Improve pinecone hybrid search retriever adding metadata support (langchain-ai#5098)

# Improve pinecone hybrid search retriever adding metadata support

I simply remove the hardwiring of metadata to the existing
implementation allowing one to pass `metadatas` attribute to the
constructors and in `get_relevant_documents`. I also add one missing pip
install to the accompanying notebook (I am not adding dependencies, they
were pre-existing).

First contribution, just hoping to help, feel free to critique :) 
my twitter username is `@andreliebschner`

While looking at hybrid search I noticed langchain-ai#3043 and langchain-ai#1743. I think the
former can be closed as following the example right now (even prior to
my improvements) works just fine, the latter I think can be also closed
safely, maybe pointing out the relevant classes and example. Should I
reply those issues mentioning someone?

@dev2049, @hwchase17

---------

Co-authored-by: Andreas Liebschner <a.liebschner@shopfully.com>

Author: lbsnrs

docs/modules/indexes/retrievers/examples/pinecone_hybrid_search.ipynb

@@ -24,7 +24,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "#!pip install pinecone-client"
+    "#!pip install pinecone-client pinecone-text"
    ]
   },
   {

langchain/retrievers/pinecone_hybrid_search.py

@@ -18,6 +18,7 @@ def create_index(
     embeddings: Embeddings,
     sparse_encoder: Any,
     ids: Optional[List[str]] = None,
+    metadatas: Optional[List[dict]] = None,
 ) -> None:
     batch_size = 32
     _iterator = range(0, len(contexts), batch_size)
@@ -38,8 +39,15 @@ def create_index(
         # extract batch
         context_batch = contexts[i:i_end]
         batch_ids = ids[i:i_end]
+        metadata_batch = (
+            metadatas[i:i_end] if metadatas else [{} for _ in context_batch]
+        )
         # add context passages as metadata
-        meta = [{"context": context} for context in context_batch]
+        meta = [
+            {"context": context, **metadata}
+            for context, metadata in zip(context_batch, metadata_batch)
+        ]
+
         # create dense vectors
         dense_embeds = embeddings.embed_documents(context_batch)
         # create sparse vectors
@@ -78,8 +86,20 @@ class Config:
         extra = Extra.forbid
         arbitrary_types_allowed = True
 
-    def add_texts(self, texts: List[str], ids: Optional[List[str]] = None) -> None:
-        create_index(texts, self.index, self.embeddings, self.sparse_encoder, ids=ids)
+    def add_texts(
+        self,
+        texts: List[str],
+        ids: Optional[List[str]] = None,
+        metadatas: Optional[List[dict]] = None,
+    ) -> None:
+        create_index(
+            texts,
+            self.index,
+            self.embeddings,
+            self.sparse_encoder,
+            ids=ids,
+            metadatas=metadatas,
+        )
 
     @root_validator()
     def validate_environment(cls, values: Dict) -> Dict:
@@ -114,7 +134,10 @@ def get_relevant_documents(self, query: str) -> List[Document]:
         )
         final_result = []
         for res in result["matches"]:
-            final_result.append(Document(page_content=res["metadata"]["context"]))
+            context = res["metadata"].pop("context")
+            final_result.append(
+                Document(page_content=context, metadata=res["metadata"])
+            )
         # return search results as json
         return final_result