# AI Embedded Knowledge Agent - Architecture Document

## 1. System Overview

The AI Embedded Knowledge Agent is a versatile knowledge management system designed to ingest, process, and retrieve information from various document types and web sources. Built for a hackathon and deployable on Hugging Face, this system enables users to upload documents or provide URLs, which are then processed, embedded, and stored for intelligent retrieval.

```mermaid
graph TD
    A[User Interface - Gradio] --> B[Document Processor]
    A --> C[URL Processor]
    B --> D[Text Extractor]
    C --> D
    D --> E[Embedding Generator - Gemini]
    E --> F[Vector Database - Pinecone]
    A --> G[Query Processor]
    G --> E
    G --> F
    G --> H[Response Generator - LangChain RAG]
    H --> A
```

## 2. Core Components

### 2.1 Document Ingestion System

This component handles the intake of various document formats and web content.

#### Document Processor

- **Responsibility**: Process uploaded documents (PDF, DOCX, CSV, PPTX, Excel)
- **Technologies**: PyMuPDF, python-docx, pandas, python-pptx, pdfplumber
- **Input**: Raw document files
- **Output**: Extracted text content

#### URL Processor

- **Responsibility**: Crawl and extract content from provided URLs, including nested documents and links
- **Technologies**: BeautifulSoup, requests, trafilatura
- **Input**: URLs
- **Output**: Extracted text content from web pages and linked documents

### 2.2 Knowledge Processing System

This component transforms raw text into queryable knowledge.

#### Text Extractor

- **Responsibility**: Clean, normalize, and chunk text from various sources
- **Technologies**: NLTK, spaCy, regex
- **Input**: Raw text from documents and web pages
- **Output**: Cleaned, normalized text chunks ready for embedding

#### Embedding Generator

- **Responsibility**: Generate vector embeddings for text chunks
- **Technology**: Gemini Embedding v3 (gemini-embedding-exp-03-07)
- **Input**: Processed text chunks
- **Output**: Vector embeddings

### 2.3 Knowledge Storage System

This component manages the storage and retrieval of vector embeddings.

#### Vector Database

- **Responsibility**: Store and index vector embeddings for efficient retrieval
- **Technology**: Pinecone
- **Input**: Vector embeddings with metadata
- **Output**: Retrieved relevant vectors based on similarity

### 2.4 Query Processing System

This component handles user queries and generates responses.

#### Query Processor

- **Responsibility**: Process user queries and convert them to vector embeddings
- **Technologies**: Gemini Embedding v3, LangChain
- **Input**: User queries
- **Output**: Query vector embeddings

#### Response Generator

- **Responsibility**: Generate coherent responses based on retrieved knowledge
- **Technology**: LangChain RAG (Retrieval Augmented Generation)
- **Input**: Retrieved relevant text chunks
- **Output**: Natural language responses

### 2.5 User Interface System

This component provides the user-facing interface.

#### Gradio UI

- **Responsibility**: Provide intuitive interface for document upload, URL input, and querying
- **Technology**: Gradio
- **Features**:
  - Document upload area
  - URL input field
  - Query input and response display
  - System status indicators

## 3. Data Flow

```mermaid
sequenceDiagram
    participant User
    participant UI as Gradio UI
    participant DP as Document Processor
    participant UP as URL Processor
    participant TE as Text Extractor
    participant EG as Embedding Generator
    participant VDB as Vector Database
    participant QP as Query Processor
    participant RG as Response Generator

    %% Document Upload Flow
    User->>UI: Upload Document
    UI->>DP: Process Document
    DP->>TE: Extract Text
    TE->>EG: Generate Embeddings
    EG->>VDB: Store Embeddings

    %% URL Processing Flow
    User->>UI: Input URL
    UI->>UP: Process URL
    UP->>TE: Extract Text
    TE->>EG: Generate Embeddings
    EG->>VDB: Store Embeddings

    %% Query Flow
    User->>UI: Submit Query
    UI->>QP: Process Query
    QP->>EG: Generate Query Embedding
    QP->>VDB: Retrieve Relevant Embeddings
    VDB->>QP: Return Relevant Chunks
    QP->>RG: Generate Response
    RG->>UI: Display Response
    UI->>User: Show Answer
```

## 4. Technical Architecture

### 4.1 Technology Stack

| Component          | Technology                                            | Purpose                                    |
| ------------------ | ----------------------------------------------------- | ------------------------------------------ |
| Document Parsing   | PyMuPDF, python-docx, pandas, python-pptx, pdfplumber | Extract text from various document formats |
| Web Scraping       | BeautifulSoup, requests, trafilatura                  | Extract content from web pages             |
| Text Processing    | NLTK, spaCy, regex                                    | Clean and chunk text                       |
| Embedding          | Gemini Embedding v3 (gemini-embedding-exp-03-07)      | Generate vector embeddings                 |
| Vector Storage     | Pinecone                                              | Store and retrieve vector embeddings       |
| RAG Implementation | LangChain                                             | Implement retrieval augmented generation   |
| User Interface     | Gradio                                                | Provide user-friendly interface            |

### 4.2 Integration Points

- **Document Processing → Text Extraction**: Raw text extraction from documents
- **URL Processing → Text Extraction**: Raw text extraction from web pages
- **Text Extraction → Embedding Generation**: Processed text chunks for embedding
- **Embedding Generation → Vector Database**: Storage of embeddings
- **Query Processing → Embedding Generation**: Query embedding generation
- **Query Processing → Vector Database**: Retrieval of relevant embeddings
- **Query Processing → Response Generation**: Generation of coherent responses
- **Response Generation → UI**: Display of responses to user

## 5. Deployment Architecture

The system is designed to be deployed on Hugging Face using their Spaces feature, which supports Gradio applications.

```mermaid
graph TD
    A[User] --> B[Hugging Face Space]
    B --> C[Gradio Application]
    C --> D[Document Processing]
    C --> E[URL Processing]
    C --> F[Query Processing]
    D --> G[Gemini API]
    E --> G
    F --> G
    D --> H[Pinecone API]
    E --> H
    F --> H
```

### 5.1 Deployment Considerations

- **API Keys**: Secure storage of Gemini and Pinecone API keys
- **Rate Limiting**: Handling API rate limits for both Gemini and Pinecone
- **Memory Management**: Efficient memory usage within Hugging Face constraints
- **Statelessness**: Designing components to be stateless where possible
- **Error Handling**: Robust error handling for API failures and timeouts

## 6. Scalability and Performance

For the hackathon version, the focus is on functionality rather than scalability. However, the architecture is designed with the following considerations:

- **Document Size Limits**: Implement reasonable limits on document sizes
- **Chunking Strategy**: Optimize text chunking for better retrieval performance
- **Caching**: Implement basic caching for frequently accessed embeddings
- **Asynchronous Processing**: Use asynchronous processing where appropriate

## 7. Future Enhancements

While not implemented in the hackathon version, the architecture supports future enhancements:

- **Authentication**: User authentication and document access control
- **Document Versioning**: Track changes to documents over time
- **Advanced RAG Techniques**: Implement more sophisticated RAG approaches
- **Multi-Modal Support**: Add support for images and other non-text content
- **Collaborative Features**: Allow multiple users to collaborate on knowledge bases
- **Custom Training**: Fine-tune models for specific domains

## 8. Folder Structure

```
rag-ai/
├── src/
│   ├── ingestion/
│   │   ├── document_processor.py
│   │   ├── url_processor.py
│   │   └── text_extractor.py
│   ├── embedding/
│   │   └── embedding_generator.py
│   ├── storage/
│   │   └── vector_db.py
│   ├── rag/
│   │   ├── query_processor.py
│   │   └── response_generator.py
│   ├── ui/
│   │   └── gradio_app.py
│   └── utils/
│       ├── config_manager.py
│       └── error_handler.py
├── config/
│   └── config.yaml
├── docs/
│   ├── architecture.md
│   └── api_documentation.md
├── tests/
│   ├── test_document_processor.py
│   ├── test_url_processor.py
│   └── ...
├── scripts/
│   ├── setup.py
│   └── deploy_to_huggingface.py
├── data/
│   ├── sample_documents/
│   └── test_data/
├── .gitignore
├── requirements.txt
├── README.md
└── app.py
```

## 9. Implementation Roadmap

1. **Phase 1: Core Infrastructure**

   - Set up project structure
   - Implement basic document processing
   - Set up Pinecone integration

2. **Phase 2: Knowledge Processing**

   - Implement text extraction and chunking
   - Integrate Gemini embedding API
   - Develop vector storage and retrieval

3. **Phase 3: Query System**

   - Implement query processing
   - Develop RAG response generation
   - Integrate components

4. **Phase 4: User Interface**

   - Develop Gradio UI
   - Integrate UI with backend components
   - Add error handling and user feedback

5. **Phase 5: URL Processing**

   - Implement URL crawling
   - Add nested document extraction
   - Integrate with existing components

6. **Phase 6: Testing and Deployment**
   - Comprehensive testing
   - Optimization for Hugging Face deployment
   - Documentation and demo preparation