# create dense vectors
        dense_embeds = embeddings.embed_documents(context_batch)
        # create sparse vectors
        sparse_embeds = sparse_encoder.encode_documents(context_batch)
        for s in sparse_embeds:
            s["values"] = [float(s1) for s1 in s["values"]]
        vectors = []
        # loop through the data and create dictionaries for upserts
        for doc_id, sparse, dense, metadata in zip(
            batch_ids, sparse_embeds, dense_embeds, meta
        ):
            vectors.append(
                {
                    "id": doc_id,
                    "sparse_values": sparse,
                    "values": dense,
                    "metadata": metadata,
                }
            )
        # upload the documents to the new hybrid index
        index.upsert(vectors)
[docs]class PineconeHybridSearchRetriever(BaseRetriever, BaseModel):
    embeddings: Embeddings
    sparse_encoder: Any
    index: Any
    top_k: int = 4
    alpha: float = 0.5
    class Config:
        """Configuration for this pydantic object."""
        extra = Extra.forbid
        arbitrary_types_allowed = True
[docs]    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:
        """Validate that api key and python package exists in environment."""
        try:
rtdocs_stable/api.python.langchain.com/en/stable/_modules/langchain/retrievers/pinecone_hybrid_search.html