +class Client: + """Drop-in replacement for openai.Client with context optimization.""" + + def __init__(self, api_key=None, optimizer=None, **kwargs): + import openai + self._client = openai.Client(api_key=api_key, **kwargs) + self._optimizer = optimizer or _get_default_optimizer() + self.chat = self._ChatNamespace(self._client, self._optimizer) + + class _ChatNamespace: + def __init__(self, client, optimizer): + self.completions = self._CompletionsNamespace(client, optimizer) + + class _CompletionsNamespace: + def create(self, messages, **kwargs): + optimized = self._optimizer.optimize(messages) + return self._client.chat.completions.create( + messages=optimized, **kwargs + ) +``` + +### 6.2 简洁的 `__init__.py` + +```python +# contextpilot/__init__.py + +from .optimizer.core import ContextOptimizer +from . import integrations as openai # cp.openai.Client(...) +from . import integrations as anthropic # cp.anthropic.Client(...) + +# Module-level convenience (uses singleton optimizer) +def optimize(messages, **kwargs): + return _get_default().optimize(messages, **kwargs) + +def explain(messages, **kwargs): + return _get_default().explain(messages, **kwargs) + +def optimize_batch(all_messages, **kwargs): + return _get_default().optimize_batch(all_messages, **kwargs) +``` + +--- + +## 7. Data Flow (一次 optimize 调用) + +``` +messages = [ + {role: "system", content: "..."}, + {role: "user", content: [{type: "tool_result", content: "file contents..."}]}, + {role: "user", content: "Transcribe this audio file"} +] + │ + ▼ + ┌─ 1. PARSE (< 5ms) ─────────────────────────────────┐ + │ ContextBlockTree: │ + │ ├── skill:whisper (8400 tokens) │ + │ │ ├── §Overview │ + │ │ ├── §Installation │ + │ │ ├── §Usage │ + │ │ └── §Advanced │ + │ ├── file:/audio/test.wav (2400 tokens) │ + │ └── user_query (12 tokens) │ + └──────────────────────────────────────────────────────┘ + │ + ▼ + ┌─ 2. ANALYZE (< 1ms) ───────────────────────────────┐ + │ Catalog lookup: │ + │ skill:whisper → freq=12, last=2min, hot ★★★★ │ + │ skill:whisper/§Advanced → freq=0, cold │ + │ file:/audio/test.wav → freq=1, new │ + └──────────────────────────────────────────────────────┘ + │ + ▼ + ┌─ 3. PLAN (< 2ms) ──────────────────────────────────┐ + │ Rules evaluated: │ + │ ✅ reorder → 2 blocks can share prefix │ + │ ✅ prune → §Advanced (freq=0) removable │ + │ ⬜ dedup → no previous turn (skip) │ + │ ⬜ prefetch → skill already present (skip) │ + │ │ + │ Plan: [reorder, prune] │ + │ Token savings: 1,200 (§Advanced section) │ + │ Cache benefit: 6,400 tokens (prefix sharing) │ + └──────────────────────────────────────────────────────┘ + │ + ▼ + ┌─ 4. EXECUTE (< 5ms) ───────────────────────────────┐ + │ Apply reorder: rearrange doc order in system msg │ + │ Apply prune: remove §Advanced from skill content │ + │ Reconstruct messages with optimized content │ + └──────────────────────────────────────────────────────┘ + │ + ▼ + optimized_messages → send to LLM + (9,612 tokens, was 10,812) +``` + +--- + +## 8. Refactoring Plan + +### Phase 0: 准备 (不改功能，只重组文件) + +``` +目标: 文件搬到新位置，旧 import 全部兼容 +方法: 新文件 import from 旧位置，_compat.py 做旧 → 新转发 + +1. 创建新目录结构 (空 __init__.py) +2. engine/ — 移入 context_index/* + context_ordering/* +3. proxy/ — 移入 server/http_server.py + intercept_parser.py +4. integrations/ — 移入 retriever/* + hooks +5. _compat.py — 旧 import 路径全部转发 +6. 跑通所有现有 tests +``` + +### Phase 1: Optimizer Core (新增，不改旧代码) + +``` +目标: ContextOptimizer 类 + 基础 rules + +1. optimizer/parser.py — ContextBlockParser (from design_context_blocks.md) +2. optimizer/catalog.py — Catalog + BlockUsageStore +3. optimizer/cost_model.py — CostModel +4. optimizer/rules/reorder.py — 包装现有 ContextIndex + Scheduler +5. optimizer/rules/dedup.py — 包装现有 ConversationTracker +6. optimizer/core.py — ContextOptimizer 主类 +7. optimizer/planner.py — 规则选择 + plan 生成 +8. 新 __init__.py — cp.optimize() API +9. tests/test_optimizer.py +``` + +### Phase 2: Client Wrappers (即插即用) + +``` +目标: cp.openai.Client, cp.anthropic.Client + +1. integrations/openai_client.py — OpenAI wrapper +2. integrations/anthropic_client.py — Anthropic wrapper +3. 更新 __init__.py — import path +4. tests/test_wrappers.py +``` + +### Phase 3: Advanced Rules (新功能) + +``` +目标: prefetch, prune, merge rules + +1. optimizer/rules/prefetch.py — SkillPrefetcher +2. optimizer/rules/prune.py — Token budget pruning +3. optimizer/rules/merge.py — Block merging +4. optimizer/catalog.py — SkillRegistry 扩展 +5. explain() 输出完善 +6. tests/test_rules.py +``` + +### Phase 4: Proxy 重构 + +``` +目标: proxy server 使用 ContextOptimizer 而不是直接调 engine + +1. proxy/server.py — 重构 _intercept_and_forward 使用 optimizer.optimize() +2. proxy/interceptor.py — 从 intercept_parser.py 提取 +3. CLI: contextpilot serve +4. 集成测试 +``` + +--- + +## 9. Migration: 旧 API 完全兼容 + +```python +# _compat.py — 所有旧 import 都工作 + +# 旧: +from contextpilot import ContextPilot +from contextpilot.pipeline import RAGPipeline +from contextpilot.context_index import ContextIndex, IndexResult +from contextpilot.context_ordering import IntraContextOrderer +import contextpilot as cp; cp.optimize(docs, query) + +# 全部通过 _compat.py 转发到新位置 +# 旧 API 标记 DeprecationWarning，但功能不变 +``` + +--- + +## 10. Why This Matters + +``` + Before After + (ContextPilot) (Context Optimizer) + ────────────── ────────────────── + + User effort 需要理解 pipeline, cp.optimize(messages) + retriever, config 一行搞定 + + Mental model "KV cache prefix "像 SQL Optimizer — + sharing library" prompt 进去， + 优化后的 prompt 出来" + + Integration 自己写 pipeline 零代码 proxy 或 + 配 server 一行 client wrapper + + Optimization 只有 reorder + reorder + dedup + + basic dedup prefetch + prune + merge + + Observability 基本日志 EXPLAIN：完整优化计划 + markmap 可视化 + usage stats dashboard + + Extensibility 改核心代码 自定义 Rule 插件 + pluggable, 注册即用 +``` From dd7353e6c8d838ea5aa1f25129cf43f7cfd876ce Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 00:51:02 +0000 Subject: [PATCH 14/76] Update architecture SVG: add _compat bridge, pipeline/utils, refactoring timeline, package structure --- docs/context_optimizer_architecture.svg | 164 +++++++++++++++++------- 1 file changed, 118 insertions(+), 46 deletions(-) diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg index 3ae6e9c..ca5164c 100644 --- a/docs/context_optimizer_architecture.svg +++ b/docs/context_optimizer_architecture.svg @@ -1,11 +1,11 @@ - + - + Context Optimizer Architecture @@ -20,7 +20,7 @@ INTEGRATION LAYER - pick one + pick one — Phase 2 L0: HTTP Proxy @@ -40,9 +40,14 @@ + + + _compat.py — old import paths forwarded here (Phase 0) + CONTEXT OPTIMIZER CORE + optimizer/ — Phase 1 @@ -112,15 +117,15 @@ Prefetch - NEW + Phase 3 Prune - NEW + Phase 3 Merge - NEW + Phase 3 Custom @@ -170,49 +175,116 @@ - + EXECUTION ENGINE - - - Cache Index - Hierarchical clustering - Prefix tree, live search - Incremental insert - - - Scheduler - Intra + Inter ordering - Search-path grouping - Batch execution order - - - Eviction Manager - LRU + callback-driven - SGLang/vLLM sync - Auto tree pruning - - + engine/ — Phase 0 reorg + + + Cache Index + Hierarchical clustering + Prefix tree, live search + Incremental insert + cache_index.py, tree.py + + + Scheduler + Intra + Inter ordering + Search-path grouping + Batch execution order + scheduler.py + + + Eviction Manager + LRU + callback-driven + SGLang/vLLM sync + Auto tree pruning + eviction.py, metadata.py + + + Pipeline (RAG) + RAG pipeline, multi-turn + Internally uses optimizer + (kept, refactored later) + pipeline/ + + - - LLM Backend - - - - Data Flow: - messages → [Parse] → BlockTree → [Analyze] → [Plan] → [Execute] → optimized_msgs → LLM - - - - Performance: - Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = Total <15ms overhead. Zero LLM calls. + + LLM Backend + OpenAI / Anthropic / SGLang / vLLM / llama.cpp + + + + Refactoring Timeline + + + + Phase 0 + File reorg + NEXT + + + + + + Phase 1 + Optimizer Core + PENDING + + + + + + Phase 2 + Client Wrappers + PENDING + + + + + + Phase 3 + Advanced Rules + PENDING + + + + + + Phase 4 + Proxy Refactor + PENDING + + + Design: DONE + docs/ + Notion page created + + + + Package Structure (Target) + + contextpilot/ + ├── optimizer/ NEW: core.py, parser.py, planner.py, catalog.py, cost_model.py + │ └── rules/ NEW: base.py, reorder.py, dedup.py, prefetch.py, prune.py, merge.py + ├── engine/ MOVED: cache_index.py, tree.py, distance.py, scheduler.py, ... + ├── proxy/ MOVED: server.py, interceptor.py + ├── integrations/ MOVED: openai_client.py, anthropic_client.py, retrievers/, hooks/ + ├── pipeline/ KEPT: rag_pipeline.py, components.py + ├── utils/ KEPT: eval_metrics.py, prompt_generator.py, tools.py + ├── _compat.py NEW: old import paths → new locations + └── __init__.py UPDATED: cp.optimize(), cp.explain(), cp.openai, cp.anthropic - - - Existing - - New - - Pluggable + + + Existing (wrap) + + New + + Pluggable + + Next step + + Compat bridge From e4efe06c4053c440eacc7bdab30bc70090eca17d Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 01:00:50 +0000 Subject: [PATCH 15/76] Redesign SVG: horizontal layout (landscape, left-to-right data flow) --- docs/context_optimizer_architecture.svg | 391 +++++++++++------------- 1 file changed, 171 insertions(+), 220 deletions(-) diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg index ca5164c..4611bb4 100644 --- a/docs/context_optimizer_architecture.svg +++ b/docs/context_optimizer_architecture.svg @@ -1,290 +1,241 @@ - + - + - Context Optimizer Architecture - Analogous to SQL Query Optimizer + Context Optimizer Architecture + Analogous to SQL Query Optimizer - - User Application + + User + Application - + - - INTEGRATION LAYER - pick one — Phase 2 + + INTEGRATION - - L0: HTTP Proxy - cp serve --backend ... + + L0: HTTP Proxy + cp serve - - L1: Client Wrapper - cp.openai.Client() + + L1: Client Wrapper + cp.openai.Client() - - L2: SDK Direct - cp.optimize(msgs) + + L2: SDK Direct + cp.optimize(msgs) - - L3: Engine Hook - sglang / vllm auto + + L3: Engine Hook + sglang / vllm - + Phase 2 - - - _compat.py — old import paths forwarded here (Phase 0) + + + _compat.py (Phase 0) + + - - CONTEXT OPTIMIZER CORE - optimizer/ — Phase 1 + + CONTEXT OPTIMIZER CORE + optimizer/ — Phase 1 - - 1. PARSER - Prompt → BlockTree - <5ms + + 1. PARSER + Prompt → BlockTree <5ms - + - - 2. ANALYZER - Usage stats lookup - <1ms + + 2. ANALYZER + Usage stats <1ms - + - - 3. PLANNER - Select rules → Plan - <2ms + + 3. PLANNER + Select rules <2ms - + - - 4. EXECUTOR - Apply → Optimized - <5ms + + 4. EXECUTOR + Apply plan <5ms - - - - + + + + - - CATALOG - - Skill Registry - scan dirs, keyword index - Usage Statistics - freq, recency, co-occurrence - Block Registry - hash, section decomposition + + CATALOG + + Skill Registry + keyword index + Usage Stats (freq, recency) + Block Registry + co-occurrence + catalog.py - - COST MODEL - - Token Estimation - chars/4 heuristic - Cache Benefit - prefix_shared_tokens - Risk Assessment - prefetch_miss_rate + + COST MODEL + + Token estimation (chars/4) + Cache benefit (prefix shared) + Risk assessment (miss rate) + cost_model.py - - REWRITE RULES (Pluggable) - + + REWRITE RULES + - - - Reorder + + + Reorder - - Dedup + + Dedup - - Prefetch - Phase 3 + + Prefetch - - Prune - Phase 3 + + Prune - - Merge - Phase 3 + + Merge - - Custom + + Custom - - cp.explain(messages) - Input: 47,832 tok → Output: 31,206 tok → Saving: 34.8% - = SQL EXPLAIN + + cp.explain(messages) → 47,832 tok → 31,206 tok Saving: 34.8% + = SQL EXPLAIN - - - CONTEXT BLOCK PARSER (Detail) + - - - skill + + - - memory + + + EXECUTION ENGINE - - searched + + Cache Index + Clustering + prefix tree + cache_index.py, tree.py - - file_based + + Scheduler + Intra + Inter ordering + scheduler.py - - Decomposition tree: - skill:whisper - ├ §Overview f=12 - ├ §Usage f=15 - └ §Advanced f=0 + + Eviction Manager + LRU + SGLang/vLLM sync + eviction.py - - Each block tracks: - block_id | type | frequency | last_used | co_occurrence - Detection: regex + tag matching, zero LLM calls, O(n) scan + + Pipeline (RAG) + Kept, uses optimizer + pipeline/ - - → export: to_markmap() for markmap.js.org/repl - → export: to_json() for usage dashboard - → persist: ~/.contextpilot/block_usage.json + engine/ — Phase 0 - + + - + + + LLM Backend + OpenAI / Anthropic + SGLang / vLLM / llama.cpp - - - EXECUTION ENGINE - engine/ — Phase 0 reorg - - - Cache Index - Hierarchical clustering - Prefix tree, live search - Incremental insert - cache_index.py, tree.py - - - Scheduler - Intra + Inter ordering - Search-path grouping - Batch execution order - scheduler.py - - - Eviction Manager - LRU + callback-driven - SGLang/vLLM sync - Auto tree pruning - eviction.py, metadata.py - - - Pipeline (RAG) - RAG pipeline, multi-turn - Internally uses optimizer - (kept, refactored later) - pipeline/ - - + + + CONTEXT BLOCK PARSER (Detail) - - - LLM Backend - OpenAI / Anthropic / SGLang / vLLM / llama.cpp + + skill - - - Refactoring Timeline + + memory + + + searched + + + file_based - - - Phase 0 - File reorg - NEXT + Section decomposition: skill:whisper → §Overview f=12, §Usage f=15, §Advanced f=0 + Regex + tag matching, zero LLM calls, persist to ~/.contextpilot/block_usage.json + + + + Refactoring Timeline + Design: DONE (docs/ + Notion) - + + + Phase 0: File Reorganization — NEXT - - - Phase 1 - Optimizer Core - PENDING + - + + Phase 1: Optimizer Core - - - Phase 2 - Client Wrappers - PENDING + - + + Phase 2: Client Wrappers - - - Phase 3 - Advanced Rules - PENDING + - + + Phase 3: Advanced Rules - - - Phase 4 - Proxy Refactor - PENDING + - - Design: DONE - docs/ + Notion page created + + Phase 4: Proxy Refactor - - Package Structure (Target) - - contextpilot/ - ├── optimizer/ NEW: core.py, parser.py, planner.py, catalog.py, cost_model.py - │ └── rules/ NEW: base.py, reorder.py, dedup.py, prefetch.py, prune.py, merge.py - ├── engine/ MOVED: cache_index.py, tree.py, distance.py, scheduler.py, ... - ├── proxy/ MOVED: server.py, interceptor.py - ├── integrations/ MOVED: openai_client.py, anthropic_client.py, retrievers/, hooks/ - ├── pipeline/ KEPT: rag_pipeline.py, components.py - ├── utils/ KEPT: eval_metrics.py, prompt_generator.py, tools.py - ├── _compat.py NEW: old import paths → new locations - └── __init__.py UPDATED: cp.optimize(), cp.explain(), cp.openai, cp.anthropic - - - - - Existing (wrap) - - New - - Pluggable - - Next step - - Compat bridge + + Package Structure (Target) + + contextpilot/ + optimizer/ NEW: core.py, parser.py, planner.py, catalog.py, cost_model.py, rules/{base,reorder,dedup,prefetch,prune,merge}.py + engine/ MOVED: cache_index.py, tree.py, distance.py, scheduler.py, live_index.py, eviction.py, metadata.py + proxy/ MOVED: server.py, interceptor.py, http_client.py + integrations/ MOVED+NEW: openai_client.py, anthropic_client.py, retrievers/{bm25,faiss,mem0,pageindex}.py, hooks/{sglang,vllm,llamacpp}.py + pipeline/, utils/ KEPT | _compat.py NEW: old imports forwarded | __init__.py UPDATED: cp.optimize(), cp.openai + + + + + Existing (wrap) + + New + + Pluggable + + Next step + + Compat bridge + Total overhead <15ms, zero LLM calls From a46d1c5a050ceaed6a4924e077c092b04b9f3b4e Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 01:06:24 +0000 Subject: [PATCH 16/76] SVG: resize to 950x680, larger fonts for Notion readability --- docs/context_optimizer_architecture.svg | 348 +++++++++++------------- 1 file changed, 164 insertions(+), 184 deletions(-) diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg index 4611bb4..ac726c2 100644 --- a/docs/context_optimizer_architecture.svg +++ b/docs/context_optimizer_architecture.svg @@ -1,241 +1,221 @@ - + - + - Context Optimizer Architecture - Analogous to SQL Query Optimizer + Context Optimizer Architecture + SQL Query Optimizer Analogy — Total overhead <15ms, zero LLM calls - - - User - Application + - + + + User + App - - - INTEGRATION + - - L0: HTTP Proxy - cp serve + + + INTEGRATION - - L1: Client Wrapper - cp.openai.Client() + + L0: Proxy - - L2: SDK Direct - cp.optimize(msgs) + + L1: Wrapper - - L3: Engine Hook - sglang / vllm + + L2: SDK - Phase 2 + + L3: Hook - - - _compat.py (Phase 0) + Phase 2 - + + + _compat.py (Phase 0) - - - CONTEXT OPTIMIZER CORE - optimizer/ — Phase 1 + - - - 1. PARSER - Prompt → BlockTree <5ms + + + OPTIMIZER CORE + Phase 1 - + + + PARSER + <5ms - - 2. ANALYZER - Usage stats <1ms + - + + ANALYZER + <1ms - - 3. PLANNER - Select rules <2ms + - + + PLANNER + <2ms - - 4. EXECUTOR - Apply plan <5ms + + + + EXEC + <5ms - - - + + + - - CATALOG - - Skill Registry + keyword index - Usage Stats (freq, recency) - Block Registry + co-occurrence - catalog.py + + CATALOG + + Skill Registry + Usage Stats + Block Registry + catalog.py - - COST MODEL - - Token estimation (chars/4) - Cache benefit (prefix shared) - Risk assessment (miss rate) - cost_model.py + + COST MODEL + + Token estimation + Cache benefit + Risk assessment + cost_model.py - - REWRITE RULES - + + RULES + - - Reorder - - - Dedup + + Reorder - - Prefetch + + Dedup - - Prune + + Prefetch - - Merge + + Prune - - Custom + Merge, Custom... - - cp.explain(messages) → 47,832 tok → 31,206 tok Saving: 34.8% - = SQL EXPLAIN - - - - - - - - - EXECUTION ENGINE + + cp.explain() → 47,832→31,206 tok (34.8%) + = SQL EXPLAIN - - Cache Index - Clustering + prefix tree - cache_index.py, tree.py + + + BLOCK PARSER - - Scheduler - Intra + Inter ordering - scheduler.py - - - Eviction Manager - LRU + SGLang/vLLM sync - eviction.py - - - Pipeline (RAG) - Kept, uses optimizer - pipeline/ - - engine/ — Phase 0 - - - + + skill - - - LLM Backend - OpenAI / Anthropic - SGLang / vLLM / llama.cpp + + memory - - - CONTEXT BLOCK PARSER (Detail) + + searched - - skill + + file - - memory + Section decomp: skill:whisper → §Overview f=12, §Usage f=15, §Advanced f=0 - - searched - - - file_based - - Section decomposition: skill:whisper → §Overview f=12, §Usage f=15, §Advanced f=0 - Regex + tag matching, zero LLM calls, persist to ~/.contextpilot/block_usage.json - - - - Refactoring Timeline - Design: DONE (docs/ + Notion) - - - - Phase 0: File Reorganization — NEXT - - - - - Phase 1: Optimizer Core - - - - - Phase 2: Client Wrappers + + - + + + ENGINE - - Phase 3: Advanced Rules + + Cache Index + Clustering + prefix tree - + + Scheduler + Intra + Inter ordering - - Phase 4: Proxy Refactor + + Eviction + LRU + engine sync - - - Package Structure (Target) + + Pipeline + RAG (kept) - contextpilot/ - optimizer/ NEW: core.py, parser.py, planner.py, catalog.py, cost_model.py, rules/{base,reorder,dedup,prefetch,prune,merge}.py - engine/ MOVED: cache_index.py, tree.py, distance.py, scheduler.py, live_index.py, eviction.py, metadata.py - proxy/ MOVED: server.py, interceptor.py, http_client.py - integrations/ MOVED+NEW: openai_client.py, anthropic_client.py, retrievers/{bm25,faiss,mem0,pageindex}.py, hooks/{sglang,vllm,llamacpp}.py - pipeline/, utils/ KEPT | _compat.py NEW: old imports forwarded | __init__.py UPDATED: cp.optimize(), cp.openai + Phase 0 reorg - - - - Existing (wrap) - - New - - Pluggable - - Next step - - Compat bridge - Total overhead <15ms, zero LLM calls + + + + + + LLM + OpenAI + Anthropic + SGLang + + + + Refactoring Timeline + Design: DONE + + + Phase 0: File Reorg — NEXT + + + Phase 1: Optimizer Core + + + Phase 2: Wrappers + + + Phase 3: Adv. Rules + + + Phase 4: Proxy Refactor + + + + Package Structure (Target) + + contextpilot/ + optimizer/ NEW: core, parser, planner, catalog, cost_model, rules/* + engine/ MOVED: cache_index, tree, distance, scheduler, eviction, metadata + proxy/ MOVED: server, interceptor, http_client + integrations/ MOVED+NEW: openai_client, anthropic_client, retrievers/*, hooks/* + pipeline/, utils/ KEPT | _compat.py NEW | __init__.py UPDATED + + + + + Existing + + New + + Next step + + Compat bridge + + + messages → [Parse] → BlockTree → [Analyze] → [Plan] → [Execute] → optimized_msgs → LLM + Performance: Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = <15ms total From de8a7665c5f9986f0ce6b82f1434bbcf6a304c15 Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 01:30:21 +0000 Subject: [PATCH 17/76] =?UTF-8?q?SVG:=20=E5=85=A8=E4=B8=AD=E6=96=87?= =?UTF-8?q?=E6=A0=87=E6=B3=A8=EF=BC=8C=E6=AF=8F=E6=AD=A5=E8=AF=B4=E6=98=8E?= =?UTF-8?q?=E5=B9=B2=E4=BB=80=E4=B9=88?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- docs/context_optimizer_architecture.svg | 376 ++++++++++++------------ 1 file changed, 195 insertions(+), 181 deletions(-) diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg index ac726c2..0d91b71 100644 --- a/docs/context_optimizer_architecture.svg +++ b/docs/context_optimizer_architecture.svg @@ -1,221 +1,235 @@ - + - + - Context Optimizer Architecture - SQL Query Optimizer Analogy — Total overhead <15ms, zero LLM calls + Context Optimizer 架构 + 类比 SQL Query Optimizer · 总额外延迟 <15ms · 零 LLM 调用 - + - - - User - App + + + 用户 + 应用 - + - - - INTEGRATION + + + 接入层 - - L0: Proxy + + L0: 代理 - - L1: Wrapper + + L1: 包装器 - - L2: SDK + + L2: SDK - - L3: Hook + + L3: Hook - Phase 2 + 零改动 ~ 一行代码 + 用户选一种接入 - - _compat.py (Phase 0) + + _compat.py 旧 import 兼容 - + - - - OPTIMIZER CORE - Phase 1 + + + 优化器核心 (optimizer/) - - - PARSER - <5ms + + + 1. 解析 + 把 prompt 拆成 + typed blocks - + - - ANALYZER - <1ms + + 2. 分析 + 查每个 block 的 + 使用频率和缓存 - + + + + 3. 规划 + 选哪些优化规则 + 生成执行计划 - - PLANNER - <2ms + - + + 4. 执行 + 应用规则 + 输出新 msgs - - EXEC - <5ms + + + + + + + + 目录 Catalog + + 存什么： + · 注册的 skill 文件 + · block 使用频率/最近时间 + · block 间共现关系 + catalog.py + + + + 代价模型 + + 算什么： + · block 占多少 token + · reorder 能省多少 cache + · prefetch 猜错的代价 + cost_model.py - - - - + + + 优化规则 + + 做什么： - - - CATALOG - - Skill Registry - Usage Stats - Block Registry - catalog.py + + 重排序 - - - COST MODEL - - Token estimation - Cache benefit - Risk assessment - cost_model.py + + 去重复 - - - RULES - + + 预取 + + + 裁剪 - - - Reorder + 合并, 自定义... + + + + cp.explain() → 看优化计划，不执行 (= SQL EXPLAIN) + + + + 解析器识别 4 种 block 类型： + + + skill + + + memory + + + search + + + file + + 每个 block 再按 heading 拆 section + + + + + + + 执行引擎 + + + 缓存索引 + 层次聚类 + 前缀树 + cache_index.py + + + 调度器 + 块内 + 块间排序 + scheduler.py + + + 淘汰管理 + LRU + 引擎同步 + eviction.py + + + RAG 管线 + 保留，内部用优化器 + + engine/ — Phase 0 搬文件 + + + + + + + LLM + OpenAI + Anthropic + SGLang + vLLM + + + + 数据流 + messages[] → 解析成 blocks → 查统计 → 选规则 → 执行优化 → 优化后的 messages[] → 发给 LLM + + + + 重构阶段 + 设计: 完成 + + + Phase 0: 搬文件 — 下一步 + + + Phase 1: 优化器核心 - - Dedup + + Phase 2: 包装器 - - Prefetch + + Phase 3: 高级规则 - - Prune + + Phase 4: 重构代理服务 - Merge, Custom... + + + 目标目录结构 - - - cp.explain() → 47,832→31,206 tok (34.8%) - = SQL EXPLAIN + contextpilot/ + optimizer/ 新增: 核心类、解析器、规划器、目录、代价模型、优化规则 + engine/ 搬入: 缓存索引、树结构、距离计算、调度器、淘汰管理 + proxy/ 搬入: HTTP 代理服务器、请求拦截器 + integrations/ 搬入+新增: OpenAI/Anthropic 包装器, retrievers, hooks + pipeline/, utils/ 保留 | _compat.py 旧路径兼容 | __init__.py 公共 API - - - BLOCK PARSER - - - skill - - - memory - - - searched - - - file - - Section decomp: skill:whisper → §Overview f=12, §Usage f=15, §Advanced f=0 - - - - - - - ENGINE - - - Cache Index - Clustering + prefix tree - - - Scheduler - Intra + Inter ordering - - - Eviction - LRU + engine sync - - - Pipeline - RAG (kept) - - Phase 0 reorg - - - - - - - LLM - OpenAI - Anthropic - SGLang - - - - Refactoring Timeline - Design: DONE - - - Phase 0: File Reorg — NEXT - - - Phase 1: Optimizer Core - - - Phase 2: Wrappers - - - Phase 3: Adv. Rules - - - Phase 4: Proxy Refactor - - - - Package Structure (Target) - - contextpilot/ - optimizer/ NEW: core, parser, planner, catalog, cost_model, rules/* - engine/ MOVED: cache_index, tree, distance, scheduler, eviction, metadata - proxy/ MOVED: server, interceptor, http_client - integrations/ MOVED+NEW: openai_client, anthropic_client, retrievers/*, hooks/* - pipeline/, utils/ KEPT | _compat.py NEW | __init__.py UPDATED - - - - - Existing - - New - - Next step - - Compat bridge - - - messages → [Parse] → BlockTree → [Analyze] → [Plan] → [Execute] → optimized_msgs → LLM - Performance: Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = <15ms total + + + + 已有(包装) + + 新增 + + 下一步 + + 兼容桥 + + + 性能预算: 解析(<5ms) + 分析(<1ms) + 规划(<2ms) + 执行(<5ms) = 总计 <15ms + 全程零 LLM 调用，纯规则引擎 From af3218e6a956cacd51ce7cfc5f0b4433956201ed Mon Sep 17 00:00:00 2001 From: SecretSettler Date: Tue, 10 Mar 2026 01:41:34 +0000 Subject: [PATCH 18/76] SVG v5: flow-oriented vertical layout, English labels, 880x720, shows rule trigger points --- docs/context_optimizer_architecture.svg | 386 ++++++++++-------------- 1 file changed, 160 insertions(+), 226 deletions(-) diff --git a/docs/context_optimizer_architecture.svg b/docs/context_optimizer_architecture.svg index 0d91b71..6c1c730 100644 --- a/docs/context_optimizer_architecture.svg +++ b/docs/context_optimizer_architecture.svg @@ -1,235 +1,169 @@ - + + + + - + - Context Optimizer 架构 - 类比 SQL Query Optimizer · 总额外延迟 <15ms · 零 LLM 调用 - - - - - - 用户 - 应用 - - - - - - 接入层 - - - L0: 代理 - - - L1: 包装器 - - - L2: SDK - - - L3: Hook - - 零改动 ~ 一行代码 - 用户选一种接入 - - - - _compat.py 旧 import 兼容 - - - - - - 优化器核心 (optimizer/) - - - - 1. 解析 - 把 prompt 拆成 - typed blocks - - - - - 2. 分析 - 查每个 block 的 - 使用频率和缓存 - - - - - 3. 规划 - 选哪些优化规则 - 生成执行计划 - - - - - 4. 执行 - 应用规则 - 输出新 msgs - - - - - - - - - 目录 Catalog - - 存什么： - · 注册的 skill 文件 - · block 使用频率/最近时间 - · block 间共现关系 - catalog.py - - - - 代价模型 - - 算什么： - · block 占多少 token - · reorder 能省多少 cache - · prefetch 猜错的代价 - cost_model.py - - - - 优化规则 - - 做什么： - - - 重排序 - - - 去重复 - - - 预取 - - - 裁剪 - - 合并, 自定义... - - - - cp.explain() → 看优化计划，不执行 (= SQL EXPLAIN) - - - - 解析器识别 4 种 block 类型： - - - skill - - - memory - - - search - - - file - - 每个 block 再按 heading 拆 section - - - - - - - 执行引擎 - - - 缓存索引 - 层次聚类 + 前缀树 - cache_index.py - - - 调度器 - 块内 + 块间排序 - scheduler.py - - - 淘汰管理 - LRU + 引擎同步 - eviction.py - - - RAG 管线 - 保留，内部用优化器 - - engine/ — Phase 0 搬文件 - - - - - - - LLM - OpenAI - Anthropic - SGLang - vLLM - - - - 数据流 - messages[] → 解析成 blocks → 查统计 → 选规则 → 执行优化 → 优化后的 messages[] → 发给 LLM - - - - 重构阶段 - 设计: 完成 - - - Phase 0: 搬文件 — 下一步 - - - Phase 1: 优化器核心 - - - Phase 2: 包装器 - - - Phase 3: 高级规则 - - - Phase 4: 重构代理服务 - - - - 目标目录结构 - - contextpilot/ - optimizer/ 新增: 核心类、解析器、规划器、目录、代价模型、优化规则 - engine/ 搬入: 缓存索引、树结构、距离计算、调度器、淘汰管理 - proxy/ 搬入: HTTP 代理服务器、请求拦截器 - integrations/ 搬入+新增: OpenAI/Anthropic 包装器, retrievers, hooks - pipeline/, utils/ 保留 | _compat.py 旧路径兼容 | __init__.py 公共 API - - - - - 已有(包装) - - 新增 - - 下一步 - - 兼容桥 - - - 性能预算: 解析(<5ms) + 分析(<1ms) + 规划(<2ms) + 执行(<5ms) = 总计 <15ms - 全程零 LLM 调用，纯规则引擎 + Context Optimizer — End-to-End Flow + Like a SQL Query Optimizer: messages in → optimized messages out. <15ms total, zero LLM calls. + + + + INPUT + messages[] — 47,832 tokens, 12 blocks + User sends prompt to LLM (OpenAI, Anthropic, ...) + + + + + + + 1. PARSE + Split prompt into typed blocks + Regex + tag matching. Detect block boundaries. + <5ms · parser.py + + + + + + Output: BlockTree + + skill + + memory + + search + + file + Each skill block further split by §headings into sections + + + + + + + 2. ANALYZE + Look up stats for each block + Query Catalog for frequency, recency, cache state. + <1ms · catalog.py + + + + + + CATALOG (usage stats + skill registry) + skill:whisper → freq=12, last=2min, hot + skill:whisper/§Advanced → freq=0, never used + search:web → freq=1, cold | file:audio.py → freq=4 + + + + + + + 3. PLAN + Evaluate rules, pick best combo + Cost Model estimates benefit of each rule. + <2ms · planner.py + cost_model.py + + + + + + RULES evaluated (each is a plugin): + + + + REORDER + Re-sort blocks so similar ones are adjacent + → share 6,400 tok in LLM prefix cache + + + + DEDUP + Remove content already sent in previous turns + → remove search:web (sent in Turn 2), save 6,100 tok + + + + PREFETCH + Predict needed skills and inject them + → skip (skill:whisper already present). Would inject if missing. + + + + PRUNE + Trim unused sections to fit token budget + → remove §Advanced (freq=0), save 1,200 tok + + + + MERGE + Combine co-occurring blocks for better caching + → skip (no co-occurring pair detected) + + + + applied + + skipped + + + + + + + 4. EXECUTE + Apply the plan to messages + Reorder blocks, remove dedup'd, trim pruned sections. + <5ms · executor.py + + + + + + Actions taken: + 1. Reorder: skill:whisper moved to top (prefix sharing) + 2. Remove: search:web block (dedup) 3. Trim: §Advanced section (prune) + + + + + + + OUTPUT + optimized messages[] — 31,206 tokens + Saved 16,626 tokens (34.8%) + + + + + + + LLM + OpenAI / Anthropic / SGLang / vLLM / llama.cpp + + + + Integration (pick one): + L0 $ contextpilot serve --port 8765 # zero code change + L1 client = cp.openai.Client() # 1-line change + L2 optimized = cp.optimize(messages) # explicit call + L3 optimizer = cp.ContextOptimizer(...) # full control + + + + cp.explain(messages) + Show this entire plan without executing — like SQL EXPLAIN + + + Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = <15ms total overhead. All regex, zero LLM calls. 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control (SGLang radix tree) + + + CLIENT SIDE — CONTEXT OPTIMIZER + - - INPUT - messages[] — 47,832 tokens, 12 blocks - User sends prompt to LLM (OpenAI, Anthropic, ...) + + INPUT + messages[] — 47,832 tokens, 12 blocks + User sends prompt to LLM (OpenAI, Anthropic, ...) - + - - 1. PARSE - Split prompt into typed blocks - Regex + tag matching. Detect block boundaries. - <5ms · parser.py + + 1. PARSE + Split prompt into typed blocks + Regex + tag matching. Detect block boundaries. + <5ms · parser.py - - - - Output: BlockTree - - skill - - memory - - search - - file - Each skill block further split by §headings into sections + + + + Output: BlockTree + + skill + + memory + + search + + file + + system + Each skill block further split by §headings into sections - + - - 2. ANALYZE - Look up stats for each block - Query Catalog for frequency, recency, cache state. - <1ms · catalog.py + + 2. ANALYZE + Look up stats for each block + Query Catalog for frequency, recency, co-occurrence. + <1ms · catalog.py - + - - CATALOG (usage stats + skill registry) - skill:whisper → freq=12, last=2min, hot - skill:whisper/§Advanced → freq=0, never used - search:web → freq=1, cold | file:audio.py → freq=4 + + CATALOG (usage stats + skill registry) + skill:whisper → freq=12, last=2min, hot + memory+file co-occur 92% | skill:whisper/§Advanced → freq=0 - - - - - 3. PLAN - Evaluate rules, pick best combo - Cost Model estimates benefit of each rule. - <2ms · planner.py + cost_model.py - - - - - - RULES evaluated (each is a plugin): - - - - REORDER - Re-sort blocks so similar ones are adjacent - → share 6,400 tok in LLM prefix cache - - - - DEDUP - Remove content already sent in previous turns - → remove search:web (sent in Turn 2), save 6,100 tok - - - - PREFETCH - Predict needed skills and inject them - → skip (skill:whisper already present). Would inject if missing. - - - - PRUNE - Trim unused sections to fit token budget - → remove §Advanced (freq=0), save 1,200 tok - - - - MERGE - Combine co-occurring blocks for better caching - → skip (no co-occurring pair detected) - - - - applied - - skipped + + + + + 3. PLAN + Evaluate rules, pick best combo + Cost Model estimates benefit of each rule. + <2ms · planner.py + cost_model.py + + + + + + RULES evaluated (each is a plugin): + + + + REORDER + Sort blocks so common prefix is maximized + → move skill:whisper to top, share 6,400 tok KV prefix + + + + DEDUP + Remove content already sent in previous turns + → remove search:web (sent in Turn 2), save 6,100 tok + + + + PREFETCH + Predict needed skills → inject + pre-warm KV + → inject skill:notion (keyword match), pre-insert KV via kv.insert() + + + + PRUNE + Trim unused sections to fit token budget + → remove §Advanced (freq=0), save 1,200 tok + + + + MERGE + Combine co-occurring blocks for better caching + → merge memory+file (co-occur 92%) into 1 block for prefix reuse + + + + applied - + - - 4. EXECUTE - Apply the plan to messages - Reorder blocks, remove dedup'd, trim pruned sections. - <5ms · executor.py + + 4. EXECUTE + Apply the plan to messages + Reorder, dedup, inject, prune, merge blocks. + <5ms · executor.py - - + + - - Actions taken: - 1. Reorder: skill:whisper moved to top (prefix sharing) - 2. Remove: search:web block (dedup) 3. Trim: §Advanced section (prune) + + Actions taken: + 1. Reorder blocks 2. Remove search:web 3. Inject skill:notion + 4. Trim §Advanced 5. Merge memory+file → single block - + - - OUTPUT - optimized messages[] — 31,206 tokens - Saved 16,626 tokens (34.8%) + + OUTPUT + optimized messages[] — 31,206 tokens + Saved 16,626 tokens (34.8%) + + + + Integration (pick one): + L0 proxy (0 code) | L1 wrapper (1 line) | L2 cp.optimize() | L3 full control + + + + + + SERVER SIDE — KV CACHE ENGINE (SGLANG RADIX TREE) + + + + + 5. KV.INSERT + Pre-compute KV for block sequences + Tokenize blocks → prefill forward pass → store in radix tree. + h = engine.kv_insert(blocks=[sys, doc1]) → handle + Returns handle with block boundaries for reuse. + + + + Radix Tree (KV cache indexed by token prefix): + + + [root] + + + sys tokens + + + + + + doc1 →h_a + + + + + doc2 →h_b + + + ← shared + KV computed once + + + + + + + 6. GENERATE + KV.REUSE + Reference handle instead of re-sending context. + engine.generate(reuse_handle=h, text="Q?") + Only new tokens need prefill — cached prefix skipped. + + + + Auto-Batch by Shared Prefix: + Req1: reuse=h_a + "Q1?" ──┐ + Req2: reuse=h_a + "Q2?" ──┤ same prefix → batched + Req3: reuse=h_b + "Q3?" ──┘ shares sys → partial batch - + - - LLM - OpenAI / Anthropic / SGLang / vLLM / llama.cpp - - - - Integration (pick one): - L0 $ contextpilot serve --port 8765 # zero code change - L1 client = cp.openai.Client() # 1-line change - L2 optimized = cp.optimize(messages) # explicit call - L3 optimizer = cp.ContextOptimizer(...) # full control + + LLM + SGLang / vLLM / llama.cpp (GPU forward pass) + + + + Total savings: 34.8% fewer tokens + KV reuse across requests - - cp.explain(messages) - Show this entire plan without executing — like SQL EXPLAIN + + cp.explain(messages) + Show this entire plan without executing — like SQL EXPLAIN - - Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = <15ms total overhead. All regex, zero LLM calls. + + Client: Parse(<5ms) + Analyze(<1ms) + Plan(<2ms) + Execute(<5ms) = <15ms total. All regex, zero LLM calls. + Server: KV.insert = 1 prefill pass (amortized). KV.reuse = 0 extra cost (radix tree lookup <0.1ms). 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