130 lines
4.5 KiB
Python
130 lines
4.5 KiB
Python
"""
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Key facts gc agent implementation.
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This agent is responsible for maintaining the knowledge base by pruning less important
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facts when the total number exceeds a specified threshold. The agent evaluates all
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key facts and deletes the least valuable ones to keep the database clean and relevant.
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"""
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from typing import List
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from langchain_core.tools import tool
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from rich.console import Console
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from rich.markdown import Markdown
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from rich.panel import Panel
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from ra_aid.agent_utils import create_agent, run_agent_with_retry
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from ra_aid.database.repositories.key_fact_repository import KeyFactRepository
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from ra_aid.llm import initialize_llm
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from ra_aid.prompts.key_facts_gc_prompts import KEY_FACTS_GC_PROMPT
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from ra_aid.tools.memory import log_work_event, _global_memory
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console = Console()
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key_fact_repository = KeyFactRepository()
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@tool
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def delete_key_facts(fact_ids: List[int]) -> str:
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"""Delete multiple key facts by their IDs.
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Args:
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fact_ids: List of IDs of the key facts to delete
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Returns:
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str: Success or failure message
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"""
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deleted_facts = []
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not_found_facts = []
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failed_facts = []
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for fact_id in fact_ids:
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# Get the fact first to display information
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fact = key_fact_repository.get(fact_id)
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if fact:
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# Delete the fact
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was_deleted = key_fact_repository.delete(fact_id)
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if was_deleted:
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deleted_facts.append((fact_id, fact.content))
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log_work_event(f"Deleted fact {fact_id}.")
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else:
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failed_facts.append(fact_id)
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else:
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not_found_facts.append(fact_id)
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# Prepare result message
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result_parts = []
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if deleted_facts:
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deleted_msg = "Successfully deleted facts:\n" + "\n".join([f"- #{fact_id}: {content}" for fact_id, content in deleted_facts])
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result_parts.append(deleted_msg)
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console.print(
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Panel(Markdown(deleted_msg), title="Facts Deleted", border_style="green")
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)
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if not_found_facts:
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not_found_msg = f"Facts not found: {', '.join([f'#{fact_id}' for fact_id in not_found_facts])}"
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result_parts.append(not_found_msg)
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if failed_facts:
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failed_msg = f"Failed to delete facts: {', '.join([f'#{fact_id}' for fact_id in failed_facts])}"
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result_parts.append(failed_msg)
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return "\n".join(result_parts)
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def run_key_facts_gc_agent() -> None:
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"""Run the key facts gc agent to maintain a reasonable number of key facts.
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The agent analyzes all key facts and determines which are the least valuable,
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deleting them to maintain a manageable collection size of high-value facts.
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"""
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# Get the count of key facts
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facts = key_fact_repository.get_all()
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fact_count = len(facts)
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# Display status panel with fact count included
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console.print(Panel(f"Gathering my thoughts...\nCurrent number of key facts: {fact_count}", title="🗑 Garbage Collection"))
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# Only run the agent if we actually have facts to clean
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if fact_count > 0:
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# Get all facts as a formatted string for the prompt
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facts_dict = key_fact_repository.get_facts_dict()
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formatted_facts = "\n".join([f"Fact #{k}: {v}" for k, v in facts_dict.items()])
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# Retrieve configuration
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llm_config = _global_memory.get("config", {})
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# Initialize the LLM model
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model = initialize_llm(
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llm_config.get("provider", "anthropic"),
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llm_config.get("model", "claude-3-7-sonnet-20250219"),
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temperature=llm_config.get("temperature")
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)
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# Create the agent with the delete_key_facts tool
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agent = create_agent(model, [delete_key_facts])
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# Format the prompt with the current facts
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prompt = KEY_FACTS_GC_PROMPT.format(key_facts=formatted_facts)
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# Set up the agent configuration
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agent_config = {
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"recursion_limit": 50 # Set a reasonable recursion limit
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}
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# Run the agent
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run_agent_with_retry(agent, prompt, agent_config)
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# Get updated count
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updated_facts = key_fact_repository.get_all()
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updated_count = len(updated_facts)
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# Show info panel with updated count
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console.print(
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Panel(
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f"Cleaned key facts: {fact_count} → {updated_count}",
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title="🗑 GC Complete"
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)
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)
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else:
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console.print(Panel("No key facts to clean.", title="🗑 GC Info")) |