221 lines
7.8 KiB
Python
221 lines
7.8 KiB
Python
import logging
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from typing import Dict, List, Optional, Any
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from chromadb import Collection, QueryResult
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from langchain.docstore.document import Document
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from embedchain.config import ChromaDbConfig
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from embedchain.helper.json_serializable import register_deserializable
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from embedchain.vectordb.base import BaseVectorDB
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try:
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import chromadb
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from chromadb.config import Settings
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from chromadb.errors import InvalidDimensionException
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except RuntimeError:
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from embedchain.utils import use_pysqlite3
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use_pysqlite3()
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import chromadb
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from chromadb.config import Settings
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from chromadb.errors import InvalidDimensionException
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@register_deserializable
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class ChromaDB(BaseVectorDB):
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"""Vector database using ChromaDB."""
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def __init__(self, config: Optional[ChromaDbConfig] = None):
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"""Initialize a new ChromaDB instance
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:param config: Configuration options for Chroma, defaults to None
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:type config: Optional[ChromaDbConfig], optional
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"""
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if config:
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self.config = config
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else:
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self.config = ChromaDbConfig()
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self.settings = Settings()
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if self.config.chroma_settings:
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for key, value in self.config.chroma_settings.items():
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if hasattr(self.settings, key):
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setattr(self.settings, key, value)
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if self.config.host and self.config.port:
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logging.info(f"Connecting to ChromaDB server: {self.config.host}:{self.config.port}")
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self.settings.chroma_server_host = self.config.host
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self.settings.chroma_server_http_port = self.config.port
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self.settings.chroma_api_impl = "chromadb.api.fastapi.FastAPI"
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else:
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if self.config.dir is None:
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self.config.dir = "db"
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self.settings.persist_directory = self.config.dir
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self.settings.is_persistent = True
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self.client = chromadb.Client(self.settings)
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super().__init__(config=self.config)
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def _initialize(self):
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"""
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This method is needed because `embedder` attribute needs to be set externally before it can be initialized.
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"""
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if not self.embedder:
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raise ValueError("Embedder not set. Please set an embedder with `set_embedder` before initialization.")
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self._get_or_create_collection(self.config.collection_name)
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def _get_or_create_db(self):
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"""Called during initialization"""
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return self.client
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def _get_or_create_collection(self, name: str) -> Collection:
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"""
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Get or create a named collection.
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:param name: Name of the collection
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:type name: str
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:raises ValueError: No embedder configured.
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:return: Created collection
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:rtype: Collection
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"""
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if not hasattr(self, "embedder") or not self.embedder:
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raise ValueError("Cannot create a Chroma database collection without an embedder.")
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self.collection = self.client.get_or_create_collection(
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name=name,
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embedding_function=self.embedder.embedding_fn,
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)
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return self.collection
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def get(self, ids=None, where=None, limit=None):
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"""
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Get existing doc ids present in vector database
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:param ids: list of doc ids to check for existence
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:type ids: List[str]
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:param where: Optional. to filter data
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:type where: Dict[str, Any]
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:return: Existing documents.
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:rtype: List[str]
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"""
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args = {}
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if ids:
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args["ids"] = ids
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if where:
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args["where"] = where
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if limit:
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args["limit"] = limit
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return self.collection.get(
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**args
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)
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def get_advanced(self, where):
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return self.collection.get(where=where, limit=1)
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def add(self, documents: List[str], metadatas: List[object], ids: List[str]) -> Any:
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"""
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Add vectors to chroma database
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:param documents: Documents
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:type documents: List[str]
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:param metadatas: Metadatas
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:type metadatas: List[object]
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:param ids: ids
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:type ids: List[str]
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"""
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self.collection.add(documents=documents, metadatas=metadatas, ids=ids)
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def _format_result(self, results: QueryResult) -> list[tuple[Document, float]]:
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"""
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Format Chroma results
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:param results: ChromaDB query results to format.
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:type results: QueryResult
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:return: Formatted results
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:rtype: list[tuple[Document, float]]
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"""
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return [
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(Document(page_content=result[0], metadata=result[1] or {}), result[2])
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for result in zip(
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results["documents"][0],
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results["metadatas"][0],
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results["distances"][0],
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)
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]
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def query(self, input_query: List[str], n_results: int, where: Dict[str, Any]) -> List[str]:
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"""
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Query contents from vector data base based on vector similarity
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:param input_query: list of query string
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:type input_query: List[str]
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:param n_results: no of similar documents to fetch from database
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:type n_results: int
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:param where: to filter data
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:type where: Dict[str, Any]
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:raises InvalidDimensionException: Dimensions do not match.
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:return: The content of the document that matched your query.
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:rtype: List[str]
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"""
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try:
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result = self.collection.query(
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query_texts=[
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input_query,
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],
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n_results=n_results,
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where=where,
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)
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except InvalidDimensionException as e:
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raise InvalidDimensionException(
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e.message()
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+ ". This is commonly a side-effect when an embedding function, different from the one used to add the embeddings, is used to retrieve an embedding from the database." # noqa E501
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) from None
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results_formatted = self._format_result(result)
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contents = [result[0].page_content for result in results_formatted]
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return contents
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def set_collection_name(self, name: str):
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"""
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Set the name of the collection. A collection is an isolated space for vectors.
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:param name: Name of the collection.
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:type name: str
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"""
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self.config.collection_name = name
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self._get_or_create_collection(self.config.collection_name)
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def count(self) -> int:
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"""
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Count number of documents/chunks embedded in the database.
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:return: number of documents
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:rtype: int
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"""
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return self.collection.count()
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def delete(self, where):
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return self.collection.delete(where=where)
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def reset(self):
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"""
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Resets the database. Deletes all embeddings irreversibly.
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"""
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# Delete all data from the database
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try:
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self.client.reset()
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except ValueError:
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raise ValueError(
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"For safety reasons, resetting is disabled."
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'Please enable it by including `chromadb_settings={"allow_reset": True}` in your ChromaDbConfig'
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) from None
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# Recreate
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self._get_or_create_collection(self.config.collection_name)
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# Todo: Automatically recreating a collection with the same name cannot be the best way to handle a reset.
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# A downside of this implementation is, if you have two instances,
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# the other instance will not get the updated `self.collection` attribute.
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# A better way would be to create the collection if it is called again after being reset.
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# That means, checking if collection exists in the db-consuming methods, and creating it if it doesn't.
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# That's an extra steps for all uses, just to satisfy a niche use case in a niche method. For now, this will do.
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