do not gc key facts/snippets associated with current human input
This commit is contained in:
AI Christianson
parent
f88ad5bc7a
commit
9a69bb173e
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@ -15,6 +15,7 @@ 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.database.repositories.human_input_repository import HumanInputRepository
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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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@ -22,6 +23,7 @@ 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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human_input_repository = HumanInputRepository()
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@tool
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@ -37,12 +39,27 @@ def delete_key_facts(fact_ids: List[int]) -> str:
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deleted_facts = []
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not_found_facts = []
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failed_facts = []
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protected_facts = []
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# Try to get the current human input to protect its facts
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current_human_input_id = None
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try:
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recent_inputs = human_input_repository.get_recent(1)
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if recent_inputs and len(recent_inputs) > 0:
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current_human_input_id = recent_inputs[0].id
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except Exception as e:
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console.print(f"Warning: Could not retrieve current human input: {str(e)}")
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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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# Check if this fact is associated with the current human input
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if current_human_input_id is not None and fact.human_input_id == current_human_input_id:
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protected_facts.append((fact_id, fact.content))
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continue
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# Delete the fact if it's not protected
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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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@ -61,6 +78,13 @@ def delete_key_facts(fact_ids: List[int]) -> str:
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Panel(Markdown(deleted_msg), title="Facts Deleted", border_style="green")
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)
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if protected_facts:
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protected_msg = "Protected facts (associated with current request):\n" + "\n".join([f"- #{fact_id}: {content}" for fact_id, content in protected_facts])
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result_parts.append(protected_msg)
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console.print(
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Panel(Markdown(protected_msg), title="Facts Protected", border_style="blue")
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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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@ -77,6 +101,7 @@ def run_key_facts_gc_agent() -> None:
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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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Facts associated with the current human input are excluded from deletion.
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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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@ -87,44 +112,75 @@ def run_key_facts_gc_agent() -> None:
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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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# Try to get the current human input ID to exclude its facts
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current_human_input_id = None
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try:
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recent_inputs = human_input_repository.get_recent(1)
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if recent_inputs and len(recent_inputs) > 0:
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current_human_input_id = recent_inputs[0].id
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except Exception as e:
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console.print(f"Warning: Could not retrieve current human input: {str(e)}")
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# Retrieve configuration
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llm_config = _global_memory.get("config", {})
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# Get all facts that are not associated with the current human input
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eligible_facts = []
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protected_facts = []
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for fact in facts:
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if current_human_input_id is not None and fact.human_input_id == current_human_input_id:
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protected_facts.append(fact)
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else:
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eligible_facts.append(fact)
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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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# Only process if we have facts that can be deleted
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if eligible_facts:
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# Format facts as a dictionary for the prompt
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facts_dict = {fact.id: fact.content for fact in eligible_facts}
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formatted_facts = "\n".join([f"Fact #{k}: {v}" for k, v in facts_dict.items()])
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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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# Retrieve configuration
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llm_config = _global_memory.get("config", {})
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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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# 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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)
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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 eligible 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 and protected facts count
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protected_count = len(protected_facts)
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if protected_count > 0:
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console.print(
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Panel(
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f"Cleaned key facts: {fact_count} → {updated_count}\nProtected facts (associated with current request): {protected_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(
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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(f"All {len(protected_facts)} facts are associated with the current request and protected from deletion.", title="🗑 GC Info"))
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else:
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console.print(Panel("No key facts to clean.", title="🗑 GC Info"))
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@ -15,6 +15,7 @@ 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_snippet_repository import KeySnippetRepository
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from ra_aid.database.repositories.human_input_repository import HumanInputRepository
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from ra_aid.llm import initialize_llm
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from ra_aid.prompts.key_snippets_gc_prompts import KEY_SNIPPETS_GC_PROMPT
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from ra_aid.tools.memory import log_work_event, _global_memory
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@ -22,6 +23,7 @@ from ra_aid.tools.memory import log_work_event, _global_memory
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console = Console()
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key_snippet_repository = KeySnippetRepository()
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human_input_repository = HumanInputRepository()
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@tool
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@ -38,13 +40,29 @@ def delete_key_snippets(snippet_ids: List[int]) -> str:
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results = []
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not_found_snippets = []
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failed_snippets = []
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protected_snippets = []
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# Try to get the current human input to protect its snippets
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current_human_input_id = None
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try:
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recent_inputs = human_input_repository.get_recent(1)
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if recent_inputs and len(recent_inputs) > 0:
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current_human_input_id = recent_inputs[0].id
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except Exception as e:
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console.print(f"Warning: Could not retrieve current human input: {str(e)}")
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for snippet_id in snippet_ids:
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# Get the snippet first to capture filepath for the message
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snippet = key_snippet_repository.get(snippet_id)
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if snippet:
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filepath = snippet.filepath
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# Delete from database
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# Check if this snippet is associated with the current human input
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if current_human_input_id is not None and snippet.human_input_id == current_human_input_id:
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protected_snippets.append((snippet_id, filepath))
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continue
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# Delete from database if not protected
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success = key_snippet_repository.delete(snippet_id)
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if success:
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success_msg = f"Successfully deleted snippet #{snippet_id} from {filepath}"
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@ -66,6 +84,13 @@ def delete_key_snippets(snippet_ids: List[int]) -> str:
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deleted_msg = "Successfully deleted snippets:\n" + "\n".join([f"- #{snippet_id}: {filepath}" for snippet_id, filepath in results])
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result_parts.append(deleted_msg)
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if protected_snippets:
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protected_msg = "Protected snippets (associated with current request):\n" + "\n".join([f"- #{snippet_id}: {filepath}" for snippet_id, filepath in protected_snippets])
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result_parts.append(protected_msg)
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console.print(
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Panel(Markdown(protected_msg), title="Snippets Protected", border_style="blue")
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)
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if not_found_snippets:
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not_found_msg = f"Snippets not found: {', '.join([f'#{snippet_id}' for snippet_id in not_found_snippets])}"
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result_parts.append(not_found_msg)
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@ -82,6 +107,7 @@ def run_key_snippets_gc_agent() -> None:
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The agent analyzes all key snippets and determines which are the least valuable,
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deleting them to maintain a manageable collection size of high-value snippets.
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Snippets associated with the current human input are excluded from deletion.
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"""
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# Get the count of key snippets
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snippets = key_snippet_repository.get_all()
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@ -92,47 +118,87 @@ def run_key_snippets_gc_agent() -> None:
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# Only run the agent if we actually have snippets to clean
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if snippet_count > 0:
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# Get all snippets as a formatted string for the prompt
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snippets_dict = key_snippet_repository.get_snippets_dict()
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formatted_snippets = "\n".join([
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f"Snippet #{k}: filepath={v['filepath']}, line_number={v['line_number']}, description={v['description']}\n```python\n{v['snippet']}\n```"
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for k, v in snippets_dict.items()
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])
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# Try to get the current human input ID to exclude its snippets
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current_human_input_id = None
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try:
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recent_inputs = human_input_repository.get_recent(1)
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if recent_inputs and len(recent_inputs) > 0:
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current_human_input_id = recent_inputs[0].id
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except Exception as e:
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console.print(f"Warning: Could not retrieve current human input: {str(e)}")
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# Retrieve configuration
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llm_config = _global_memory.get("config", {})
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# Get all snippets that are not associated with the current human input
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eligible_snippets = []
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protected_snippets = []
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for snippet in snippets:
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if current_human_input_id is not None and snippet.human_input_id == current_human_input_id:
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protected_snippets.append(snippet)
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else:
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eligible_snippets.append(snippet)
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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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# Only process if we have snippets that can be deleted
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if eligible_snippets:
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# Get eligible snippets as a formatted string for the prompt
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snippets_dict = {
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snippet.id: {
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'filepath': snippet.filepath,
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'line_number': snippet.line_number,
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'snippet': snippet.snippet,
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'description': snippet.description
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}
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for snippet in eligible_snippets
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}
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# Create the agent with the delete_key_snippets tool
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agent = create_agent(model, [delete_key_snippets])
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formatted_snippets = "\n".join([
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f"Snippet #{k}: filepath={v['filepath']}, line_number={v['line_number']}, description={v['description']}\n```python\n{v['snippet']}\n```"
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for k, v in snippets_dict.items()
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])
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# Format the prompt with the current snippets
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prompt = KEY_SNIPPETS_GC_PROMPT.format(key_snippets=formatted_snippets)
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# Retrieve configuration
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llm_config = _global_memory.get("config", {})
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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_snippets = key_snippet_repository.get_all()
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updated_count = len(updated_snippets)
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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 snippets: {snippet_count} → {updated_count}",
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title="🗑 GC Complete"
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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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)
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# Create the agent with the delete_key_snippets tool
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agent = create_agent(model, [delete_key_snippets])
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# Format the prompt with the eligible snippets
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prompt = KEY_SNIPPETS_GC_PROMPT.format(key_snippets=formatted_snippets)
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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_snippets = key_snippet_repository.get_all()
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updated_count = len(updated_snippets)
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# Show info panel with updated count and protected snippets count
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protected_count = len(protected_snippets)
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if protected_count > 0:
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console.print(
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Panel(
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f"Cleaned key snippets: {snippet_count} → {updated_count}\nProtected snippets (associated with current request): {protected_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(
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Panel(
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f"Cleaned key snippets: {snippet_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(f"All {len(protected_snippets)} snippets are associated with the current request and protected from deletion.", title="🗑 GC Info"))
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else:
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console.print(Panel("No key snippets to clean.", title="🗑 GC Info"))
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@ -37,31 +37,44 @@ with suppress(ImportError):
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def migrate(migrator: Migrator, database: pw.Database, *, fake=False):
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"""Add human_input_id foreign key field to KeyFact and KeySnippet tables."""
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# Add human_input_id field to KeyFact model
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migrator.add_fields(
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'key_fact',
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human_input_id=pw.ForeignKeyField(
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'human_input',
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null=True,
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field='id',
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on_delete='SET NULL'
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)
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)
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# Get the HumanInput model from migrator.orm
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HumanInput = migrator.orm['human_input']
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# Add human_input_id field to KeySnippet model
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migrator.add_fields(
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'key_snippet',
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human_input_id=pw.ForeignKeyField(
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'human_input',
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null=True,
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field='id',
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on_delete='SET NULL'
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# Skip adding fields if they already exist
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# Check if the column exists before trying to add it
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try:
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# Check if key_fact table has human_input_id column
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database.execute_sql("SELECT human_input_id FROM key_fact LIMIT 1")
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except pw.OperationalError:
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# Column doesn't exist, safe to add
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migrator.add_fields(
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'key_fact',
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human_input=pw.ForeignKeyField(
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HumanInput,
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null=True,
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field='id',
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on_delete='SET NULL'
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)
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)
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try:
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# Check if key_snippet table has human_input_id column
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database.execute_sql("SELECT human_input_id FROM key_snippet LIMIT 1")
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except pw.OperationalError:
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# Column doesn't exist, safe to add
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migrator.add_fields(
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'key_snippet',
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human_input=pw.ForeignKeyField(
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HumanInput,
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null=True,
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field='id',
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on_delete='SET NULL'
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)
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)
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)
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def rollback(migrator: Migrator, database: pw.Database, *, fake=False):
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"""Remove human_input_id field from KeyFact and KeySnippet tables."""
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migrator.remove_fields('key_fact', 'human_input_id')
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migrator.remove_fields('key_snippet', 'human_input_id')
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migrator.remove_fields('key_fact', 'human_input')
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migrator.remove_fields('key_snippet', 'human_input')
|
||||
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Reference in New Issue