https://github.com/yash-rewalia/introductory_qna_system
This project is a Question-and-Answer (QnA) system that allows users to extract information from documents using semantic search and vector embeddings. The system processes documents, converts their content into embeddings, stores them in a knowledge base, and retrieves relevant answers based on user queries.
https://github.com/yash-rewalia/introductory_qna_system
gemini-api generative-ai llama-index python streamlit
Last synced: 4 months ago
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This project is a Question-and-Answer (QnA) system that allows users to extract information from documents using semantic search and vector embeddings. The system processes documents, converts their content into embeddings, stores them in a knowledge base, and retrieves relevant answers based on user queries.
- Host: GitHub
- URL: https://github.com/yash-rewalia/introductory_qna_system
- Owner: yash-rewalia
- Created: 2025-02-16T19:25:24.000Z (4 months ago)
- Default Branch: master
- Last Pushed: 2025-02-18T05:06:20.000Z (4 months ago)
- Last Synced: 2025-02-18T06:22:25.790Z (4 months ago)
- Topics: gemini-api, generative-ai, llama-index, python, streamlit
- Language: Python
- Homepage:
- Size: 41 KB
- Stars: 0
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Document-Based QnA System using LlamaIndex
## Introduction
This project is a Question-and-Answer (QnA) system that allows users to extract information from documents using semantic search and vector embeddings. The system processes documents, converts their content into embeddings, stores them in a knowledge base, and retrieves relevant answers based on user queries.
## Project Workflow
Below is the step-by-step process of how this system works:
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1. **Document Input**: A document is provided for data extraction.
2. **Data Extraction & Chunking**: The document is processed, and its content is split into smaller chunks.
3. **Vector Embedding Generation**: Each chunk is converted into a vector embedding.
4. **Semantic Indexing**: A semantic index is built using LlamaIndex.
5. **Storage**: The embeddings are stored in a knowledge base or vector storage.
6. **User Query**: A user submits a question.
7. **Query Embedding Generation**: The query is transformed into an embedding.
8. **Semantic Search**: The system performs a semantic search in the knowledge base.
9. **Best Match Selection**: The most relevant answer is identified.
10. **Response**: The answer is returned to the user.
## Technologies Used
- **LlamaIndex**: For building the semantic index.
- **Vector Embeddings**: Used for document and query representation.
- **Semantic Search**: To retrieve the most relevant answers.
## Usage
1. Upload a document.
2. Ask a question related to the document.
3. Receive the best possible answer based on the content.
## Conclusion
This system enables efficient information retrieval from documents using AI-powered search techniques. Happy querying!