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SearchQnA Application

Search Question and Answering (SearchQnA) harnesses the synergy between search engines, like Google Search, and large language models (LLMs) to enhance QA quality. While LLMs excel at general knowledge, they face limitations in accessing real-time or specific details due to their reliance on prior training data. By integrating a search engine, SearchQnA bridges this gap.

Operating within the LangChain framework, the Google Search QnA chatbot mimics human behavior by iteratively searching, selecting, and synthesizing information. Here's how it works:

  • Diverse Search Queries: The system employs an LLM to generate multiple search queries from a single prompt, ensuring a wide range of query terms essential for comprehensive results.

  • Parallel Search Execution: Queries are executed simultaneously, accelerating data collection. This concurrent approach enables the bot to 'read' multiple pages concurrently, a unique advantage of AI.

  • Top Link Prioritization: Algorithms identify top K links for each query, and the bot scrapes full page content in parallel. This prioritization ensures the extraction of the most relevant information.

  • Efficient Data Indexing: Extracted data is meticulously indexed into a dedicated vector store (Chroma DB), optimizing retrieval and comparison in subsequent steps.

  • Contextual Result Matching: The bot matches original search queries with relevant documents stored in the vector store, presenting users with accurate and contextually appropriate results.

By integrating search capabilities with LLMs within the LangChain framework, this Google Search QnA chatbot delivers comprehensive and precise answers, akin to human search behavior.

Table of contents

  1. Architecture
  2. Deployment Options

Architecture

The architecture of the SearchQnA Application is illustrated below:

architecture

The SearchQnA example is implemented using the component-level microservices defined in GenAIComps. The flow chart below shows the information flow between different microservices for this example.

---
config:
  flowchart:
    nodeSpacing: 400
    rankSpacing: 100
    curve: linear
  themeVariables:
    fontSize: 50px
---
flowchart LR
    %% Colors %%
    classDef blue fill:#ADD8E6,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef orange fill:#FBAA60,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef orchid fill:#C26DBC,stroke:#ADD8E6,stroke-width:2px,fill-opacity:0.5
    classDef invisible fill:transparent,stroke:transparent;
    style SearchQnA-MegaService stroke:#000000

    %% Subgraphs %%
    subgraph SearchQnA-MegaService["SearchQnA MegaService "]
        direction LR
        EM([Embedding MicroService]):::blue
        RET([Web Retrieval MicroService]):::blue
        RER([Rerank MicroService]):::blue
        LLM([LLM MicroService]):::blue
    end
    subgraph UserInterface[" User Interface "]
        direction LR
        a([User Input Query]):::orchid
        UI([UI server<br>]):::orchid
    end



    TEI_RER{{Reranking service<br>}}
    TEI_EM{{Embedding service <br>}}
    VDB{{Vector DB<br><br>}}
    R_RET{{Web Retriever service <br>}}
    LLM_gen{{LLM Service <br>}}
    GW([SearchQnA GateWay<br>]):::orange

    %% Questions interaction
    direction LR
    a[User Input Query] --> UI
    UI --> GW
    GW <==> SearchQnA-MegaService
    EM ==> RET
    RET ==> RER
    RER ==> LLM

    %% Embedding service flow
    direction LR
    EM <-.-> TEI_EM
    RET <-.-> R_RET
    RER <-.-> TEI_RER
    LLM <-.-> LLM_gen

    direction TB
    %% Vector DB interaction
    R_RET <-.-> VDB
Loading

This SearchQnA use case performs Search-augmented Question Answering across multiple platforms. Currently, we provide the example for Intel® Gaudi® 2 and Intel® Xeon® Scalable Processors, and we invite contributions from other hardware vendors to expand OPEA ecosystem.

Deployment Options

The table below lists the available deployment options and their implementation details for different hardware platforms.

Category Deployment Option Description
On-premise Deployments Docker Compose (Xeon) DocSum deployment on Xeon
Docker Compose (Gaudi) DocSum deployment on Gaudi
Docker Compose (ROCm) DocSum deployment on AMD ROCm