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(
self,
query: str,
k: int = 4,
filter: Optional[Dict[str, str]] = None,
embedder_name: Optional[str] = "default",
**kwargs: Any,
) -> List[Document]:
"""Return meilisearch documents most similar to the query.
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.
"""
docs_and_scores = self.similarity_search_with_score(
query=query,
embedder_name=embedder_name,
k=k,
filter=filter,
kwargs=kwargs,
)
return [doc for doc, _ in docs_and_scores]
[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,
"showRankingScore": True,
},
)
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["_rankingScore"]
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
