Help with Query Embeddings Serialization (using stored Query embeddings in RAG pipeline)

[gody10]

Help with Query Embeddings Serialization (using stored Query embeddings in RAG pipeline)

Task description:

In my RAG pipeline I want to create embeddings for my query dataset beforehand and use them during the RAG process instead of generating them every time on the go, which causes additional costs and time delays.

Issue:

I believe that serializing the embeddings should be straightforward, but I don't see it somewhere clear in the documentation. Have I understood something wrong? Most solutions point to creating a custom VectorStoreIndex, but I would be delighted if someone could point out the best way to do it.

Code:

Some of the already established code that I use is the following (hope it will help you understand the context):

# This is the code that I use currently for my RAG pipeline, which does not do any serialization of the query embeddings

# Create Textnode objects with ids, text and embeddings
for i, embedding in enumerate(embeddings):
    node = TextNode(
        id_=str(identifiers[i]),  # replace with appropriate id
        text=texts_to_embed[i],  
        embedding= embedding,
        metadata={},
    )
    nodes.append(node)
 
# Creating the index
index = VectorStoreIndex(
    nodes=nodes,
    service_context=ServiceContext.from_defaults(
        embed_model=embed_model
    )
)
 
# Initialize for retriever class
retriever = index.as_retriever(similarity_top_k=self.k)
 
retrieved_nodes = retriever.retrieve(row['query']) # I want here to not calculate the embedding with the existing embed_model
# but rather load it from a pickle or a json file
 
## Version
- llama-index==0.9.40
 

[dosubot[bot]]

To efficiently use stored query embeddings in your RAG pipeline, follow these steps:

1. Generate and Serialize Embeddings: First, generate embeddings for your query dataset and serialize them. This can be done by processing your queries through an embedding model and storing the resulting embeddings in a format like pickle or JSON, which can be easily accessed later.

2. Persisting Embeddings: Store these serialized embeddings in a location that allows for quick retrieval. This could be a vector database or a simple file system storage, depending on your specific needs and the scale of your application.

3. Loading Persisted Embeddings for Querying: When initializing your RAG pipeline for querying, configure it to load the pre-generated embeddings. This setup should bypass the need for on-the-fly embedding generation, leveraging the serialized embeddings for retrieval operations.

4. Querying with Pre-Generated Embeddings: With the pre-generated embeddings loaded, your RAG pipeline can now perform retrieval operations more efficiently, as the computationally intensive embedding generation step has been eliminated.

For practical implementation, consider looking into the RagCLI class and the save_embedding and load_embed_model functions within the LlamaIndex documentation. These resources provide examples and tools for embedding serialization and retrieval in a RAG pipeline context.

This approach will enhance the performance and scalability of your RAG pipeline by reducing the computational overhead associated with on-the-fly embedding generation.

Details

Sources

• llama-index-legacy/llama_index/legacy/command_line/rag.py
• docs/docs/optimizing/production_rag.md
• llama-index-cli/llama_index/cli/rag/base.py
• llama-index-core/llama_index/core/embeddings/utils.py
• llama-index-legacy/llama_index/legacy/embeddings/utils.py
• llama-index-core/llama_index/core/embeddings/loading.py
• llama-index-legacy/llama_index/legacy/embeddings/loading.py
• llama-index-integrations/embeddings/llama-index-embeddings-gradient/llama_index/embeddings/gradient/base.py
• docs/docs/module_guides/indexing/vector_store_index.md
• llama-index-core/llama_index/core/indices/vector_store/base.py
• benchmarks/embeddings/bench_embeddings.py

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[gody10]

Answer

I found the answer to my question. The queries can be serialized through the QueryBundle class. You just have to add the following Code before retrieving the nodes. The embeddings_query_df is a DataFrame that contains the embeddings for all the queries.

#Creating Query Bundle Object
    query_bundle = QueryBundle(
        query_str=row['query'],
        embedding=embeddings_query_df[i],
    )

   retrieved_nodes = vector_retriever.retrieve(query_bundle)