document-chat-interface

What this skill does

# Document Chat Interface

Build intelligent chat interfaces that allow users to query and interact with documents using natural language, transforming static documents into interactive knowledge sources.

## Overview

A document chat interface combines three capabilities:
1. **Document Processing** - Extract and prepare documents
2. **Semantic Understanding** - Understand questions and find relevant content
3. **Conversational Interface** - Maintain context and provide natural responses

### Common Applications

- **PDF Q&A**: Answer questions about research papers, reports, books
- **Email Search**: Find information in email archives conversationally
- **GitHub Explorer**: Ask questions about code repositories
- **Knowledge Base**: Interactive access to company documentation
- **Contract Review**: Query legal documents with natural language
- **Research Assistant**: Explore academic papers interactively

## Architecture

```
Document Source
    ↓
Document Processor
    ├→ Extract text
    ├→ Process content
    └→ Generate embeddings
    ↓
Vector Database
    ↓
Chat Interface ← User Question
    ├→ Retrieve relevant content
    ├→ Maintain conversation history
    └→ Generate response
```

## Core Components

### 1. Document Sources

See [examples/document_processors.py](examples/document_processors.py) for implementations:

#### PDF Documents
- Extract text from PDF pages
- Preserve document structure and metadata
- Handle scanned PDFs with OCR (pytesseract)
- Extract tables (pdfplumber)

#### GitHub Repositories
- Extract code files from repositories
- Parse repository structure
- Process multiple file types

#### Email Archives
- Extract email metadata (from, to, subject, date)
- Parse email body content
- Handle multiple mailbox formats

#### Web Pages
- Extract page text and structure
- Preserve heading hierarchy
- Extract links and navigation

#### YouTube/Audio
- Get transcripts from YouTube videos
- Transcribe audio files
- Handle multiple formats

### 2. Document Processing

See [examples/text_processor.py](examples/text_processor.py) for implementations:

#### Text Extraction & Cleaning
- Remove extra whitespace and special characters
- Smart text chunking with overlap
- Intelligent sentence boundary detection

#### Metadata Extraction
- Extract title, author, date, language
- Calculate word count and document statistics
- Track document source and format

#### Structure Preservation
- Keep heading hierarchy in chunks
- Preserve section context
- Enable hierarchical retrieval

### 3. Chat Interface Design

See [examples/conversation_manager.py](examples/conversation_manager.py) for implementations:

#### Conversation Management
- Maintain conversation history with size limits
- Track message metadata (timestamps, roles)
- Provide context for LLM integration
- Clear history as needed

#### Question Refinement
- Expand implicit references in questions
- Handle pronouns and context references
- Improve question clarity with previous context

#### Response Generation
- Use document context for answering
- Maintain conversation history in prompts
- Provide source citations
- Handle out-of-scope questions

### 4. User Experience Features

#### Citation & Sources
```python
def format_response_with_citations(response: str, sources: List[Dict]) -> str:
    """Add source citations to response"""

    formatted = response + "\n\n**Sources:**\n"
    for i, source in enumerate(sources, 1):
        formatted += f"[{i}] Page {source['page']} of {source['source']}\n"
        if 'excerpt' in source:
            formatted += f"    \"{source['excerpt'][:100]}...\"\n"

    return formatted
```

#### Clarifying Questions
```python
def generate_follow_up_questions(context: str, response: str) -> List[str]:
    """Suggest follow-up questions to user"""

    prompt = f"""
    Based on this Q&A, generate 3 relevant follow-up questions:
    Context: {context[:500]}
    Response: {response[:500]}
    """

    follow_ups = llm.generate(prompt)
    return follow_ups
```

#### Error Handling
```python
def handle_query_failure(question: str, error: Exception) -> str:
    """Handle when no relevant documents found"""

    if isinstance(error, NoRelevantDocuments):
        return (
            "I couldn't find information about that in the documents. "
            "Try asking about different topics like: "
            + ", ".join(get_main_topics())
        )
    elif isinstance(error, ContextTooLarge):
        return (
            "The answer requires too much context. "
            "Can you be more specific about what you'd like to know?"
        )
    else:
        return f"I encountered an issue: {str(error)[:100]}"
```

## Implementation Frameworks

### Using LangChain
```python
from langchain.document_loaders import PDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain

# Load document
loader = PDFLoader("document.pdf")
documents = loader.load()

# Split into chunks
splitter = CharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(documents)

# Create embeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)

# Create chat chain
llm = ChatOpenAI(model="gpt-4")
qa = ConversationalRetrievalChain.from_llm(
    llm=llm,
    retriever=vectorstore.as_retriever(),
    return_source_documents=True
)

# Chat interface
chat_history = []
while True:
    question = input("You: ")
    result = qa({"question": question, "chat_history": chat_history})
    print(f"Assistant: {result['answer']}")
    chat_history.append((question, result['answer']))
```

### Using LlamaIndex
```python
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ChatMemoryBuffer
from llama_index.llms import ChatMessage, MessageRole

# Load documents
documents = SimpleDirectoryReader("./docs").load_data()

# Create index
index = GPTVectorStoreIndex.from_documents(documents)

# Create chat engine with memory
chat_engine = index.as_chat_engine(
    memory=ChatMemoryBuffer.from_defaults(token_limit=3900),
    llm="gpt-4"
)

# Chat loop
while True:
    question = input("You: ")
    response = chat_engine.chat(question)
    print(f"Assistant: {response}")
```

### Using RAG-Based Approach
```python
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np

# Load and embed documents
model = SentenceTransformer('all-MiniLM-L6-v2')
documents = load_documents("document.pdf")
embeddings = model.encode(documents)

# Create FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings).astype('float32'))

# Chat function
def chat(question):
    # Embed question
    q_embedding = model.encode(question)

    # Retrieve documents
    k = 5
    distances, indices = index.search(
        np.array([q_embedding]).astype('float32'), k
    )

    # Get relevant documents
    context = " ".join([documents[i] for i in indices[0]])

    # Generate response
    response = llm.generate(
        f"Context: {context}\nQuestion: {question}\nAnswer:"
    )
    return response
```

## Best Practices

### Document Handling
- ✓ Support multiple formats (PDF, TXT, docx, etc.)
- ✓ Handle large documents efficiently
- ✓ Preserve document structure
- ✓ Extract metadata
- ✓ Handle multiple languages
- ✓ Implement OCR for scanned PDFs

### Conversation Quality
- ✓ Maintain conversation context
- ✓ Ask clarifying questions
- ✓ Cite sources
- ✓ Handle ambiguity
- ✓ Suggest follow-up questions
- ✓ Handle out-of-scope questions

### Performance
- ✓ Optimize retrieval speed
- ✓ Implement caching
- ✓ Handle large document sets
- ✓ Batch process documents
- ✓ Monitor latency
- ✓ Implement pagination

### User Experience
- ✓ Clear response formatting
- ✓ Ability to cite sources
- ✓ Document browser