lightrag-cn-git/lightrag/kg/deprecated/chroma_impl.py

import asyncio
import os
from dataclasses import dataclass
from typing import Any, final
import numpy as np

from lightrag.base import BaseVectorStorage
from lightrag.utils import logger
import pipmaster as pm

if not pm.is_installed("chromadb"):
    pm.install("chromadb")

from chromadb import HttpClient, PersistentClient  # type: ignore
from chromadb.config import Settings  # type: ignore


@final
@dataclass
class ChromaVectorDBStorage(BaseVectorStorage):
    """ChromaDB vector storage implementation."""

    def __post_init__(self):
        self._validate_embedding_func()
        try:
            config = self.global_config.get("vector_db_storage_cls_kwargs", {})
            cosine_threshold = config.get("cosine_better_than_threshold")
            if cosine_threshold is None:
                raise ValueError(
                    "cosine_better_than_threshold must be specified in vector_db_storage_cls_kwargs"
                )
            self.cosine_better_than_threshold = cosine_threshold

            user_collection_settings = config.get("collection_settings", {})
            # Default HNSW index settings for ChromaDB
            default_collection_settings = {
                # Distance metric used for similarity search (cosine similarity)
                "hnsw:space": "cosine",
                # Number of nearest neighbors to explore during index construction
                # Higher values = better recall but slower indexing
                "hnsw:construction_ef": 128,
                # Number of nearest neighbors to explore during search
                # Higher values = better recall but slower search
                "hnsw:search_ef": 128,
                # Number of connections per node in the HNSW graph
                # Higher values = better recall but more memory usage
                "hnsw:M": 16,
                # Number of vectors to process in one batch during indexing
                "hnsw:batch_size": 100,
                # Number of updates before forcing index synchronization
                # Lower values = more frequent syncs but slower indexing
                "hnsw:sync_threshold": 1000,
            }
            collection_settings = {
                **default_collection_settings,
                **user_collection_settings,
            }

            local_path = config.get("local_path", None)
            if local_path:
                self._client = PersistentClient(
                    path=local_path,
                    settings=Settings(
                        allow_reset=True,
                        anonymized_telemetry=False,
                    ),
                )
            else:
                auth_provider = config.get(
                    "auth_provider", "chromadb.auth.token_authn.TokenAuthClientProvider"
                )
                auth_credentials = config.get("auth_token", "secret-token")
                headers = {}

                if "token_authn" in auth_provider:
                    headers = {
                        config.get(
                            "auth_header_name", "X-Chroma-Token"
                        ): auth_credentials
                    }
                elif "basic_authn" in auth_provider:
                    auth_credentials = config.get("auth_credentials", "admin:admin")

                self._client = HttpClient(
                    host=config.get("host", "localhost"),
                    port=config.get("port", 8000),
                    headers=headers,
                    settings=Settings(
                        chroma_api_impl="rest",
                        chroma_client_auth_provider=auth_provider,
                        chroma_client_auth_credentials=auth_credentials,
                        allow_reset=True,
                        anonymized_telemetry=False,
                    ),
                )

            self._collection = self._client.get_or_create_collection(
                name=self.namespace,
                metadata={
                    **collection_settings,
                    "dimension": self.embedding_func.embedding_dim,
                },
            )
            # Use batch size from collection settings if specified
            self._max_batch_size = self.global_config.get(
                "embedding_batch_num", collection_settings.get("hnsw:batch_size", 32)
            )
        except Exception as e:
            logger.error(f"ChromaDB initialization failed: {str(e)}")
            raise

    async def upsert(self, data: dict[str, dict[str, Any]]) -> None:
        logger.debug(f"Inserting {len(data)} to {self.namespace}")
        if not data:
            return

        try:
            import time

            current_time = int(time.time())

            ids = list(data.keys())
            documents = [v["content"] for v in data.values()]
            metadatas = [
                {
                    **{k: v for k, v in item.items() if k in self.meta_fields},
                    "created_at": current_time,
                }
                or {"_default": "true", "created_at": current_time}
                for item in data.values()
            ]

            # Process in batches
            batches = [
                documents[i : i + self._max_batch_size]
                for i in range(0, len(documents), self._max_batch_size)
            ]

            embedding_tasks = [self.embedding_func(batch) for batch in batches]
            embeddings_list = []

            # Pre-allocate embeddings_list with known size
            embeddings_list = [None] * len(embedding_tasks)

            # Use asyncio.gather instead of as_completed if order doesn't matter
            embeddings_results = await asyncio.gather(*embedding_tasks)
            embeddings_list = list(embeddings_results)

            embeddings = np.concatenate(embeddings_list)

            # Upsert in batches
            for i in range(0, len(ids), self._max_batch_size):
                batch_slice = slice(i, i + self._max_batch_size)

                self._collection.upsert(
                    ids=ids[batch_slice],
                    embeddings=embeddings[batch_slice].tolist(),
                    documents=documents[batch_slice],
                    metadatas=metadatas[batch_slice],
                )

            return ids

        except Exception as e:
            logger.error(f"Error during ChromaDB upsert: {str(e)}")
            raise

    async def query(self, query: str, top_k: int) -> list[dict[str, Any]]:
        try:
            embedding = await self.embedding_func(
                [query], _priority=5
            )  # higher priority for query

            results = self._collection.query(
                query_embeddings=embedding.tolist()
                if not isinstance(embedding, list)
                else embedding,
                n_results=top_k * 2,  # Request more results to allow for filtering
                include=["metadatas", "distances", "documents"],
            )

            # Filter results by cosine similarity threshold and take top k
            # We request 2x results initially to have enough after filtering
            # ChromaDB returns cosine similarity (1 = identical, 0 = orthogonal)
            # We convert to distance (0 = identical, 1 = orthogonal) via (1 - similarity)
            # Only keep results with distance below threshold, then take top k
            return [
                {
                    "id": results["ids"][0][i],
                    "distance": 1 - results["distances"][0][i],
                    "content": results["documents"][0][i],
                    "created_at": results["metadatas"][0][i].get("created_at"),
                    **results["metadatas"][0][i],
                }
                for i in range(len(results["ids"][0]))
                if (1 - results["distances"][0][i]) >= self.cosine_better_than_threshold
            ][:top_k]

        except Exception as e:
            logger.error(f"Error during ChromaDB query: {str(e)}")
            raise

    async def index_done_callback(self) -> None:
        # ChromaDB handles persistence automatically
        pass

    async def delete_entity(self, entity_name: str) -> None:
        """Delete an entity by its ID.

        Args:
            entity_name: The ID of the entity to delete
        """
        try:
            logger.info(f"Deleting entity with ID {entity_name} from {self.namespace}")
            self._collection.delete(ids=[entity_name])
        except Exception as e:
            logger.error(f"Error during entity deletion: {str(e)}")
            raise

    async def delete_entity_relation(self, entity_name: str) -> None:
        """Delete an entity and its relations by ID.
        In vector DB context, this is equivalent to delete_entity.

        Args:
            entity_name: The ID of the entity to delete
        """
        await self.delete_entity(entity_name)

    async def delete(self, ids: list[str]) -> None:
        """Delete vectors with specified IDs

        Args:
            ids: List of vector IDs to be deleted
        """
        try:
            self._collection.delete(ids=ids)
            logger.debug(
                f"Successfully deleted {len(ids)} vectors from {self.namespace}"
            )
        except Exception as e:
            logger.error(f"Error while deleting vectors from {self.namespace}: {e}")
            raise

        except Exception as e:
            logger.error(f"Error during prefix search in ChromaDB: {str(e)}")
            raise

    async def get_by_id(self, id: str) -> dict[str, Any] | None:
        """Get vector data by its ID

        Args:
            id: The unique identifier of the vector

        Returns:
            The vector data if found, or None if not found
        """
        try:
            # Query the collection for a single vector by ID
            result = self._collection.get(
                ids=[id], include=["metadatas", "embeddings", "documents"]
            )

            if not result or not result["ids"] or len(result["ids"]) == 0:
                return None

            # Format the result to match the expected structure
            return {
                "id": result["ids"][0],
                "vector": result["embeddings"][0],
                "content": result["documents"][0],
                "created_at": result["metadatas"][0].get("created_at"),
                **result["metadatas"][0],
            }
        except Exception as e:
            logger.error(f"Error retrieving vector data for ID {id}: {e}")
            return None

    async def get_by_ids(self, ids: list[str]) -> list[dict[str, Any]]:
        """Get multiple vector data by their IDs

        Args:
            ids: List of unique identifiers

        Returns:
            List of vector data objects that were found
        """
        if not ids:
            return []

        try:
            # Query the collection for multiple vectors by IDs
            result = self._collection.get(
                ids=ids, include=["metadatas", "embeddings", "documents"]
            )

            if not result or not result["ids"] or len(result["ids"]) == 0:
                return []

            # Format the results to match the expected structure and preserve ordering
            formatted_map: dict[str, dict[str, Any]] = {}
            for i, result_id in enumerate(result["ids"]):
                record = {
                    "id": result_id,
                    "vector": result["embeddings"][i],
                    "content": result["documents"][i],
                    "created_at": result["metadatas"][i].get("created_at"),
                    **result["metadatas"][i],
                }
                formatted_map[str(result_id)] = record

            ordered_results: list[dict[str, Any] | None] = []
            for requested_id in ids:
                ordered_results.append(formatted_map.get(str(requested_id)))

            return ordered_results
        except Exception as e:
            logger.error(f"Error retrieving vector data for IDs {ids}: {e}")
            return []

    async def drop(self) -> dict[str, str]:
        """Drop all vector data from storage and clean up resources

        This method will delete all documents from the ChromaDB collection.

        Returns:
            dict[str, str]: Operation status and message
            - On success: {"status": "success", "message": "data dropped"}
            - On failure: {"status": "error", "message": "<error details>"}
        """
        try:
            # Get all IDs in the collection
            result = self._collection.get(include=[])
            if result and result["ids"] and len(result["ids"]) > 0:
                # Delete all documents
                self._collection.delete(ids=result["ids"])

            logger.info(
                f"Process {os.getpid()} drop ChromaDB collection {self.namespace}"
            )
            return {"status": "success", "message": "data dropped"}
        except Exception as e:
            logger.error(f"Error dropping ChromaDB collection {self.namespace}: {e}")
            return {"status": "error", "message": str(e)}