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.

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

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