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102 lines
3.4 KiB
Python
102 lines
3.4 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
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from uuid import uuid4
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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.vectorstores.base import VectorStore
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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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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 it 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._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(data_properties, self._index_name, _id)
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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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"""Look up similar documents in weaviate."""
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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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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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@classmethod
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def from_texts(
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cls,
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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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) -> VectorStore:
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"""Not implemented for Weaviate yet."""
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raise NotImplementedError("weaviate does not currently support `from_texts`.")
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