Hybrid search indexing how to
Labels: name: auto:documentation

### Issue with current documentation:


https://python.langchain.com/docs/modules/data_connection/retrievers/integrations/pinecone_hybrid_search



### Idea or request for content:

Its not clear on this page how to index data in pinecone for hybrid search, I am already indexing like this and it works, but for sparse values and the bm25encoder this is very confusing.

```
    df = loadSqlData()
    df.to_csv('profiles.csv', index=False)

    # Iterate through DataFrame rows
    # Time Complexity: O(n), where n is the number of rows in the DataFrame
    for _, record in df.iterrows():
        start_time = time.time()
        
        # Get metadata for this record
        # Time Complexity: O(1)
        metadata = {
            'IdentityId': str(record['IdentityId'])
        }
        st.write(f'Time taken for metadata extraction: {time.time() - start_time} seconds')

        start_time = time.time()
        # Split record text into chunks
        # Time Complexity: O(m), where m is the size of the text
        record_texts = text_splitter.split_text(record['content'])
        st.write(f'Time taken for text splitting: {time.time() - start_time} seconds')

        start_time = time.time()
        # Create metadata for each chunk
        # Time Complexity: O(k), where k is the number of chunks in the text
        record_metadatas = [{
            "chunk": j, "text": text, **metadata
        } for j, text in enumerate(record_texts)]
        st.write(f'Time taken for metadata dictionary creation: {time.time() - start_time} seconds')

        start_time = time.time()
        # Append chunks and metadata to current batches
        # Time Complexity: O(1)
        texts.extend(record_texts)
        metadatas.extend(record_metadatas)
        st.write(f'Time taken for data appending: {time.time() - start_time} seconds')

        # If batch_limit is reached, upsert vectors
        # Time Complexity: Depends on the upsert implementation
        if len(texts) >= batch_limit:
            start_time = time.time()
            ids = [str(uuid4()) for _ in range(len(texts))]
            # Simulating embedding and upserting here
            Pinecone.from_texts(
                    texts,
                    embed,
                    index_name="xx",
                    metadatas=metadatas,
                    namespace="vwProfiles2")
            texts = []
            metadatas = []
            st.write(f'Time taken for vector upsertion (simulated): {time.time() - start_time} seconds')

    # Upsert any remaining vectors after the loop
    # Time Complexity: Depends on the upsert implementation
    if len(texts) > 0:
        start_time = time.time()
        ids = [str(uuid4()) for _ in range(len(texts))]
        # Simulating embedding and upserting here
        Pinecone.from_texts(
                texts,
                embed,
                index_name="x",
                metadatas=metadatas,
                namespace="vwProfiles2")
        st.write(f'Time taken for remaining vector upsertion (simulated): {time.time() - start_time} seconds')
        st.write('Rows indexed: ', len(df))
```