Customizing AzureAISearchRetriever for butter retrieval results

[AsmaaMHadir]

I built a RAG graph with LangGraph and I have a retrieval node which looks like this:

    def get_azure_index_retriever(self, filters=None):
        """Initialize an Azure AI Search Retriever."""
        return AzureAISearchRetriever(
            content_key="content",
            top_k=5,
            index_name=os.getenv("AZURE_AI_SEARCH_RETRIEVAL_INDEX_NAME"),
            service_name=os.getenv("AZURE_AI_SEARCH_SERVICE_NAME"),
            api_key=os.getenv("VECTOR_STORE_PASSWORD"),
            filter=filters
        )

    async def retrieve_documents(self, state: State):
        """Retrieve documents based on user query."""
        user_msg = state["messages"][-1].content
        filter_query = LangGraphUtils.construct_filter_query(state.get("doc_filters", []))
        retriever = self.get_azure_index_retriever(filters=filter_query)
        try:
            docs = await retriever.ainvoke(user_msg)
            state["context"].extend(docs)
            return {"context": state["context"]}
        except Exception:
            logger.error("Error retrieving documents:\n" + traceback.format_exc())
            return None

I noticed that the retriever doesn't always retrieve the relevant chunks and in general needs more enhancements. So I have a couple of questions:

• What are the retriever kwargs that I can customize to control for example the similarity threshold?
• What are some techniques I can use to make the retriever be more consistent when retrieving documents from my index?
• How can I incorporate an Azure Reranker to improve my similarity search results and what are some other retrieval enhancement techniques that I can add to my retrieval process to improve the relevancy of the chunks?

[dosubot[bot]]

I found similar discussions that might be helpful:

1. How to pass a filtering query to an AzureVectorIndexRetriever - This discussion is open and might provide insights into handling filtering queries with Azure retrievers ^[1].

2. Retrieval from Azure Index (RAG Implementation) - This is another open discussion that could offer relevant information on retrieval implementations with Azure ^[2].

To address your questions:

1. Customizing Similarity Threshold:
   You can control the similarity threshold by using the similarity_search_with_relevance_scores method and specifying a score_threshold. This filters out documents that do not meet the minimum relevance score. Here's an example:

   docs = [
       doc
       for doc, _ in self.vectorstore.similarity_search_with_relevance_scores(
           query, k=self.k, score_threshold=desired_threshold, **params
       )
   ]

   Replace desired_threshold with your preferred threshold value ^[3].

2. Improving Consistency in Retrieval:
   Adjust the k parameter to control the number of documents returned and use the fetch_k parameter to manage the number of documents considered before applying the Maximal Marginal Relevance (MMR) algorithm. This can help achieve more consistent results:

   docs = self.vectorstore.hybrid_search(
       query, k=self.k, fetch_k=desired_fetch_k, **params
   )

   Replace desired_fetch_k with the number of documents you want to fetch initially ^[3].

3. Incorporating an Azure Reranker:
   Use the asemantic_hybrid_search_with_score_and_rerank method to incorporate an Azure Reranker, which allows for reranking the results:

   docs_and_scores = await self.vectorstore.asemantic_hybrid_search_with_score_and_rerank(
       query, k=self.k, **params
   )

   This method returns documents with their scores, which you can further process as needed ^[3].

These techniques should help enhance the retrieval process and improve the relevancy of the chunks retrieved.

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[dosubot[bot]]

To define a Semantic Configuration for your existing Azure Search index, you need to configure it in the Azure portal or via the Azure SDK. While I don't have specific steps from the LangChain repository, here's a general approach you can follow:

1. Access Azure Portal:

   • Log in to the Azure portal and navigate to your Azure Search service.

2. Select Your Index:

   • Go to the "Indexes" section and select the index you want to configure.

3. Add Semantic Configuration:

   • Look for a section related to "Semantic Configuration" or "Semantic Search" within the index settings.
   • Create a new semantic configuration by specifying the fields you want to prioritize for semantic search. This typically involves selecting fields that contain the main content and any metadata that might be relevant for semantic understanding.

4. Define Prioritized Fields:

   • Specify which fields should be used for content, title, and other semantic roles. This helps the search service understand the context and importance of different parts of your documents.

5. Save and Apply:

   • Save the configuration and apply it to your index. Ensure that the configuration is set as the default if required.

6. Update Your Application:

   • In your application code, ensure that you reference this semantic configuration when initializing the AzureSearch vector store, as shown in the previous example.

By following these steps, you can enhance your search capabilities with semantic understanding, improving the relevance and context-awareness of your search results.

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[AsmaaMHadir]

@dosu

Can you explain in detail the differences between these types of search: 'similarity', 'similarity_score_threshold', 'hybrid', 'hybrid_score_threshold', 'semantic_hybrid', 'semantic_hybrid_score_threshold'

And give examples showcasng how they impact the retrieval results? how does similarity search differ from semantic and semantic_hybrid? please explain how each search type differs from each other search type.

[dosubot[bot]]

Here are the differences between the various search types in LangChain, along with examples of how they impact retrieval results:

1. Similarity Search:

   • Functionality: Retrieves documents based on vector similarity to the query. It uses embeddings to find the most similar documents.
   • Example Impact: If you search for "climate change," it retrieves documents with similar vector representations, focusing on semantic similarity.
   • Difference: It doesn't apply any score filtering; all results are based purely on similarity.

2. Similarity Score Threshold:

   • Functionality: Similar to similarity search but includes a score threshold to filter results.
   • Example Impact: Only documents with a similarity score above a certain threshold (e.g., 0.7) are returned, ensuring higher relevance.
   • Difference: Adds a filtering layer to exclude less relevant documents based on a score threshold.

3. Hybrid Search:

   • Functionality: Combines vector similarity with other search techniques like full-text or BM25.
   • Example Impact: A search for "climate change" might return documents that are both semantically similar and contain the exact phrase.
   • Difference: Integrates multiple search strategies to enhance retrieval, balancing between semantic and keyword matches.

4. Hybrid Score Threshold:

   • Functionality: Similar to hybrid search but applies a score threshold to filter results.
   • Example Impact: Only documents that meet both semantic and keyword criteria and exceed a score threshold are returned.
   • Difference: Adds a filtering mechanism to hybrid search, ensuring only highly relevant documents are included.

5. Semantic Hybrid:

   • Functionality: Integrates semantic understanding with hybrid search techniques, enhancing context and meaning in retrieval.
   • Example Impact: A search for "climate change" considers semantic context, returning documents that understand the topic's nuances.
   • Difference: Focuses on semantic understanding, combining it with hybrid search for more context-aware results.

6. Semantic Hybrid Score Threshold:

   • Functionality: Combines semantic hybrid search with a score threshold for filtering.
   • Example Impact: Only documents that are semantically relevant and exceed a score threshold are returned, ensuring high-quality results.
   • Difference: Adds a score-based filtering layer to semantic hybrid search, refining results further.

Comparison:

• Similarity vs. Semantic: Similarity search focuses on vector similarity, while semantic search incorporates understanding of context and meaning.
• Hybrid vs. Semantic Hybrid: Hybrid combines multiple search strategies, while semantic hybrid adds semantic understanding to this mix.
• Score Threshold Variants: These add a filtering mechanism to ensure only documents meeting a certain relevance score are returned, enhancing result quality.

Each search type offers different strengths, allowing you to tailor retrieval strategies to specific needs, balancing between precision, recall, and semantic understanding.

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[AsmaaMHadir]

@dosu

Am I correctly pass the search kwargs here:

    retriever = AzureSearchVectorStoreRetriever(
        k=k,search_kwargs={"fetch_k":fetch_k,
                           "score_threshold":sim_threshold,
                           "filters":filter},
        search_type=search_type,
        vectorstore=vectorstore
    )

Can I pass fetch_k parameter in this case?

[dosubot[bot]]

Yes, you can correctly pass the fetch_k parameter in the search_kwargs when initializing the AzureSearchVectorStoreRetriever. The search_kwargs dictionary is designed to accept various search parameters, including fetch_k. This parameter is used to determine the number of documents to initially fetch for consideration by the Maximal Marginal Relevance (MMR) algorithm ^[1].

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