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312 lines
10 KiB
Python
312 lines
10 KiB
Python
from __future__ import annotations
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import uuid
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from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
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from langchain_core.documents import Document
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from langchain_core.embeddings import Embeddings
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from langchain_core.utils import get_from_env
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from langchain_core.vectorstores import VectorStore
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if TYPE_CHECKING:
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from meilisearch import Client
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def _create_client(
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client: Optional[Client] = None,
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url: Optional[str] = None,
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api_key: Optional[str] = None,
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) -> Client:
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try:
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import meilisearch
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except ImportError:
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raise ImportError(
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"Could not import meilisearch python package. "
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"Please install it with `pip install meilisearch`."
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)
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if not client:
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url = url or get_from_env("url", "MEILI_HTTP_ADDR")
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try:
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api_key = api_key or get_from_env("api_key", "MEILI_MASTER_KEY")
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except Exception:
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pass
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client = meilisearch.Client(url=url, api_key=api_key)
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elif not isinstance(client, meilisearch.Client):
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raise ValueError(
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f"client should be an instance of meilisearch.Client, "
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f"got {type(client)}"
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)
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try:
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client.version()
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except ValueError as e:
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raise ValueError(f"Failed to connect to Meilisearch: {e}")
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return client
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class Meilisearch(VectorStore):
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"""`Meilisearch` vector store.
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To use this, you need to have `meilisearch` python package installed,
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and a running Meilisearch instance.
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To learn more about Meilisearch Python, refer to the in-depth
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Meilisearch Python documentation: https://meilisearch.github.io/meilisearch-python/.
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See the following documentation for how to run a Meilisearch instance:
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https://www.meilisearch.com/docs/learn/getting_started/quick_start.
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Example:
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.. code-block:: python
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from langchain_community.vectorstores import Meilisearch
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from langchain_community.embeddings.openai import OpenAIEmbeddings
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import meilisearch
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# api_key is optional; provide it if your meilisearch instance requires it
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client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
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embeddings = OpenAIEmbeddings()
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vectorstore = Meilisearch(
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embedding=embeddings,
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client=client,
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index_name='langchain_demo',
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text_key='text')
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"""
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def __init__(
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self,
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embedding: Embeddings,
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client: Optional[Client] = None,
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url: Optional[str] = None,
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api_key: Optional[str] = None,
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index_name: str = "langchain-demo",
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text_key: str = "text",
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metadata_key: str = "metadata",
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):
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"""Initialize with Meilisearch client."""
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client = _create_client(client=client, url=url, api_key=api_key)
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self._client = client
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self._index_name = index_name
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self._embedding = embedding
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self._text_key = text_key
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self._metadata_key = metadata_key
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def add_texts(
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self,
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texts: Iterable[str],
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metadatas: Optional[List[dict]] = None,
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ids: Optional[List[str]] = None,
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**kwargs: Any,
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) -> List[str]:
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"""Run more texts through the embedding and add them to the vector store.
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Args:
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texts (Iterable[str]): Iterable of strings/text to add to the vectorstore.
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metadatas (Optional[List[dict]]): Optional list of metadata.
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Defaults to None.
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ids Optional[List[str]]: Optional list of IDs.
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Defaults to None.
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Returns:
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List[str]: List of IDs of the texts added to the vectorstore.
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"""
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texts = list(texts)
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# Embed and create the documents
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docs = []
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if ids is None:
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ids = [uuid.uuid4().hex for _ in texts]
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if metadatas is None:
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metadatas = [{} for _ in texts]
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embedding_vectors = self._embedding.embed_documents(texts)
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for i, text in enumerate(texts):
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id = ids[i]
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metadata = metadatas[i]
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metadata[self._text_key] = text
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embedding = embedding_vectors[i]
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docs.append(
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{
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"id": id,
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"_vectors": embedding,
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f"{self._metadata_key}": metadata,
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}
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)
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# Send to Meilisearch
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self._client.index(str(self._index_name)).add_documents(docs)
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return ids
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def similarity_search(
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self,
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return meilisearch documents most similar to the query.
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Args:
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query (str): Query text for which to find similar documents.
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k (int): Number of documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata.
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Defaults to None.
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Returns:
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List[Document]: List of Documents most similar to the query
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text and score for each.
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"""
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docs_and_scores = self.similarity_search_with_score(
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query=query,
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k=k,
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filter=filter,
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kwargs=kwargs,
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)
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return [doc for doc, _ in docs_and_scores]
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def similarity_search_with_score(
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self,
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query: str,
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return meilisearch documents most similar to the query, along with scores.
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Args:
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query (str): Query text for which to find similar documents.
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k (int): Number of documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata.
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Defaults to None.
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Returns:
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List[Document]: List of Documents most similar to the query
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text and score for each.
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"""
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_query = self._embedding.embed_query(query)
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docs = self.similarity_search_by_vector_with_scores(
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embedding=_query,
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k=k,
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filter=filter,
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kwargs=kwargs,
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)
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return docs
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def similarity_search_by_vector_with_scores(
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self,
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embedding: List[float],
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k: int = 4,
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filter: Optional[Dict[str, Any]] = None,
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**kwargs: Any,
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) -> List[Tuple[Document, float]]:
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"""Return meilisearch documents most similar to embedding vector.
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Args:
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embedding (List[float]): Embedding to look up similar documents.
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k (int): Number of documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata.
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Defaults to None.
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Returns:
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List[Document]: List of Documents most similar to the query
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vector and score for each.
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"""
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docs = []
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results = self._client.index(str(self._index_name)).search(
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"", {"vector": embedding, "limit": k, "filter": filter}
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)
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for result in results["hits"]:
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metadata = result[self._metadata_key]
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if self._text_key in metadata:
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text = metadata.pop(self._text_key)
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semantic_score = result["_semanticScore"]
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docs.append(
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(Document(page_content=text, metadata=metadata), semantic_score)
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)
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return docs
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def similarity_search_by_vector(
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self,
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embedding: List[float],
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k: int = 4,
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filter: Optional[Dict[str, str]] = None,
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**kwargs: Any,
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) -> List[Document]:
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"""Return meilisearch documents most similar to embedding vector.
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Args:
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embedding (List[float]): Embedding to look up similar documents.
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k (int): Number of documents to return. Defaults to 4.
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filter (Optional[Dict[str, str]]): Filter by metadata.
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Defaults to None.
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Returns:
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List[Document]: List of Documents most similar to the query
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vector and score for each.
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"""
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docs = self.similarity_search_by_vector_with_scores(
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embedding=embedding,
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k=k,
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filter=filter,
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kwargs=kwargs,
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)
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return [doc for doc, _ in docs]
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@classmethod
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def from_texts(
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cls: Type[Meilisearch],
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texts: List[str],
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embedding: Embeddings,
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metadatas: Optional[List[dict]] = None,
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client: Optional[Client] = None,
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url: Optional[str] = None,
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api_key: Optional[str] = None,
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index_name: str = "langchain-demo",
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ids: Optional[List[str]] = None,
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text_key: Optional[str] = "text",
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metadata_key: Optional[str] = "metadata",
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**kwargs: Any,
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) -> Meilisearch:
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"""Construct Meilisearch wrapper from raw documents.
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This is a user-friendly interface that:
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1. Embeds documents.
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2. Adds the documents to a provided Meilisearch index.
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This is intended to be a quick way to get started.
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Example:
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.. code-block:: python
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from langchain_community.vectorstores import Meilisearch
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from langchain_community.embeddings import OpenAIEmbeddings
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import meilisearch
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# The environment should be the one specified next to the API key
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# in your Meilisearch console
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client = meilisearch.Client(url='http://127.0.0.1:7700', api_key='***')
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embeddings = OpenAIEmbeddings()
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docsearch = Meilisearch.from_texts(
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client=client,
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embeddings=embeddings,
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)
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"""
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client = _create_client(client=client, url=url, api_key=api_key)
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vectorstore = cls(
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embedding=embedding,
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client=client,
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index_name=index_name,
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)
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vectorstore.add_texts(
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texts=texts,
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metadatas=metadatas,
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ids=ids,
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text_key=text_key,
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metadata_key=metadata_key,
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)
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return vectorstore
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