115 lines
3.4 KiB
Python
115 lines
3.4 KiB
Python
from langchain_core.tools import tool
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from rich.console import Console
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from rich.panel import Panel
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from rich.markdown import Markdown
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from langchain_openai import ChatOpenAI
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from .memory import get_memory_value
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console = Console()
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model = ChatOpenAI(model_name="o1-preview")
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# Keep track of context globally
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expert_context = []
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@tool("emit_expert_context")
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def emit_expert_context(context: str) -> str:
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"""Add context for the next expert question.
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This should be highly detailed contents such as entire sections of source code, etc.
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Do not include your question in the additional context.
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Err on the side of adding more context rather than less.
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You must give the complete contents.
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Expert context will be reset after the ask_expert tool is called.
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Args:
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context: The context to add
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Returns:
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Confirmation message
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"""
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expert_context.append(context)
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# Create and display status panel
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panel_content = f"Added expert context ({len(context)} characters)"
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console.print(Panel(panel_content, title="Expert Context", border_style="blue"))
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return f"Context added."
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@tool("ask_expert")
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def ask_expert(question: str) -> str:
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"""Ask a question to an expert AI model.
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Keep your questions specific, but long and detailed.
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You only query the expert when you have a specific question in mind.
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The expert can be extremely useful at logic questions, debugging, and reviewing complex source code, but you must provide all context including source manually.
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The query will automatically include any key facts and code snippets from memory, along with any additional context you've provided.
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Try to phrase your question in a way that it does not expand the scope of our top-level task.
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The expert can be prone to overthinking depending on what and how you ask it.
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Args:
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question: The question to ask the expert
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Returns:
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The expert's response
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"""
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global expert_context
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# Build query with context and key facts
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query_parts = []
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# Add key facts if they exist
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key_facts = get_memory_value('key_facts')
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if key_facts and len(key_facts) > 0:
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query_parts.append("# Key Facts About This Project")
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query_parts.append(key_facts)
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# Add key snippets if they exist
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key_snippets = get_memory_value('key_snippets')
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if key_snippets and len(key_snippets) > 0:
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query_parts.append('# Key Snippets')
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query_parts.append(key_snippets)
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# Add other context if it exists
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if expert_context:
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query_parts.append("\n# Additional Context")
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query_parts.append("\n".join(expert_context))
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# Add the question last
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if query_parts: # If we have context/facts, add a newline before question
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query_parts.append("\n# Question")
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query_parts.append(question)
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# Join all parts
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query = "\n".join(query_parts)
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# Display the query in a panel before making the call
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console.print(Panel(
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Markdown(query),
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title="🤔 Expert Query",
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border_style="yellow"
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))
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# Clear context after use
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expert_context.clear()
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# Get response
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response = model.invoke(query)
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# Format and display response
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console.print(Panel(
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Markdown(response.content),
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title="Expert Response",
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border_style="blue"
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))
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return response.content
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