Enabling Intelligent Knowledge Retrieval through Multi-Source AI Query Processing
A technology-driven organization aimed to build an intelligent AI system capable of answering natural language queries using both real-time web information and static document repositories. The objective was to create a scalable conversational platform that leverages Sequential Language Models (SLMs) and multiple external tools to provide accurate, context-aware, and up-to-date responses. The solution was designed to combine live search, document retrieval, and AI orchestration for seamless knowledge discovery across public and proprietary content sources.
LLaMA-7B and Gemma2-9B-it; LangChain; Wikimedia API and Google Search via Groq API; ChromaDB; Python
The challenge
A technology-driven organization aimed to build an intelligent AI system capable of answering natural language queries using both real-time web information and static document repositories. The objective was to create a scalable conversational platform that leverages Sequential Language Models (SLMs) and multiple external tools to provide accurate, context-aware, and up-to-date responses. The solution was designed to combine live search, document retrieval, and AI orchestration for seamless knowledge discovery across public and proprietary content sources.
Key challenges
- Managing information retrieval from multiple data sources simultaneously
- Ensuring accurate and context-aware responses from live and static knowledge bases
- Difficulty integrating real-time web search with document-based retrieval systems
- Maintaining scalability and low-latency response generation
- Handling diverse query types spanning general and proprietary information
Technology used
- Models Used: LLaMA-7B and Gemma2-9B-it for local inference and response generation
- Framework: LangChain for agent orchestration, document loading, and text processing
- Search Tools: Wikimedia API and Google Search via Groq API for live information retrieval
- Vector Database: ChromaDB for embedding storage and semantic document retrieval
- Programming Language: Python for orchestration and deployment logic
What we built
- Document & Tool Ingestion: Processes PDFs and external sources using LangChain document loaders
- Text Splitting & Embedding: Converts documents into embeddings for semantic retrieval
- Tool Orchestration: Dynamically routes queries between vector DB, Wikipedia, and Google Search
- Hybrid Query Processing: Combines static document knowledge with real-time web information
- Response Generation: Produces contextual responses using SLM-based reasoning and retrieval pipelines
Objectives
- Build a multi-agent AI system leveraging multiple search and retrieval tools
- Enable natural language querying across real-time web and static documents
- Improve response relevance using intelligent tool orchestration
- Support context-aware question answering across multiple domains
- Combine document intelligence with live search capabilities
- Deliver accurate and up-to-date responses for dynamic user queries
Outcomes
- Hybrid retrieval combining static documents and live web search for comprehensive coverage
- Improved response accuracy through intelligent tool selection and orchestration
- Scalable architecture supporting high-volume query processing
- Faster and more contextual answers across multiple knowledge domains
- Always up-to-date responses through live Google and Wikipedia integration
- Flexible system architecture for adding new APIs, documents, and retrieval sources