name: document-chat-interface
description: Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.

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 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 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 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

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

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

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

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

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

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/explorer
• ✓ Search suggestions
• ✓ Query history
• ✓ Export conversations

Common Challenges & Solutions

Challenge: Irrelevant Answers

Solutions:
- Improve retrieval (more context, better embeddings)
- Validate answer against context
- Ask clarifying questions
- Implement confidence scoring
- Use hybrid search

Challenge: Lost Context Across Turns

Solutions:
- Maintain conversation memory
- Update retrieval based on history
- Summarize long conversations
- Re-weight previous queries

Challenge: Handling Long Documents

Solutions:
- Hierarchical chunking
- Summarize first
- Question refinement
- Multi-hop retrieval
- Document navigation

Challenge: Limited Context Window

Solutions:
- Compress retrieved context
- Use document summarization
- Hierarchical retrieval
- Focus on most relevant sections
- Iterative refinement

Advanced Features

Multi-Document Analysis

def compare_documents(question: str, documents: List[str]):
    """Analyze and compare across multiple documents"""
    results = []

    for doc in documents:
        response = query_document(doc, question)
        results.append({
            "document": doc.name,
            "answer": response
        })

    # Compare and synthesize
    comparison = llm.generate(
        f"Compare these answers: {results}"
    )
    return comparison

Interactive Document Exploration

class DocumentExplorer:
    def __init__(self, documents):
        self.documents = documents

    def browse_by_topic(self, topic):
        """Find documents by topic"""
        pass

    def get_related_documents(self, doc_id):
        """Find similar documents"""
        pass

    def get_key_terms(self, document):
        """Extract key terms and concepts"""
        pass

Resources

Document Processing Libraries

• PyPDF: PDF handling
• python-docx: Word document handling
• BeautifulSoup: Web scraping
• youtube-transcript-api: YouTube transcripts

Chat Frameworks

• LangChain: Comprehensive framework
• LlamaIndex: Document-focused
• RAG libraries: Vector DB integration

Implementation Checklist

• [ ] Choose document source(s) to support
• [ ] Implement document loading and processing
• [ ] Set up vector database/embeddings
• [ ] Build chat interface
• [ ] Implement conversation management
• [ ] Add source citation
• [ ] Handle edge cases (large docs, OCR, etc.)
• [ ] Implement error handling
• [ ] Add performance monitoring
• [ ] Test with real documents
• [ ] Deploy and monitor

Getting Started

1. Start Simple: Single PDF, basic chat
2. Add Features: Multi-document, conversation history
3. Improve Quality: Better chunking, retrieval
4. Scale: Support more formats, larger documents
5. Polish: UX improvements, error handling