97fa6781cf
Image processing pipeline added OpenAI GPT-4.1 support Chat tool prompt enhancement Lint and code quality improvements
205 lines
8.5 KiB
Python
205 lines
8.5 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=(
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"Your thorough, expressive question with as much context as possible. Remember: you're talking to "
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"another Claude assistant who has deep expertise and can provide nuanced insights. Include your "
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"current thinking, specific challenges, background context, what you've already tried, and what "
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"kind of response would be most helpful. The more context and detail you provide, the more "
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"valuable and targeted the response will be."
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),
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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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images: Optional[list[str]] = Field(
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default_factory=list,
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description=(
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"Optional images for visual context. Useful for UI discussions, diagrams, visual problems, "
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"error screens, or architectural mockups."
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),
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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 "
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"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": (
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"Your thorough, expressive question with as much context as possible. Remember: you're "
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"talking to another Claude assistant who has deep expertise and can provide nuanced "
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"insights. Include your current thinking, specific challenges, background context, what "
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"you've already tried, and what kind of response would be most helpful. The more context "
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"and detail you provide, the more valuable and targeted the response will be."
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),
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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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"images": {
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"type": "array",
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"items": {"type": "string"},
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"description": (
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"Optional images for visual context. Useful for UI discussions, diagrams, visual "
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"problems, error screens, or architectural mockups."
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),
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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": (
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"Thinking depth: minimal (0.5% of model max), low (8%), medium (33%), high (67%), "
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"max (100% of model max)"
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),
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},
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"use_websearch": {
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"type": "boolean",
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"description": (
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"Enable web search for documentation, best practices, and current information. "
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"Particularly useful for: brainstorming sessions, architectural design discussions, "
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"exploring industry best practices, working with specific frameworks/technologies, "
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"researching solutions to complex problems, or when current documentation and "
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"community insights would enhance the analysis."
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),
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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": (
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"Thread continuation ID for multi-turn conversations. Can be used to continue "
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"conversations across different tools. Only provide this if continuing a previous "
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"conversation thread."
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),
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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 (
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f"{response}\n\n---\n\n**Claude's Turn:** Evaluate this perspective alongside your analysis to "
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"form a comprehensive solution and continue with the user's request and task at hand."
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)
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