🛠️Step 2: Choosing the Framework

Framework Options

1. LlamaIndex:

   • Pros: Optimized for large-scale indexing and retrieval tasks.

   • Cons: May require more setup for complex workflows.

2. LangChain:

   • Pros: Flexible and modular approach for chaining multiple LLMs and tools.

   • Cons: Requires a good understanding of LLM interactions.

3. CrewAI:

   • Pros: High-level abstractions and extensive tool integrations.

   • Cons: May have limitations in customizability.

Considerations

1. Project Requirements: Evaluate the specific needs of your project (e.g., data volume, processing speed, integration complexity).

2. Community Support: Consider the community and developer support available for each framework.

3. Scalability: Assess the scalability options provided by the framework for future growth.

Select a minimalist approach

• Use OpenAI's API directly

• Implement custom ChatBot class

Set up API connection

1. Install openai library:

   pip install openai

2. Configure API key:

   import openai
   openai.api_key = 'your-api-key-here'

Create ChatBot class:

• Initialize with system prompt

• Implement call method for interactions

• Use openai.ChatCompletion.create() for requests

class ChatBot:
    def __init__(self, api_key, system_prompt):
        self.api_key = api_key
        openai.api_key = self.api_key
        self.system_prompt = system_prompt
        self.messages = [{"role": "system", "content": system_prompt}]

    def call(self, user_input):
        self.messages.append({"role": "user", "content": user_input})
        response = openai.ChatCompletion.create(
            model="gpt-3.5-turbo",
            messages=self.messages
        )
        ai_response = response.choices[0].message['content']
        self.messages.append({"role": "assistant", "content": ai_response})
        return ai_response

Resources

• LangChain vs LlamaIndex vs CrewAI Comparison

• Llama Index Docs

• Langchain Docs

• CrewAI Docs