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f4bf832 | 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 61 62 | import os
import json
import chromadb
from chromadb.utils.embedding_functions import GoogleGenerativeAiEmbeddingFunction
_collection: chromadb.Collection | None = None
_DATA_PATH = os.path.join(os.path.dirname(__file__), "../data/labor_law_chunks.json")
def _get_collection() -> chromadb.Collection:
global _collection
if _collection is not None:
return _collection
db_path = os.getenv("VECTOR_DB_PATH", "./data/chromadb")
client = chromadb.PersistentClient(path=db_path)
embedding_fn = GoogleGenerativeAiEmbeddingFunction(
api_key=os.getenv("GEMINI_API_KEY", ""),
model_name="models/text-embedding-004",
)
_collection = client.get_or_create_collection(
name="labor_law",
embedding_function=embedding_fn,
)
if _collection.count() == 0:
_seed_collection(_collection)
return _collection
def _seed_collection(col: chromadb.Collection) -> None:
with open(_DATA_PATH, encoding="utf-8") as f:
chunks: list[dict] = json.load(f)
col.add(
ids=[c["id"] for c in chunks],
documents=[c["content"] for c in chunks],
metadatas=[
{
"topic": c["topic"],
"law": c["law"],
"keywords": ", ".join(c.get("keywords", [])),
}
for c in chunks
],
)
def search(query: str, n_results: int = 3) -> list[dict]:
col = _get_collection()
results = col.query(query_texts=[query], n_results=n_results)
docs = results["documents"][0]
metas = results["metadatas"][0]
return [
{"content": doc, "topic": meta["topic"], "law": meta["law"]}
for doc, meta in zip(docs, metas)
]
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