mirror of
https://github.com/open-webui/open-webui
synced 2024-11-07 17:19:53 +00:00
114 lines
4.2 KiB
Python
114 lines
4.2 KiB
Python
import chromadb
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from chromadb import Settings
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from chromadb.utils.batch_utils import create_batches
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from typing import Optional
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from open_webui.apps.rag.vector.main import VectorItem, QueryResult
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from open_webui.config import (
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CHROMA_DATA_PATH,
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CHROMA_HTTP_HOST,
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CHROMA_HTTP_PORT,
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CHROMA_HTTP_HEADERS,
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CHROMA_HTTP_SSL,
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CHROMA_TENANT,
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CHROMA_DATABASE,
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)
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class ChromaClient:
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def __init__(self):
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if CHROMA_HTTP_HOST != "":
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self.client = chromadb.HttpClient(
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host=CHROMA_HTTP_HOST,
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port=CHROMA_HTTP_PORT,
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headers=CHROMA_HTTP_HEADERS,
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ssl=CHROMA_HTTP_SSL,
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tenant=CHROMA_TENANT,
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database=CHROMA_DATABASE,
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settings=Settings(allow_reset=True, anonymized_telemetry=False),
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)
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else:
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self.client = chromadb.PersistentClient(
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path=CHROMA_DATA_PATH,
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settings=Settings(allow_reset=True, anonymized_telemetry=False),
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tenant=CHROMA_TENANT,
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database=CHROMA_DATABASE,
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)
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def has_collection(self, collection_name: str) -> bool:
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# Check if the collection exists based on the collection name.
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collections = self.client.list_collections()
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return collection_name in [collection.name for collection in collections]
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def delete_collection(self, collection_name: str):
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# Delete the collection based on the collection name.
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return self.client.delete_collection(name=collection_name)
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def search(
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self, collection_name: str, vectors: list[list[float | int]], limit: int
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) -> Optional[QueryResult]:
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# Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
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collection = self.client.get_collection(name=collection_name)
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if collection:
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result = collection.query(
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query_embeddings=vectors,
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n_results=limit,
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)
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return {
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"ids": result["ids"],
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"distances": result["distances"],
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"documents": result["documents"],
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"metadatas": result["metadatas"],
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}
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return None
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def get(self, collection_name: str) -> Optional[QueryResult]:
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# Get all the items in the collection.
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collection = self.client.get_collection(name=collection_name)
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if collection:
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return collection.get()
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return None
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def insert(self, collection_name: str, items: list[VectorItem]):
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# Insert the items into the collection, if the collection does not exist, it will be created.
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collection = self.client.get_or_create_collection(name=collection_name)
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ids = [item["id"] for item in items]
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documents = [item["text"] for item in items]
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embeddings = [item["vector"] for item in items]
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metadatas = [item["metadata"] for item in items]
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for batch in create_batches(
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api=self.client,
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documents=documents,
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embeddings=embeddings,
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ids=ids,
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metadatas=metadatas,
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):
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collection.add(*batch)
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def upsert(self, collection_name: str, items: list[VectorItem]):
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# Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
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collection = self.client.get_or_create_collection(name=collection_name)
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ids = [item["id"] for item in items]
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documents = [item["text"] for item in items]
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embeddings = [item["vector"] for item in items]
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metadata = [item["metadata"] for item in items]
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collection.upsert(
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ids=ids, documents=documents, embeddings=embeddings, metadata=metadata
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)
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def delete(self, collection_name: str, ids: list[str]):
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# Delete the items from the collection based on the ids.
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collection = self.client.get_collection(name=collection_name)
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if collection:
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collection.delete(ids=ids)
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def reset(self):
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# Resets the database. This will delete all collections and item entries.
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return self.client.reset()
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