12 RAG Pain Points and Proposed Solutions

Things that might lead to failure of RAG pipeline. Mostly taken from the blog

Pain point: * and solutions

1: Missing Content:

• Clean your data
• Better prompting

2: Missed the Top Ranked Documents

• Hyperparameter tuning for chunk_size and similarity_top_k as in Hyperparameter Optimization for RAG.
• Reranking notebook usses Improving Retrieval Performance by Fine-tuning Cohere Reranker with LlamaIndex and CohereRank to rerank the results

      import os
      from llama_index.postprocessor.cohere_rerank import CohereRerank
  
      api_key = os.environ["COHERE_API_KEY"]
      cohere_rerank = CohereRerank(api_key=api_key, top_n=2) # return top 2 nodes from reranker
  
      query_engine = index.as_query_engine(
          similarity_top_k=10, # we can set a high top_k here to ensure maximum relevant retrieval
          node_postprocessors=[cohere_rerank], # pass the reranker to node_postprocessors
      )
  
      response = query_engine.query(
          "What did Sam Altman do in this essay?",
      )
  

3: Not in Context — Consolidation Strategy Limitations

• Tweak retrieval strategies
• Finetune embeddings

4: Not Extracted

• Clean your Data
• Prompt Compression
• Long Context Reorder (put crucial content at beginning and end)

5: Wrong Format

• Output Parsing
• Pydantic

6: Incorrect Specificity

• small-to-big retrieval
• sentence window retrieval
• recursive retrieval
• Advanced Retriever

7: Incomplete and Impartial Responses

• Query Transformations
• Pipeline Parallelization

8: Data Ingestion Scalability

• Chain of table and Llama solution
• Mix-Self-Consistency Pack based on Rethinking Tabular Data Understanding with Large Language Models Llama solution

9: Structured Data QA

• Use Llama index ChainOfTablePack based on Chain of Table
• Use Llama index MixSelfConsistencyQueryEngine based on Rethinking Tabular Data Understanding with Large Language Models

10: Data Extraction from Complex PDFs

• Use pdf2htmlEX
• Use EmbeddedTablesUnstructuredRetrieverPack in LlamaIndex

11: Fallback Model(s): Use a model router like - Neutrino

    from llama_index.llms import Neutrino
    from llama_index.llms import ChatMessage

    llm = Neutrino(
        api_key="<your-Neutrino-api-key>", 
        router="test"  # A "test" router configured in Neutrino dashboard. You treat a router as a LLM. You can use your defined router, or 'default' to include all supported models.
    )

    response = llm.complete("What is large language model?")
    print(f"Optimal model: {response.raw['model']}")

• Openrouter

    from llama_index.llms import OpenRouter
    from llama_index.llms import ChatMessage

    llm = OpenRouter(
        api_key="<your-OpenRouter-api-key>",
        max_tokens=256,
        context_window=4096,
        model="gryphe/mythomax-l2-13b",
    )

    message = ChatMessage(role="user", content="Tell me a joke")
    resp = llm.chat([message])
    print(resp)

12: LLM Security

• Use things like Llama Guard