Source code for langchain_community.vectorstores.meilisearch

from __future__ import annotations

import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type

from langchain_core.documents import Document
from langchain_core.embeddings import Embeddings
from langchain_core.utils import get_from_env
from langchain_core.vectorstores import VectorStore

if TYPE_CHECKING:
    from meilisearch import Client


def _create_client(
    client: Optional[Client] = None,
    url: Optional[str] = None,
    api_key: Optional[str] = None,
) -> Client:
    try:
        import meilisearch
    except ImportError:
        raise ImportError(
            "Could not import meilisearch python package. "
            "Please install it with `pip install meilisearch`."
        )
    if not client:
        url = url or get_from_env("url", "MEILI_HTTP_ADDR")
        try:
            api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY")
        except Exception:
            pass
        client = meilisearch.Client(url=url, api_key=api_key)
    elif not isinstance(client, meilisearch.Client):
        raise ValueError(
            f"client should be an instance of meilisearch.Client, "
            f"got {type(client)}"
        )
    try:
        client.version()
    except ValueError as e:
        raise ValueError(f"Failed to connect to Meilisearch: {e}")
    return client


[docs]class Meilisearch(VectorStore): """`Meilisearch` vector store. To use this, you need to have `meilisearch` python package installed, and a running Meilisearch instance. To learn more about Meilisearch Python, refer to the in-depth Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/. See the following documentation for how to run a Meilisearch instance: https://www.meilisearch.com/docs/learn/getting_started/quick_start. Example: .. code-block:: python from langchain_community.vectorstores import Meilisearch from langchain_community.embeddings.openai import OpenAIEmbeddings import meilisearch # api_key is optional; provide it if your meilisearch instance requires it client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embeddings = OpenAIEmbeddings() embedders = { "theEmbedderName": { "source": "userProvided", "dimensions": "1536" } } vectorstore = Meilisearch( embedding=embeddings, embedders=embedders, client=client, index_name='langchain_demo', text_key='text') """
[docs] def __init__( self, embedding: Embeddings, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", text_key: str = "text", metadata_key: str = "metadata", *, embedders: Optional[Dict[str, Any]] = None, ): """Initialize with Meilisearch client.""" client = _create_client(client=client, url=url, api_key=api_key) self._client = client self._index_name = index_name self._embedding = embedding self._text_key = text_key self._metadata_key = metadata_key self._embedders = embedders self._embedders_settings = self._client.index( str(self._index_name) ).update_embedders(embedders)
[docs] def add_texts( self, texts: Iterable[str], metadatas: Optional[List[dict]] = None, ids: Optional[List[str]] = None, embedder_name: Optional[str] = "default", **kwargs: Any, ) -> List[str]: """Run more texts through the embedding and add them to the vector store. Args: texts (Iterable[str]): Iterable of strings/text to add to the vectorstore. embedder_name: Name of the embedder. Defaults to "default". metadatas (Optional[List[dict]]): Optional list of metadata. Defaults to None. ids Optional[List[str]]: Optional list of IDs. Defaults to None. Returns: List[str]: List of IDs of the texts added to the vectorstore. """ texts = list(texts) # Embed and create the documents docs = [] if ids is None: ids = [uuid.uuid4().hex for _ in texts] if metadatas is None: metadatas = [{} for _ in texts] embedding_vectors = self._embedding.embed_documents(texts) for i, text in enumerate(texts): id = ids[i] metadata = metadatas[i] metadata[self._text_key] = text embedding = embedding_vectors[i] docs.append( { "id": id, "_vectors": {f"{embedder_name}": embedding}, f"{self._metadata_key}": metadata, } ) # Send to Meilisearch self._client.index(str(self._index_name)).add_documents(docs) return ids
[docs] def similarity_search_with_score( self, query: str, k: int = 4, filter: Optional[Dict[str, str]] = None, embedder_name: Optional[str] = "default", **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to the query, along with scores. Args: query (str): Query text for which to find similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query text and score for each. """ _query = self._embedding.embed_query(query) docs = self.similarity_search_by_vector_with_scores( embedding=_query, embedder_name=embedder_name, k=k, filter=filter, kwargs=kwargs, ) return docs
[docs] def similarity_search_by_vector_with_scores( self, embedding: List[float], embedder_name: Optional[str] = "default", k: int = 4, filter: Optional[Dict[str, Any]] = None, **kwargs: Any, ) -> List[Tuple[Document, float]]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = [] results = self._client.index(str(self._index_name)).search( "", { "vector": embedding, "hybrid": {"semanticRatio": 1.0, "embedder": embedder_name}, "limit": k, "filter": filter, }, ) for result in results["hits"]: metadata = result[self._metadata_key] if self._text_key in metadata: text = metadata.pop(self._text_key) semantic_score = result["_semanticScore"] docs.append( (Document(page_content=text, metadata=metadata), semantic_score) ) return docs
[docs] def similarity_search_by_vector( self, embedding: List[float], k: int = 4, filter: Optional[Dict[str, str]] = None, embedder_name: Optional[str] = "default", **kwargs: Any, ) -> List[Document]: """Return meilisearch documents most similar to embedding vector. Args: embedding (List[float]): Embedding to look up similar documents. embedder_name: Name of the embedder to be used. Defaults to "default". k (int): Number of documents to return. Defaults to 4. filter (Optional[Dict[str, str]]): Filter by metadata. Defaults to None. Returns: List[Document]: List of Documents most similar to the query vector and score for each. """ docs = self.similarity_search_by_vector_with_scores( embedding=embedding, embedder_name=embedder_name, k=k, filter=filter, kwargs=kwargs, ) return [doc for doc, _ in docs]
[docs] @classmethod def from_texts( cls: Type[Meilisearch], texts: List[str], embedding: Embeddings, metadatas: Optional[List[dict]] = None, client: Optional[Client] = None, url: Optional[str] = None, api_key: Optional[str] = None, index_name: str = "langchain-demo", ids: Optional[List[str]] = None, text_key: Optional[str] = "text", metadata_key: Optional[str] = "metadata", embedders: Dict[str, Any] = {}, embedder_name: Optional[str] = "default", **kwargs: Any, ) -> Meilisearch: """Construct Meilisearch wrapper from raw documents. This is a user-friendly interface that: 1. Embeds documents. 2. Adds the documents to a provided Meilisearch index. This is intended to be a quick way to get started. Example: .. code-block:: python from langchain_community.vectorstores import Meilisearch from langchain_community.embeddings import OpenAIEmbeddings import meilisearch # The environment should be the one specified next to the API key # in your Meilisearch console client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***') embedding = OpenAIEmbeddings() embedders: Embedders index setting. embedder_name: Name of the embedder. Defaults to "default". docsearch = Meilisearch.from_texts( client=client, embedding=embedding, ) """ client = _create_client(client=client, url=url, api_key=api_key) vectorstore = cls( embedding=embedding, embedders=embedders, client=client, index_name=index_name, ) vectorstore.add_texts( texts=texts, embedder_name=embedder_name, metadatas=metadatas, ids=ids, text_key=text_key, metadata_key=metadata_key, ) return vectorstore