Market Research Assistant
Generative Text-to-SQL on Data Lake
Overview
This market research assistant utilizes a modern data architecture and generative AI techniques to empower an organization’s market research efforts. By integrating various data sources and leveraging natural language capabilities, the assistant allows non-technical users to easily query and analyze market research data using conversational natural language.
What Is It?
This conversational research assistant will allow you to ask fact-based questions in natural language, eliminating the need for complex SQL queries. The assistant will utilize natural language processing and generative AI models, such as large language models (LLMs) from Amazon Bedrock and JumpStart, to identify the relevant data sources, generate SQL queries, and provide coherent responses with embedded results.
How It Works
- A business user provides a natural language question prompt.
- An AWS Glue crawler is scheduled to run at frequent intervals to extract metadata from databases and create table definitions in the AWS Glue Data Catalog. The Data Catalog is input to the chain sequence.
- LangChain, a tool to work with LLMs and prompts, is used in a Python application running on Amazon ECS. LangChain requires an LLM to be defined. As part of chain sequence, the prompt and Data Catalog metadata are passed to an LLM, hosted on Amazon Bedrock, to identify the relevant database and table using LangChain.
- The prompt and identified database and table are passed to a second chain sequence.
- LangChain establishes a connection to the database and runs the SQL query to get the results.
- The results are passed to the LLM to generate a natural language answer with the data.
- The user receives a natural language answer to their prompt, querying data from different databases.
Architecture
The hybrid architecture uses multiple databases and LLMs, with foundation models from Amazon Bedrock for data source identification, SQL generation, and text generation with results.
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