ilia
4b9ffb5ddf
- Enhanced `ARCHITECTURE.md` with details on LLM models for work (Llama 3.1 70B Q4) and family agents (Phi-3 Mini 3.8B Q4). - Introduced new documents: - `ASR_EVALUATION.md` for ASR engine evaluation and selection. - `HARDWARE.md` outlining hardware requirements and purchase plans. - `IMPLEMENTATION_GUIDE.md` for Milestone 2 implementation steps. - `LLM_CAPACITY.md` assessing VRAM and context window limits. - `LLM_MODEL_SURVEY.md` surveying open-weight LLM models. - `LLM_USAGE_AND_COSTS.md` detailing LLM usage and operational costs. - `MCP_ARCHITECTURE.md` describing the Model Context Protocol architecture. - `MCP_IMPLEMENTATION_SUMMARY.md` summarizing MCP implementation status. These updates provide comprehensive guidance for the next phases of development and ensure clarity in project documentation.
65 lines
1.1 KiB
Markdown
65 lines
1.1 KiB
Markdown
# MCP-LLM Adapter
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Adapter that connects LLM function calls to MCP tool server.
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## Overview
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This adapter:
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- Converts LLM function calls (OpenAI format) to MCP JSON-RPC calls
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- Converts MCP responses back to LLM format
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- Handles tool discovery and registration
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- Manages errors and retries
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## Architecture
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```
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LLM Server (Ollama/vLLM)
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↓ (function call)
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MCP Adapter
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↓ (JSON-RPC)
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MCP Server
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↓ (tool result)
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MCP Adapter
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↓ (function result)
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LLM Server
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```
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## Quick Start
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```bash
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# Run tests
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./run_test.sh
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# Or manually
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python test_adapter.py
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```
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## Usage
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```python
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from adapter import MCPAdapter
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# Initialize adapter
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adapter = MCPAdapter(mcp_server_url="http://localhost:8000/mcp")
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# Discover tools
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tools = adapter.discover_tools()
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# Convert LLM function call to MCP call
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llm_function_call = {
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"name": "weather",
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"arguments": {"location": "San Francisco"}
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}
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result = adapter.call_tool(llm_function_call)
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# Result is in LLM format
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print(result) # "Weather in San Francisco: 72°F, sunny..."
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```
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## Integration
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The adapter can be integrated into:
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- LLM routing layer
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- Direct LLM server integration
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- Standalone service
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