ada466879e
<!-- .github/pull_request_template.md --> ## Description <!-- Provide a clear description of the changes in this PR --> ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin <!-- This is an auto-generated comment: release notes by coderabbit.ai --> ## Summary by CodeRabbit - **New Features** - Enhanced the task execution process by enabling default values for certain parameters, allowing users to trigger task processing without supplying every input explicitly. - **Bug Fixes** - Adjusted asynchronous handling for the `retrieved_edges_to_string` function to ensure proper execution flow in various components. - **Documentation** - Updated markdown formatting in the Jupyter notebook for improved readability and structure. <!-- end of auto-generated comment: release notes by coderabbit.ai --> --------- Co-authored-by: hajdul88 <52442977+hajdul88@users.noreply.github.com>
77 lines
2.4 KiB
Python
77 lines
2.4 KiB
Python
import asyncio
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import cognee
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import logging
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from cognee.modules.pipelines import Task, run_tasks
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from cognee.shared.utils import setup_logging
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from cognee.tasks.temporal_awareness import build_graph_with_temporal_awareness
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from cognee.infrastructure.databases.relational import (
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create_db_and_tables as create_relational_db_and_tables,
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)
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from cognee.tasks.temporal_awareness.index_graphiti_objects import (
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index_and_transform_graphiti_nodes_and_edges,
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)
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from cognee.modules.retrieval.brute_force_triplet_search import brute_force_triplet_search
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from cognee.tasks.completion.graph_query_completion import retrieved_edges_to_string
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from cognee.infrastructure.llm.prompts import read_query_prompt, render_prompt
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from cognee.infrastructure.llm.get_llm_client import get_llm_client
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text_list = [
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"Kamala Harris is the Attorney General of California. She was previously "
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"the district attorney for San Francisco.",
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"As AG, Harris was in office from January 3, 2011 – January 3, 2017",
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]
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async def main():
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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await create_relational_db_and_tables()
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for text in text_list:
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await cognee.add(text)
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tasks = [
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Task(build_graph_with_temporal_awareness, text_list=text_list),
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]
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pipeline = run_tasks(tasks)
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async for result in pipeline:
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print(result)
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await index_and_transform_graphiti_nodes_and_edges()
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query = "When was Kamala Harris in office?"
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triplets = await brute_force_triplet_search(
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query=query,
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top_k=3,
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collections=["graphitinode_content", "graphitinode_name", "graphitinode_summary"],
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)
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args = {
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"question": query,
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"context": await retrieved_edges_to_string(triplets),
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}
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user_prompt = render_prompt("graph_context_for_question.txt", args)
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system_prompt = read_query_prompt("answer_simple_question_restricted.txt")
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llm_client = get_llm_client()
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computed_answer = await llm_client.acreate_structured_output(
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text_input=user_prompt,
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system_prompt=system_prompt,
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response_model=str,
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)
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print(computed_answer)
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if __name__ == "__main__":
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setup_logging(logging.ERROR)
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loop = asyncio.new_event_loop()
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asyncio.set_event_loop(loop)
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try:
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loop.run_until_complete(main())
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finally:
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loop.run_until_complete(loop.shutdown_asyncgens())
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