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323 lines
11 KiB
Python
323 lines
11 KiB
Python
"""Wrapper around weaviate vector database."""
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from __future__ import annotations
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from typing import Any, Dict, Iterable, List, Optional, Type
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from uuid import uuid4
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import numpy as np
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from langchain.docstore.document import Document
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from langchain.embeddings.base import Embeddings
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from langchain.utils import get_from_dict_or_env
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from langchain.vectorstores.base import VectorStore
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from langchain.vectorstores.utils import maximal_marginal_relevance
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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(**kwargs: Any) -> Any:
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client = kwargs.get("client")
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if client is not None:
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return client
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weaviate_url = get_from_dict_or_env(kwargs, "weaviate_url", "WEAVIATE_URL")
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weaviate_api_key = get_from_dict_or_env(
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kwargs, "weaviate_api_key", "WEAVIATE_API_KEY", None
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)
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try:
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import weaviate
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except ImportError:
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raise ValueError(
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"Could not import weaviate python package. "
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"Please install it with `pip instal weaviate-client`"
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)
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auth = (
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weaviate.auth.AuthApiKey(api_key=weaviate_api_key)
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if weaviate_api_key is not None
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else None
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)
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client = weaviate.Client(weaviate_url, auth_client_secret=auth)
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return client
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class Weaviate(VectorStore):
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"""Wrapper around Weaviate vector database.
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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.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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):
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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 ValueError(
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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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if attributes is not None:
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self._query_attrs.extend(attributes)
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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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with self._client.batch as batch:
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ids = []
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for i, doc in enumerate(texts):
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data_properties = {
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self._text_key: doc,
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}
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if metadatas is not None:
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for key in metadatas[i].keys():
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data_properties[key] = metadatas[i][key]
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_id = get_valid_uuid(uuid4())
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batch.add_data_object(
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data_object=data_properties, class_name=self._index_name, uuid=_id
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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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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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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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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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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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@classmethod
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def from_texts(
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cls: Type[Weaviate],
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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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**kwargs: Any,
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) -> Weaviate:
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"""Construct Weaviate 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. Creates a new index for the embeddings in the Weaviate instance.
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3. Adds the documents to the newly created Weaviate 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.vectorstores.weaviate import Weaviate
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from langchain.embeddings import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings()
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weaviate = Weaviate.from_texts(
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texts,
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embeddings,
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weaviate_url="http://localhost:8080"
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)
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"""
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client = _create_weaviate_client(**kwargs)
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from weaviate.util import get_valid_uuid
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index_name = kwargs.get("index_name", f"LangChain_{uuid4().hex}")
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embeddings = embedding.embed_documents(texts) if embedding else None
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text_key = "text"
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schema = _default_schema(index_name)
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attributes = list(metadatas[0].keys()) if metadatas else None
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# check whether the index already exists
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if not client.schema.contains(schema):
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client.schema.create_class(schema)
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with client.batch as batch:
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for i, text in enumerate(texts):
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data_properties = {
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text_key: text,
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}
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if metadatas is not None:
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for key in metadatas[i].keys():
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data_properties[key] = metadatas[i][key]
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_id = get_valid_uuid(uuid4())
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# if an embedding strategy is not provided, we let
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# weaviate create the embedding. Note that this will only
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# work if weaviate has been installed with a vectorizer module
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# like text2vec-contextionary for example
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params = {
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"uuid": _id,
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"data_object": data_properties,
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"class_name": index_name,
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}
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if embeddings is not None:
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params["vector"] = embeddings[i]
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batch.add_data_object(**params)
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batch.flush()
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return cls(client, index_name, text_key, embedding, attributes)
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