Merge branch 'dev' into COG-970-refactor-tokenizing
This commit is contained in:
Igor Ilic
commit
49f60971bb
15 changed files with 2180 additions and 1010 deletions
24
.github/workflows/clean_stale_pr.yaml
vendored
Normal file
24
.github/workflows/clean_stale_pr.yaml
vendored
Normal file
|
|
@ -0,0 +1,24 @@
|
|||
name: clean | remove stale PRs
|
||||
|
||||
on:
|
||||
# Run this action periodically (daily at 0:00 UTC in this example).
|
||||
schedule:
|
||||
- cron: "0 0 * * *"
|
||||
# Optionally, also run when pull requests are labeled, unlabeled, synchronized, or reopened
|
||||
# to update the stale timer as needed. Uncomment if desired.
|
||||
# pull_request:
|
||||
# types: [labeled, unlabeled, synchronize, reopened]
|
||||
|
||||
jobs:
|
||||
stale:
|
||||
runs-on: ubuntu-latest
|
||||
steps:
|
||||
- name: Mark and Close Stale
|
||||
uses: actions/stale@v6
|
||||
with:
|
||||
# Number of days of inactivity before the pull request is marked stale
|
||||
days-before-stale: 60
|
||||
# Number of days of inactivity after being marked stale before the pull request is closed
|
||||
days-before-close: 7
|
||||
# Comment to post when marking as stale
|
||||
stale-pr-message: "This pull request has been automatically marke
|
||||
11
README.md
11
README.md
|
|
@ -12,7 +12,11 @@ We build for developers who need a reliable, production-ready data layer for AI
|
|||
|
||||
## What is cognee?
|
||||
|
||||
Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you to interconnect and retrieve past conversations, documents, and audio transcriptions while reducing hallucinations, developer effort, and cost.
|
||||
Cognee implements scalable, modular ECL (Extract, Cognify, Load) pipelines that allow you to interconnect and retrieve past conversations, documents, and audio transcriptions while reducing hallucinations, developer effort, and cost.
|
||||
|
||||
Cognee merges graph and vector databases to uncover hidden relationships and new patterns in your data. You can automatically model, load and retrieve entities and objects representing your business domain and analyze their relationships, uncovering insights that neither vector stores nor graph stores alone can provide. Learn more about use-cases [here](https://docs.cognee.ai/use_cases)
|
||||
|
||||
|
||||
Try it in a Google Colab <a href="https://colab.research.google.com/drive/1g-Qnx6l_ecHZi0IOw23rg0qC4TYvEvWZ?usp=sharing">notebook</a> or have a look at our <a href="https://docs.cognee.ai">documentation</a>
|
||||
|
||||
If you have questions, join our <a href="https://discord.gg/NQPKmU5CCg">Discord</a> community
|
||||
|
|
@ -217,10 +221,11 @@ Cognee supports a variety of tools and services for different operations:
|
|||
|
||||
## Demo
|
||||
|
||||
Check out our demo notebook [here](https://github.com/topoteretes/cognee/blob/main/notebooks/cognee_demo.ipynb)
|
||||
Check out our demo notebook [here](https://github.com/topoteretes/cognee/blob/main/notebooks/cognee_demo.ipynb) or watch the Youtube video bellow
|
||||
|
||||
|
||||
[<img src="https://i3.ytimg.com/vi/-ARUfIzhzC4/maxresdefault.jpg" width="100%">](https://www.youtube.com/watch?v=BDFt4xVPmro "Learn about cognee: 55")
|
||||
[<img src="https://img.youtube.com/vi/fI4hDzguN5k/maxresdefault.jpg" width="100%">](https://www.youtube.com/watch?v=fI4hDzguN5k "Learn about cognee: 55")
|
||||
|
||||
|
||||
|
||||
## Get Started
|
||||
|
|
|
|||
|
|
@ -1,6 +1,8 @@
|
|||
from .server import mcp
|
||||
from .server import main as server_main
|
||||
|
||||
|
||||
def main():
|
||||
"""Main entry point for the package."""
|
||||
mcp.run(transport="stdio")
|
||||
import asyncio
|
||||
|
||||
asyncio.run(server_main())
|
||||
|
|
|
|||
15
cognee-mcp/src/client.py
Normal file → Executable file
15
cognee-mcp/src/client.py
Normal file → Executable file
|
|
@ -4,8 +4,8 @@ from mcp.client.stdio import stdio_client
|
|||
|
||||
# Create server parameters for stdio connection
|
||||
server_params = StdioServerParameters(
|
||||
command="mcp", # Executable
|
||||
args=["run", "src/server.py"], # Optional command line arguments
|
||||
command="uv", # Executable
|
||||
args=["--directory", ".", "run", "cognee"], # Optional command line arguments
|
||||
env=None, # Optional environment variables
|
||||
)
|
||||
|
||||
|
|
@ -33,10 +33,15 @@ async def run():
|
|||
async with ClientSession(read, write, timedelta(minutes=3)) as session:
|
||||
await session.initialize()
|
||||
|
||||
toolResult = await session.call_tool("cognify", arguments={"text": text})
|
||||
# toolResult = await session.call_tool("search", arguments={"search_query": "AI"})
|
||||
toolResult = await session.list_tools()
|
||||
|
||||
print(f"Cognify result: {toolResult}")
|
||||
toolResult = await session.call_tool("prune", arguments={})
|
||||
|
||||
toolResult = await session.call_tool("cognify", arguments={"text": text})
|
||||
|
||||
toolResult = await session.call_tool("search", arguments={"search_query": "AI"})
|
||||
|
||||
print(f"Cognify result: {toolResult.content}")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
|
|
|||
120
cognee-mcp/src/server.py
Normal file → Executable file
120
cognee-mcp/src/server.py
Normal file → Executable file
|
|
@ -1,16 +1,97 @@
|
|||
import os
|
||||
import cognee
|
||||
import logging
|
||||
import importlib.util
|
||||
from contextlib import redirect_stderr, redirect_stdout
|
||||
|
||||
# from PIL import Image as PILImage
|
||||
from mcp.server.fastmcp import FastMCP
|
||||
import mcp.types as types
|
||||
from mcp.server import Server, NotificationOptions
|
||||
from mcp.server.models import InitializationOptions
|
||||
from cognee.api.v1.search import SearchType
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
|
||||
mcp = FastMCP("cognee", timeout=120000)
|
||||
mcp = Server("cognee")
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@mcp.list_tools()
|
||||
async def list_tools() -> list[types.Tool]:
|
||||
return [
|
||||
types.Tool(
|
||||
name="cognify",
|
||||
description="Cognifies text into knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"text": {
|
||||
"type": "string",
|
||||
"description": "The text to cognify",
|
||||
},
|
||||
"graph_model_file": {
|
||||
"type": "string",
|
||||
"description": "The path to the graph model file",
|
||||
},
|
||||
"graph_model_name": {
|
||||
"type": "string",
|
||||
"description": "The name of the graph model",
|
||||
},
|
||||
},
|
||||
"required": ["text"],
|
||||
},
|
||||
),
|
||||
types.Tool(
|
||||
name="search",
|
||||
description="Searches for information in knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {
|
||||
"search_query": {
|
||||
"type": "string",
|
||||
"description": "The query to search for",
|
||||
},
|
||||
},
|
||||
"required": ["search_query"],
|
||||
},
|
||||
),
|
||||
types.Tool(
|
||||
name="prune",
|
||||
description="Prunes knowledge graph",
|
||||
inputSchema={
|
||||
"type": "object",
|
||||
"properties": {},
|
||||
},
|
||||
),
|
||||
]
|
||||
|
||||
|
||||
@mcp.call_tool()
|
||||
async def call_tools(name: str, arguments: dict) -> list[types.TextContent]:
|
||||
try:
|
||||
with open(os.devnull, "w") as fnull:
|
||||
with redirect_stdout(fnull), redirect_stderr(fnull):
|
||||
if name == "cognify":
|
||||
await cognify(
|
||||
text=arguments["text"],
|
||||
graph_model_file=arguments.get("graph_model_file", None),
|
||||
graph_model_name=arguments.get("graph_model_name", None),
|
||||
)
|
||||
|
||||
return [types.TextContent(type="text", text="Ingested")]
|
||||
elif name == "search":
|
||||
search_results = await search(arguments["search_query"])
|
||||
|
||||
return [types.TextContent(type="text", text=search_results)]
|
||||
elif name == "prune":
|
||||
await prune()
|
||||
|
||||
return [types.TextContent(type="text", text="Pruned")]
|
||||
except Exception as e:
|
||||
logger.error(f"Error calling tool '{name}': {str(e)}")
|
||||
return [types.TextContent(type="text", text=f"Error calling tool '{name}': {str(e)}")]
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def cognify(text: str, graph_model_file: str = None, graph_model_name: str = None) -> str:
|
||||
"""Build knowledge graph from the input text"""
|
||||
if graph_model_file and graph_model_name:
|
||||
|
|
@ -25,10 +106,7 @@ async def cognify(text: str, graph_model_file: str = None, graph_model_name: str
|
|||
except Exception as e:
|
||||
raise ValueError(f"Failed to cognify: {str(e)}")
|
||||
|
||||
return "Ingested"
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def search(search_query: str) -> str:
|
||||
"""Search the knowledge graph"""
|
||||
search_results = await cognee.search(SearchType.INSIGHTS, query_text=search_query)
|
||||
|
|
@ -38,16 +116,36 @@ async def search(search_query: str) -> str:
|
|||
return results
|
||||
|
||||
|
||||
@mcp.tool()
|
||||
async def prune() -> str:
|
||||
"""Reset the knowledge graph"""
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
return "Pruned"
|
||||
|
||||
async def main():
|
||||
try:
|
||||
from mcp.server.stdio import stdio_server
|
||||
|
||||
async with stdio_server() as (read_stream, write_stream):
|
||||
await mcp.run(
|
||||
read_stream=read_stream,
|
||||
write_stream=write_stream,
|
||||
initialization_options=InitializationOptions(
|
||||
server_name="cognee",
|
||||
server_version="0.1.0",
|
||||
capabilities=mcp.get_capabilities(
|
||||
notification_options=NotificationOptions(),
|
||||
experimental_capabilities={},
|
||||
),
|
||||
),
|
||||
raise_exceptions=True,
|
||||
)
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Server failed to start: {str(e)}", exc_info=True)
|
||||
raise
|
||||
|
||||
|
||||
# @mcp.tool()
|
||||
# async def visualize() -> Image:
|
||||
# """Visualize the knowledge graph"""
|
||||
# try:
|
||||
|
|
@ -116,4 +214,6 @@ def load_class(model_file, model_name):
|
|||
|
||||
if __name__ == "__main__":
|
||||
# Initialize and run the server
|
||||
mcp.run(transport="stdio")
|
||||
import asyncio
|
||||
|
||||
asyncio.run(main())
|
||||
|
|
|
|||
89
cognee-mcp/uv.lock
generated
89
cognee-mcp/uv.lock
generated
|
|
@ -1,7 +1,9 @@
|
|||
version = 1
|
||||
requires-python = ">=3.10"
|
||||
resolution-markers = [
|
||||
"python_full_version >= '3.12'",
|
||||
"python_full_version >= '3.13'",
|
||||
"python_full_version >= '3.12.4' and python_full_version < '3.13'",
|
||||
"python_full_version >= '3.12' and python_full_version < '3.12.4'",
|
||||
"python_full_version == '3.11.*'",
|
||||
"python_full_version < '3.11'",
|
||||
]
|
||||
|
|
@ -534,7 +536,7 @@ requires-dist = [
|
|||
{ name = "deepeval", marker = "extra == 'deepeval'", specifier = ">=2.0.1,<3.0.0" },
|
||||
{ name = "dlt", extras = ["sqlalchemy"], specifier = ">=1.4.1,<2.0.0" },
|
||||
{ name = "falkordb", marker = "extra == 'falkordb'", specifier = "==1.0.9" },
|
||||
{ name = "fastapi", specifier = ">=0.109.2,<0.116.0" },
|
||||
{ name = "fastapi", specifier = "==0.115.7" },
|
||||
{ name = "fastapi-users", extras = ["sqlalchemy"], specifier = "==14.0.0" },
|
||||
{ name = "filetype", specifier = ">=1.2.0,<2.0.0" },
|
||||
{ name = "graphistry", specifier = ">=0.33.5,<0.34.0" },
|
||||
|
|
@ -542,6 +544,7 @@ requires-dist = [
|
|||
{ name = "gunicorn", specifier = ">=20.1.0,<21.0.0" },
|
||||
{ name = "httpx", specifier = "==0.27.0" },
|
||||
{ name = "instructor", specifier = "==1.7.2" },
|
||||
{ name = "jedi", marker = "extra == 'codegraph'", specifier = ">=0.19.2,<0.20.0" },
|
||||
{ name = "jinja2", specifier = ">=3.1.3,<4.0.0" },
|
||||
{ name = "lancedb", specifier = "==0.16.0" },
|
||||
{ name = "langchain-text-splitters", marker = "extra == 'langchain'", specifier = "==0.3.2" },
|
||||
|
|
@ -558,6 +561,7 @@ requires-dist = [
|
|||
{ name = "numpy", specifier = "==1.26.4" },
|
||||
{ name = "openai", specifier = "==1.59.4" },
|
||||
{ name = "pandas", specifier = "==2.2.3" },
|
||||
{ name = "parso", marker = "extra == 'codegraph'", specifier = ">=0.8.4,<0.9.0" },
|
||||
{ name = "pgvector", marker = "extra == 'postgres'", specifier = ">=0.3.5,<0.4.0" },
|
||||
{ name = "posthog", marker = "extra == 'posthog'", specifier = ">=3.5.0,<4.0.0" },
|
||||
{ name = "pre-commit", specifier = ">=4.0.1,<5.0.0" },
|
||||
|
|
@ -863,16 +867,16 @@ wheels = [
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.115.6"
|
||||
version = "0.115.7"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "pydantic" },
|
||||
{ name = "starlette" },
|
||||
{ name = "typing-extensions" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/93/72/d83b98cd106541e8f5e5bfab8ef2974ab45a62e8a6c5b5e6940f26d2ed4b/fastapi-0.115.6.tar.gz", hash = "sha256:9ec46f7addc14ea472958a96aae5b5de65f39721a46aaf5705c480d9a8b76654", size = 301336 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/a2/f5/3f921e59f189e513adb9aef826e2841672d50a399fead4e69afdeb808ff4/fastapi-0.115.7.tar.gz", hash = "sha256:0f106da6c01d88a6786b3248fb4d7a940d071f6f488488898ad5d354b25ed015", size = 293177 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/52/b3/7e4df40e585df024fac2f80d1a2d579c854ac37109675db2b0cc22c0bb9e/fastapi-0.115.6-py3-none-any.whl", hash = "sha256:e9240b29e36fa8f4bb7290316988e90c381e5092e0cbe84e7818cc3713bcf305", size = 94843 },
|
||||
{ url = "https://files.pythonhosted.org/packages/e6/7f/bbd4dcf0faf61bc68a01939256e2ed02d681e9334c1a3cef24d5f77aba9f/fastapi-0.115.7-py3-none-any.whl", hash = "sha256:eb6a8c8bf7f26009e8147111ff15b5177a0e19bb4a45bc3486ab14804539d21e", size = 94777 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2339,44 +2343,34 @@ bcrypt = [
|
|||
|
||||
[[package]]
|
||||
name = "pyarrow"
|
||||
version = "19.0.0"
|
||||
version = "15.0.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/7b/01/fe1fd04744c2aa038e5a11c7a4adb3d62bce09798695e54f7274b5977134/pyarrow-19.0.0.tar.gz", hash = "sha256:8d47c691765cf497aaeed4954d226568563f1b3b74ff61139f2d77876717084b", size = 1129096 }
|
||||
dependencies = [
|
||||
{ name = "numpy" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/b3/1b/bc36a07706f630709bfd5a7936d2875e153e3d084a6d95dae583c3ad9de7/pyarrow-15.0.0.tar.gz", hash = "sha256:876858f549d540898f927eba4ef77cd549ad8d24baa3207cf1b72e5788b50e83", size = 1063077 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/1c/02/1ad80ffd3c558916858a49c83b6e494a9d93009bbebc603cf0cb8263bea7/pyarrow-19.0.0-cp310-cp310-macosx_12_0_arm64.whl", hash = "sha256:c318eda14f6627966997a7d8c374a87d084a94e4e38e9abbe97395c215830e0c", size = 30686262 },
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||||
{ url = "https://files.pythonhosted.org/packages/1b/f0/adab5f142eb8203db8bfbd3a816816e37a85423ae684567e7f3555658315/pyarrow-19.0.0-cp310-cp310-macosx_12_0_x86_64.whl", hash = "sha256:62ef8360ff256e960f57ce0299090fb86423afed5e46f18f1225f960e05aae3d", size = 32100005 },
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||||
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||||
{ url = "https://files.pythonhosted.org/packages/31/55/f05fc5608cc96060c2b24de505324d641888bd62d4eed2fa1dacd872a1e1/pyarrow-19.0.0-cp310-cp310-manylinux_2_28_x86_64.whl", hash = "sha256:e675a3ad4732b92d72e4d24009707e923cab76b0d088e5054914f11a797ebe44", size = 42067488 },
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|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.41.3"
|
||||
version = "0.45.3"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
dependencies = [
|
||||
{ name = "anyio" },
|
||||
]
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/1a/4c/9b5764bd22eec91c4039ef4c55334e9187085da2d8a2df7bd570869aae18/starlette-0.41.3.tar.gz", hash = "sha256:0e4ab3d16522a255be6b28260b938eae2482f98ce5cc934cb08dce8dc3ba5835", size = 2574159 }
|
||||
sdist = { url = "https://files.pythonhosted.org/packages/ff/fb/2984a686808b89a6781526129a4b51266f678b2d2b97ab2d325e56116df8/starlette-0.45.3.tar.gz", hash = "sha256:2cbcba2a75806f8a41c722141486f37c28e30a0921c5f6fe4346cb0dcee1302f", size = 2574076 }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/96/00/2b325970b3060c7cecebab6d295afe763365822b1306a12eeab198f74323/starlette-0.41.3-py3-none-any.whl", hash = "sha256:44cedb2b7c77a9de33a8b74b2b90e9f50d11fcf25d8270ea525ad71a25374ff7", size = 73225 },
|
||||
{ url = "https://files.pythonhosted.org/packages/d9/61/f2b52e107b1fc8944b33ef56bf6ac4ebbe16d91b94d2b87ce013bf63fb84/starlette-0.45.3-py3-none-any.whl", hash = "sha256:dfb6d332576f136ec740296c7e8bb8c8a7125044e7c6da30744718880cdd059d", size = 71507 },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
|
|
@ -77,14 +77,51 @@ class SQLAlchemyAdapter:
|
|||
text(f"DROP TABLE IF EXISTS {schema_name}.{table_name} CASCADE;")
|
||||
)
|
||||
|
||||
async def insert_data(self, schema_name: str, table_name: str, data: list[dict]):
|
||||
columns = ", ".join(data[0].keys())
|
||||
values = ", ".join([f"({', '.join([f':{key}' for key in row.keys()])})" for row in data])
|
||||
insert_query = text(f"INSERT INTO {schema_name}.{table_name} ({columns}) VALUES {values};")
|
||||
async def insert_data(
|
||||
self,
|
||||
table_name: str,
|
||||
data: list[dict],
|
||||
schema_name: Optional[str] = "public",
|
||||
) -> int:
|
||||
"""
|
||||
Insert data into specified table using SQLAlchemy Core with batch optimization
|
||||
Returns number of inserted rows
|
||||
|
||||
async with self.engine.begin() as connection:
|
||||
await connection.execute(insert_query, data)
|
||||
await connection.close()
|
||||
Usage Example:
|
||||
from cognee.infrastructure.databases.relational.get_relational_engine import get_relational_engine
|
||||
from uuid import UUID
|
||||
db = get_relational_engine()
|
||||
table_name = "groups"
|
||||
data = {
|
||||
"id": UUID("c70a3cec-3309-44df-8ee6-eced820cf438"),
|
||||
"name": "test"
|
||||
}
|
||||
await db.insert_data(table_name, data)
|
||||
"""
|
||||
if not data:
|
||||
logger.info("No data provided for insertion")
|
||||
return 0
|
||||
|
||||
try:
|
||||
# Use SQLAlchemy Core insert with execution options
|
||||
async with self.engine.begin() as conn:
|
||||
# Dialect-agnostic table reference
|
||||
if self.engine.dialect.name == "sqlite":
|
||||
# Foreign key constraints are disabled by default in SQLite (for backwards compatibility),
|
||||
# so must be enabled for each database connection/session separately.
|
||||
await conn.execute(text("PRAGMA foreign_keys=ON"))
|
||||
table = await self.get_table(table_name) # SQLite ignores schemas
|
||||
else:
|
||||
table = await self.get_table(table_name, schema_name)
|
||||
|
||||
result = await conn.execute(table.insert().values(data))
|
||||
|
||||
# Return rowcount for validation
|
||||
return result.rowcount
|
||||
|
||||
except Exception as e:
|
||||
logger.error(f"Insert failed: {str(e)}")
|
||||
raise e # Re-raise for error handling upstream
|
||||
|
||||
async def get_schema_list(self) -> List[str]:
|
||||
"""
|
||||
|
|
|
|||
|
|
@ -14,9 +14,9 @@ from ...relational.ModelBase import Base
|
|||
from ...relational.sqlalchemy.SqlAlchemyAdapter import SQLAlchemyAdapter
|
||||
from ..embeddings.EmbeddingEngine import EmbeddingEngine
|
||||
from ..models.ScoredResult import ScoredResult
|
||||
from ..utils import normalize_distances
|
||||
from ..vector_db_interface import VectorDBInterface
|
||||
from .serialize_data import serialize_data
|
||||
from ..utils import normalize_distances
|
||||
|
||||
|
||||
class IndexSchema(DataPoint):
|
||||
|
|
@ -247,12 +247,22 @@ class PGVectorAdapter(SQLAlchemyAdapter, VectorDBInterface):
|
|||
|
||||
# Extract distances and find min/max for normalization
|
||||
for vector in closest_items:
|
||||
# TODO: Add normalization of similarity score
|
||||
vector_list.append(vector)
|
||||
vector_list.append(
|
||||
{
|
||||
"id": UUID(str(vector.id)),
|
||||
"payload": vector.payload,
|
||||
"_distance": vector.similarity,
|
||||
}
|
||||
)
|
||||
|
||||
# Normalize vector distance and add this as score information to vector_list
|
||||
normalized_values = normalize_distances(vector_list)
|
||||
for i in range(0, len(normalized_values)):
|
||||
vector_list[i]["score"] = normalized_values[i]
|
||||
|
||||
# Create and return ScoredResult objects
|
||||
return [
|
||||
ScoredResult(id=UUID(str(row.id)), payload=row.payload, score=row.similarity)
|
||||
ScoredResult(id=row.get("id"), payload=row.get("payload"), score=row.get("score"))
|
||||
for row in vector_list
|
||||
]
|
||||
|
||||
|
|
|
|||
|
|
@ -26,10 +26,6 @@ class DataPoint(BaseModel):
|
|||
topological_rank: Optional[int] = 0
|
||||
_metadata: Optional[MetaData] = {"index_fields": [], "type": "DataPoint"}
|
||||
|
||||
# Override the Pydantic configuration
|
||||
class Config:
|
||||
underscore_attrs_are_private = True
|
||||
|
||||
@classmethod
|
||||
def get_embeddable_data(self, data_point):
|
||||
if (
|
||||
|
|
|
|||
File diff suppressed because one or more lines are too long
File diff suppressed because it is too large
Load diff
1
notebooks/data/graphrag
Submodule
1
notebooks/data/graphrag
Submodule
|
|
@ -0,0 +1 @@
|
|||
Subproject commit 130b84db9270734756d16918e5c86034777140fc
|
||||
|
|
@ -618,76 +618,339 @@
|
|||
"cell_type": "markdown",
|
||||
"id": "e519e30c0423c2a",
|
||||
"metadata": {},
|
||||
"source": "## Let's add evals"
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"id": "b22ae3d868fa5606",
|
||||
"metadata": {
|
||||
"ExecuteTime": {
|
||||
"end_time": "2024-12-19T18:01:11.387716Z",
|
||||
"start_time": "2024-12-19T18:01:11.278042Z"
|
||||
}
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"ename": "ModuleNotFoundError",
|
||||
"evalue": "No module named 'deepeval'",
|
||||
"output_type": "error",
|
||||
"traceback": [
|
||||
"\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)",
|
||||
"Cell \u001b[0;32mIn[3], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mevals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meval_on_hotpot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m eval_on_hotpotQA\n\u001b[1;32m 2\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mevals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meval_on_hotpot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m answer_with_cognee\n\u001b[1;32m 3\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mevals\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01meval_on_hotpot\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m answer_without_cognee\n",
|
||||
"File \u001b[0;32m~/cognee/evals/eval_on_hotpot.py:7\u001b[0m\n\u001b[1;32m 4\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mstatistics\u001b[39;00m\n\u001b[1;32m 5\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mpathlib\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m Path\n\u001b[0;32m----> 7\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mdeepeval\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mmetrics\u001b[39;00m\n\u001b[1;32m 8\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mwget\u001b[39;00m\n\u001b[1;32m 9\u001b[0m \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01mdeepeval\u001b[39;00m\u001b[38;5;21;01m.\u001b[39;00m\u001b[38;5;21;01mdataset\u001b[39;00m \u001b[38;5;28;01mimport\u001b[39;00m EvaluationDataset\n",
|
||||
"\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'deepeval'"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"from evals.eval_on_hotpot import eval_on_hotpotQA\n",
|
||||
"from evals.eval_on_hotpot import answer_with_cognee\n",
|
||||
"from evals.eval_on_hotpot import answer_without_cognee\n",
|
||||
"from evals.eval_on_hotpot import eval_answers\n",
|
||||
"from cognee.base_config import get_base_config\n",
|
||||
"from pathlib import Path\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import wget\n",
|
||||
"import json\n",
|
||||
"import statistics"
|
||||
"## Let's add evals"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "728355d390e3a01b",
|
||||
"id": "3845443e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install \"cognee[deepeval]\""
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7a2c3c70",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from evals.eval_on_hotpot import deepeval_answers, answer_qa_instance\n",
|
||||
"from evals.qa_dataset_utils import load_qa_dataset\n",
|
||||
"from evals.qa_metrics_utils import get_metrics\n",
|
||||
"from evals.qa_context_provider_utils import qa_context_providers\n",
|
||||
"from pathlib import Path\n",
|
||||
"from tqdm import tqdm\n",
|
||||
"import statistics\n",
|
||||
"import random"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "53a609d8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"answer_provider = answer_with_cognee # For native LLM answers use answer_without_cognee\n",
|
||||
"num_samples = 10 # With cognee, it takes ~1m10s per sample\n",
|
||||
"dataset_name_or_filename = \"hotpotqa\"\n",
|
||||
"dataset = load_qa_dataset(dataset_name_or_filename)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7351ab8f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"context_provider_name = \"cognee\"\n",
|
||||
"context_provider = qa_context_providers[context_provider_name]"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9346115b",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"random.seed(42)\n",
|
||||
"instances = dataset if not num_samples else random.sample(dataset, num_samples)\n",
|
||||
"\n",
|
||||
"base_config = get_base_config()\n",
|
||||
"data_root_dir = base_config.data_root_directory\n",
|
||||
"out_path = \"out\" \n",
|
||||
"if not Path(out_path).exists():\n",
|
||||
" Path(out_path).mkdir()\n",
|
||||
"contexts_filename = out_path / Path(\n",
|
||||
" f\"contexts_{dataset_name_or_filename.split('.')[0]}_{context_provider_name}.json\"\n",
|
||||
" )\n",
|
||||
"\n",
|
||||
"if not Path(data_root_dir).exists():\n",
|
||||
" Path(data_root_dir).mkdir()\n",
|
||||
"\n",
|
||||
"filepath = data_root_dir / Path(\"hotpot_dev_fullwiki_v1.json\")\n",
|
||||
"if not filepath.exists():\n",
|
||||
" url = \"http://curtis.ml.cmu.edu/datasets/hotpot/hotpot_dev_fullwiki_v1.json\"\n",
|
||||
" wget.download(url, out=data_root_dir)\n",
|
||||
"\n",
|
||||
"with open(filepath, \"r\") as file:\n",
|
||||
" dataset = json.load(file)\n",
|
||||
"\n",
|
||||
"instances = dataset if not num_samples else dataset[:num_samples]\n",
|
||||
"answers = []\n",
|
||||
"for instance in tqdm(instances, desc=\"Getting answers\"):\n",
|
||||
" answer = answer_provider(instance)\n",
|
||||
" answer = await answer_qa_instance(instance, context_provider, contexts_filename)\n",
|
||||
" answers.append(answer)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1e7d872d",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"#### Define Metrics for Evaluation and Calculate Score\n",
|
||||
"**Options**: \n",
|
||||
"- **Correctness**: Is the actual output factually correct based on the expected output?\n",
|
||||
"- **Comprehensiveness**: How much detail does the answer provide to cover all aspects and details of the question?\n",
|
||||
"- **Diversity**: How varied and rich is the answer in providing different perspectives and insights on the question?\n",
|
||||
"- **Empowerment**: How well does the answer help the reader understand and make informed judgements about the topic?\n",
|
||||
"- **Directness**: How specifically and clearly does the answer address the question?\n",
|
||||
"- **F1 Score**: the harmonic mean of the precision and recall, using word-level Exact Match\n",
|
||||
"- **EM Score**: the rate at which the predicted strings exactly match their references, ignoring white spaces and capitalization."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "c81e2b46",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculate `\"Correctness\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "ae728344",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"Correctness\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "764aac6d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Correctness = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(Correctness)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "6d3bbdc5",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating `\"Comprehensiveness\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9793ef78",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"Comprehensiveness\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "9add448a",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Comprehensiveness = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(Comprehensiveness)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "bce2fa25",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating `\"Diversity\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "f60a179e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"Diversity\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "7ccbd0ab",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Diversity = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(Diversity)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "191cab63",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating`\"Empowerment\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "66bec0bf",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"Empowerment\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "1b043a8f",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Empowerment = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(Empowerment)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2cac3be9",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating `\"Directness\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "adaa17c0",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"Directness\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "3a8f97c9",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"Directness = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(Directness)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "1ad6feb8",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating `\"F1\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "bdc48259",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"F1\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "c43c17c8",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8bfcc46d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"F1_score = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(F1_score)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "2583f948",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"##### Calculating `\"EM\"`"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "90a8f630",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"metric_name_list = [\"EM\"]\n",
|
||||
"eval_metrics = get_metrics(metric_name_list)\n",
|
||||
"eval_results = await deepeval_answers(instances, answers, eval_metrics[\"deepeval_metrics\"]) "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "8d1b1ea1",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"EM = statistics.mean(\n",
|
||||
" [result.metrics_data[0].score for result in eval_results.test_results]\n",
|
||||
")\n",
|
||||
"print(EM)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "288ab570",
|
||||
|
|
@ -700,7 +963,7 @@
|
|||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": ".venv",
|
||||
"display_name": "cognee-c83GrcRT-py3.11",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
|
|
@ -714,7 +977,7 @@
|
|||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.8"
|
||||
"version": "3.11.10"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
|
|
|
|||
350
poetry.lock
generated
350
poetry.lock
generated
|
|
@ -452,13 +452,13 @@ test = ["distro (>=1.9.0,<1.10.0)", "flake8 (>=6.1,<7.0)", "flake8-pyi (>=24.1.0
|
|||
|
||||
[[package]]
|
||||
name = "attrs"
|
||||
version = "24.3.0"
|
||||
version = "25.1.0"
|
||||
description = "Classes Without Boilerplate"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "attrs-24.3.0-py3-none-any.whl", hash = "sha256:ac96cd038792094f438ad1f6ff80837353805ac950cd2aa0e0625ef19850c308"},
|
||||
{file = "attrs-24.3.0.tar.gz", hash = "sha256:8f5c07333d543103541ba7be0e2ce16eeee8130cb0b3f9238ab904ce1e85baff"},
|
||||
{file = "attrs-25.1.0-py3-none-any.whl", hash = "sha256:c75a69e28a550a7e93789579c22aa26b0f5b83b75dc4e08fe092980051e1090a"},
|
||||
{file = "attrs-25.1.0.tar.gz", hash = "sha256:1c97078a80c814273a76b2a298a932eb681c87415c11dee0a6921de7f1b02c3e"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -609,17 +609,17 @@ xyzservices = ">=2021.09.1"
|
|||
|
||||
[[package]]
|
||||
name = "boto3"
|
||||
version = "1.36.2"
|
||||
version = "1.36.6"
|
||||
description = "The AWS SDK for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "boto3-1.36.2-py3-none-any.whl", hash = "sha256:76cfc9a705be46e8d22607efacc8d688c064f923d785a01c00b28e9a96425d1a"},
|
||||
{file = "boto3-1.36.2.tar.gz", hash = "sha256:fde1c29996b77274a60b7bc9f741525afa6267bb1716eb644a764fb7c124a0d2"},
|
||||
{file = "boto3-1.36.6-py3-none-any.whl", hash = "sha256:6d473f0f340d02b4e9ad5b8e68786a09728101a8b950231b89ebdaf72b6dca21"},
|
||||
{file = "boto3-1.36.6.tar.gz", hash = "sha256:b36feae061dc0793cf311468956a0a9e99215ce38bc99a1a4e55a5b105f16297"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
botocore = ">=1.36.2,<1.37.0"
|
||||
botocore = ">=1.36.6,<1.37.0"
|
||||
jmespath = ">=0.7.1,<2.0.0"
|
||||
s3transfer = ">=0.11.0,<0.12.0"
|
||||
|
||||
|
|
@ -628,13 +628,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
|
|||
|
||||
[[package]]
|
||||
name = "botocore"
|
||||
version = "1.36.2"
|
||||
version = "1.36.6"
|
||||
description = "Low-level, data-driven core of boto 3."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "botocore-1.36.2-py3-none-any.whl", hash = "sha256:bc3b7e3b573a48af2bd7116b80fe24f9a335b0b67314dcb2697a327d009abf29"},
|
||||
{file = "botocore-1.36.2.tar.gz", hash = "sha256:a1fe6610983f0214b0c7655fe6990b6a731746baf305b182976fc7b568fc3cb0"},
|
||||
{file = "botocore-1.36.6-py3-none-any.whl", hash = "sha256:f77bbbb03fb420e260174650fb5c0cc142ec20a96967734eed2b0ef24334ef34"},
|
||||
{file = "botocore-1.36.6.tar.gz", hash = "sha256:4864c53d638da191a34daf3ede3ff1371a3719d952cc0c6bd24ce2836a38dd77"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1257,13 +1257,13 @@ optimize = ["orjson"]
|
|||
|
||||
[[package]]
|
||||
name = "deepeval"
|
||||
version = "2.1.9"
|
||||
version = "2.2.6"
|
||||
description = "The Open-Source LLM Evaluation Framework."
|
||||
optional = true
|
||||
python-versions = "<3.13,>=3.9"
|
||||
python-versions = "<3.14,>=3.9"
|
||||
files = [
|
||||
{file = "deepeval-2.1.9-py3-none-any.whl", hash = "sha256:c225f8ab6ab910de50026dfd46e2ea38541b3697b189831482a6f02162ead536"},
|
||||
{file = "deepeval-2.1.9.tar.gz", hash = "sha256:b6c9e90fd0ab639c5b0af5023f2e3fd20ce1906b05d7dc9bfc0bd2f46d0545e0"},
|
||||
{file = "deepeval-2.2.6-py3-none-any.whl", hash = "sha256:0148a3fc05bff9aa4a21b7c4bd23c7b610957469fe55440f5826b4b7c38797a3"},
|
||||
{file = "deepeval-2.2.6.tar.gz", hash = "sha256:337b5a8a07dc94e5f72c6db0f5670bc77445edb662a019ccec61f438d150c281"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -1308,20 +1308,20 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "deprecated"
|
||||
version = "1.2.15"
|
||||
version = "1.2.17"
|
||||
description = "Python @deprecated decorator to deprecate old python classes, functions or methods."
|
||||
optional = true
|
||||
python-versions = "!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,>=2.7"
|
||||
files = [
|
||||
{file = "Deprecated-1.2.15-py2.py3-none-any.whl", hash = "sha256:353bc4a8ac4bfc96800ddab349d89c25dec1079f65fd53acdcc1e0b975b21320"},
|
||||
{file = "deprecated-1.2.15.tar.gz", hash = "sha256:683e561a90de76239796e6b6feac66b99030d2dd3fcf61ef996330f14bbb9b0d"},
|
||||
{file = "Deprecated-1.2.17-py2.py3-none-any.whl", hash = "sha256:69cdc0a751671183f569495e2efb14baee4344b0236342eec29f1fde25d61818"},
|
||||
{file = "deprecated-1.2.17.tar.gz", hash = "sha256:0114a10f0bbb750b90b2c2296c90cf7e9eaeb0abb5cf06c80de2c60138de0a82"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
wrapt = ">=1.10,<2"
|
||||
|
||||
[package.extras]
|
||||
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "jinja2 (>=3.0.3,<3.1.0)", "setuptools", "sphinx (<2)", "tox"]
|
||||
dev = ["PyTest", "PyTest-Cov", "bump2version (<1)", "setuptools", "tox"]
|
||||
|
||||
[[package]]
|
||||
name = "deprecation"
|
||||
|
|
@ -1592,13 +1592,13 @@ testing = ["hatch", "pre-commit", "pytest", "tox"]
|
|||
|
||||
[[package]]
|
||||
name = "executing"
|
||||
version = "2.1.0"
|
||||
version = "2.2.0"
|
||||
description = "Get the currently executing AST node of a frame, and other information"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "executing-2.1.0-py2.py3-none-any.whl", hash = "sha256:8d63781349375b5ebccc3142f4b30350c0cd9c79f921cde38be2be4637e98eaf"},
|
||||
{file = "executing-2.1.0.tar.gz", hash = "sha256:8ea27ddd260da8150fa5a708269c4a10e76161e2496ec3e587da9e3c0fe4b9ab"},
|
||||
{file = "executing-2.2.0-py2.py3-none-any.whl", hash = "sha256:11387150cad388d62750327a53d3339fad4888b39a6fe233c3afbb54ecffd3aa"},
|
||||
{file = "executing-2.2.0.tar.gz", hash = "sha256:5d108c028108fe2551d1a7b2e8b713341e2cb4fc0aa7dcf966fa4327a5226755"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -1619,23 +1619,23 @@ redis = ">=5.0.1,<6.0.0"
|
|||
|
||||
[[package]]
|
||||
name = "fastapi"
|
||||
version = "0.115.6"
|
||||
version = "0.115.7"
|
||||
description = "FastAPI framework, high performance, easy to learn, fast to code, ready for production"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "fastapi-0.115.6-py3-none-any.whl", hash = "sha256:e9240b29e36fa8f4bb7290316988e90c381e5092e0cbe84e7818cc3713bcf305"},
|
||||
{file = "fastapi-0.115.6.tar.gz", hash = "sha256:9ec46f7addc14ea472958a96aae5b5de65f39721a46aaf5705c480d9a8b76654"},
|
||||
{file = "fastapi-0.115.7-py3-none-any.whl", hash = "sha256:eb6a8c8bf7f26009e8147111ff15b5177a0e19bb4a45bc3486ab14804539d21e"},
|
||||
{file = "fastapi-0.115.7.tar.gz", hash = "sha256:0f106da6c01d88a6786b3248fb4d7a940d071f6f488488898ad5d354b25ed015"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
pydantic = ">=1.7.4,<1.8 || >1.8,<1.8.1 || >1.8.1,<2.0.0 || >2.0.0,<2.0.1 || >2.0.1,<2.1.0 || >2.1.0,<3.0.0"
|
||||
starlette = ">=0.40.0,<0.42.0"
|
||||
starlette = ">=0.40.0,<0.46.0"
|
||||
typing-extensions = ">=4.8.0"
|
||||
|
||||
[package.extras]
|
||||
all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=2.11.2)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.7)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
|
||||
standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=2.11.2)", "python-multipart (>=0.0.7)", "uvicorn[standard] (>=0.12.0)"]
|
||||
all = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "itsdangerous (>=1.1.0)", "jinja2 (>=3.1.5)", "orjson (>=3.2.1)", "pydantic-extra-types (>=2.0.0)", "pydantic-settings (>=2.0.0)", "python-multipart (>=0.0.18)", "pyyaml (>=5.3.1)", "ujson (>=4.0.1,!=4.0.2,!=4.1.0,!=4.2.0,!=4.3.0,!=5.0.0,!=5.1.0)", "uvicorn[standard] (>=0.12.0)"]
|
||||
standard = ["email-validator (>=2.0.0)", "fastapi-cli[standard] (>=0.0.5)", "httpx (>=0.23.0)", "jinja2 (>=3.1.5)", "python-multipart (>=0.0.18)", "uvicorn[standard] (>=0.12.0)"]
|
||||
|
||||
[[package]]
|
||||
name = "fastapi-users"
|
||||
|
|
@ -1694,18 +1694,18 @@ devel = ["colorama", "json-spec", "jsonschema", "pylint", "pytest", "pytest-benc
|
|||
|
||||
[[package]]
|
||||
name = "filelock"
|
||||
version = "3.16.1"
|
||||
version = "3.17.0"
|
||||
description = "A platform independent file lock."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "filelock-3.16.1-py3-none-any.whl", hash = "sha256:2082e5703d51fbf98ea75855d9d5527e33d8ff23099bec374a134febee6946b0"},
|
||||
{file = "filelock-3.16.1.tar.gz", hash = "sha256:c249fbfcd5db47e5e2d6d62198e565475ee65e4831e2561c8e313fa7eb961435"},
|
||||
{file = "filelock-3.17.0-py3-none-any.whl", hash = "sha256:533dc2f7ba78dc2f0f531fc6c4940addf7b70a481e269a5a3b93be94ffbe8338"},
|
||||
{file = "filelock-3.17.0.tar.gz", hash = "sha256:ee4e77401ef576ebb38cd7f13b9b28893194acc20a8e68e18730ba9c0e54660e"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
docs = ["furo (>=2024.8.6)", "sphinx (>=8.0.2)", "sphinx-autodoc-typehints (>=2.4.1)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.6.1)", "diff-cover (>=9.2)", "pytest (>=8.3.3)", "pytest-asyncio (>=0.24)", "pytest-cov (>=5)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.26.4)"]
|
||||
docs = ["furo (>=2024.8.6)", "sphinx (>=8.1.3)", "sphinx-autodoc-typehints (>=3)"]
|
||||
testing = ["covdefaults (>=2.3)", "coverage (>=7.6.10)", "diff-cover (>=9.2.1)", "pytest (>=8.3.4)", "pytest-asyncio (>=0.25.2)", "pytest-cov (>=6)", "pytest-mock (>=3.14)", "pytest-timeout (>=2.3.1)", "virtualenv (>=20.28.1)"]
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||||
typing = ["typing-extensions (>=4.12.2)"]
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||||
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||||
[[package]]
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||||
|
|
@ -1721,61 +1721,61 @@ files = [
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|||
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||||
[[package]]
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||||
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version = "4.55.6"
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{file = "fonttools-4.55.6-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:03701e7de70c71eb5965cb200986b0c11dfa3cf8e843e4f517ee30a0f43f0a25"},
|
||||
{file = "fonttools-4.55.6-cp38-cp38-win32.whl", hash = "sha256:f66561fbfb75785d06513b8025a50be37bf970c3c413e87581cc6eff10bc78f1"},
|
||||
{file = "fonttools-4.55.6-cp38-cp38-win_amd64.whl", hash = "sha256:edf159a8f1e48dc4683a715b36da76dd2f82954b16bfe11a215d58e963d31cfc"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-macosx_10_9_universal2.whl", hash = "sha256:61aa1997c520bee4cde14ffabe81efc4708c500c8c81dce37831551627a2be56"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:7954ea66a8d835f279c17d8474597a001ddd65a2c1ca97e223041bfbbe11f65e"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3f4e88f15f5ed4d2e4bdfcc98540bb3987ae25904f9be304be9a604e7a7050a1"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2d419483a6295e83cabddb56f1c7b7bfdc8169de2fcb5c68d622bd11140355f9"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:acc74884afddc2656bffc50100945ff407574538c152931c402fccddc46f0abc"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:a55489c7e9d5ea69690a2afad06723c3d0c48c6d276a25391ea97cb31a16b37c"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-win32.whl", hash = "sha256:8c9de8d16d02ecc8b65e3f3d2d1e3002be2c4a3f094d580faf76d7f768bd45fe"},
|
||||
{file = "fonttools-4.55.6-cp39-cp39-win_amd64.whl", hash = "sha256:471961af7a4b8461fac0c8ee044b4986e6fe3746d4c83a1aacbdd85b4eb53f93"},
|
||||
{file = "fonttools-4.55.6-py3-none-any.whl", hash = "sha256:d20ab5a78d0536c26628eaadba661e7ae2427b1e5c748a0a510a44d914e1b155"},
|
||||
{file = "fonttools-4.55.6.tar.gz", hash = "sha256:1beb4647a0df5ceaea48015656525eb8081af226fe96554089fd3b274d239ef0"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -2540,13 +2540,13 @@ test = ["eth_utils (>=2.0.0)", "hypothesis (>=3.44.24,<=6.31.6)", "pytest (>=7.0
|
|||
|
||||
[[package]]
|
||||
name = "hpack"
|
||||
version = "4.0.0"
|
||||
description = "Pure-Python HPACK header compression"
|
||||
version = "4.1.0"
|
||||
description = "Pure-Python HPACK header encoding"
|
||||
optional = true
|
||||
python-versions = ">=3.6.1"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "hpack-4.0.0-py3-none-any.whl", hash = "sha256:84a076fad3dc9a9f8063ccb8041ef100867b1878b25ef0ee63847a5d53818a6c"},
|
||||
{file = "hpack-4.0.0.tar.gz", hash = "sha256:fc41de0c63e687ebffde81187a948221294896f6bdc0ae2312708df339430095"},
|
||||
{file = "hpack-4.1.0-py3-none-any.whl", hash = "sha256:157ac792668d995c657d93111f46b4535ed114f0c9c8d672271bbec7eae1b496"},
|
||||
{file = "hpack-4.1.0.tar.gz", hash = "sha256:ec5eca154f7056aa06f196a557655c5b009b382873ac8d1e66e79e87535f1dca"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2701,24 +2701,24 @@ tests = ["freezegun", "pytest", "pytest-cov"]
|
|||
|
||||
[[package]]
|
||||
name = "hyperframe"
|
||||
version = "6.0.1"
|
||||
description = "HTTP/2 framing layer for Python"
|
||||
version = "6.1.0"
|
||||
description = "Pure-Python HTTP/2 framing"
|
||||
optional = true
|
||||
python-versions = ">=3.6.1"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "hyperframe-6.0.1-py3-none-any.whl", hash = "sha256:0ec6bafd80d8ad2195c4f03aacba3a8265e57bc4cff261e802bf39970ed02a15"},
|
||||
{file = "hyperframe-6.0.1.tar.gz", hash = "sha256:ae510046231dc8e9ecb1a6586f63d2347bf4c8905914aa84ba585ae85f28a914"},
|
||||
{file = "hyperframe-6.1.0-py3-none-any.whl", hash = "sha256:b03380493a519fce58ea5af42e4a42317bf9bd425596f7a0835ffce80f1a42e5"},
|
||||
{file = "hyperframe-6.1.0.tar.gz", hash = "sha256:f630908a00854a7adeabd6382b43923a4c4cd4b821fcb527e6ab9e15382a3b08"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "identify"
|
||||
version = "2.6.5"
|
||||
version = "2.6.6"
|
||||
description = "File identification library for Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "identify-2.6.5-py2.py3-none-any.whl", hash = "sha256:14181a47091eb75b337af4c23078c9d09225cd4c48929f521f3bf16b09d02566"},
|
||||
{file = "identify-2.6.5.tar.gz", hash = "sha256:c10b33f250e5bba374fae86fb57f3adcebf1161bce7cdf92031915fd480c13bc"},
|
||||
{file = "identify-2.6.6-py2.py3-none-any.whl", hash = "sha256:cbd1810bce79f8b671ecb20f53ee0ae8e86ae84b557de31d89709dc2a48ba881"},
|
||||
{file = "identify-2.6.6.tar.gz", hash = "sha256:7bec12768ed44ea4761efb47806f0a41f86e7c0a5fdf5950d4648c90eca7e251"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -3549,18 +3549,18 @@ tenacity = ">=8.1.0,<8.4.0 || >8.4.0,<10"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-core"
|
||||
version = "0.3.30"
|
||||
version = "0.3.31"
|
||||
description = "Building applications with LLMs through composability"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_core-0.3.30-py3-none-any.whl", hash = "sha256:0a4c4e02fac5968b67fbb0142c00c2b976c97e45fce62c7ac9eb1636a6926493"},
|
||||
{file = "langchain_core-0.3.30.tar.gz", hash = "sha256:0f1281b4416977df43baf366633ad18e96c5dcaaeae6fcb8a799f9889c853243"},
|
||||
{file = "langchain_core-0.3.31-py3-none-any.whl", hash = "sha256:882e64ad95887c951dce8e835889e43263b11848c394af3b73e06912624bd743"},
|
||||
{file = "langchain_core-0.3.31.tar.gz", hash = "sha256:5ffa56354c07de9efaa4139609659c63e7d9b29da2c825f6bab9392ec98300df"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
jsonpatch = ">=1.33,<2.0"
|
||||
langsmith = ">=0.1.125,<0.3"
|
||||
langsmith = ">=0.1.125,<0.4"
|
||||
packaging = ">=23.2,<25"
|
||||
pydantic = [
|
||||
{version = ">=2.5.2,<3.0.0", markers = "python_full_version < \"3.12.4\""},
|
||||
|
|
@ -3572,17 +3572,17 @@ typing-extensions = ">=4.7"
|
|||
|
||||
[[package]]
|
||||
name = "langchain-openai"
|
||||
version = "0.3.1"
|
||||
version = "0.3.2"
|
||||
description = "An integration package connecting OpenAI and LangChain"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langchain_openai-0.3.1-py3-none-any.whl", hash = "sha256:5cf2a1e115b12570158d89c22832fa381803c3e1e11d1eb781195c8d9e454bd5"},
|
||||
{file = "langchain_openai-0.3.1.tar.gz", hash = "sha256:cce314f1437b2cad73e0ed2b55e74dc399bc1bbc43594c4448912fb51c5e4447"},
|
||||
{file = "langchain_openai-0.3.2-py3-none-any.whl", hash = "sha256:8674183805e26d3ae3f78cc44f79fe0b2066f61e2de0e7e18be3b86f0d3b2759"},
|
||||
{file = "langchain_openai-0.3.2.tar.gz", hash = "sha256:c2c80ac0208eb7cefdef96f6353b00fa217979ffe83f0a21cc8666001df828c1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
langchain-core = ">=0.3.30,<0.4.0"
|
||||
langchain-core = ">=0.3.31,<0.4.0"
|
||||
openai = ">=1.58.1,<2.0.0"
|
||||
tiktoken = ">=0.7,<1"
|
||||
|
||||
|
|
@ -3616,13 +3616,13 @@ six = "*"
|
|||
|
||||
[[package]]
|
||||
name = "langfuse"
|
||||
version = "2.57.11"
|
||||
version = "2.57.12"
|
||||
description = "A client library for accessing langfuse"
|
||||
optional = false
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "langfuse-2.57.11-py3-none-any.whl", hash = "sha256:c9a074c68de62b7a7b144c02577a1a124df84274f13c80488f077147e93d6e78"},
|
||||
{file = "langfuse-2.57.11.tar.gz", hash = "sha256:f1c220decdd9c858fb58916af1775ac999836859553c6ffef33ebf2197030697"},
|
||||
{file = "langfuse-2.57.12-py3-none-any.whl", hash = "sha256:11f7b0a002ef08c1de129384c866a389aa7997d6620c5a7282678ea769b93857"},
|
||||
{file = "langfuse-2.57.12.tar.gz", hash = "sha256:e74e7c7ef790475d222a9ee6e5163524495a899817db18736df2ab14bc72615f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3694,13 +3694,13 @@ proxy = ["PyJWT (>=2.8.0,<3.0.0)", "apscheduler (>=3.10.4,<4.0.0)", "backoff", "
|
|||
|
||||
[[package]]
|
||||
name = "llama-index-core"
|
||||
version = "0.12.12"
|
||||
version = "0.12.14"
|
||||
description = "Interface between LLMs and your data"
|
||||
optional = true
|
||||
python-versions = "<4.0,>=3.9"
|
||||
files = [
|
||||
{file = "llama_index_core-0.12.12-py3-none-any.whl", hash = "sha256:cea491e87f65e6b775b5aef95720de302b85af1bdc67d779c4b09170a30e5b98"},
|
||||
{file = "llama_index_core-0.12.12.tar.gz", hash = "sha256:068b755bbc681731336e822f5977d7608585e8f759c6293ebd812e2659316a37"},
|
||||
{file = "llama_index_core-0.12.14-py3-none-any.whl", hash = "sha256:6fdb30e3fadf98e7df75f9db5d06f6a7f8503ca545a71e048d786ff88012bd50"},
|
||||
{file = "llama_index_core-0.12.14.tar.gz", hash = "sha256:378bbf5bf4d1a8c692d3a980c1a6ed3be7a9afb676a4960429dea15f62d06cd3"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -4022,13 +4022,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "marshmallow"
|
||||
version = "3.25.1"
|
||||
version = "3.26.0"
|
||||
description = "A lightweight library for converting complex datatypes to and from native Python datatypes."
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "marshmallow-3.25.1-py3-none-any.whl", hash = "sha256:ec5d00d873ce473b7f2ffcb7104286a376c354cab0c2fa12f5573dab03e87210"},
|
||||
{file = "marshmallow-3.25.1.tar.gz", hash = "sha256:f4debda3bb11153d81ac34b0d582bf23053055ee11e791b54b4b35493468040a"},
|
||||
{file = "marshmallow-3.26.0-py3-none-any.whl", hash = "sha256:1287bca04e6a5f4094822ac153c03da5e214a0a60bcd557b140f3e66991b8ca1"},
|
||||
{file = "marshmallow-3.26.0.tar.gz", hash = "sha256:eb36762a1cc76d7abf831e18a3a1b26d3d481bbc74581b8e532a3d3a8115e1cb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5531,13 +5531,13 @@ tests = ["pytest (>=5.4.1)", "pytest-cov (>=2.8.1)", "pytest-mypy (>=0.8.0)", "p
|
|||
|
||||
[[package]]
|
||||
name = "posthog"
|
||||
version = "3.8.4"
|
||||
version = "3.10.0"
|
||||
description = "Integrate PostHog into any python application."
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "posthog-3.8.4-py2.py3-none-any.whl", hash = "sha256:a6f781310fda9c18a36e697400b7f8be8bd46e998f152560273e62b88d1c9f73"},
|
||||
{file = "posthog-3.8.4.tar.gz", hash = "sha256:ba8cd14bca58686a199b1ba5655d3bad67c09a3a381062347eb30908282df1da"},
|
||||
{file = "posthog-3.10.0-py2.py3-none-any.whl", hash = "sha256:8481949321ba84059bfc8778d358ffec008c64efe834ac7c8eae80243fafa090"},
|
||||
{file = "posthog-3.10.0.tar.gz", hash = "sha256:c07113c0558fde279d0462010e4ad87b6a2a76cb970cae0122d5a31d629fc27b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -5551,7 +5551,7 @@ six = ">=1.5"
|
|||
dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
|
||||
langchain = ["langchain (>=0.2.0)"]
|
||||
sentry = ["django", "sentry-sdk"]
|
||||
test = ["anthropic", "coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "mock (>=2.0.0)", "openai", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
|
||||
test = ["anthropic", "coverage", "django", "flake8", "freezegun (==0.3.15)", "langchain-anthropic (>=0.2.0)", "langchain-community (>=0.2.0)", "langchain-openai (>=0.2.0)", "langgraph", "mock (>=2.0.0)", "openai", "pylint", "pytest", "pytest-asyncio", "pytest-timeout"]
|
||||
|
||||
[[package]]
|
||||
name = "pre-commit"
|
||||
|
|
@ -6154,13 +6154,13 @@ testutils = ["gitpython (>3)"]
|
|||
|
||||
[[package]]
|
||||
name = "pymdown-extensions"
|
||||
version = "10.14"
|
||||
version = "10.14.1"
|
||||
description = "Extension pack for Python Markdown."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pymdown_extensions-10.14-py3-none-any.whl", hash = "sha256:202481f716cc8250e4be8fce997781ebf7917701b59652458ee47f2401f818b5"},
|
||||
{file = "pymdown_extensions-10.14.tar.gz", hash = "sha256:741bd7c4ff961ba40b7528d32284c53bc436b8b1645e8e37c3e57770b8700a34"},
|
||||
{file = "pymdown_extensions-10.14.1-py3-none-any.whl", hash = "sha256:637951cbfbe9874ba28134fb3ce4b8bcadd6aca89ac4998ec29dcbafd554ae08"},
|
||||
{file = "pymdown_extensions-10.14.1.tar.gz", hash = "sha256:b65801996a0cd4f42a3110810c306c45b7313c09b0610a6f773730f2a9e3c96b"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6172,13 +6172,13 @@ extra = ["pygments (>=2.19.1)"]
|
|||
|
||||
[[package]]
|
||||
name = "pymilvus"
|
||||
version = "2.5.3"
|
||||
version = "2.5.4"
|
||||
description = "Python Sdk for Milvus"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "pymilvus-2.5.3-py3-none-any.whl", hash = "sha256:64ca63594284586937274800be27a402f3be2d078130bf81d94ab8d7798ac9c8"},
|
||||
{file = "pymilvus-2.5.3.tar.gz", hash = "sha256:68bc3797b7a14c494caf116cee888894ffd6eba7b96a3ac841be85d60694cc5d"},
|
||||
{file = "pymilvus-2.5.4-py3-none-any.whl", hash = "sha256:3f7ddaeae0c8f63554b8e316b73f265d022e05a457d47c366ce47293434a3aea"},
|
||||
{file = "pymilvus-2.5.4.tar.gz", hash = "sha256:611732428ff669d57ded3d1f823bdeb10febf233d0251cce8498b287e5a10ce8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6693,13 +6693,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
|
|||
|
||||
[[package]]
|
||||
name = "qdrant-client"
|
||||
version = "1.13.0"
|
||||
version = "1.13.2"
|
||||
description = "Client library for the Qdrant vector search engine"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "qdrant_client-1.13.0-py3-none-any.whl", hash = "sha256:63a063d5232618b609f2c438caf6f3afd3bd110dd80d01be20c596e516efab6b"},
|
||||
{file = "qdrant_client-1.13.0.tar.gz", hash = "sha256:9708e3194081619b38194c99e7c369064e3f3f328d8a8ef1d71a87425a5ddf0c"},
|
||||
{file = "qdrant_client-1.13.2-py3-none-any.whl", hash = "sha256:db97e759bd3f8d483a383984ba4c2a158eef56f2188d83df7771591d43de2201"},
|
||||
{file = "qdrant_client-1.13.2.tar.gz", hash = "sha256:c8cce87ce67b006f49430a050a35c85b78e3b896c0c756dafc13bdeca543ec13"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -6838,13 +6838,13 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"
|
|||
|
||||
[[package]]
|
||||
name = "referencing"
|
||||
version = "0.36.1"
|
||||
version = "0.36.2"
|
||||
description = "JSON Referencing + Python"
|
||||
optional = false
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "referencing-0.36.1-py3-none-any.whl", hash = "sha256:363d9c65f080d0d70bc41c721dce3c7f3e77fc09f269cd5c8813da18069a6794"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -7195,40 +7195,40 @@ pyasn1 = ">=0.1.3"
|
|||
|
||||
[[package]]
|
||||
name = "ruff"
|
||||
version = "0.9.2"
|
||||
version = "0.9.3"
|
||||
description = "An extremely fast Python linter and code formatter, written in Rust."
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
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||||
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||||
[[package]]
|
||||
name = "s3transfer"
|
||||
version = "0.11.1"
|
||||
version = "0.11.2"
|
||||
description = "An Amazon S3 Transfer Manager"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "s3transfer-0.11.1-py3-none-any.whl", hash = "sha256:8fa0aa48177be1f3425176dfe1ab85dcd3d962df603c3dbfc585e6bf857ef0ff"},
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{file = "s3transfer-0.11.1.tar.gz", hash = "sha256:3f25c900a367c8b7f7d8f9c34edc87e300bde424f779dc9f0a8ae4f9df9264f6"},
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{file = "s3transfer-0.11.2-py3-none-any.whl", hash = "sha256:be6ecb39fadd986ef1701097771f87e4d2f821f27f6071c872143884d2950fbc"},
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{file = "s3transfer-0.11.2.tar.gz", hash = "sha256:3b39185cb72f5acc77db1a58b6e25b977f28d20496b6e58d6813d75f464d632f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -7387,13 +7387,13 @@ test = ["Cython", "array-api-strict (>=2.0,<2.1.1)", "asv", "gmpy2", "hypothesis
|
|||
|
||||
[[package]]
|
||||
name = "semver"
|
||||
version = "3.0.2"
|
||||
version = "3.0.4"
|
||||
description = "Python helper for Semantic Versioning (https://semver.org)"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "semver-3.0.2-py3-none-any.whl", hash = "sha256:b1ea4686fe70b981f85359eda33199d60c53964284e0cfb4977d243e37cf4bf4"},
|
||||
{file = "semver-3.0.2.tar.gz", hash = "sha256:6253adb39c70f6e51afed2fa7152bcd414c411286088fb4b9effb133885ab4cc"},
|
||||
{file = "semver-3.0.4-py3-none-any.whl", hash = "sha256:9c824d87ba7f7ab4a1890799cec8596f15c1241cb473404ea1cb0c55e4b04746"},
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{file = "semver-3.0.4.tar.gz", hash = "sha256:afc7d8c584a5ed0a11033af086e8af226a9c0b206f313e0301f8dd7b6b589602"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -7789,20 +7789,20 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
|
|||
|
||||
[[package]]
|
||||
name = "starlette"
|
||||
version = "0.41.3"
|
||||
version = "0.45.3"
|
||||
description = "The little ASGI library that shines."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
python-versions = ">=3.9"
|
||||
files = [
|
||||
{file = "starlette-0.41.3-py3-none-any.whl", hash = "sha256:44cedb2b7c77a9de33a8b74b2b90e9f50d11fcf25d8270ea525ad71a25374ff7"},
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{file = "starlette-0.41.3.tar.gz", hash = "sha256:0e4ab3d16522a255be6b28260b938eae2482f98ce5cc934cb08dce8dc3ba5835"},
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{file = "starlette-0.45.3-py3-none-any.whl", hash = "sha256:dfb6d332576f136ec740296c7e8bb8c8a7125044e7c6da30744718880cdd059d"},
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{file = "starlette-0.45.3.tar.gz", hash = "sha256:2cbcba2a75806f8a41c722141486f37c28e30a0921c5f6fe4346cb0dcee1302f"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
anyio = ">=3.4.0,<5"
|
||||
anyio = ">=3.6.2,<5"
|
||||
|
||||
[package.extras]
|
||||
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.7)", "pyyaml"]
|
||||
full = ["httpx (>=0.27.0,<0.29.0)", "itsdangerous", "jinja2", "python-multipart (>=0.0.18)", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
|
|
@ -8234,13 +8234,13 @@ typing-extensions = ">=3.7.4"
|
|||
|
||||
[[package]]
|
||||
name = "tzdata"
|
||||
version = "2024.2"
|
||||
version = "2025.1"
|
||||
description = "Provider of IANA time zone data"
|
||||
optional = false
|
||||
python-versions = ">=2"
|
||||
files = [
|
||||
{file = "tzdata-2024.2-py2.py3-none-any.whl", hash = "sha256:a48093786cdcde33cad18c2555e8532f34422074448fbc874186f0abd79565cd"},
|
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{file = "tzdata-2024.2.tar.gz", hash = "sha256:7d85cc416e9382e69095b7bdf4afd9e3880418a2413feec7069d533d6b4e31cc"},
|
||||
{file = "tzdata-2025.1-py2.py3-none-any.whl", hash = "sha256:7e127113816800496f027041c570f50bcd464a020098a3b6b199517772303639"},
|
||||
{file = "tzdata-2025.1.tar.gz", hash = "sha256:24894909e88cdb28bd1636c6887801df64cb485bd593f2fd83ef29075a81d694"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -8332,13 +8332,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "unstructured"
|
||||
version = "0.16.14"
|
||||
version = "0.16.15"
|
||||
description = "A library that prepares raw documents for downstream ML tasks."
|
||||
optional = true
|
||||
python-versions = "<3.13,>=3.9.0"
|
||||
files = [
|
||||
{file = "unstructured-0.16.14-py3-none-any.whl", hash = "sha256:7b3c2eb21e65d2f61240de7a5241fd7734d97be2c9cfa5f70934e10470318131"},
|
||||
{file = "unstructured-0.16.14.tar.gz", hash = "sha256:cec819461090226cd478429c1e0fda19a66ba49ab9ade1ea1fd9ec79c279d7ac"},
|
||||
{file = "unstructured-0.16.15-py3-none-any.whl", hash = "sha256:5b0931eb92fb858b983fada18111efdf9c2a0c861ef8e9b58c4e05b1daa50e35"},
|
||||
{file = "unstructured-0.16.15.tar.gz", hash = "sha256:18fb850d47b5a2a6ea45b2f7e0eda687f903a2f2e58909b1defd48e2b3126ff4"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8374,19 +8374,19 @@ wrapt = "*"
|
|||
xlrd = {version = "*", optional = true, markers = "extra == \"xlsx\""}
|
||||
|
||||
[package.extras]
|
||||
all-docs = ["effdet", "google-cloud-vision", "markdown", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.2)", "python-pptx (>=1.0.1)", "unstructured-inference (==0.8.1)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
|
||||
all-docs = ["effdet", "google-cloud-vision", "markdown", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.2)", "python-pptx (>=1.0.1)", "unstructured-inference (>=0.8.6)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
|
||||
csv = ["pandas"]
|
||||
doc = ["python-docx (>=1.1.2)"]
|
||||
docx = ["python-docx (>=1.1.2)"]
|
||||
epub = ["pypandoc"]
|
||||
huggingface = ["langdetect", "sacremoses", "sentencepiece", "torch", "transformers"]
|
||||
image = ["effdet", "google-cloud-vision", "onnx", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypdf", "unstructured-inference (==0.8.1)", "unstructured.pytesseract (>=0.3.12)"]
|
||||
local-inference = ["effdet", "google-cloud-vision", "markdown", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.2)", "python-pptx (>=1.0.1)", "unstructured-inference (==0.8.1)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
|
||||
image = ["effdet", "google-cloud-vision", "onnx", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypdf", "unstructured-inference (>=0.8.6)", "unstructured.pytesseract (>=0.3.12)"]
|
||||
local-inference = ["effdet", "google-cloud-vision", "markdown", "networkx", "onnx", "openpyxl", "pandas", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypandoc", "pypdf", "python-docx (>=1.1.2)", "python-pptx (>=1.0.1)", "unstructured-inference (>=0.8.6)", "unstructured.pytesseract (>=0.3.12)", "xlrd"]
|
||||
md = ["markdown"]
|
||||
odt = ["pypandoc", "python-docx (>=1.1.2)"]
|
||||
org = ["pypandoc"]
|
||||
paddleocr = ["paddlepaddle (==3.0.0b1)", "unstructured.paddleocr (==2.8.1.0)"]
|
||||
pdf = ["effdet", "google-cloud-vision", "onnx", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypdf", "unstructured-inference (==0.8.1)", "unstructured.pytesseract (>=0.3.12)"]
|
||||
pdf = ["effdet", "google-cloud-vision", "onnx", "pdf2image", "pdfminer.six", "pi-heif", "pikepdf", "pypdf", "unstructured-inference (>=0.8.6)", "unstructured.pytesseract (>=0.3.12)"]
|
||||
ppt = ["python-pptx (>=1.0.1)"]
|
||||
pptx = ["python-pptx (>=1.0.1)"]
|
||||
rst = ["pypandoc"]
|
||||
|
|
@ -8765,13 +8765,13 @@ test = ["pytest", "pytest-cov"]
|
|||
|
||||
[[package]]
|
||||
name = "xlsxwriter"
|
||||
version = "3.2.0"
|
||||
version = "3.2.1"
|
||||
description = "A Python module for creating Excel XLSX files."
|
||||
optional = true
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "XlsxWriter-3.2.0-py3-none-any.whl", hash = "sha256:ecfd5405b3e0e228219bcaf24c2ca0915e012ca9464a14048021d21a995d490e"},
|
||||
{file = "XlsxWriter-3.2.0.tar.gz", hash = "sha256:9977d0c661a72866a61f9f7a809e25ebbb0fb7036baa3b9fe74afcfca6b3cb8c"},
|
||||
{file = "XlsxWriter-3.2.1-py3-none-any.whl", hash = "sha256:7e8f7c60b7a1660ef791d46ab5de78469cb978b991ca841af61f5832d2f9f4fe"},
|
||||
{file = "XlsxWriter-3.2.1.tar.gz", hash = "sha256:97618759cb264fb6a93397f660cca156ffa9561743b1823dafb60dc4474e1902"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -9053,4 +9053,4 @@ weaviate = ["weaviate-client"]
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.10.0,<3.13"
|
||||
content-hash = "ddc6f5406ee0205107a278cf46918b082d12dbc51471ff6464011731cfd41890"
|
||||
content-hash = "480675c274cd85a76a95bf03af865b1a0b462f25bbc21d7427b0a0b8e21c13db"
|
||||
|
|
|
|||
|
|
@ -22,7 +22,7 @@ python = ">=3.10.0,<3.13"
|
|||
openai = "1.59.4"
|
||||
pydantic = "2.10.5"
|
||||
python-dotenv = "1.0.1"
|
||||
fastapi = ">=0.109.2,<0.116.0"
|
||||
fastapi = "0.115.7"
|
||||
uvicorn = "0.34.0"
|
||||
requests = "2.32.3"
|
||||
aiohttp = "3.10.10"
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue