pinecone changes (#590)

Co-authored-by: Smit Shah <who828@gmail.com>
Co-authored-by: iocuydi <46613640+iocuydi@users.noreply.github.com>

langchain/vectorstores/pinecone.py

@@ -2,7 +2,7 @@
 from __future__ import annotations
 
 import uuid
-from typing import Any, Callable, Iterable, List, Optional
+from typing import Any, Callable, Iterable, List, Optional, Tuple
 
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
@@ -46,16 +46,21 @@ class Pinecone(VectorStore):
         self._text_key = text_key
 
     def add_texts(
-        self, texts: Iterable[str], metadatas: Optional[List[dict]] = None
+        self,
+        texts: Iterable[str],
+        metadatas: Optional[List[dict]] = None,
+        namespace: Optional[str] = None,
     ) -> List[str]:
         """Run more texts through the embeddings and add to the vectorstore.
 
         Args:
             texts: Iterable of strings to add to the vectorstore.
             metadatas: Optional list of metadatas associated with the texts.
+            namespace: Optional pinecone namespace to add the texts to.
 
         Returns:
             List of ids from adding the texts into the vectorstore.
+
         """
         # Embed and create the documents
         docs = []
@@ -68,14 +73,57 @@ class Pinecone(VectorStore):
             docs.append((id, embedding, metadata))
             ids.append(id)
         # upsert to Pinecone
-        self._index.upsert(vectors=docs)
+        self._index.upsert(vectors=docs, namespace=namespace)
         return ids
 
-    def similarity_search(self, query: str, k: int = 5) -> List[Document]:
-        """Look up similar documents in pinecone."""
+    def similarity_search_with_score(
+        self,
+        query: str,
+        k: int = 5,
+        namespace: Optional[str] = None,
+    ) -> List[Tuple[Document, float]]:
+        """Return pinecone documents most similar to query, along with scores.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            namespace: Namespace to search in. Default will search in '' namespace.
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
+        query_obj = self._embedding_function(query)
+        docs = []
+        results = self._index.query(
+            [query_obj], top_k=k, include_metadata=True, namespace=namespace
+        )
+        for res in results["matches"]:
+            metadata = res["metadata"]
+            text = metadata.pop(self._text_key)
+            docs.append((Document(page_content=text, metadata=metadata), res["score"]))
+        return docs
+
+    def similarity_search(
+        self,
+        query: str,
+        k: int = 5,
+        namespace: Optional[str] = None,
+    ) -> List[Document]:
+        """Return pinecone documents most similar to query.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            namespace: Namespace to search in. Default will search in '' namespace.
+
+        Returns:
+            List of Documents most similar to the query and score for each
+        """
         query_obj = self._embedding_function(query)
         docs = []
-        results = self._index.query([query_obj], top_k=k, include_metadata=True)
+        results = self._index.query(
+            [query_obj], top_k=k, include_metadata=True, namespace=namespace
+        )
         for res in results["matches"]:
             metadata = res["metadata"]
             text = metadata.pop(self._text_key)
@@ -132,7 +180,7 @@ class Pinecone(VectorStore):
             i_end = min(i + batch_size, len(texts))
             # get batch of texts and ids
             lines_batch = texts[i : i + batch_size]
-            ids_batch = [str(n) for n in range(i, i_end)]
+            ids_batch = [str(uuid.uuid4()) for n in range(i, i_end)]
             # create embeddings
             embeds = embedding.embed_documents(lines_batch)
             # prep metadata and upsert batch
@@ -150,3 +198,18 @@ class Pinecone(VectorStore):
             # upsert to Pinecone
             index.upsert(vectors=list(to_upsert), namespace=namespace)
         return cls(index, embedding.embed_query, text_key)
+
+    @classmethod
+    def from_existing_index(
+        cls, index_name: str, embedding: Embeddings, text_key: str = "text"
+    ) -> Pinecone:
+        """Load pinecone vectorstore from index name."""
+        try:
+            import pinecone
+        except ImportError:
+            raise ValueError(
+                "Could not import pinecone python package. "
+                "Please install it with `pip install pinecone-client`."
+            )
+
+        return cls(pinecone.Index(index_name), embedding.embed_query, text_key)