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add reflexion (#212)

Browse source 
* add reflexion

* clean up boolean logic

* update conditional

* cap reflexion iterations

* don't do an extra reflection step
This commit is contained in:
Preston Rasmussen 2024-11-13 11:58:56 -05:00 • committed by GitHub
parent 471741c05a
commit eba9f40ca2
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9 changed files with 369 additions and 198 deletions
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6
examples/podcast/podcast_runner.py
View file

@ -69,6 +69,7 @@ async def main(use_bulk: bool = True):
episode_body=f'{message.speaker_name} ({message.role}): {message.content}', episode_body=f'{message.speaker_name} ({message.role}): {message.content}',
reference_time=message.actual_timestamp, reference_time=message.actual_timestamp,
source_description='Podcast Transcript', source_description='Podcast Transcript',
group_id='podcast',
) )
# build communities # build communities
@ -81,7 +82,7 @@ async def main(use_bulk: bool = True):
episode_body=f'{message.speaker_name} ({message.role}): {message.content}', episode_body=f'{message.speaker_name} ({message.role}): {message.content}',
reference_time=message.actual_timestamp, reference_time=message.actual_timestamp,
source_description='Podcast Transcript', source_description='Podcast Transcript',
group_id='1', group_id='podcast',
update_communities=True, update_communities=True,
) )
@ -94,11 +95,12 @@ async def main(use_bulk: bool = True):
source=EpisodeType.message, source=EpisodeType.message,
source_description='Podcast Transcript', source_description='Podcast Transcript',
reference_time=message.actual_timestamp, reference_time=message.actual_timestamp,
group_id='podcast',
) )
for i, message in enumerate(messages[3:20]) 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)) asyncio.run(main(False))

1
graphiti_core/helpers.py
View file

@ -25,6 +25,7 @@ load_dotenv()
DEFAULT_DATABASE = os.getenv('DEFAULT_DATABASE', None) DEFAULT_DATABASE = os.getenv('DEFAULT_DATABASE', None)
USE_PARALLEL_RUNTIME = bool(os.getenv('USE_PARALLEL_RUNTIME', False)) 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: def parse_db_date(neo_date: neo4j_time.DateTime | None) -> datetime | None:

37
graphiti_core/prompts/extract_edges.py
View file

@ -23,11 +23,13 @@ from .models import Message, PromptFunction, PromptVersion
class Prompt(Protocol): class Prompt(Protocol):
v1: PromptVersion v1: PromptVersion
v2: PromptVersion v2: PromptVersion
reflexion: PromptVersion
class Versions(TypedDict): class Versions(TypedDict):
v1: PromptFunction v1: PromptFunction
v2: PromptFunction v2: PromptFunction
reflexion: PromptFunction
def v1(context: dict[str, Any]) -> list[Message]: 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. Extract entity edges based on the content of the current episode, the given nodes, and context from previous episodes.
{context['custom_prompt']}
Guidelines: Guidelines:
1. Create edges only between the provided nodes. 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}

51
graphiti_core/prompts/extract_nodes.py
View file

@ -25,6 +25,7 @@ class Prompt(Protocol):
v2: PromptVersion v2: PromptVersion
extract_json: PromptVersion extract_json: PromptVersion
extract_text: PromptVersion extract_text: PromptVersion
reflexion: PromptVersion
class Versions(TypedDict): class Versions(TypedDict):
@ -32,6 +33,7 @@ class Versions(TypedDict):
v2: PromptFunction v2: PromptFunction
extract_json: PromptFunction extract_json: PromptFunction
extract_text: PromptFunction extract_text: PromptFunction
reflexion: PromptFunction
def v1(context: dict[str, Any]) -> list[Message]: def v1(context: dict[str, Any]) -> list[Message]:
@ -87,6 +89,8 @@ Conversation:
<CURRENT MESSAGE> <CURRENT MESSAGE>
{context["episode_content"]} {context["episode_content"]}
{context['custom_prompt']}
Guidelines: Guidelines:
1. ALWAYS extract the speaker/actor as the first node. The speaker is the part before the colon in each line of dialogue. 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. 2. Extract other significant entities, concepts, or actors mentioned in the conversation.
@ -125,6 +129,8 @@ Source Description:
JSON: JSON:
{context["episode_content"]} {context["episode_content"]}
{context['custom_prompt']}
Guidelines: Guidelines:
1. Always try to extract an entities that the JSON represents. This will often be something like a "name" or "user field 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 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""" user_prompt = f"""
Given the following conversation, extract entity nodes from the CURRENT MESSAGE that are explicitly or implicitly mentioned: 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)} {json.dumps([ep['content'] for ep in context['previous_episodes']], indent=2)}
</CONVERSATION>
<CURRENT MESSAGE> <CURRENT MESSAGE>
{context["episode_content"]} {context["episode_content"]}
</CURRENT MESSAGE>
{context['custom_prompt']}
Guidelines: Guidelines:
2. Extract significant entities, concepts, or actors mentioned in the conversation. 1. Extract significant entities, concepts, or actors mentioned in the conversation.
3. Provide concise but informative summaries for each extracted node. 2. Provide concise but informative summaries for each extracted node.
4. Avoid creating nodes for relationships or actions. 3. 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). 4. 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. 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: 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 = { versions: Versions = {
'v1': v1, 'v1': v1,
'v2': v2, 'v2': v2,
'extract_json': extract_json, 'extract_json': extract_json,
'extract_text': extract_text, 'extract_text': extract_text,
'reflexion': reflexion,
} }

14
graphiti_core/search/search.py
View file

@ -32,8 +32,10 @@ from graphiti_core.search.search_config import (
CommunitySearchConfig, CommunitySearchConfig,
EdgeReranker, EdgeReranker,
EdgeSearchConfig, EdgeSearchConfig,
EdgeSearchMethod,
NodeReranker, NodeReranker,
NodeSearchConfig, NodeSearchConfig,
NodeSearchMethod,
SearchConfig, SearchConfig,
SearchResults, 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} edge_uuid_map = {edge.uuid: edge for result in search_results for edge in result}
reranked_uuids: list[str] = [] 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] 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} node_uuid_map = {node.uuid: node for result in search_results for node in result}

27
graphiti_core/utils/maintenance/edge_operations.py
View file

@ -21,6 +21,7 @@ from time import time
from typing import List from typing import List
from graphiti_core.edges import CommunityEdge, EntityEdge, EpisodicEdge 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.llm_client import LLMClient
from graphiti_core.nodes import CommunityNode, EntityNode, EpisodicNode from graphiti_core.nodes import CommunityNode, EntityNode, EpisodicNode
from graphiti_core.prompts import prompt_library from graphiti_core.prompts import prompt_library
@ -91,10 +92,32 @@ async def extract_edges(
} }
for ep in previous_episodes for ep in previous_episodes
], ],
'custom_prompt': '',
} }
llm_response = await llm_client.generate_response(prompt_library.extract_edges.v2(context)) facts_missed = True
edges_data = llm_response.get('edges', []) 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() end = time()
logger.debug(f'Extracted new edges: {edges_data} in {(end - start) * 1000} ms') logger.debug(f'Extracted new edges: {edges_data} in {(end - start) * 1000} ms')

77
graphiti_core/utils/maintenance/node_operations.py
View file

@ -20,6 +20,7 @@ from datetime import datetime, timezone
from time import time from time import time
from typing import Any from typing import Any
from graphiti_core.helpers import MAX_REFLEXION_ITERATIONS
from graphiti_core.llm_client import LLMClient from graphiti_core.llm_client import LLMClient
from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode from graphiti_core.nodes import EntityNode, EpisodeType, EpisodicNode
from graphiti_core.prompts import prompt_library from graphiti_core.prompts import prompt_library
@ -28,7 +29,10 @@ logger = logging.getLogger(__name__)
async def extract_message_nodes( 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]]: ) -> list[dict[str, Any]]:
# Prepare context for LLM # Prepare context for LLM
context = { context = {
@ -41,6 +45,7 @@ async def extract_message_nodes(
} }
for ep in previous_episodes for ep in previous_episodes
], ],
'custom_prompt': custom_prompt,
} }
llm_response = await llm_client.generate_response(prompt_library.extract_nodes.v2(context)) 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( 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]]: ) -> list[dict[str, Any]]:
# Prepare context for LLM # Prepare context for LLM
context = { context = {
@ -62,6 +70,7 @@ async def extract_text_nodes(
} }
for ep in previous_episodes for ep in previous_episodes
], ],
'custom_prompt': custom_prompt,
} }
llm_response = await llm_client.generate_response( llm_response = await llm_client.generate_response(
@ -72,14 +81,14 @@ async def extract_text_nodes(
async def extract_json_nodes( async def extract_json_nodes(
llm_client: LLMClient, llm_client: LLMClient, episode: EpisodicNode, custom_prompt=''
episode: EpisodicNode,
) -> list[dict[str, Any]]: ) -> list[dict[str, Any]]:
# Prepare context for LLM # Prepare context for LLM
context = { context = {
'episode_content': episode.content, 'episode_content': episode.content,
'episode_timestamp': episode.valid_at.isoformat(), 'episode_timestamp': episode.valid_at.isoformat(),
'source_description': episode.source_description, 'source_description': episode.source_description,
'custom_prompt': custom_prompt,
} }
llm_response = await llm_client.generate_response( llm_response = await llm_client.generate_response(
@ -89,6 +98,33 @@ async def extract_json_nodes(
return extracted_node_data 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( async def extract_nodes(
llm_client: LLMClient, llm_client: LLMClient,
episode: EpisodicNode, episode: EpisodicNode,
@ -96,12 +132,33 @@ async def extract_nodes(
) -> list[EntityNode]: ) -> list[EntityNode]:
start = time() start = time()
extracted_node_data: list[dict[str, Any]] = [] extracted_node_data: list[dict[str, Any]] = []
if episode.source == EpisodeType.message: custom_prompt = ''
extracted_node_data = await extract_message_nodes(llm_client, episode, previous_episodes) entities_missed = True
elif episode.source == EpisodeType.text: reflexion_iterations = 0
extracted_node_data = await extract_text_nodes(llm_client, episode, previous_episodes) while entities_missed and reflexion_iterations < MAX_REFLEXION_ITERATIONS:
elif episode.source == EpisodeType.json: if episode.source == EpisodeType.message:
extracted_node_data = await extract_json_nodes(llm_client, episode) 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() end = time()
logger.debug(f'Extracted new nodes: {extracted_node_data} in {(end - start) * 1000} ms') logger.debug(f'Extracted new nodes: {extracted_node_data} in {(end - start) * 1000} ms')

352
poetry.lock generated
View file

@ -641,37 +641,37 @@ test = ["pytest"]
[[package]] [[package]]
name = "debugpy" name = "debugpy"
version = "1.8.7" version = "1.8.8"
description = "An implementation of the Debug Adapter Protocol for Python" description = "An implementation of the Debug Adapter Protocol for Python"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.8"
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{file = "ruff-0.7.2.tar.gz", hash = "sha256:2b14e77293380e475b4e3a7a368e14549288ed2931fce259a6f99978669e844f"}, {file = "ruff-0.7.3.tar.gz", hash = "sha256:e1d1ba2e40b6e71a61b063354d04be669ab0d39c352461f3d789cac68b54a313"},
] ]
[[package]] [[package]]
@ -3946,13 +3949,13 @@ win32 = ["pywin32"]
[[package]] [[package]]
name = "sentence-transformers" name = "sentence-transformers"
version = "3.2.1" version = "3.3.0"
description = "State-of-the-Art Text Embeddings" description = "State-of-the-Art Text Embeddings"
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.9"
files = [ files = [
{file = "sentence_transformers-3.2.1-py3-none-any.whl", hash = "sha256:c507e069eea33d15f1f2c72f74d7ea93abef298152cc235ab5af5e3a7584f738"}, {file = "sentence_transformers-3.3.0-py3-none-any.whl", hash = "sha256:5897c376fde1fea5f22a90ead2612278a464e52b8e42f1af95f84092c36bc23c"},
{file = "sentence_transformers-3.2.1.tar.gz", hash = "sha256:9fc38e620e5e1beba31d538a451778c9ccdbad77119d90f59f5bce49c4148e79"}, {file = "sentence_transformers-3.3.0.tar.gz", hash = "sha256:b91f0aea4ada72ed5a7cdbe8a6245a7152d0d9f84f336383778f8568e406b008"},
] ]
[package.dependencies] [package.dependencies]
@ -3965,7 +3968,7 @@ tqdm = "*"
transformers = ">=4.41.0,<5.0.0" transformers = ">=4.41.0,<5.0.0"
[package.extras] [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 = ["optimum[onnxruntime] (>=1.23.1)"]
onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.1)"] onnx-gpu = ["optimum[onnxruntime-gpu] (>=1.23.1)"]
openvino = ["optimum-intel[openvino] (>=1.20.0)"] openvino = ["optimum-intel[openvino] (>=1.20.0)"]
@ -4386,13 +4389,13 @@ files = [
[[package]] [[package]]
name = "tqdm" name = "tqdm"
version = "4.66.6" version = "4.67.0"
description = "Fast, Extensible Progress Meter" description = "Fast, Extensible Progress Meter"
optional = false optional = false
python-versions = ">=3.7" python-versions = ">=3.7"
files = [ files = [
{file = "tqdm-4.66.6-py3-none-any.whl", hash = "sha256:223e8b5359c2efc4b30555531f09e9f2f3589bcd7fdd389271191031b49b7a63"}, {file = "tqdm-4.67.0-py3-none-any.whl", hash = "sha256:0cd8af9d56911acab92182e88d763100d4788bdf421d251616040cc4d44863be"},
{file = "tqdm-4.66.6.tar.gz", hash = "sha256:4bdd694238bef1485ce839d67967ab50af8f9272aab687c0d7702a01da0be090"}, {file = "tqdm-4.67.0.tar.gz", hash = "sha256:fe5a6f95e6fe0b9755e9469b77b9c3cf850048224ecaa8293d7d2d31f97d869a"},
] ]
[package.dependencies] [package.dependencies]
@ -4400,6 +4403,7 @@ colorama = {version = "*", markers = "platform_system == \"Windows\""}
[package.extras] [package.extras]
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"] dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
discord = ["requests"]
notebook = ["ipywidgets (>=6)"] notebook = ["ipywidgets (>=6)"]
slack = ["slack-sdk"] slack = ["slack-sdk"]
telegram = ["requests"] telegram = ["requests"]
@ -4594,19 +4598,15 @@ files = [
[[package]] [[package]]
name = "webcolors" name = "webcolors"
version = "24.8.0" version = "24.11.1"
description = "A library for working with the color formats defined by HTML and CSS." description = "A library for working with the color formats defined by HTML and CSS."
optional = false optional = false
python-versions = ">=3.8" python-versions = ">=3.9"
files = [ files = [
{file = "webcolors-24.8.0-py3-none-any.whl", hash = "sha256:fc4c3b59358ada164552084a8ebee637c221e4059267d0f8325b3b560f6c7f0a"}, {file = "webcolors-24.11.1-py3-none-any.whl", hash = "sha256:515291393b4cdf0eb19c155749a096f779f7d909f7cceea072791cb9095b92e9"},
{file = "webcolors-24.8.0.tar.gz", hash = "sha256:08b07af286a01bcd30d583a7acadf629583d1f79bfef27dd2c2c5c263817277d"}, {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]] [[package]]
name = "webencodings" name = "webencodings"
version = "0.5.1" version = "0.5.1"
@ -4733,4 +4733,4 @@ propcache = ">=0.2.0"
[metadata] [metadata]
lock-version = "2.0" lock-version = "2.0"
python-versions = "^3.10" python-versions = "^3.10"
content-hash = "92b1a17a3a07b0be1d08371eab8ab4abb278834441e0bf7ae6138db0e66d562c" content-hash = "b4ad73c5ab1bf928f5107e1392c62d030d67741415b072ae06463a1d0e6aff1e"

2
pyproject.toml
View file

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