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46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
from __future__ import annotations
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import chromadb
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from typing import Any
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from chromadb import PersistentClient
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from pkg.vector.vdb import VectorDatabase
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from pkg.core import app
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class ChromaVectorDatabase(VectorDatabase):
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def __init__(self, ap: app.Application, base_path: str = './data/chroma'):
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self.ap = ap
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self.client = PersistentClient(path=base_path)
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self._collections = {}
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def get_or_create_collection(self, collection: str) -> chromadb.Collection:
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if collection not in self._collections:
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self._collections[collection] = self.client.get_or_create_collection(name=collection)
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self.ap.logger.info(f"Chroma collection '{collection}' accessed/created.")
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return self._collections[collection]
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def add_embeddings(
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self,
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collection: str,
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ids: list[str],
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embeddings_list: list[list[float]],
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metadatas: list[dict[str, Any]],
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) -> None:
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col = self.get_or_create_collection(collection)
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col.add(embeddings=embeddings_list, ids=ids, metadatas=metadatas)
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self.ap.logger.info(f"Added {len(ids)} embeddings to Chroma collection '{collection}'.")
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def search(self, collection: str, query_embedding: list[float], k: int = 5) -> dict[str, Any]:
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col = self.get_or_create_collection(collection)
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results = col.query(
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query_embeddings=query_embedding,
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n_results=k,
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include=['metadatas', 'distances', 'documents'],
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)
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self.ap.logger.info(f"Chroma search in '{collection}' returned {len(results.get('ids', [[]])[0])} results.")
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return results
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def delete_by_metadata(self, collection: str, where: dict[str, Any]) -> None:
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col = self.get_or_create_collection(collection)
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col.delete(where=where)
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self.ap.logger.info(f"Deleted embeddings from Chroma collection '{collection}' with filter: {where}")
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