Context
In companies, there is often a need for rapid access to HR, Legal, and RFP (Request for Proposal) documents. HR and Legal teams frequently need to review documents, retrieve information from previous documents, or verify specific details they had provided earlier. Additionally, in scenarios where new documents must be generated for clients or projects, these teams need the capability to retrieve previous information and update specific sections accordingly. To address these needs, a chatbot solution was developed using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). This solution allows for efficient document retrieval, generation of new content based on existing documents, and seamless updates to specific sections, enhancing overall document management and operational efficiency.
Requirements
Real-Time Information Retrieval:
Provide instant access to HR and Legal documents, allowing users to quickly retrieve relevant information.
Enable real-time retrieval of previous RFP documents and responses, ensuring accurate and timely updates for new proposals.
Facilitate quick access to specific sections or clauses within documents, allowing users to verify details without manually searching through the entire content.
Dynamic Query Handling:
Understand and respond to a wide range of user queries related to HR policies, legal clauses, and RFP requirements.
Utilize LLMs to manage complex and varied user intents, ensuring precise and relevant information retrieval or document generation.
Support the ability to update specific sections of documents, based on user queries, while preserving the integrity of the original content.
User Access and Interface:
Ensure the chatbot is easily accessible to HR, Legal, and RFP teams, providing a user-friendly interface for interaction.
Enable intuitive search and query functionalities, allowing users to retrieve or update documents with minimal effort.
Scalability and Reliability:
Ensure the solution can handle multiple queries simultaneously without performance degradation, supporting large teams and diverse document needs.
Maintain high availability and reliability to support continuous operations and avoid any downtime that could delay document access or generation.
Approach
Requirement Analysis:
Conducted detailed discussions with HR, Legal, and RFP teams to understand their document management needs and expectations.
Identified key features required in the chatbot, such as real-time document retrieval, dynamic query handling, and document generation capabilities.
Design and Architecture:
Designed the architecture of the chatbot solution, focusing on scalability, reliability, and seamless integration with existing document management systems.
Planned the integration of LLMs for enhanced query handling and Retrieval-Augmented Generation (RAG) for document retrieval and generation.
Implementation:
Developed the chatbot using LLMs for natural language understanding and query management.
Utilized Langchain for orchestrating document retrieval and generation tasks, enabling dynamic responses based on user queries.
Deployed FastAPI to handle API requests efficiently, ensuring that document retrieval and generation tasks are processed quickly and reliably.
Testing and Validation:
Conducted extensive testing to ensure the chatbot accurately understands and responds to user queries related to HR, Legal, and RFP documents.
Validated the document retrieval and generation process to ensure accuracy and timeliness.
Deployment and Monitoring:
Deployed the solution on a scalable infrastructure, ensuring reliability and high availability.
Implemented monitoring and logging to track chatbot performance, usage patterns, and promptly address any issues.
Technologies Used
Large Language Models (LLMs):
Languages: Python
Libraries/Frameworks: Hugging Face Transformers, Langchain
Purpose: Enhances dynamic query handling and document generation. Ensures the chatbot can manage a wide range of user queries accurately and generate new content based on previous documents.
Langchain:
Languages: Python
Libraries/Frameworks: Langchain
Purpose: Orchestrates the retrieval and generation of document content, enabling the chatbot to handle complex queries and provide relevant responses by integrating with LLMs.
FastAPI:
Languages: Python
Libraries/Frameworks: FastAPI
Purpose: A modern, fast (high-performance) web framework for building APIs with Python. Handles API requests related to document retrieval, generation, and updates.
Retrieval-Augmented Generation (RAG):
Languages: Python
Libraries/Frameworks: Langchain, Hugging Face Transformers
Purpose: Enhances the chatbot's capability to retrieve relevant information from existing documents and generate new content by combining retrieval and generation tasks.