[Question]: Getting empty response from a basic RAG pipeline

[GildeshAbhay]

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• [X] I have searched both the documentation and discord for an answer.

Question

the generates summary is "Empty Response". How to poceed further?

# -*- coding: utf-8 -*-
"""
Created on Tue Apr  2 23:20:29 2024

@author: abhay.saini
"""

db = "staging"
db = "historical_sample"
collection =  "abhay_test"
collection = "sample_output"
documents = mongo_init(mongo_uri, db, collection, find_query)
cnt=1
for document in documents:
    print('Plain')
    if cnt <= 2:
        break;
    cnt = cnt+1

pathname = os.path.join(os.getcwd(),'Emeddings')
pathname = os.path.join(pathname, document['DOI_CROSSREF'])
content = document["content"]
doc = Document(text=content)
def mongo_init(mongo_uri, db, collection, find_query):
    client = MongoClient(mongo_uri)
    db = client[db]
    collection = db[collection]
    num_documents = collection.count_documents(find_query)
    print("Number of documents:", num_documents)
    documents = collection.find(find_query)
    return documents
chunk_size = 1024
node_parser = SentenceSplitter(chunk_size=chunk_size)
nodes = node_parser.get_nodes_from_documents([doc])
service_context = ServiceContext.from_defaults(llm=llm, embed_model=embed_model,node_parser=node_parser)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
index = VectorStoreIndex(nodes, storage_context=storage_context, service_context=service_context)
pathname = os.path.join(pathname, document['DOI_CROSSREF'])
index.storage_context.persist(persist_dir=pathname)
postproc = SimilarityPostprocessor(similarity_cutoff = pp_metadata)

reranker = LLMRerank(top_n = rr_metadata)
retriever_type, retrieval_metadata = retrieval_args

retriever = VectorIndexRetriever(index=index,similarity_top_k=retrieval_metadata)
response_mode = 'compact'
response_synthesizer = get_response_synthesizer(response_mode=response_mode)
node_postprocessors = [postproc, reranker]
query_engine = RetrieverQueryEngine(retriever=retriever,response_synthesizer=response_synthesizer,node_postprocessors = node_postprocessors)

summary_whole = query_engine.query(rag_prompt_original) #len(summary.source_nodes)
display_response(summary_whole)
summary_text = str(summary_whole)  # Convert the summary object to a string

[dosubot[bot]]

Hey there, @GildeshAbhay! 😄 Long time no see in the issue tracker. Hope you haven't been getting too many "Empty Responses" in life outside of coding. Let me dive into this RAG conundrum of yours and I'll get back to you with some insights shortly!

[logan-markewich]

Try removing the post processors -- your similarity post processor might be remove all nodes

[dosubot[bot]]

It appears there was a misunderstanding in the instructions provided. I'm unable to execute actions such as running code or making changes to GitHub repositories directly. However, based on the conversation and the advice given by the maintainer, Logan Markewich, I can offer some guidance on how to proceed with the issue of receiving an "Empty Response" from a basic RAG pipeline.

The suggestion from Logan to try removing the post processors, specifically pointing out that the similarity post processor might be removing all nodes, is a solid starting point. The post processors in a RAG (Retrieval-Augmented Generation) pipeline can sometimes be too restrictive based on the criteria they use to filter or rank the nodes (documents or text segments). If the similarity threshold is set too high, it might result in no nodes being considered relevant, leading to an empty response.

Here's a step you can take to debug and potentially solve the issue:

1. Remove or Adjust the Post Processors: Start by temporarily removing the post processors from your pipeline, particularly the SimilarityPostprocessor. Run your query again to see if you receive a non-empty response. If you do, this indicates that the post processors were indeed filtering out all potential responses.

2. Adjust the Similarity Threshold: If removing the post processors solves the issue, try adding them back one by one, starting with the SimilarityPostprocessor. Adjust the similarity_cutoff parameter to a lower value to make the filtering less strict. This might allow some nodes to pass through, giving you a non-empty response while still maintaining some level of relevance filtering.

3. Review the Reranker Settings: If you're using a LLMRerank or any other reranking mechanism, ensure its settings are not too restrictive. The top_n parameter, for example, determines how many of the top retrieved documents to consider. If set too low, it might not include the most relevant documents in the final response.

4. Debugging and Logging: Add logging statements before and after significant steps in your pipeline (e.g., after retrieval, after post-processing) to see where the pipeline might be failing or returning unexpected results. This can help you pinpoint exactly where adjustments need to be made.

Remember, tuning a RAG pipeline can be a bit of trial and error to find the right balance between relevance and response richness. Start broad with less restrictive settings and gradually tighten them as you observe the results.

If you continue to face issues or have specific questions about the adjustments, feel free to reach out. The community and maintainers like Dosu are here to help.

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