update pyenvector 1.4.x

README.md

@@ -37,8 +37,9 @@ Key dataclasses live in `libs/envector/config.py`:
 - Client-side filtering requires the JSON envelope to include an object under `metadata`.
 
 ## Limitations
-- Item-level delete/update is unsupported (drop the index to reset).
-- Manual item IDs are not accepted; returned IDs from `add_texts` are ephemeral.
+- Item-level updates are unsupported (re-insert to update; drop the index to reset).
+- Manual item IDs are not accepted; use the `item_id` values returned by `add_texts` / `add_documents` for subsequent `delete` calls.
+- Fetch-by-ID (`get_by_ids`) is unsupported.
 - Filtering happens client-side; ensure metadata is JSON for structured filters.
 
 ## Examples
@@ -55,8 +56,8 @@ Key dataclasses live in `libs/envector/config.py`:
       key=KeyConfig(
         key_path=ENVECTOR_KEY_PATH, 
         key_id=ENVECTOR_KEY_ID, 
-        preset="ip", 
-        eval_mode="rmp"
+        preset="ip2", 
+        eval_mode="mm32"
       ),
       index=IndexSettings(
         index_name=INDEX_NAME, 
@@ -113,7 +114,6 @@ for doc, score in results:
     print(f"* [SIM={score:.3f}] {doc.page_content} [{doc.metadata}]")
 ```
 
-
 #### Similarity Search with Vector
 
 ```python

libs/envector/examples/basic_usage.py

@@ -8,21 +8,21 @@
 
 from __future__ import annotations
 
-from libs.envector.config import (
+from langchain_envector.config import (
     ConnectionConfig,
     EnvectorConfig,
     IndexSettings,
     KeyConfig,
 )
-from libs.envector.vectorstore import Envector
+from langchain_envector.vectorstore import Envector
 
 
 def main():
     # Replace with your actual settings
     cfg = EnvectorConfig(
         connection=ConnectionConfig(address="localhost:50050"),
         key=KeyConfig(
-            key_path="./keys", key_id="example_key", preset="ip", eval_mode="rmp"
+            key_path="./keys", key_id="example_key", preset="ip2", eval_mode="mm32"
         ),
         index=IndexSettings(index_name="demo", dim=384, query_encryption="plain"),
         create_if_missing=True,

libs/envector/examples/ingest_synthetic_1k.py

@@ -19,13 +19,13 @@
 from pathlib import Path
 from typing import List
 
-from libs.envector.config import (
+from langchain_envector.config import (
     ConnectionConfig,
     EnvectorConfig,
     IndexSettings,
     KeyConfig,
 )
-from libs.envector.vectorstore import Envector
+from langchain_envector.vectorstore import Envector
 
 
 def batched(seq, n):
@@ -63,7 +63,7 @@ def main():
     cfg = EnvectorConfig(
         connection=ConnectionConfig(address=args.address),
         key=KeyConfig(
-            key_path=args.key_path, key_id=args.key_id, preset="ip", eval_mode="rmp"
+            key_path=args.key_path, key_id=args.key_id, preset="ip2", eval_mode="mm32"
         ),
         index=IndexSettings(
             index_name=args.index_name,

libs/envector/langchain_envector/client.py

@@ -28,19 +28,16 @@ def init(self):
 
         # Connection
         if c.address:
-            ev_client.init_connect(address=c.address, access_token=c.access_token)
+            ev_client.init_connect(
+                address=c.address, access_token=c.access_token, secure=c.secure
+            )
         else:
             if not (c.host and c.port):
                 raise ValueError("Either address or host+port must be provided.")
             ev_client.init_connect(
-                host=c.host, port=c.port, access_token=c.access_token
+                host=c.host, port=c.port, access_token=c.access_token, secure=c.secure
             )
 
-        # Key path baseline for Index
-        from pyenvector.index import Index as _Index
-
-        _Index.init_key_path(k.key_path)
-
         # Index config + key setup
         ev_client.init_index_config(
             index_name=i.index_name,
@@ -55,13 +52,17 @@ def init(self):
             index_encryption="cipher",  # server vectors are always encrypted
             index_type=i.index_type,
             auto_key_setup=True,
+            description=i.description,
+            metadata_encryption=i.metadata_encryption,
         )
 
         # Create index if missing
         if self.config.create_if_missing:
             idx_list = ev_client.get_index_list()
             if i.index_name not in idx_list:
-                ev_client.create_index(index_name=i.index_name, dim=i.dim)
+                ev_client.create_index(
+                    index_name=i.index_name, dim=i.dim, index_params=i.index_params
+                )
 
         # Bind index instance
         self._index = ev.Index(i.index_name)

libs/envector/langchain_envector/config.py

@@ -10,6 +10,7 @@ class ConnectionConfig:
     host: Optional[str] = None
     port: Optional[int] = None
     access_token: Optional[str] = None
+    secure: bool = True  # default to secure connection if not specified
 
 
 @dataclass
@@ -26,11 +27,14 @@ class KeyConfig:
 class IndexSettings:
     index_name: str
     dim: int
-    query_encryption: str = "plain"  # plain | cipher
-    index_encryption: str = "cipher"  # fixed to cipher
+    query_encryption: str = "plain"
+    index_encryption: str = "cipher"
     index_type: str = "flat"
     output_fields: List[str] = field(default_factory=lambda: ["metadata"])
     fetch_k: Optional[int] = None  # over-fetch to support client-side filters
+    description: Optional[str] = None
+    metadata_encryption: Optional[str] = None
+    index_params: Optional[dict] = None  # catch-all for any additional index parameters
 
 
 @dataclass

libs/envector/langchain_envector/vectorstore.py

@@ -96,6 +96,39 @@ def add_texts(
         # but they are NOT persisted/addressable.
         return result_ids
 
+    def delete(
+        self,
+        ids: Optional[List[Any]] = None,
+        *,
+        await_completion: bool = False,
+        timeout_s: float = 600.0,
+        poll_interval_s: float = 1.0,
+        **kwargs: Any,
+    ) -> Optional[bool]:
+        """Delete items from the encrypted index by item ID.
+
+        Accepts the ``item_id`` values returned from ``add_texts`` /
+        ``add_documents``. Both ``int`` and ``str`` (numeric) IDs are accepted
+        and coerced to ``int`` before being passed to the SDK.
+        """
+        if not ids:
+            return False
+        try:
+            item_ids = [int(x) for x in ids]
+        except (TypeError, ValueError) as e:
+            raise ValueError(
+                "Envector.delete expects integer item IDs (or numeric strings) "
+                "as returned by add_texts/add_documents."
+            ) from e
+
+        self.client.index.delete(
+            item_ids=item_ids,
+            await_completion=await_completion,
+            timeout_s=timeout_s,
+            poll_interval_s=poll_interval_s,
+        )
+        return True
+
     def _similarity_search_with_scores(
         self,
         *,

pyproject.toml

@@ -7,7 +7,7 @@ build-backend = "setuptools.build_meta"
 
 [project]
 name = "langchain-envector"
-version = "0.1.3"
+version = "0.2.0"
 description = "LangChain VectorStore integration for Envector"
 readme = "README.md"
 license = {text = "MIT"}
@@ -16,7 +16,7 @@ authors = [
   { name = "Envector Contributors" }
 ]
 dependencies = [
-  "pyenvector",
+  "pyenvector>=1.4.0",
   "langchain>=0.2.0",
   "langchain-core>=1.2.5,<2.0",
 ]

tests/conftest.py

@@ -20,12 +20,30 @@ def embed_query(self, text: str) -> List[float]:
 @dataclass
 class FakeIndex:
     inserted: List[Dict[str, Any]] = field(default_factory=list)
+    deleted: List[Dict[str, Any]] = field(default_factory=list)
     search_payload: Optional[List[List[Dict[str, Any]]]] = None
 
     def insert(self, data: List[List[float]], metadata: List[str]):
         self.inserted.append({"data": data, "metadata": metadata})
         return [len(self.inserted) + i + 1 for i in range(len(metadata))]
 
+    def delete(
+        self,
+        item_ids: List[int],
+        await_completion: bool = True,
+        timeout_s: float = 600.0,
+        poll_interval_s: float = 1.0,
+    ) -> str:
+        self.deleted.append(
+            {
+                "item_ids": list(item_ids),
+                "await_completion": await_completion,
+                "timeout_s": timeout_s,
+                "poll_interval_s": poll_interval_s,
+            }
+        )
+        return f"req-del-{len(self.deleted)}"
+
     def search(self, query: List[float], top_k: int, output_fields: List[str]):
         if self.search_payload is not None:
             return self.search_payload

tests/integration_tests/test_e2e.py

@@ -85,7 +85,7 @@ def test_e2e_vectorstore_plain_and_cipher():
     # Plain query mode
     cfg_plain = EnvectorConfig(
         connection=ConnectionConfig(address=address),
-        key=KeyConfig(key_path=key_path, key_id=key_id, preset="ip", eval_mode="rmp"),
+        key=KeyConfig(key_path=key_path, key_id=key_id, preset="ip2", eval_mode="mm32"),
         index=IndexSettings(
             index_name=f"{base_index_name}_plain", dim=dim, query_encryption="plain"
         ),
@@ -164,7 +164,7 @@ def test_e2e_vectorstore_plain_and_cipher():
     # Cipher query mode
     cfg_cc = EnvectorConfig(
         connection=ConnectionConfig(address=address),
-        key=KeyConfig(key_path=key_path, key_id=key_id, preset="ip", eval_mode="rmp"),
+        key=KeyConfig(key_path=key_path, key_id=key_id, preset="ip2", eval_mode="mm32"),
         index=IndexSettings(
             index_name=f"{base_index_name}_cipher", dim=dim, query_encryption="cipher"
         ),

tests/test_vectorstore.py

@@ -284,6 +284,59 @@ def test_add_documents_requires_vectors_when_no_embeddings():
         assert "embeddings is None and vectors not provided" in str(e)
 
 
+def test_delete_passes_item_ids_to_sdk():
+    client = FakeClient()
+    store = Envector(config=_cfg(), embeddings=FakeEmbeddings(dim=4), client=client)
+    ids = store.add_texts(["t1", "t2", "t3"])
+    assert ids == [2, 3, 4]
+
+    assert store.delete(ids=[ids[0], ids[2]]) is True
+    assert len(client.index.deleted) == 1
+    call = client.index.deleted[0]
+    assert call["item_ids"] == [2, 4]
+    assert call["await_completion"] is False
+
+
+def test_delete_accepts_string_ids():
+    client = FakeClient()
+    store = Envector(config=_cfg(), embeddings=FakeEmbeddings(dim=4), client=client)
+    ids = store.add_texts(["a", "b"])
+
+    assert store.delete(ids=[str(ids[0])]) is True
+    assert client.index.deleted[0]["item_ids"] == [ids[0]]
+
+
+def test_delete_empty_or_none_returns_false():
+    client = FakeClient()
+    store = Envector(config=_cfg(), embeddings=FakeEmbeddings(dim=4), client=client)
+
+    assert store.delete(ids=None) is False
+    assert store.delete(ids=[]) is False
+    assert client.index.deleted == []
+
+
+def test_delete_rejects_non_numeric_ids():
+    client = FakeClient()
+    store = Envector(config=_cfg(), embeddings=FakeEmbeddings(dim=4), client=client)
+    try:
+        store.delete(ids=["abc"])
+        assert False, "Expected ValueError for non-numeric ids"
+    except ValueError as e:
+        assert "integer item IDs" in str(e)
+
+
+def test_delete_forwards_await_kwargs():
+    client = FakeClient()
+    store = Envector(config=_cfg(), embeddings=FakeEmbeddings(dim=4), client=client)
+    ids = store.add_texts(["x"])
+
+    store.delete(ids=ids, await_completion=False, timeout_s=12.0, poll_interval_s=0.5)
+    call = client.index.deleted[0]
+    assert call["await_completion"] is False
+    assert call["timeout_s"] == 12.0
+    assert call["poll_interval_s"] == 0.5
+
+
 def test_add_documents_with_explicit_vectors():
     client = FakeClient()
     store = Envector(config=_cfg(), embeddings=None, client=client)