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

@@ -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))

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:

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:
+
+<CONVERSATION>:
+{json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)}
+</CONVERSATION>
+<CURRENT MESSAGE>
+{context["episode_content"]}
+</CURRENT MESSAGE>
+<EXTRACTED ENTITIES>
+{context["nodes"]}
+</EXTRACTED ENTITIES>
+<EXTRACTED FACTS>
+{context["extracted_facts"]}
+</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}

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:
 <CURRENT MESSAGE>
 {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:
+<CONVERSATION>
 {json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)}
+</CONVERSATION>
 <CURRENT MESSAGE>
 {context["episode_content"]}
+</CURRENT MESSAGE>
+
+{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:
+
+<CONVERSATION>:
+{json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)}
+</CONVERSATION>
+<CURRENT MESSAGE>
+{context["episode_content"]}
+</CURRENT MESSAGE>
+<EXTRACTED ENTITIES>
+{context["extracted_entities"]}
+</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,
 }

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}
 

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')

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')

poetry.lock

@@ -641,37 +641,37 @@ test = ["pytest"]
 
 [[package]]
 name = "debugpy"
-version = "1.8.7"
+version = "1.8.8"
 description = "An implementation of the Debug Adapter Protocol for Python"
 optional = false
 python-versions = ">=3.8"
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 ]
 
 [[package]]
@@ -1188,22 +1188,22 @@ arrow = ">=0.15.0"
 
 [[package]]
 name = "jedi"
-version = "0.19.1"
+version = "0.19.2"
 description = "An autocompletion tool for Python that can be used for text editors."
 optional = false
 python-versions = ">=3.6"
 files = [
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 ]
 
 [package.dependencies]
-parso = ">=0.8.3,<0.9.0"
+parso = ">=0.8.4,<0.9.0"
 
 [package.extras]
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-testing = ["Django", "attrs", "colorama", "docopt", "pytest (<7.0.0)"]
+testing = ["Django", "attrs", "colorama", "docopt", "pytest (<9.0.0)"]
 
 [[package]]
 name = "jinja2"
@@ -1317,15 +1317,18 @@ files = [
 
 [[package]]
 name = "json5"
-version = "0.9.25"
+version = "0.9.28"
 description = "A Python implementation of the JSON5 data format."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.8.0"
 files = [
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 ]
 
+[package.extras]
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+
 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1705,13 +1708,13 @@ orjson = ">=3.10.1"
 
 [[package]]
 name = "langsmith"
-version = "0.1.140"
+version = "0.1.142"
 description = "Client library to connect to the LangSmith LLM Tracing and Evaluation Platform."
 optional = false
 python-versions = "<4.0,>=3.8.1"
 files = [
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 ]
 
 [package.dependencies]
@@ -2444,13 +2447,13 @@ files = [
 
 [[package]]
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-version = "1.54.1"
+version = "1.54.3"
 description = "The official Python library for the openai API"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [package.dependencies]
@@ -2546,13 +2549,13 @@ files = [
 
 [[package]]
 name = "packaging"
-version = "24.1"
+version = "24.2"
 description = "Core utilities for Python packages"
 optional = false
 python-versions = ">=3.8"
 files = [
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 ]
 
 [[package]]
@@ -3409,105 +3412,105 @@ rpds-py = ">=0.7.0"
 
 [[package]]
 name = "regex"
-version = "2024.9.11"
+version = "2024.11.6"
 description = "Alternative regular expression module, to replace re."
 optional = false
 python-versions = ">=3.8"
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 [[package]]
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 [[package]]
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+version = "3.3.0"
 description = "State-of-the-Art Text Embeddings"
 optional = false
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 files = [
-    {file = "sentence_transformers-3.2.1-py3-none-any.whl", hash = "sha256:c507e069eea33d15f1f2c72f74d7ea93abef298152cc235ab5af5e3a7584f738"},
-    {file = "sentence_transformers-3.2.1.tar.gz", hash = "sha256:9fc38e620e5e1beba31d538a451778c9ccdbad77119d90f59f5bce49c4148e79"},
+    {file = "sentence_transformers-3.3.0-py3-none-any.whl", hash = "sha256:5897c376fde1fea5f22a90ead2612278a464e52b8e42f1af95f84092c36bc23c"},
+    {file = "sentence_transformers-3.3.0.tar.gz", hash = "sha256:b91f0aea4ada72ed5a7cdbe8a6245a7152d0d9f84f336383778f8568e406b008"},
 ]
 
 [package.dependencies]
@@ -3965,7 +3968,7 @@ tqdm = "*"
 transformers = ">=4.41.0,<5.0.0"
 
 [package.extras]
-dev = ["accelerate (>=0.20.3)", "datasets", "pre-commit", "pytest", "pytest-cov"]
+dev = ["accelerate (>=0.20.3)", "datasets", "peft", "pre-commit", "pytest", "pytest-cov"]
 onnx = ["optimum[onnxruntime] (>=1.23.1)"]
 onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.1)"]
 openvino = ["optimum-intel[openvino] (>=1.20.0)"]
@@ -4386,13 +4389,13 @@ files = [
 
 [[package]]
 name = "tqdm"
-version = "4.66.6"
+version = "4.67.0"
 description = "Fast, Extensible Progress Meter"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"},
-    {file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"},
+    {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
+    {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
 ]
 
 [package.dependencies]
@@ -4400,6 +4403,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
 
 [package.extras]
 dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
+discord = ["requests"]
 notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
@@ -4594,19 +4598,15 @@ files = [
 
 [[package]]
 name = "webcolors"
-version = "24.8.0"
+version = "24.11.1"
 description = "A library for working with the color formats defined by HTML and CSS."
 optional = false
-python-versions = ">=3.8"
+python-versions = ">=3.9"
 files = [
-    {file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"},
-    {file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"},
+    {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
+    {file = "webcolors-24.11.1.tar.gz", hash = "sha256:ecb3d768f32202af770477b8b65f318fa4f566c22948673a977b00d589dd80f6"},
 ]
 
-[package.extras]
-docs = ["furo", "sphinx", "sphinx-copybutton", "sphinx-inline-tabs", "sphinx-notfound-page", "sphinxext-opengraph"]
-tests = ["coverage[toml]"]
-
 [[package]]
 name = "webencodings"
 version = "0.5.1"
@@ -4733,4 +4733,4 @@ propcache = ">=0.2.0"
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.10"
-content-hash = "92b1a17a3a07b0be1d08371eab8ab4abb278834441e0bf7ae6138db0e66d562c"
+content-hash = "b4ad73c5ab1bf928f5107e1392c62d030d67741415b072ae06463a1d0e6aff1e"

pyproject.toml

@@ -20,10 +20,10 @@ diskcache = "^5.6.3"
 openai = "^1.53.0"
 tenacity = "9.0.0"
 numpy = ">=1.0.0"
+python-dotenv = "^1.0.1"
 
 [tool.poetry.dev-dependencies]
 pytest = "^8.3.3"
-python-dotenv = "^1.0.1"
 pytest-asyncio = "^0.24.0"
 pytest-xdist = "^3.6.1"
 ruff = "^0.7.1"