Implement max_marginal_relevance_search in VectorStore of Pinecone (#6056)

This adds implementation of MMR search in pinecone; and I have two
semi-related observations about this vector store class:
- Maybe we should also have a
`similarity_search_by_vector_returning_embeddings` like in supabase, but
it's not in the base `VectorStore` class so I didn't implement
- Talking about the base class, there's
`similarity_search_with_relevance_scores`, but in pinecone it is called
`similarity_search_with_score`; maybe we should consider renaming it to
align with other `VectorStore` base and sub classes (or add that as an
alias for backward compatibility)

#### Who can review?

Tag maintainers/contributors who might be interested:
 - VectorStores / Retrievers / Memory - @dev2049

docs/modules/indexes/vectorstores/examples/pinecone.ipynb

@@ -24,7 +24,7 @@
    },
    "outputs": [],
    "source": [
-    "!pip install pinecone-client"
+    "!pip install pinecone-client openai tiktoken"
    ]
   },
   {
@@ -70,7 +70,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "aac9563e",
    "metadata": {
     "tags": []
@@ -85,7 +85,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "id": "a3c3999a",
    "metadata": {},
    "outputs": [],
@@ -135,13 +135,51 @@
     "print(docs[0].page_content)"
    ]
   },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "d46d1452",
+   "metadata": {},
+   "source": [
+    "### Maximal Marginal Relevance Searches\n",
+    "\n",
+    "In addition to using similarity search in the retriever object, you can also use `mmr` as retriever.\n"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "a359ed74",
    "metadata": {},
    "outputs": [],
-   "source": []
+   "source": [
+    "retriever = docsearch.as_retriever(search_type=\"mmr\")\n",
+    "matched_docs = retriever.get_relevant_documents(query)\n",
+    "for i, d in enumerate(matched_docs):\n",
+    "    print(f\"\\n## Document {i}\\n\")\n",
+    "    print(d.page_content)"
+   ]
+  },
+  {
+   "attachments": {},
+   "cell_type": "markdown",
+   "id": "7c477287",
+   "metadata": {},
+   "source": [
+    "Or use `max_marginal_relevance_search` directly:"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "9ca82740",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "found_docs = docsearch.max_marginal_relevance_search(query, k=2, fetch_k=10)\n",
+    "for i, doc in enumerate(found_docs):\n",
+    "    print(f\"{i + 1}.\", doc.page_content, \"\\n\")"
+   ]
   }
  ],
  "metadata": {

langchain/vectorstores/pinecone.py

@@ -5,9 +5,12 @@ import logging
 import uuid
 from typing import Any, Callable, Iterable, List, Optional, Tuple
 
+import numpy as np
+
 from langchain.docstore.document import Document
 from langchain.embeddings.base import Embeddings
 from langchain.vectorstores.base import VectorStore
+from langchain.vectorstores.utils import maximal_marginal_relevance
 
 logger = logging.getLogger(__name__)
 
@@ -157,6 +160,85 @@ class Pinecone(VectorStore):
         )
         return [doc for doc, _ in docs_and_scores]
 
+    def max_marginal_relevance_search_by_vector(
+        self,
+        embedding: List[float],
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[dict] = None,
+        namespace: Optional[str] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            embedding: Embedding to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        if namespace is None:
+            namespace = self._namespace
+        results = self._index.query(
+            [embedding],
+            top_k=fetch_k,
+            include_values=True,
+            include_metadata=True,
+            namespace=namespace,
+            filter=filter,
+        )
+        mmr_selected = maximal_marginal_relevance(
+            np.array([embedding], dtype=np.float32),
+            [item["values"] for item in results["matches"]],
+            k=k,
+            lambda_mult=lambda_mult,
+        )
+        selected = [results["matches"][i]["metadata"] for i in mmr_selected]
+        return [
+            Document(page_content=metadata.pop((self._text_key)), metadata=metadata)
+            for metadata in selected
+        ]
+
+    def max_marginal_relevance_search(
+        self,
+        query: str,
+        k: int = 4,
+        fetch_k: int = 20,
+        lambda_mult: float = 0.5,
+        filter: Optional[dict] = None,
+        namespace: Optional[str] = None,
+        **kwargs: Any,
+    ) -> List[Document]:
+        """Return docs selected using the maximal marginal relevance.
+
+        Maximal marginal relevance optimizes for similarity to query AND diversity
+        among selected documents.
+
+        Args:
+            query: Text to look up documents similar to.
+            k: Number of Documents to return. Defaults to 4.
+            fetch_k: Number of Documents to fetch to pass to MMR algorithm.
+            lambda_mult: Number between 0 and 1 that determines the degree
+                        of diversity among the results with 0 corresponding
+                        to maximum diversity and 1 to minimum diversity.
+                        Defaults to 0.5.
+        Returns:
+            List of Documents selected by maximal marginal relevance.
+        """
+        embedding = self._embedding_function(query)
+        return self.max_marginal_relevance_search_by_vector(
+            embedding, k, fetch_k, lambda_mult, filter, namespace
+        )
+
     @classmethod
     def from_texts(
         cls,