.env.example

# LangChain embedding provider defaults
# Copy this file to .env and uncomment/edit as needed.

# Embedding provider: "docker", "ollama", or "openrouter"
EMBEDDING_PROVIDER=docker
# Embedding model name (Docker Model Runner default: ai/qwen3-embedding)
EMBEDDING_MODEL=ai/qwen3-embedding:4B

# Max recursive split depth when a chunk exceeds the provider's context length (default: 3)
# EMBEDDING_MAX_SPLIT_DEPTH=3

# Docker Model Runner (default provider)
# DOCKER_MODEL_RUNNER_URL=http://localhost:12434/engines/v1

# Ollama (legacy local provider)
# OLLAMA_BASE_URL=http://localhost:11434/v1

# OpenRouter (cloud provider)
# OPENROUTER_BASE_URL=https://openrouter.ai/api/v1
# OPENROUTER_API_KEY=

# REPL agent + evalkit judge LLM (reads JUDGE_LLM_* / OPENROUTER_API_KEY)
# Used by `codeknow repl` and the eval judge. Fill in JUDGE_LLM_API_KEY
# (or rely on OPENROUTER_API_KEY above as the fallback).
# JUDGE_LLM_MODEL=deepseek-v4-pro
# JUDGE_LLM_BASE_URL=https://openrouter.ai/api/v1
# JUDGE_LLM_API_KEY=

# Pipeline paths — defaults are under ~/.codeknow if unset
# CODEKNOW_INPUT_DIR=~/.codeknow/repos
# CODEKNOW_OUTPUT_DIR=~/.codeknow/graph

# ChromaDB vector store
# CHROMA_HOST=localhost
# CHROMA_PORT=8018

# Search response cache (Redis)
# CODEKNOW_REDIS_URL=redis://localhost:6379/0
# CODEKNOW_CACHE_TTL=300

# E2E hybrid search — graph BFS traversal depth (default: 3)
# E2E_TRAVERSAL_DEPTH=3