An intelligent multi-agent AI system that enables users to interact with documents using natural language.
Built with LangGraph, OpenAI, ChromaDB, LangChain, and Streamlit, IntelliFlow AI combines Retrieval-Augmented Generation (RAG), conversational memory, and specialized AI agents to deliver accurate answers, summaries, analytics, and document insights.
Organizations generate large volumes of documents, reports, manuals, policies, contracts, and datasets.
Finding information within these documents is often:
- Time-consuming
- Manual
- Inefficient
- Dependent on keyword searches
Traditional document search tools struggle to provide contextual answers and meaningful insights.
IntelliFlow AI transforms static documents into an interactive knowledge system.
Users can upload documents and ask questions in natural language while a team of specialized AI agents collaborates to:
- Answer questions
- Generate summaries
- Perform document analytics
- Retrieve conversation history
- Manage uploaded documents
The platform uses Retrieval-Augmented Generation (RAG) to ensure responses are grounded in document content rather than relying solely on LLM knowledge.
User
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Streamlit Interface
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LangGraph Router Agent
├── Question Answering Agent
├── Summarization Agent
├── Analytics Agent
├── Memory Agent
└── Document Management Agent
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RAG Pipeline
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ChromaDB Vector Store
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OpenAI LLM
User Request
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Router Agent
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Intent Classification
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├── Question Answering
├── Summarization
├── Analytics
├── Memory Retrieval
└── Document Management
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RAG Retrieval
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Response
Provides accurate answers grounded in document content using Retrieval-Augmented Generation.
Example:
- What are the key requirements in this policy document?
- What does section 5 explain?
Generates concise summaries of uploaded documents.
Examples:
- Summarize this report.
- Give me a one-page overview.
Extracts patterns, insights, and important information from document content.
Examples:
- What are the most discussed topics?
- Identify important trends.
Maintains conversational context across interactions.
Examples:
- What did we discuss earlier?
- Continue from the previous analysis.
Handles document ingestion and management across supported formats.
Uses LangGraph to orchestrate specialized AI agents.
Combines vector search and LLM reasoning to produce grounded responses.
Maintains context throughout user interactions.
Uses embeddings and vector similarity search for relevant document retrieval.
Supports:
- DOCX
- TXT
- CSV
Built with Streamlit for a user-friendly experience.
Document Upload
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Text Extraction
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Chunking
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Embedding Generation
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ChromaDB Storage
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Semantic Retrieval
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LLM Response Generation
| Category | Technology |
|---|---|
| Language | Python |
| Agent Framework | LangGraph |
| RAG Framework | LangChain |
| LLM | OpenAI |
| Vector Database | ChromaDB |
| Embeddings | Sentence Transformers |
| Frontend | Streamlit |
| Document Processing | PDF, DOCX, TXT, CSV |
Ask questions directly about uploaded documents.
Generate concise summaries of long reports and documents.
Retrieve contextually relevant information using vector search.
Extract insights and trends from document collections.
Maintain conversational context and continuity.
- Multi-Document Reasoning
- Citation-Based Answers
- Hybrid Search (Keyword + Vector)
- Azure AI Search Integration
- Multi-User Support
- Role-Based Access Control
- Dashboard Analytics
- Enterprise Knowledge Base Integration
- Agentic AI Design
- LangGraph Workflow Orchestration
- Retrieval-Augmented Generation (RAG)
- Vector Databases
- Semantic Search
- Multi-Agent Systems
- Conversational Memory
- OpenAI Integration
- Document Intelligence
- Prompt Engineering
- Streamlit Application Development
Sifat Ullah Khan
AI Engineer | Agentic AI | RAG Systems | LangGraph | LangChain | OpenAI | ChromaDB