Extend the apis with claude, gemini ect. similar to langchain

[ArpanDhot]

Feature Set Requirements for API Integration (Claude, Gemini, etc.)

1. Core API Integration Framework

• API Compatibility Layer: Implement a flexible API interface to support various advanced language models (Claude, Gemini, GPT, etc.). The framework should allow seamless switching between providers without significant code changes.
• API Key Management: Provide a secure and scalable system for handling API keys for multiple providers. Keys should be encrypted, and the system should support rotation, expiration handling, and permissions-based access.
• Request Throttling and Rate Limiting: Handle rate limits for each API provider by automatically adjusting request flow and retrying after cool-off periods. Implement strategies for parallelizing requests where allowed.

2. Advanced Query Routing and Orchestration

• Model Selection Logic: Build logic to automatically choose the best AI model (Claude, Gemini, GPT, etc.) based on query complexity, expected latency, or cost constraints. The system should also allow manual override by the user.
• Multi-Step Query Processing: Design a pipeline that breaks complex queries into simpler sub-queries. Each step in the pipeline can utilize a different model or provider as needed.
• Response Aggregation: Implement functionality to aggregate responses from multiple AI models and combine them into a cohesive answer, with automatic handling of contradictions or incomplete responses.

3. Dynamic Answer Refinement

• Basic vs. Advanced Query Handling: Introduce a mechanism to categorize user queries as simple or complex. Simple queries can be handled by a lightweight process, while complex ones can involve multi-step reasoning or model handoffs (e.g., Claude for abstract thinking, Gemini for factual data).
• Query Escalation: If a simple query cannot be answered satisfactorily, it should be escalated automatically to more powerful models or processed with a multi-step refinement approach, such as LangChain's agent-based systems.

4. Data Storage and Retrieval

• Memory Persistence: Implement a memory module that stores interaction history, allowing the models to have context in future interactions. This is similar to LangChain’s memory systems.
• Long-term Knowledge Integration: For repetitive queries, enable long-term storage of specific knowledge derived from interactions with models (e.g., storing output summaries or conclusions from multiple queries).

5. Tool and External API Interfacing

• Integration with External Tools: Similar to LangChain’s ability to extend with external tools, the system should have plugins or integrations to interact with APIs for:
  • Search engines (e.g., Google, Bing)
  • Databases (SQL, NoSQL)
  • Task automation tools
  • Knowledge bases like Wikipedia, news APIs, etc.
• Custom Tooling: Allow users to define and integrate custom tools for use in API interactions, extending the system’s capabilities beyond pre-built connectors.