LangChain Configuration¶
This page covers environment variables and configuration for the LangChain integration. For architecture details and advanced usage, see LangChain Integration in the Advanced section.
Installation¶
LangChain support is an optional extra. Install with:
# Core LangChain packages (RDF QA fusion retriever + property graph retrievers)
uv pip install -e ".[langchain]"
# Extended graph backends (ArangoDB, Spanner, AGE, Gremlin)
uv pip install -e ".[langchain,langchain-extras]"
This installs: langchain, langchain-community, langchain-openai, langchain-anthropic, langchain-aws, langchain-ollama, langchain-google-genai, langchain-google-vertexai, langchain-groq, langchain-fireworks, langchain-neo4j.
RDF QA Fusion (USE_LANGCHAIN_RDF)¶
Enable SPARQL-based retrieval fused into the hybrid search pipeline:
When enabled, the LangChain RDF retriever generates SPARQL queries from natural language and fuses results alongside vector, BM25, and graph results.
Property Graph Retrievers (USE_LANGCHAIN_PG)¶
Enable LangChain property graph retrievers for Neo4j:
Enables:
- TextToGraphQueryRetriever — converts natural language to Cypher/SPARQL for Neo4j
- GraphEntityVectorRetriever — entity vector similarity search in Neo4j
- GraphNeighborhoodRetriever — k-hop graph neighborhood expansion
Synonym Expansion¶
Expand query keywords for broader retrieval coverage:
Scope Tags¶
Control which retrievers are active in hybrid search:
# Comma-separated list of active retriever tags
RETRIEVER_SCOPE_TAGS=llamaindex_vector,llamaindex_search,llamaindex_pg_graph,langchain_rdf_graph
Available tags:
- llamaindex_vector — LlamaIndex vector retriever
- llamaindex_search — LlamaIndex BM25/ES/OpenSearch retriever
- llamaindex_pg_graph — LlamaIndex property graph retriever
- langchain_pg_vector — LangChain Neo4j entity vector retriever
- langchain_rdf_graph — LangChain RDF SPARQL retriever
- langchain_pg_graph — LangChain property graph (Cypher) retriever
- langchain_pg_neighborhood — LangChain k-hop neighborhood retriever