299f7d3897
- Add collaboration demo showing dynamic context requests - Implement chat tool for general conversations and brainstorming - Add tool selection guide with clear boundaries - Introduce models configuration system - Update prompts for better tool descriptions - Refactor server to remove redundant functionality - Add comprehensive tests for collaboration features - Enhance base tool with collaborative features This enables Claude to request additional context from Gemini during tool execution, improving analysis quality and accuracy. 🤖 Generated with [Claude Code](https://claude.ai/code) Co-Authored-By: Claude <noreply@anthropic.com>
112 lines
3.8 KiB
Python
112 lines
3.8 KiB
Python
"""
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Chat tool - General development chat and collaborative thinking
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"""
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from typing import Any, Dict, List, Optional
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from pydantic import Field
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from config import MAX_CONTEXT_TOKENS, TEMPERATURE_BALANCED
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from prompts import CHAT_PROMPT
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from utils import check_token_limit, read_files
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from .base import BaseTool, ToolRequest
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class ChatRequest(ToolRequest):
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"""Request model for chat tool"""
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prompt: str = Field(
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...,
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description="Your question, topic, or current thinking to discuss with Gemini",
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)
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context_files: Optional[List[str]] = Field(
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default_factory=list, description="Optional files for context"
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)
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class ChatTool(BaseTool):
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"""General development chat and collaborative thinking tool"""
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def get_name(self) -> str:
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return "chat"
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def get_description(self) -> str:
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return (
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"GENERAL CHAT & COLLABORATIVE THINKING - Use Gemini as your thinking partner! "
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"Perfect for: bouncing ideas during your own analysis, getting second opinions on your plans, "
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"collaborative brainstorming, validating your checklists and approaches, exploring alternatives. "
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"Also great for: explanations, comparisons, general development questions. "
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"Triggers: 'ask gemini', 'brainstorm with gemini', 'get gemini's opinion', 'discuss with gemini', "
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"'share my thinking with gemini', 'explain', 'what is', 'how do I'."
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)
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def get_input_schema(self) -> Dict[str, Any]:
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return {
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"type": "object",
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"properties": {
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"prompt": {
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"type": "string",
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"description": "Your question, topic, or current thinking to discuss with Gemini",
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},
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"context_files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional files for context",
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},
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"temperature": {
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"type": "number",
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"description": "Response creativity (0-1, default 0.5)",
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"minimum": 0,
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"maximum": 1,
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},
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"thinking_mode": {
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"type": "string",
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"enum": ["minimal", "low", "medium", "high", "max"],
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"description": "Thinking depth: minimal (128), low (2048), medium (8192), high (16384), max (32768)",
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},
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},
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"required": ["prompt"],
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}
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def get_system_prompt(self) -> str:
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return CHAT_PROMPT
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def get_default_temperature(self) -> float:
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return TEMPERATURE_BALANCED
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def get_request_model(self):
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return ChatRequest
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async def prepare_prompt(self, request: ChatRequest) -> str:
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"""Prepare the chat prompt with optional context files"""
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user_content = request.prompt
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# Add context files if provided
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if request.context_files:
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file_content, _ = read_files(request.context_files)
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user_content = f"{request.prompt}\n\n=== CONTEXT FILES ===\n{file_content}\n=== END CONTEXT ==="
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# Check token limits
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within_limit, estimated_tokens = check_token_limit(user_content)
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if not within_limit:
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raise ValueError(
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f"Content too large (~{estimated_tokens:,} tokens). "
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f"Maximum is {MAX_CONTEXT_TOKENS:,} tokens."
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)
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# Combine system prompt with user content
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full_prompt = f"""{self.get_system_prompt()}
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=== USER REQUEST ===
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{user_content}
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=== END REQUEST ===
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Please provide a thoughtful, comprehensive response:"""
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return full_prompt
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def format_response(self, response: str, request: ChatRequest) -> str:
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"""Format the chat response (no special formatting needed)"""
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return response
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