Creating a chatbot with contextual retrieval using Cohere command-r and Streamlit

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 demonstrates how modern artificial intelligence can transform document interaction through intelligent, context-aware conversations.

Architectural Components

The application is built around four main Python modules:

1. app.py: Main application entry point
2. chat_manager.py: Manage chat interactions
3. cohere_client.py: handles AI interaction
4. file_handler.py: Process uploaded documents

Application Architecture Diagram

<code>graph TD
    A[用户界面 - Streamlit] --> B[文件上传]
    A --> C[聊天输入]
    B --> D[文件处理器]
    C --> E[聊天管理器]
    D --> F[Cohere 客户端]
    E --> F
    F --> G[AI 响应生成]
    G --> A</code>

Key implementation details

File handling strategy

The FileHandler class demonstrates a flexible approach to document handling:

def process_file(self, uploaded_file):
    if uploaded_file.type == "application/pdf":
        return self.extract_text_from_pdf(uploaded_file)
    else:
        # 可扩展以支持未来的文件类型
        return uploaded_file.read().decode()

Smart reminder project

CohereClient build context-aware hints:

def build_prompt(self, user_input, context=None):
    context_str = f"{context}\n\n" if context else ""
    return (
        f"{context_str}"
        f"问题：{user_input}\n"
        f"除非被告知要详细说明，否则请直接给出答案，并使用可用的指标和历史数据。"
    )

Conversation Management

Chat management includes smart history tracking:

def chat(self, user_input, context=None):
    # 保持对话历史记录
    self.conversation_history.append({"role": "user", "content": user_input})

    # 限制历史记录以防止上下文溢出
    if len(self.conversation_history) > 10:
        self.conversation_history = self.conversation_history[-10:]

Technical Challenges Solved

1. Context Search: Dynamically integrate the context of uploaded documents
2. Session persistence: Maintain session state
3. Streaming response: Real-time AI response generation

Technology stack

• Web Framework: Streamlit
• AI Integration: Cohere Command R
• Document processing: PyPDF2
• Language: Python 3.9

Performance Notes

• Token Limitation: Configurable via max_tokens parameter
• Temperature Control: Creativity through Temperature Adjustment Response
• Model Flexibility: Easily switch models in configuration

Future Roadmap

1. Enhanced error handling
2. Support other file types
3. Advanced contextual chunking
4. Sentiment Analysis Integration

Deployment Notes

Requirements

<code>cohere==5.13.11
streamlit==1.41.1
PyPDF2==3.0.1</code>

Quick Start

# 创建虚拟环境
python3 -m venv chatish_env

# 激活环境
source chatish_env/bin/activate

# 安装依赖项
pip install -r requirements.txt

# 运行应用程序
streamlit run app.py

Safety and ethical considerations

• API Key Protection
• Explicit user warning about AI hallucinations
• Transparent context management

Conclusion

Chatish represents a practical implementation of contextual AI interaction that bridges advanced language models with user-friendly document analysis.

Key Points

• Modular, scalable architecture
• Intelligent contextual integration
• Simplified user experience

Explore, experiment, expand!

GitHub Repository

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