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:
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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.
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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.
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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.
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Efficient Data Indexing: Extracted data is meticulously indexed into a dedicated vector store (Chroma DB), optimizing retrieval and comparison in subsequent steps.
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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.
The workflow falls into the following architecture:
The SearchQnA service can be effortlessly deployed on either Intel Gaudi2 or Intel XEON Scalable Processors.
Refer to the Xeon Guide for instructions on deploying SearchQnA on Xeon.
Refer to the Gaudi Guide for instructions on deploying SearchQnA on Gaudi.