64a1d8664e
When directories were provided to tools, only the directory path was stored in conversation history instead of the individual expanded files. This caused file filtering to incorrectly skip files in continued conversations. Changes: - Modified _prepare_file_content_for_prompt to return (content, processed_files) - Updated all tools to track actually processed files for conversation memory - Ensures directories are tracked as their expanded individual files Fixes issue where Swift directory with 46 files was not properly embedded in conversation continuations.
161 lines
6.6 KiB
Python
161 lines
6.6 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 TYPE_CHECKING, Any, Optional
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from pydantic import Field
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if TYPE_CHECKING:
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from tools.models import ToolModelCategory
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from config import TEMPERATURE_BALANCED
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from systemprompts import CHAT_PROMPT
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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",
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)
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files: Optional[list[str]] = Field(
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default_factory=list,
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description="Optional files for context (must be absolute paths)",
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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 the AI model 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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"Use this when you want to ask questions, brainstorm ideas, get opinions, discuss topics, "
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"share your thinking, or need explanations about concepts and approaches. "
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"Note: If you're not currently using a top-tier model such as Opus 4 or above, these tools can provide enhanced capabilities."
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)
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def get_input_schema(self) -> dict[str, Any]:
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schema = {
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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",
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},
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"files": {
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"type": "array",
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"items": {"type": "string"},
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"description": "Optional files for context (must be absolute paths)",
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},
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"model": self.get_model_field_schema(),
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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 (0.5% of model max), low (8%), medium (33%), high (67%), max (100% of model max)",
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},
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"use_websearch": {
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"type": "boolean",
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"description": "Enable web search for documentation, best practices, and current information. Particularly useful for: brainstorming sessions, architectural design discussions, exploring industry best practices, working with specific frameworks/technologies, researching solutions to complex problems, or when current documentation and community insights would enhance the analysis.",
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"default": True,
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},
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"continuation_id": {
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"type": "string",
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"description": "Thread continuation ID for multi-turn conversations. Can be used to continue conversations across different tools. Only provide this if continuing a previous conversation thread.",
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},
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},
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"required": ["prompt"] + (["model"] if self.is_effective_auto_mode() else []),
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}
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return schema
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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_model_category(self) -> "ToolModelCategory":
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"""Chat prioritizes fast responses and cost efficiency"""
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from tools.models import ToolModelCategory
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return ToolModelCategory.FAST_RESPONSE
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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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# Check for prompt.txt in files
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prompt_content, updated_files = self.handle_prompt_file(request.files)
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# Use prompt.txt content if available, otherwise use the prompt field
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user_content = prompt_content if prompt_content else request.prompt
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# Check user input size at MCP transport boundary (before adding internal content)
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size_check = self.check_prompt_size(user_content)
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if size_check:
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# Need to return error, but prepare_prompt returns str
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# Use exception to handle this cleanly
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from tools.models import ToolOutput
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raise ValueError(f"MCP_SIZE_CHECK:{ToolOutput(**size_check).model_dump_json()}")
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# Update request files list
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if updated_files is not None:
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request.files = updated_files
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# Add context files if provided (using centralized file handling with filtering)
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if request.files:
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file_content, processed_files = self._prepare_file_content_for_prompt(
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request.files, request.continuation_id, "Context files"
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)
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self._actually_processed_files = processed_files
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if file_content:
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user_content = f"{user_content}\n\n=== CONTEXT FILES ===\n{file_content}\n=== END CONTEXT ===="
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# Check token limits
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self._validate_token_limit(user_content, "Content")
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# Add web search instruction if enabled
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websearch_instruction = self.get_websearch_instruction(
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request.use_websearch,
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"""When discussing topics, consider if searches for these would help:
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- Documentation for any technologies or concepts mentioned
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- Current best practices and patterns
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- Recent developments or updates
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- Community discussions and solutions""",
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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()}{websearch_instruction}
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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, model_info: Optional[dict] = None) -> str:
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"""Format the chat response"""
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return f"{response}\n\n---\n\n**Claude's Turn:** Evaluate this perspective alongside your analysis to form a comprehensive solution and continue with the user's request and task at hand."
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