Project Overview
Chatish is an innovative Streamlit web application that demonstrates the power of contextual retrieval using large language models, specifically Cohere's Command R model. This project showcases how modern AI can transform document interaction through intelligent, context-aware conversations.
Architectural Components
The application is structured around four primary Python modules:
- app.py: Main application entry point
- chat_manager.py: Manages chat interactions
- cohere_client.py: Handles AI interactions
- file_handler.py: Processes uploaded documents
Application Architecture Diagram
graph TD
A[User Interface - Streamlit] --> B[File Upload]
A --> C[Chat Input]
B --> D[File Handler]
C --> E[Chat Manager]
D --> F[Cohere Client]
E --> F
F --> G[AI Response Generation]
G --> A
Key Implementation Details
File Handling Strategy
The FileHandler
class demonstrates a flexible approach to document processing:
def process_file(self, uploaded_file):
if uploaded_file.type == "application/pdf":
return self.extract_text_from_pdf(uploaded_file)
else:
# Extensible for future file type support
return uploaded_file.read().decode()
Intelligent Prompt Engineering
The CohereClient
builds context-aware prompts:
def build_prompt(self, user_input, context=None):
context_str = f"{context}\n\n" if context else ""
return (
f"{context_str}"
f"Question: {user_input}\n"
f"Provide straight to the point response except when told "
f"to elaborate using available metrics and historical data"
)
Conversation Management
The chat management includes intelligent history tracking:
def chat(self, user_input, context=None):
# Maintain conversation history
self.conversation_history.append({"role": "user", "content": user_input})
# Limit history to prevent context overflow
if len(self.conversation_history) > 10:
self.conversation_history = self.conversation_history[-10:]
Technical Challenges Addressed
- Context Retrieval: Dynamically incorporating uploaded document context
- Conversation Persistence: Maintaining conversational state
- Streaming Response: Implementing real-time AI response generation
Technology Stack
- Web Framework: Streamlit
- AI Integration: Cohere Command R
- Document Processing: PyPDF2
- Language: Python 3.9+
Performance Considerations
-
Token Limits: Configurable with
max_tokens
parameter -
Temperature Control: Adjustable response creativity via
temperature
- Model Flexibility: Easy model swapping in configuration
Future Roadmap
- Enhanced error handling
- Support for additional file types
- Advanced context chunking
- Sentiment analysis integration
Deployment Considerations
Requirements
cohere==5.13.11
streamlit==1.41.1
PyPDF2==3.0.1
Quick Start
# Create virtual environment
python3 -m venv chatish_env
# Activate environment
source chatish_env/bin/activate
# Install dependencies
pip install -r requirements.txt
# Run application
streamlit run app.py
Security and Ethical Considerations
- API key protection
- Explicit user warnings about AI hallucinations
- Transparent context management
Conclusion
Chatish represents a pragmatic implementation of contextual AI interaction, bridging advanced language models with user-friendly document analysis.
Key Takeaways
- Modular, extensible architecture
- Intelligent context incorporation
- Streamlined user experience
Explore, Experiment, Extend!
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