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@ -1,6 +1,7 @@
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"""Wrapper around Pinecone vector database."""
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from __future__ import annotations
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import logging
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import uuid
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from typing import Any, Callable, Iterable, List, Optional, Tuple
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@ -8,6 +9,8 @@ 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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logger = logging.getLogger(__name__)
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class Pinecone(VectorStore):
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"""Wrapper around Pinecone vector database.
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@ -120,8 +123,14 @@ class Pinecone(VectorStore):
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)
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for res in results["matches"]:
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metadata = res["metadata"]
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text = metadata.pop(self._text_key)
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docs.append((Document(page_content=text, metadata=metadata), res["score"]))
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if self._text_key in metadata:
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text = metadata.pop(self._text_key)
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score = res["score"]
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docs.append((Document(page_content=text, metadata=metadata), score))
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else:
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logger.warning(
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f"Found document with no `{self._text_key}` key. Skipping."
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)
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return docs
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def similarity_search(
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@ -143,22 +152,10 @@ class Pinecone(VectorStore):
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Returns:
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List of Documents most similar to the query and score for each
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"""
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if namespace is None:
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namespace = self._namespace
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query_obj = self._embedding_function(query)
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docs = []
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results = self._index.query(
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[query_obj],
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top_k=k,
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include_metadata=True,
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namespace=namespace,
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filter=filter,
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docs_and_scores = self.similarity_search_with_score(
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query, k=k, filter=filter, namespace=namespace, **kwargs
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)
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for res in results["matches"]:
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metadata = res["metadata"]
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text = metadata.pop(self._text_key)
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docs.append(Document(page_content=text, metadata=metadata))
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return docs
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return [doc for doc, _ in docs_and_scores]
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@classmethod
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def from_texts(
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