trelix-llama-index
LlamaIndex retriever for trelix — semantic code search using Tree-sitter AST parsing, hybrid BM25+vector search, and call-graph expansion.
Install
pip install trelix-llama-index
Usage
from trelix_llama_index import TrelixIndexRetriever
# First index your repo (one-time)
# trelix index /path/to/repo
retriever = TrelixIndexRetriever(repo_path="/path/to/repo", k=10)
nodes = retriever.retrieve("how does authentication work?")
for node in nodes:
print(node.node.metadata["file"], node.score)
print(node.node.text[:200])
With LlamaIndex query engine
from llama_index.core import VectorStoreIndex
from llama_index.core.query_engine import RetrieverQueryEngine
from trelix_llama_index import TrelixIndexRetriever
retriever = TrelixIndexRetriever(repo_path="/path/to/repo", k=10)
query_engine = RetrieverQueryEngine.from_args(retriever)
response = query_engine.query("How does the authentication middleware work?")
print(response)
Configuration
| Env var | Default | Description |
|---|---|---|
TRELIX_EMBEDDER_PROVIDER |
local |
Embedding provider: local | openai | azure | bedrock-cohere | bedrock-titan |
OPENAI_API_KEY |
— | Required for openai provider |
AZURE_API_KEY |
— | Required for azure provider |
AWS_ACCESS_KEY_ID |
— | Required for Bedrock providers |
Provider switching (v0.7.0+)
# Use Bedrock Cohere embeddings (best retrieval, reuses AWS creds)
TRELIX_EMBEDDER_PROVIDER=bedrock-cohere trelix index /path/to/repo
# Use local embeddings (no API key needed)
TRELIX_EMBEDDER_PROVIDER=local trelix index /path/to/repo
Links
- trelix on GitHub
- trelix on PyPI
- trelix-mcp — MCP server for Claude Code, Cursor, Windsurf
- trelix-langchain — LangChain retriever
1 maintainer
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