File size: 1,956 Bytes
93df7ed | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 | import chromadb
from config import get_settings
from rag.embedding import embed_query
def get_chroma_client():
settings = get_settings()
return chromadb.PersistentClient(path=settings.vector_db_path)
def build_vector_store(embedded_chunks: list[dict]):
client = get_chroma_client()
# Always start fresh — delete any data from the previously ingested repo
# so that stale chunks never bleed into the current analysis session.
try:
client.delete_collection("codebase")
except Exception:
pass # collection didn't exist yet — that's fine
collection = client.create_collection("codebase")
ids = [f"{c['file_path']}::{c['chunk_index']}" for c in embedded_chunks]
embeddings = [c["embedding"] for c in embedded_chunks]
documents = [c["content"] for c in embedded_chunks]
metadatas = [
{
"file_path": c["file_path"],
"language": c["language"],
"start_line": c["start_line"],
"end_line": c["end_line"],
}
for c in embedded_chunks
]
collection.add(ids=ids, embeddings=embeddings, documents=documents, metadatas=metadatas)
return collection
def load_vector_store():
client = get_chroma_client()
return client.get_or_create_collection("codebase")
def retrieve_relevant_chunks(query: str, k: int = 5) -> list[dict]:
collection = load_vector_store()
query_vector = embed_query(query)
results = collection.query(query_embeddings=[query_vector], n_results=k)
return format_results(results)
def format_results(results: dict) -> list[dict]:
formatted = []
documents = results.get("documents", [[]])[0]
metadatas = results.get("metadatas", [[]])[0]
for doc_text, meta in zip(documents, metadatas):
formatted.append({"content": doc_text, "metadata": meta})
return formatted
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