mirror of
https://github.com/hwchase17/langchain
synced 2024-11-10 01:10:59 +00:00
529 lines
19 KiB
Python
529 lines
19 KiB
Python
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from __future__ import annotations
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import datetime
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import os
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from typing import (
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TYPE_CHECKING,
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Any,
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Callable,
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Dict,
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Iterable,
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List,
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Optional,
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Tuple,
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)
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from uuid import uuid4
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import numpy as np
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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.vectorstores import VectorStore
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from langchain_community.vectorstores.utils import maximal_marginal_relevance
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if TYPE_CHECKING:
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import weaviate
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def _default_schema(index_name: str) -> Dict:
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return {
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"class": index_name,
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"properties": [
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{
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"name": "text",
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"dataType": ["text"],
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}
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],
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}
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def _create_weaviate_client(
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url: Optional[str] = None,
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api_key: Optional[str] = None,
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**kwargs: Any,
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) -> weaviate.Client:
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try:
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import weaviate
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except ImportError:
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raise ImportError(
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"Could not import weaviate python package. "
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"Please install it with `pip install weaviate-client`"
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)
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url = url or os.environ.get("WEAVIATE_URL")
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api_key = api_key or os.environ.get("WEAVIATE_API_KEY")
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auth = weaviate.auth.AuthApiKey(api_key=api_key) if api_key else None
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return weaviate.Client(url=url, auth_client_secret=auth, **kwargs)
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def _default_score_normalizer(val: float) -> float:
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return 1 - 1 / (1 + np.exp(val))
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def _json_serializable(value: Any) -> Any:
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if isinstance(value, datetime.datetime):
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return value.isoformat()
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return value
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class Weaviate(VectorStore):
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"""`Weaviate` vector store.
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To use, you should have the ``weaviate-client`` python package installed.
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Example:
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.. code-block:: python
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import weaviate
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from langchain_community.vectorstores import Weaviate
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client = weaviate.Client(url=os.environ["WEAVIATE_URL"], ...)
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weaviate = Weaviate(client, index_name, text_key)
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"""
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def __init__(
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self,
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client: Any,
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index_name: str,
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text_key: str,
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embedding: Optional[Embeddings] = None,
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attributes: Optional[List[str]] = None,
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relevance_score_fn: Optional[
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Callable[[float], float]
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] = _default_score_normalizer,
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by_text: bool = True,
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):
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"""Initialize with Weaviate client."""
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try:
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import weaviate
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except ImportError:
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raise ImportError(
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"Could not import weaviate python package. "
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"Please install it with `pip install weaviate-client`."
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)
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if not isinstance(client, weaviate.Client):
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raise ValueError(
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f"client should be an instance of weaviate.Client, got {type(client)}"
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)
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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._query_attrs = [self._text_key]
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self.relevance_score_fn = relevance_score_fn
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self._by_text = by_text
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if attributes is not None:
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self._query_attrs.extend(attributes)
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@property
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def embeddings(self) -> Optional[Embeddings]:
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return self._embedding
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def _select_relevance_score_fn(self) -> Callable[[float], float]:
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return (
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self.relevance_score_fn
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if self.relevance_score_fn
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else _default_score_normalizer
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)
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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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**kwargs: Any,
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) -> List[str]:
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"""Upload texts with metadata (properties) to Weaviate."""
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from weaviate.util import get_valid_uuid
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ids = []
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embeddings: Optional[List[List[float]]] = None
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if self._embedding:
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if not isinstance(texts, list):
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texts = list(texts)
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embeddings = self._embedding.embed_documents(texts)
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with self._client.batch as batch:
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for i, text in enumerate(texts):
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data_properties = {self._text_key: text}
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if metadatas is not None:
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for key, val in metadatas[i].items():
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data_properties[key] = _json_serializable(val)
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# Allow for ids (consistent w/ other methods)
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# # Or uuids (backwards compatible w/ existing arg)
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# If the UUID of one of the objects already exists
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# then the existing object will be replaced by the new object.
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_id = get_valid_uuid(uuid4())
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if "uuids" in kwargs:
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_id = kwargs["uuids"][i]
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elif "ids" in kwargs:
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_id = kwargs["ids"][i]
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batch.add_data_object(
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data_object=data_properties,
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class_name=self._index_name,
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uuid=_id,
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vector=embeddings[i] if embeddings else None,
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tenant=kwargs.get("tenant"),
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)
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ids.append(_id)
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return ids
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def similarity_search(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query.
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"""
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if self._by_text:
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return self.similarity_search_by_text(query, k, **kwargs)
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else:
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if self._embedding is None:
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raise ValueError(
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"_embedding cannot be None for similarity_search when "
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"_by_text=False"
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)
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embedding = self._embedding.embed_query(query)
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return self.similarity_search_by_vector(embedding, k, **kwargs)
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def similarity_search_by_text(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Return docs most similar to query.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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Returns:
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List of Documents most similar to the query.
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"""
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content: Dict[str, Any] = {"concepts": [query]}
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if kwargs.get("search_distance"):
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content["certainty"] = kwargs.get("search_distance")
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query_obj = self._client.query.get(self._index_name, self._query_attrs)
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if kwargs.get("where_filter"):
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query_obj = query_obj.with_where(kwargs.get("where_filter"))
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if kwargs.get("tenant"):
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query_obj = query_obj.with_tenant(kwargs.get("tenant"))
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if kwargs.get("additional"):
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query_obj = query_obj.with_additional(kwargs.get("additional"))
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result = query_obj.with_near_text(content).with_limit(k).do()
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if "errors" in result:
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raise ValueError(f"Error during query: {result['errors']}")
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docs = []
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for res in result["data"]["Get"][self._index_name]:
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text = res.pop(self._text_key)
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docs.append(Document(page_content=text, metadata=res))
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return docs
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def similarity_search_by_vector(
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self, embedding: List[float], k: int = 4, **kwargs: Any
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) -> List[Document]:
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"""Look up similar documents by embedding vector in Weaviate."""
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vector = {"vector": embedding}
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query_obj = self._client.query.get(self._index_name, self._query_attrs)
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if kwargs.get("where_filter"):
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query_obj = query_obj.with_where(kwargs.get("where_filter"))
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if kwargs.get("tenant"):
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query_obj = query_obj.with_tenant(kwargs.get("tenant"))
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if kwargs.get("additional"):
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query_obj = query_obj.with_additional(kwargs.get("additional"))
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result = query_obj.with_near_vector(vector).with_limit(k).do()
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if "errors" in result:
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raise ValueError(f"Error during query: {result['errors']}")
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docs = []
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for res in result["data"]["Get"][self._index_name]:
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text = res.pop(self._text_key)
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docs.append(Document(page_content=text, metadata=res))
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return docs
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def max_marginal_relevance_search(
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self,
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query: str,
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k: int = 4,
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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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query: Text to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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if self._embedding is not None:
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embedding = self._embedding.embed_query(query)
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else:
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raise ValueError(
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"max_marginal_relevance_search requires a suitable Embeddings object"
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)
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return self.max_marginal_relevance_search_by_vector(
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embedding, k=k, fetch_k=fetch_k, lambda_mult=lambda_mult, **kwargs
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)
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def max_marginal_relevance_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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fetch_k: int = 20,
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lambda_mult: float = 0.5,
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**kwargs: Any,
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) -> List[Document]:
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"""Return docs selected using the maximal marginal relevance.
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Maximal marginal relevance optimizes for similarity to query AND diversity
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among selected documents.
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Args:
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embedding: Embedding to look up documents similar to.
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k: Number of Documents to return. Defaults to 4.
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fetch_k: Number of Documents to fetch to pass to MMR algorithm.
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lambda_mult: Number between 0 and 1 that determines the degree
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of diversity among the results with 0 corresponding
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to maximum diversity and 1 to minimum diversity.
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Defaults to 0.5.
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Returns:
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List of Documents selected by maximal marginal relevance.
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"""
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vector = {"vector": embedding}
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query_obj = self._client.query.get(self._index_name, self._query_attrs)
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if kwargs.get("where_filter"):
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query_obj = query_obj.with_where(kwargs.get("where_filter"))
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if kwargs.get("tenant"):
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query_obj = query_obj.with_tenant(kwargs.get("tenant"))
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results = (
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query_obj.with_additional("vector")
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.with_near_vector(vector)
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.with_limit(fetch_k)
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.do()
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)
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payload = results["data"]["Get"][self._index_name]
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embeddings = [result["_additional"]["vector"] for result in payload]
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mmr_selected = maximal_marginal_relevance(
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np.array(embedding), embeddings, k=k, lambda_mult=lambda_mult
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)
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docs = []
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for idx in mmr_selected:
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text = payload[idx].pop(self._text_key)
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payload[idx].pop("_additional")
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meta = payload[idx]
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docs.append(Document(page_content=text, metadata=meta))
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return docs
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def similarity_search_with_score(
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self, query: str, k: int = 4, **kwargs: Any
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) -> List[Tuple[Document, float]]:
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"""
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Return list of documents most similar to the query
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text and cosine distance in float for each.
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Lower score represents more similarity.
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"""
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if self._embedding is None:
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raise ValueError(
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"_embedding cannot be None for similarity_search_with_score"
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)
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content: Dict[str, Any] = {"concepts": [query]}
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if kwargs.get("search_distance"):
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content["certainty"] = kwargs.get("search_distance")
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query_obj = self._client.query.get(self._index_name, self._query_attrs)
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if kwargs.get("where_filter"):
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query_obj = query_obj.with_where(kwargs.get("where_filter"))
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if kwargs.get("tenant"):
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query_obj = query_obj.with_tenant(kwargs.get("tenant"))
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embedded_query = self._embedding.embed_query(query)
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if not self._by_text:
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vector = {"vector": embedded_query}
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result = (
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query_obj.with_near_vector(vector)
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.with_limit(k)
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.with_additional("vector")
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.do()
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)
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else:
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result = (
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query_obj.with_near_text(content)
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.with_limit(k)
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.with_additional("vector")
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.do()
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)
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if "errors" in result:
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raise ValueError(f"Error during query: {result['errors']}")
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docs_and_scores = []
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||
|
|
for res in result["data"]["Get"][self._index_name]:
|
||
|
|
text = res.pop(self._text_key)
|
||
|
|
score = np.dot(res["_additional"]["vector"], embedded_query)
|
||
|
|
docs_and_scores.append((Document(page_content=text, metadata=res), score))
|
||
|
|
return docs_and_scores
|
||
|
|
|
||
|
|
@classmethod
|
||
|
|
def from_texts(
|
||
|
|
cls,
|
||
|
|
texts: List[str],
|
||
|
|
embedding: Embeddings,
|
||
|
|
metadatas: Optional[List[dict]] = None,
|
||
|
|
*,
|
||
|
|
client: Optional[weaviate.Client] = None,
|
||
|
|
weaviate_url: Optional[str] = None,
|
||
|
|
weaviate_api_key: Optional[str] = None,
|
||
|
|
batch_size: Optional[int] = None,
|
||
|
|
index_name: Optional[str] = None,
|
||
|
|
text_key: str = "text",
|
||
|
|
by_text: bool = False,
|
||
|
|
relevance_score_fn: Optional[
|
||
|
|
Callable[[float], float]
|
||
|
|
] = _default_score_normalizer,
|
||
|
|
**kwargs: Any,
|
||
|
|
) -> Weaviate:
|
||
|
|
"""Construct Weaviate wrapper from raw documents.
|
||
|
|
|
||
|
|
This is a user-friendly interface that:
|
||
|
|
1. Embeds documents.
|
||
|
|
2. Creates a new index for the embeddings in the Weaviate instance.
|
||
|
|
3. Adds the documents to the newly created Weaviate index.
|
||
|
|
|
||
|
|
This is intended to be a quick way to get started.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
texts: Texts to add to vector store.
|
||
|
|
embedding: Text embedding model to use.
|
||
|
|
metadatas: Metadata associated with each text.
|
||
|
|
client: weaviate.Client to use.
|
||
|
|
weaviate_url: The Weaviate URL. If using Weaviate Cloud Services get it
|
||
|
|
from the ``Details`` tab. Can be passed in as a named param or by
|
||
|
|
setting the environment variable ``WEAVIATE_URL``. Should not be
|
||
|
|
specified if client is provided.
|
||
|
|
weaviate_api_key: The Weaviate API key. If enabled and using Weaviate Cloud
|
||
|
|
Services, get it from ``Details`` tab. Can be passed in as a named param
|
||
|
|
or by setting the environment variable ``WEAVIATE_API_KEY``. Should
|
||
|
|
not be specified if client is provided.
|
||
|
|
batch_size: Size of batch operations.
|
||
|
|
index_name: Index name.
|
||
|
|
text_key: Key to use for uploading/retrieving text to/from vectorstore.
|
||
|
|
by_text: Whether to search by text or by embedding.
|
||
|
|
relevance_score_fn: Function for converting whatever distance function the
|
||
|
|
vector store uses to a relevance score, which is a normalized similarity
|
||
|
|
score (0 means dissimilar, 1 means similar).
|
||
|
|
**kwargs: Additional named parameters to pass to ``Weaviate.__init__()``.
|
||
|
|
|
||
|
|
Example:
|
||
|
|
.. code-block:: python
|
||
|
|
|
||
|
|
from langchain_community.embeddings import OpenAIEmbeddings
|
||
|
|
from langchain_community.vectorstores import Weaviate
|
||
|
|
|
||
|
|
embeddings = OpenAIEmbeddings()
|
||
|
|
weaviate = Weaviate.from_texts(
|
||
|
|
texts,
|
||
|
|
embeddings,
|
||
|
|
weaviate_url="http://localhost:8080"
|
||
|
|
)
|
||
|
|
"""
|
||
|
|
|
||
|
|
try:
|
||
|
|
from weaviate.util import get_valid_uuid
|
||
|
|
except ImportError as e:
|
||
|
|
raise ImportError(
|
||
|
|
"Could not import weaviate python package. "
|
||
|
|
"Please install it with `pip install weaviate-client`"
|
||
|
|
) from e
|
||
|
|
|
||
|
|
client = client or _create_weaviate_client(
|
||
|
|
url=weaviate_url,
|
||
|
|
api_key=weaviate_api_key,
|
||
|
|
)
|
||
|
|
if batch_size:
|
||
|
|
client.batch.configure(batch_size=batch_size)
|
||
|
|
|
||
|
|
index_name = index_name or f"LangChain_{uuid4().hex}"
|
||
|
|
schema = _default_schema(index_name)
|
||
|
|
# check whether the index already exists
|
||
|
|
if not client.schema.exists(index_name):
|
||
|
|
client.schema.create_class(schema)
|
||
|
|
|
||
|
|
embeddings = embedding.embed_documents(texts) if embedding else None
|
||
|
|
attributes = list(metadatas[0].keys()) if metadatas else None
|
||
|
|
|
||
|
|
# If the UUID of one of the objects already exists
|
||
|
|
# then the existing object will be replaced by the new object.
|
||
|
|
if "uuids" in kwargs:
|
||
|
|
uuids = kwargs.pop("uuids")
|
||
|
|
else:
|
||
|
|
uuids = [get_valid_uuid(uuid4()) for _ in range(len(texts))]
|
||
|
|
|
||
|
|
with client.batch as batch:
|
||
|
|
for i, text in enumerate(texts):
|
||
|
|
data_properties = {
|
||
|
|
text_key: text,
|
||
|
|
}
|
||
|
|
if metadatas is not None:
|
||
|
|
for key in metadatas[i].keys():
|
||
|
|
data_properties[key] = metadatas[i][key]
|
||
|
|
|
||
|
|
_id = uuids[i]
|
||
|
|
|
||
|
|
# if an embedding strategy is not provided, we let
|
||
|
|
# weaviate create the embedding. Note that this will only
|
||
|
|
# work if weaviate has been installed with a vectorizer module
|
||
|
|
# like text2vec-contextionary for example
|
||
|
|
params = {
|
||
|
|
"uuid": _id,
|
||
|
|
"data_object": data_properties,
|
||
|
|
"class_name": index_name,
|
||
|
|
}
|
||
|
|
if embeddings is not None:
|
||
|
|
params["vector"] = embeddings[i]
|
||
|
|
|
||
|
|
batch.add_data_object(**params)
|
||
|
|
|
||
|
|
batch.flush()
|
||
|
|
|
||
|
|
return cls(
|
||
|
|
client,
|
||
|
|
index_name,
|
||
|
|
text_key,
|
||
|
|
embedding=embedding,
|
||
|
|
attributes=attributes,
|
||
|
|
relevance_score_fn=relevance_score_fn,
|
||
|
|
by_text=by_text,
|
||
|
|
**kwargs,
|
||
|
|
)
|
||
|
|
|
||
|
|
def delete(self, ids: Optional[List[str]] = None, **kwargs: Any) -> None:
|
||
|
|
"""Delete by vector IDs.
|
||
|
|
|
||
|
|
Args:
|
||
|
|
ids: List of ids to delete.
|
||
|
|
"""
|
||
|
|
|
||
|
|
if ids is None:
|
||
|
|
raise ValueError("No ids provided to delete.")
|
||
|
|
|
||
|
|
# TODO: Check if this can be done in bulk
|
||
|
|
for id in ids:
|
||
|
|
self._client.data_object.delete(uuid=id)
|