From eba9f40ca265a954a5c4d735a2cea3f23d0d091a Mon Sep 17 00:00:00 2001 From: Preston Rasmussen <109292228+prasmussen15@users.noreply.github.com> Date: Wed, 13 Nov 2024 11:58:56 -0500 Subject: [PATCH] add reflexion (#212) * add reflexion * clean up boolean logic * update conditional * cap reflexion iterations * don't do an extra reflection step --- examples/podcast/podcast_runner.py | 6 +- graphiti_core/helpers.py | 1 + graphiti_core/prompts/extract_edges.py | 37 +- graphiti_core/prompts/extract_nodes.py | 51 ++- graphiti_core/search/search.py | 14 + .../utils/maintenance/edge_operations.py | 27 +- .../utils/maintenance/node_operations.py | 77 +++- poetry.lock | 352 +++++++++--------- pyproject.toml | 2 +- 9 files changed, 369 insertions(+), 198 deletions(-) diff --git a/examples/podcast/podcast_runner.py b/examples/podcast/podcast_runner.py index 43ad29fc..b33bc7bc 100644 --- a/examples/podcast/podcast_runner.py +++ b/examples/podcast/podcast_runner.py @@ -69,6 +69,7 @@ async def main(use_bulk: bool = True): episode_body=f'{message.speaker_name} ({message.role}): {message.content}', reference_time=message.actual_timestamp, source_description='Podcast Transcript', + group_id='podcast', ) # build communities @@ -81,7 +82,7 @@ async def main(use_bulk: bool = True): episode_body=f'{message.speaker_name} ({message.role}): {message.content}', reference_time=message.actual_timestamp, source_description='Podcast Transcript', - group_id='1', + group_id='podcast', update_communities=True, ) @@ -94,11 +95,12 @@ async def main(use_bulk: bool = True): source=EpisodeType.message, source_description='Podcast Transcript', reference_time=message.actual_timestamp, + group_id='podcast', ) for i, message in enumerate(messages[3:20]) ] - await client.add_episode_bulk(episodes, None) + await client.add_episode_bulk(episodes, '') asyncio.run(main(False)) diff --git a/graphiti_core/helpers.py b/graphiti_core/helpers.py index 28a1df2d..253509c9 100644 --- a/graphiti_core/helpers.py +++ b/graphiti_core/helpers.py @@ -25,6 +25,7 @@ load_dotenv() DEFAULT_DATABASE = os.getenv('DEFAULT_DATABASE', None) USE_PARALLEL_RUNTIME = bool(os.getenv('USE_PARALLEL_RUNTIME', False)) +MAX_REFLEXION_ITERATIONS = 2 def parse_db_date(neo_date: neo4j_time.DateTime | None) -> datetime | None: diff --git a/graphiti_core/prompts/extract_edges.py b/graphiti_core/prompts/extract_edges.py index 9d39c116..7f6636dd 100644 --- a/graphiti_core/prompts/extract_edges.py +++ b/graphiti_core/prompts/extract_edges.py @@ -23,11 +23,13 @@ from .models import Message, PromptFunction, PromptVersion class Prompt(Protocol): v1: PromptVersion v2: PromptVersion + reflexion: PromptVersion class Versions(TypedDict): v1: PromptFunction v2: PromptFunction + reflexion: PromptFunction def v1(context: dict[str, Any]) -> list[Message]: @@ -107,6 +109,8 @@ def v2(context: dict[str, Any]) -> list[Message]: Extract entity edges based on the content of the current episode, the given nodes, and context from previous episodes. + + {context['custom_prompt']} Guidelines: 1. Create edges only between the provided nodes. @@ -137,4 +141,35 @@ def v2(context: dict[str, Any]) -> list[Message]: ] -versions: Versions = {'v1': v1, 'v2': v2} +def reflexion(context: dict[str, Any]) -> list[Message]: + sys_prompt = """You are an AI assistant that determines which facts have not been extracted from the given context""" + + user_prompt = f""" +Given the following conversation, current message, list of extracted entities, and list of extracted facts; +determine if any facts haven't been extracted: + +: +{json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)} + + +{context["episode_content"]} + + +{context["nodes"]} + + +{context["extracted_facts"]} + + +Respond with a JSON object in the following format: +{{ + "missing_facts": [ "facts that weren't extracted", ...] +}} +""" + return [ + Message(role='system', content=sys_prompt), + Message(role='user', content=user_prompt), + ] + + +versions: Versions = {'v1': v1, 'v2': v2, 'reflexion': reflexion} diff --git a/graphiti_core/prompts/extract_nodes.py b/graphiti_core/prompts/extract_nodes.py index fd52159d..4693ce67 100644 --- a/graphiti_core/prompts/extract_nodes.py +++ b/graphiti_core/prompts/extract_nodes.py @@ -25,6 +25,7 @@ class Prompt(Protocol): v2: PromptVersion extract_json: PromptVersion extract_text: PromptVersion + reflexion: PromptVersion class Versions(TypedDict): @@ -32,6 +33,7 @@ class Versions(TypedDict): v2: PromptFunction extract_json: PromptFunction extract_text: PromptFunction + reflexion: PromptFunction def v1(context: dict[str, Any]) -> list[Message]: @@ -87,6 +89,8 @@ Conversation: {context["episode_content"]} +{context['custom_prompt']} + Guidelines: 1. ALWAYS extract the speaker/actor as the first node. The speaker is the part before the colon in each line of dialogue. 2. Extract other significant entities, concepts, or actors mentioned in the conversation. @@ -125,6 +129,8 @@ Source Description: JSON: {context["episode_content"]} +{context['custom_prompt']} + Guidelines: 1. Always try to extract an entities that the JSON represents. This will often be something like a "name" or "user field 2. Do NOT extract any properties that contain dates @@ -152,17 +158,21 @@ def extract_text(context: dict[str, Any]) -> list[Message]: user_prompt = f""" Given the following conversation, extract entity nodes from the CURRENT MESSAGE that are explicitly or implicitly mentioned: -Conversation: + {json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)} + {context["episode_content"]} + + +{context['custom_prompt']} Guidelines: -2. Extract significant entities, concepts, or actors mentioned in the conversation. -3. Provide concise but informative summaries for each extracted node. -4. Avoid creating nodes for relationships or actions. -5. Avoid creating nodes for temporal information like dates, times or years (these will be added to edges later). -6. Be as explicit as possible in your node names, using full names and avoiding abbreviations. +1. Extract significant entities, concepts, or actors mentioned in the conversation. +2. Provide concise but informative summaries for each extracted node. +3. Avoid creating nodes for relationships or actions. +4. Avoid creating nodes for temporal information like dates, times or years (these will be added to edges later). +5. Be as explicit as possible in your node names, using full names and avoiding abbreviations. Respond with a JSON object in the following format: {{ @@ -181,9 +191,38 @@ Respond with a JSON object in the following format: ] +def reflexion(context: dict[str, Any]) -> list[Message]: + sys_prompt = """You are an AI assistant that determines which entities have not been extracted from the given context""" + + user_prompt = f""" +Given the following conversation, current message, and list of extracted entities; determine if any entities haven't been +extracted: + +: +{json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)} + + +{context["episode_content"]} + + +{context["extracted_entities"]} + + +Respond with a JSON object in the following format: +{{ + "missed_entities": [ "name of entity that wasn't extracted", ...] +}} +""" + return [ + Message(role='system', content=sys_prompt), + Message(role='user', content=user_prompt), + ] + + versions: Versions = { 'v1': v1, 'v2': v2, 'extract_json': extract_json, 'extract_text': extract_text, + 'reflexion': reflexion, } diff --git a/graphiti_core/search/search.py b/graphiti_core/search/search.py index 3b294ca7..857f5053 100644 --- a/graphiti_core/search/search.py +++ b/graphiti_core/search/search.py @@ -32,8 +32,10 @@ from graphiti_core.search.search_config import ( CommunitySearchConfig, EdgeReranker, EdgeSearchConfig, + EdgeSearchMethod, NodeReranker, NodeSearchConfig, + NodeSearchMethod, SearchConfig, SearchResults, ) @@ -150,6 +152,12 @@ async def edge_search( ) ) + if EdgeSearchMethod.bfs in config.search_methods and bfs_origin_node_uuids is None: + source_node_uuids = [edge.source_node_uuid for result in search_results for edge in result] + search_results.append( + await edge_bfs_search(driver, source_node_uuids, config.bfs_max_depth, 2 * limit) + ) + edge_uuid_map = {edge.uuid: edge for result in search_results for edge in result} reranked_uuids: list[str] = [] @@ -229,6 +237,12 @@ async def node_search( ) ) + if NodeSearchMethod.bfs in config.search_methods and bfs_origin_node_uuids is None: + origin_node_uuids = [node.uuid for result in search_results for node in result] + search_results.append( + await node_bfs_search(driver, origin_node_uuids, config.bfs_max_depth, 2 * limit) + ) + search_result_uuids = [[node.uuid for node in result] for result in search_results] node_uuid_map = {node.uuid: node for result in search_results for node in result} diff --git a/graphiti_core/utils/maintenance/edge_operations.py b/graphiti_core/utils/maintenance/edge_operations.py index 15ec7951..c7f0f541 100644 --- a/graphiti_core/utils/maintenance/edge_operations.py +++ b/graphiti_core/utils/maintenance/edge_operations.py @@ -21,6 +21,7 @@ from time import time from typing import List from graphiti_core.edges import CommunityEdge, EntityEdge, EpisodicEdge +from graphiti_core.helpers import MAX_REFLEXION_ITERATIONS from graphiti_core.llm_client import LLMClient from graphiti_core.nodes import CommunityNode, EntityNode, EpisodicNode from graphiti_core.prompts import prompt_library @@ -91,10 +92,32 @@ async def extract_edges( } for ep in previous_episodes ], + 'custom_prompt': '', } - llm_response = await llm_client.generate_response(prompt_library.extract_edges.v2(context)) - edges_data = llm_response.get('edges', []) + facts_missed = True + reflexion_iterations = 0 + while facts_missed and reflexion_iterations < MAX_REFLEXION_ITERATIONS: + llm_response = await llm_client.generate_response(prompt_library.extract_edges.v2(context)) + edges_data = llm_response.get('edges', []) + + context['extracted_facts'] = [edge_data['fact'] for edge_data in edges_data] + + reflexion_iterations += 1 + if reflexion_iterations < MAX_REFLEXION_ITERATIONS: + reflexion_response = await llm_client.generate_response( + prompt_library.extract_edges.reflexion(context) + ) + + missing_facts = reflexion_response.get('missing_facts', []) + + custom_prompt = 'The following facts were missed in a previous extraction: ' + for fact in missing_facts: + custom_prompt += f'\n{fact},' + + context['custom_prompt'] = custom_prompt + + facts_missed = len(missing_facts) != 0 end = time() logger.debug(f'Extracted new edges: {edges_data} in {(end - start) * 1000} ms') diff --git a/graphiti_core/utils/maintenance/node_operations.py b/graphiti_core/utils/maintenance/node_operations.py index dd940f07..f54c033d 100644 --- a/graphiti_core/utils/maintenance/node_operations.py +++ b/graphiti_core/utils/maintenance/node_operations.py @@ -20,6 +20,7 @@ from datetime import datetime, timezone from time import time from typing import Any +from graphiti_core.helpers import MAX_REFLEXION_ITERATIONS from graphiti_core.llm_client import LLMClient from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode from graphiti_core.prompts import prompt_library @@ -28,7 +29,10 @@ logger = logging.getLogger(__name__) async def extract_message_nodes( - llm_client: LLMClient, episode: EpisodicNode, previous_episodes: list[EpisodicNode] + llm_client: LLMClient, + episode: EpisodicNode, + previous_episodes: list[EpisodicNode], + custom_prompt='', ) -> list[dict[str, Any]]: # Prepare context for LLM context = { @@ -41,6 +45,7 @@ async def extract_message_nodes( } for ep in previous_episodes ], + 'custom_prompt': custom_prompt, } llm_response = await llm_client.generate_response(prompt_library.extract_nodes.v2(context)) @@ -49,7 +54,10 @@ async def extract_message_nodes( async def extract_text_nodes( - llm_client: LLMClient, episode: EpisodicNode, previous_episodes: list[EpisodicNode] + llm_client: LLMClient, + episode: EpisodicNode, + previous_episodes: list[EpisodicNode], + custom_prompt='', ) -> list[dict[str, Any]]: # Prepare context for LLM context = { @@ -62,6 +70,7 @@ async def extract_text_nodes( } for ep in previous_episodes ], + 'custom_prompt': custom_prompt, } llm_response = await llm_client.generate_response( @@ -72,14 +81,14 @@ async def extract_text_nodes( async def extract_json_nodes( - llm_client: LLMClient, - episode: EpisodicNode, + llm_client: LLMClient, episode: EpisodicNode, custom_prompt='' ) -> list[dict[str, Any]]: # Prepare context for LLM context = { 'episode_content': episode.content, 'episode_timestamp': episode.valid_at.isoformat(), 'source_description': episode.source_description, + 'custom_prompt': custom_prompt, } llm_response = await llm_client.generate_response( @@ -89,6 +98,33 @@ async def extract_json_nodes( return extracted_node_data +async def extract_nodes_reflexion( + llm_client: LLMClient, + episode: EpisodicNode, + previous_episodes: list[EpisodicNode], + node_names: list[str], +) -> list[str]: + # Prepare context for LLM + context = { + 'episode_content': episode.content, + 'previous_episodes': [ + { + 'content': ep.content, + 'timestamp': ep.valid_at.isoformat(), + } + for ep in previous_episodes + ], + 'extracted_entities': node_names, + } + + llm_response = await llm_client.generate_response( + prompt_library.extract_nodes.reflexion(context) + ) + missed_entities = llm_response.get('missed_entities', []) + + return missed_entities + + async def extract_nodes( llm_client: LLMClient, episode: EpisodicNode, @@ -96,12 +132,33 @@ async def extract_nodes( ) -> list[EntityNode]: start = time() extracted_node_data: list[dict[str, Any]] = [] - if episode.source == EpisodeType.message: - extracted_node_data = await extract_message_nodes(llm_client, episode, previous_episodes) - elif episode.source == EpisodeType.text: - extracted_node_data = await extract_text_nodes(llm_client, episode, previous_episodes) - elif episode.source == EpisodeType.json: - extracted_node_data = await extract_json_nodes(llm_client, episode) + custom_prompt = '' + entities_missed = True + reflexion_iterations = 0 + while entities_missed and reflexion_iterations < MAX_REFLEXION_ITERATIONS: + if episode.source == EpisodeType.message: + extracted_node_data = await extract_message_nodes( + llm_client, episode, previous_episodes, custom_prompt + ) + elif episode.source == EpisodeType.text: + extracted_node_data = await extract_text_nodes( + llm_client, episode, previous_episodes, custom_prompt + ) + elif episode.source == EpisodeType.json: + extracted_node_data = await extract_json_nodes(llm_client, episode, custom_prompt) + + reflexion_iterations += 1 + if reflexion_iterations < MAX_REFLEXION_ITERATIONS: + entity_names = [node_data['name'] for node_data in extracted_node_data] + missing_entities = await extract_nodes_reflexion( + llm_client, episode, previous_episodes, entity_names + ) + + entities_missed = len(missing_entities) != 0 + + custom_prompt = 'The following entities were missed in a previous extraction: ' + for entity in missing_entities: + custom_prompt += f'\n{entity},' end = time() logger.debug(f'Extracted new nodes: {extracted_node_data} in {(end - start) * 1000} ms') diff --git a/poetry.lock b/poetry.lock index e5fa1da2..bfa8e42e 100644 --- a/poetry.lock +++ b/poetry.lock @@ -641,37 +641,37 @@ test = ["pytest"] [[package]] name = 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