trelix-langchain

LangChain retriever for trelix — semantic code search using Tree-sitter AST parsing, hybrid BM25+vector search, and call-graph expansion.

Install

pip install trelix-langchain

For AWS Bedrock embeddings (Cohere or Titan):

pip install "trelix-langchain[bedrock]"

Basic Usage

from trelix_langchain import TrelixRetriever

# First index your repo (one-time)
# trelix index /path/to/repo

retriever = TrelixRetriever(repo_path="/path/to/repo", k=10)
docs = retriever.invoke("how does authentication work?")

for doc in docs:
    print(doc.metadata["source"], doc.metadata["score"])
    print(doc.page_content[:200])

Each returned Document carries rich metadata:

LangChain RAG Chain (LCEL)

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnablePassthrough
from trelix_langchain import TrelixRetriever

retriever = TrelixRetriever(repo_path="/path/to/repo", k=8)

prompt = ChatPromptTemplate.from_template(
    "Answer the question using only the code context below.\n\n"
    "Context:\n{context}\n\n"
    "Question: {question}"
)

def format_docs(docs):
    return "\n\n".join(
        f"# {d.metadata['source']} ({d.metadata['symbol']})\n{d.page_content}"
        for d in docs
    )

chain = (
    {"context": retriever | format_docs, "question": RunnablePassthrough()}
    | prompt
    | ChatOpenAI(model="gpt-4o")
    | StrOutputParser()
)

answer = chain.invoke("How does the authentication middleware work?")
print(answer)

RetrievalQA (classic interface)

from langchain.chains import RetrievalQA
from langchain_openai import ChatOpenAI
from trelix_langchain import TrelixRetriever

retriever = TrelixRetriever(repo_path="/path/to/repo", k=10)
llm = ChatOpenAI(model="gpt-4o")

qa = RetrievalQA.from_chain_type(
    llm=llm,
    retriever=retriever,
    return_source_documents=True,
)

result = qa.invoke({"query": "Where is rate limiting applied?"})
print(result["result"])
for doc in result["source_documents"]:
    print(" -", doc.metadata["source"])

Configuration

You can also set the provider directly on the retriever instance:

retriever = TrelixRetriever(repo_path="/path/to/repo", provider="openai", k=10)

Provider Switching (v0.7.0+)

# Use Bedrock Cohere embeddings (best retrieval quality, reuses AWS credentials)
TRELIX_EMBEDDER_PROVIDER=bedrock-cohere trelix index /path/to/repo

# Use Azure OpenAI embeddings
TRELIX_EMBEDDER_PROVIDER=azure trelix index /path/to/repo

# Use local sentence-transformers (no API key needed, works offline)
TRELIX_EMBEDDER_PROVIDER=local trelix index /path/to/repo

The index and the retriever must use the same provider — re-index whenever you switch.

Links

• trelix on GitHub
• trelix on PyPI
• trelix-mcp — MCP server for Claude Code, Cursor, Windsurf
• trelix-llama-index — LlamaIndex retriever