open-webui/backend/open_webui/apps/retrieval/vector/dbs/chroma.py

import chromadb
from chromadb import Settings
from chromadb.utils.batch_utils import create_batches

from typing import Optional

from open_webui.apps.retrieval.vector.main import VectorItem, SearchResult, GetResult
from open_webui.config import (
    CHROMA_DATA_PATH,
    CHROMA_HTTP_HOST,
    CHROMA_HTTP_PORT,
    CHROMA_HTTP_HEADERS,
    CHROMA_HTTP_SSL,
    CHROMA_TENANT,
    CHROMA_DATABASE,
)


class ChromaClient:
    def __init__(self):
        if CHROMA_HTTP_HOST != "":
            self.client = chromadb.HttpClient(
                host=CHROMA_HTTP_HOST,
                port=CHROMA_HTTP_PORT,
                headers=CHROMA_HTTP_HEADERS,
                ssl=CHROMA_HTTP_SSL,
                tenant=CHROMA_TENANT,
                database=CHROMA_DATABASE,
                settings=Settings(allow_reset=True, anonymized_telemetry=False),
            )
        else:
            self.client = chromadb.PersistentClient(
                path=CHROMA_DATA_PATH,
                settings=Settings(allow_reset=True, anonymized_telemetry=False),
                tenant=CHROMA_TENANT,
                database=CHROMA_DATABASE,
            )

    def has_collection(self, collection_name: str) -> bool:
        # Check if the collection exists based on the collection name.
        collections = self.client.list_collections()
        return collection_name in [collection.name for collection in collections]

    def delete_collection(self, collection_name: str):
        # Delete the collection based on the collection name.
        return self.client.delete_collection(name=collection_name)

    def search(
        self, collection_name: str, vectors: list[list[float | int]], limit: int
    ) -> Optional[SearchResult]:
        # Search for the nearest neighbor items based on the vectors and return 'limit' number of results.
        collection = self.client.get_collection(name=collection_name)
        if collection:
            result = collection.query(
                query_embeddings=vectors,
                n_results=limit,
            )

            return SearchResult(
                **{
                    "ids": result["ids"],
                    "distances": result["distances"],
                    "documents": result["documents"],
                    "metadatas": result["metadatas"],
                }
            )
        return None

    def query(
        self, collection_name: str, filter: dict, limit: int = 1
    ) -> Optional[GetResult]:
        # Query the items from the collection based on the filter.
        collection = self.client.get_collection(name=collection_name)
        if collection:
            result = collection.get(
                where=filter,
                limit=limit,
            )

            return GetResult(
                **{
                    "ids": result["ids"],
                    "documents": result["documents"],
                    "metadatas": result["metadatas"],
                }
            )
        return None

    def get(self, collection_name: str) -> Optional[GetResult]:
        # Get all the items in the collection.
        collection = self.client.get_collection(name=collection_name)
        if collection:
            result = collection.get()
            return GetResult(
                **{
                    "ids": [result["ids"]],
                    "documents": [result["documents"]],
                    "metadatas": [result["metadatas"]],
                }
            )
        return None

    def insert(self, collection_name: str, items: list[VectorItem]):
        # Insert the items into the collection, if the collection does not exist, it will be created.
        collection = self.client.get_or_create_collection(name=collection_name)

        ids = [item["id"] for item in items]
        documents = [item["text"] for item in items]
        embeddings = [item["vector"] for item in items]
        metadatas = [item["metadata"] for item in items]

        for batch in create_batches(
            api=self.client,
            documents=documents,
            embeddings=embeddings,
            ids=ids,
            metadatas=metadatas,
        ):
            collection.add(*batch)

    def upsert(self, collection_name: str, items: list[VectorItem]):
        # Update the items in the collection, if the items are not present, insert them. If the collection does not exist, it will be created.
        collection = self.client.get_or_create_collection(name=collection_name)

        ids = [item["id"] for item in items]
        documents = [item["text"] for item in items]
        embeddings = [item["vector"] for item in items]
        metadatas = [item["metadata"] for item in items]

        collection.upsert(
            ids=ids, documents=documents, embeddings=embeddings, metadatas=metadatas
        )

    def delete(
        self,
        collection_name: str,
        ids: Optional[list[str]] = None,
        filter: Optional[dict] = None,
    ):
        # Delete the items from the collection based on the ids.
        collection = self.client.get_collection(name=collection_name)
        if collection:
            if ids:
                collection.delete(ids=ids)
            elif filter:
                collection.delete(where=filter)

    def reset(self):
        # Resets the database. This will delete all collections and item entries.
        return self.client.reset()