In rag how to make sure it doesnt answer out of context

[JINO-ROHIT]

Question Validation

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Question

I have a RAG for specific debugging problems, but if asked question about random things, it still answers, how to restrict this? Im using zephyr 7b

[dosubot[bot]]

Hey there, @JINO-ROHIT! Great to see you back here, diving into more intriguing questions. How's everything going on your end?

To restrict your RAG from answering out-of-context or unrelated questions when using Zephyr 7b for specific debugging problems, you can implement several strategies:

1. Decoupling Chunks for Retrieval vs. Synthesis: By separating the chunks used for retrieval from those used for synthesis, you can ensure that the retrieval process is more focused and relevant to the debugging context. Implementing document summaries and sentence embeddings that link to more detailed context can help in retrieving only the most relevant information. This is crucial for debugging, where precision in the retrieved context can significantly impact the effectiveness of the RAG's responses.

2. Structured Retrieval for Larger Document Sets: Utilizing metadata filters and document hierarchies can improve the precision of the retrieval process. For debugging, you can tag documents or sections with metadata related to specific debugging contexts or error types. This allows the RAG to filter and retrieve information that is only relevant to the debugging task at hand.

3. Dynamically Retrieve Chunks Depending on Your Task: Leveraging task-specific retrieval techniques, such as using the router and data agent modules from LlamaIndex, can help tailor the retrieval process to the specific needs of debugging. This approach ensures that the RAG dynamically adjusts its retrieval strategy based on the debugging context, leading to more accurate and contextually relevant answers.

4. Optimize Context Embeddings: Fine-tuning the embedding model to better capture the nuances of your debugging data can improve the relevance of the retrieved information. By optimizing embeddings for your specific debugging context, the RAG can more effectively distinguish between relevant and irrelevant information, reducing the likelihood of answering out-of-context or unrelated questions.

You can find more details on these strategies in the production_rag.md document in the LlamaIndex repository.

As for Zephyr 7b, I couldn't find specific features for restricting out-of-context responses in a RAG within the LlamaIndex repository. It's possible that this information might be available elsewhere or I might have missed it.

Regarding LlamaIndex, it provides several features or modules designed to enhance the precision of retrieval and synthesis in a Retrieval-Augmented Generation (RAG) system:

1. VectorStoreIndex: This module is used for creating and managing a vector store index. It is crucial for efficient retrieval as it allows for the quick lookup of vectors that are closest to the query vector. This is achieved by converting text data into vectors using embeddings and then indexing these vectors. The VectorStoreIndex.from_vector_store method is used to initialize a retriever with the vector store, facilitating efficient retrieval based on vector similarity.

2. QueryPipeline: This feature orchestrates the query processing workflow. It allows for the modular composition of different processing steps, such as query transformation, retrieval, and response synthesis. The QueryPipeline class is used to define a pipeline that includes a retriever and a response synthesizer, among other components. This modular approach enables flexible customization of the query handling process.

3. CompactAndRefine: This module acts as a response synthesizer. It is designed to refine and compact the retrieved documents or text snippets into a coherent and concise response. The CompactAndRefine class is utilized to synthesize responses from the nodes retrieved by the retriever, improving the quality and relevance of the generated answers.

4. CondenseQuestionChatEngine: This chat engine is tailored for handling conversational queries. It leverages the QueryPipeline for processing questions and generating responses. The CondenseQuestionChatEngine.from_defaults method initializes the chat engine with default settings, including the query engine and language model. This engine is particularly useful for interactive applications where precision and context retention in conversations are critical.

5. IngestionPipeline: While not directly involved in the retrieval and synthesis process, the IngestionPipeline is essential for preparing and ingesting data into the system. It processes documents or text data, applies transformations (such as embeddings), and stores the resulting vectors in the vector store. This preparation step is crucial for ensuring that the retrieval system has access to high-quality, vectorized data.

You can find more details on these features in the base.py file in the LlamaIndex repository.

I hope this helps! If you have any more questions or need further clarification, feel free to ask.

Sources

• docs/optimizing/production_rag.md
• llama-index-cli/llama_index/cli/rag/base.py

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[logan-markewich]

It's basically just prompt engineering. Or introducing a second llm call to tryvand get the llm to filter itself

[mirix]

You can use the an evaluator such as the FaithfulnessEvaluator (unfortunately, this is a binary evaluator).

For my current project, however, I am just measuring how relevant is the the most relevant node (as assessed by the re-ranker):

    if response.source_nodes[0].score < 0.25:
        response_text = 'I cannot find an answer to that question in en.swissquote.lu. Please, rephrase the question.'
        source = 'No sources'
    else:
        response_text = str(response)

[JINO-ROHIT]

ive tried prompt engineering but its still kinda bad, i tried to add a threshold for the filtered source nodes but that didnt work either, a second llm call is expensive for me

[JINO-ROHIT]

guess ill try faithfulness eval

[JINO-ROHIT]

i tried add a post context prompt and it worked fairly well, thanks

[Aekansh-Ak]

Hi @JINO-ROHIT,

Can you help me with how you added post context prompt?

[JINO-ROHIT]

@Aekansh-Ak hey sure, what i did was - post_prompt = """ if the information isnt available in the given context to formulate the answer, just reply with NO ANSWER"""" response = query_engine.query(SYS_PROMPT + user_query + post_prompt)

this is what worked for me