Is that ok to use Recursive Retriever with Pandas Parser in dealing Structured Data?

[mraguth]

While experimenting with my query plan, I explored both the retriever and pandas query engine, and the results were remarkably outstanding compared to solely utilizing the retriever. Now, the concern arises: what are the potential drawbacks of using these two together? Your insights are appreciated. My query plan is below.... and some links that I referenced as well..

My query plan:

query_engine = RAGStringQueryEngine(
    retriever=recursive_retriever,
    response_synthesizer=response_synthesizer,
    pandas_prompt= pandas_prompt,
    pandas_output_parser= pandas_output_parser,
    llm=llm,
    qa_prompt=qa_prompt,
    synthesize_response=True,
    response_synthesis_prompt=response_synthesis_prompt,
)

References based on:
https://docs.llamaindex.ai/en/stable/examples/query_engine/custom_query_engine/?h=response_synthesizer
https://github.com/run-llama/llama_index/blob/dd3a25c8a2423d1f8f5077237e7b68e7065e6907/docs/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb

[dosubot[bot]]

Integrating a Recursive Retriever with the Pandas Parser in your custom query engine setup, as outlined in your query plan, can indeed yield impressive results for handling structured data. However, there are several considerations to keep in mind:

1. Performance Overheads: The combination of recursive retrieval and dynamic decision-making can introduce significant performance overheads, especially when processing complex queries or large datasets.

2. Complexity in Setup and Maintenance: The multi-layered approach, involving document agents for tasks like semantic search or summarization, adds complexity, making the system harder to maintain and debug.

3. Dependency on External Libraries and Services: Your setup relies on external libraries and services, such as those provided by OpenAI. This introduces risks related to library updates, API changes, and the long-term availability of these services.

4. Scalability Challenges: The architecture's reliance on in-memory data structures and complex recursive queries may limit scalability, particularly in handling an increasing volume of documents or queries.

5. Data Privacy and Security: Using external services raises data privacy concerns, and integrating complex systems increases the attack surface, necessitating careful data handling and input sanitization.

6. Decoupling Challenges and Dynamic Task Performance: The system's design to decouple retrieval from synthesis and the dynamic nature of document agents' tasks can affect the accuracy and relevance of query responses, requiring careful tuning.

For detailed examples and further exploration, the LlamaIndex repository provides comprehensive guides and notebooks, such as the Recursive Retriever example, which can offer more insights into implementing such systems effectively.

Details

Sources

• Is that possible to get my response via Query pipe line instead of 'Query_engine_query'?
• docs/docs/examples/query_engine/pdf_tables/recursive_retriever.ipynb
• docs/docs/examples/query_engine/recursive_retriever_agents.ipynb
• llama-index-packs/llama-index-packs-self-rag/llama_index/packs/self_rag/base.py
• llama-index-experimental/tests/test_pandas.py
• llama-index-core/llama_index/core/query_engine/pandas/output_parser.py
• llama-index-packs/llama-index-packs-recursive-retriever/llama_index/packs/recursive_retriever/embedded_tables_unstructured/base.py
• llama-index-core/llama_index/core/retrievers/recursive_retriever.py
• llama-index-packs/llama-index-packs-recursive-retriever/README.md
• docs/docs/examples/retrievers/auto_vs_recursive_retriever.ipynb
• llama-index-legacy/llama_index/legacy/retrievers/recursive_retriever.py
• llama-index-packs/llama-index-packs-recursive-retriever/requirements.txt
• llama-index-packs/llama-index-packs-recursive-retriever/pyproject.toml
• docs/docs/examples/retrievers/recursive_retriever_nodes.ipynb

About Dosu
This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

To continue the conversation, mention @dosu.