infrastructure-lambda-serverless-deploying-langchain-pinecone-vector-store-pinecone-e0acd10b

THEN Use langchain version 0.0.275 or earlier, or modify the `add_texts` method in LangChain's Pinecone wrapper to pass `async_req=False` to the Pinecone upsert call, forcing synchronous requests. This avoids the unsupported multiprocessing in AWS Lambda environment.

def add_texts(self, texts, metadatas=None, ids=None, namespace=None, batch_size=32, embedding_chunk_size=1000, **kwargs):
    # ... existing code ...
    for i in range(0, len(texts), embedding_chunk_size):
        chunk_texts = texts[i:i+embedding_chunk_size]
        chunk_ids = ids[i:i+embedding_chunk_size]
        chunk_metadatas = metadatas[i:i+embedding_chunk_size]
        embeddings = self.embedding.embed_documents(chunk_texts)
        # Use synchronous upsert by setting async_req=False
        self._index.upsert(vectors=zip(chunk_ids, embeddings, chunk_metadatas), namespace=namespace, async_req=False)