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Merge branch 'main' into feat/code-knowledge-graph

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Vasilije 2024-04-28 17:10:56 +02:00 • committed by GitHub
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39 changed files with 752 additions and 372 deletions
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6
.github/workflows/py_lint.yml vendored
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@ -59,7 +59,7 @@ jobs:
- name: Install dependencies
# if: steps.cached-poetry-dependencies.outputs.cache-hit != 'true'
run: poetry install --all-extras
run: poetry install --all-extras --no-root
- name: Set Script Permissions
run: chmod +x ./tools/check-package.sh
@ -72,7 +72,7 @@ jobs:
poetry run python ./tools/check-lockfile.py
poetry run mypy --config-file mypy.ini cognee
poetry run flake8 --max-line-length=200 cognee
poetry run black cognee docs tests --diff --extend-exclude=".*syntax_error.py"
# poetry run black cognee --exclude docs --diff --extend-exclude=".*syntax_error.py"
# poetry run isort ./ --diff
poetry run bandit -r cognee/ -n 3 -l
@ -85,4 +85,4 @@ jobs:
- name: Check matrix job results
if: contains(needs.*.result, 'failure') || contains(needs.*.result, 'cancelled')
run: |
echo "One or more matrix job tests failed or were cancelled. You may need to re-run them." && exit 1
echo "One or more matrix job tests failed or were cancelled. You may need to re-run them." && exit 1

23
.github/workflows/release_discord_action.yml vendored Normal file
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@ -0,0 +1,23 @@
name: Send Release to Discord
on:
release:
types: [published]
jobs:
github-releases-to-discord:
runs-on: ubuntu-latest
steps:
- name: Checkout
uses: actions/checkout@v3
- name: Github Releases To Discord
uses: SethCohen/github-releases-to-discord@v1.15.0
with:
webhook_url: ${{ secrets.WEBHOOK_URL }}
color: "2105893"
username: "Release Changelog"
avatar_url: "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png"
content: "||@everyone||"
footer_title: "Changelog"
footer_icon_url: "https://cdn.discordapp.com/avatars/487431320314576937/bd64361e4ba6313d561d54e78c9e7171.png"
footer_timestamp: true

137
.github/workflows/test_common.yml vendored
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@ -4,7 +4,6 @@ on:
pull_request:
branches:
- main
- devel
workflow_dispatch:
concurrency:
@ -38,6 +37,7 @@ jobs:
defaults:
run:
shell: bash
runs-on: ${{ matrix.os }}
steps:
@ -70,87 +70,84 @@ jobs:
installer-parallel: true
- name: Install dependencies
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
run: poetry install --no-interaction
- name: Build with Poetry
run: poetry build
- name: Install Package
run: |
cd dist
pip install *.whl
- name: Download NLTK Punkt Tokenizer Models
run: |
python -m nltk.downloader punkt
python -m nltk.downloader stopwords
python -m nltk.downloader wordnet
python -m nltk.downloader averaged_perceptron_tagger
# - name: Build with Poetry
# run: poetry build
# - name: Install Package
# run: |
# cd dist
# pip install *.whl
# - name: Download NLTK Punkt Tokenizer Models
# run: |
# python -m nltk.downloader punkt
# python -m nltk.downloader stopwords
# python -m nltk.downloader wordnet
# python -m nltk.downloader averaged_perceptron_tagger
- name: Run test script
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
QDRANT_URL: ${{ secrets.QDRANT_API_URL }}
run: poetry run python ./cognee/tests/test_library.py
- run: |
poetry run
if: runner.os != 'Windows'
name: Run common tests with minimum dependencies Linux/MAC
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
- run: |
poetry run python ./cognee/tests/test_library.py
if: runner.os == 'Windows'
name: Run common tests with minimum dependencies Windows
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
shell: cmd
- name: Install dependencies
run: poetry install --no-interaction
# - run: |
# poetry run
# if: runner.os != 'Windows'
# name: Run common tests with minimum dependencies Linux/MAC
# env:
# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
# QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
# QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
- name: Build with Poetry
run: poetry build
# - run: |
# poetry run python ./cognee/tests/test_library.py
# if: runner.os == 'Windows'
# name: Run common tests with minimum dependencies Windows
# env:
# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
# QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
# QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
# shell: cmd
- name: Install Package
run: |
cd dist
pip install *.whl
# - name: Install dependencies
# run: poetry install --no-interaction
# - name: Build with Poetry
# run: poetry build
# - name: Install Package
# run: |
# cd dist
# pip install *.whl
- name: Download NLTK Punkt Tokenizer Models
run: |
python -m nltk.downloader punkt
- run: |
poetry run python ./cognee/tests/test_library.py
if: runner.os != 'Windows'
name: Run pipeline smoke tests with minimum deps Linux/MAC
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
- name: Download NLTK Punkt Tokenizer Models
run: |
python -m nltk.downloader punkt
- run: |
poetry run python ./cognee/tests/test_library.py
if: runner.os == 'Windows'
name: Run smoke tests with minimum deps Windows
env:
OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
shell: cmd
# - name: Download NLTK Punkt Tokenizer Models
# run: |
# python -m nltk.downloader punkt
# - run: |
# poetry run python ./cognee/tests/test_library.py
# if: runner.os != 'Windows'
# name: Run pipeline smoke tests with minimum deps Linux/MAC
# env:
# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
# QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
# QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
# - name: Download NLTK Punkt Tokenizer Models
# run: |
# python -m nltk.downloader punkt
# - run: |
# poetry run python ./cognee/tests/test_library.py
# if: runner.os == 'Windows'
# name: Run smoke tests with minimum deps Windows
# env:
# OPENAI_API_KEY: ${{ secrets.OPENAI_API_KEY }}
# QDRANT_API_KEY: ${{ secrets.QDRANT_API_KEY }}
# QDRANT_API_URL: ${{ secrets.QDRANT_API_URL }}
# shell: cmd
matrix_job_required_check:

35
cognee/api/client.py
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@ -1,6 +1,8 @@
""" FastAPI server for the Cognee API. """
import os
from uuid import UUID
import aiohttp
import uvicorn
import logging
@ -67,14 +69,37 @@ async def add(payload: AddPayload):
from v1.add.add import add
try:
await add(
payload.data,
payload.dataset_name,
)
data = payload.data
if isinstance(data, str) and data.startswith('http'):
if 'github' in data:
# Perform git clone if the URL is from GitHub
repo_name = data.split('/')[-1].replace('.git', '')
os.system(f'git clone {data} .data/{repo_name}')
await add(
"data://.data/",
f"{repo_name}",
)
else:
# Fetch and store the data from other types of URL using curl
async with aiohttp.ClientSession() as session:
async with session.get(data) as resp:
if resp.status == 200:
file_data = await resp.read()
with open(f'.data/{data.split("/")[-1]}', 'wb') as f:
f.write(file_data)
await add(
f"data://.data/",
f"{data.split('/')[-1]}",
)
else:
await add(
payload.data,
payload.dataset_name,
)
except Exception as error:
return JSONResponse(
status_code = 409,
content = { "error": error }
content = { "error": str(error) }
)
@app.post("/cognify", response_model=dict)

4
cognee/api/v1/add/add.py
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@ -7,11 +7,9 @@ import cognee.modules.ingestion as ingestion
from cognee.infrastructure import infrastructure_config
from cognee.infrastructure.files.storage import LocalStorage
from cognee.modules.discovery import discover_directory_datasets
from cognee.utils import send_telemetry
async def add(data_path: Union[str, List[str]], dataset_name: str = None):
if isinstance(data_path, str):
# data_path is a data directory path
@ -105,7 +103,7 @@ async def add_files(file_paths: List[str], dataset_name: str):
dataset_name = dataset_name.replace(" ", "_").replace(".", "_") if dataset_name is not None else "main_dataset",
write_disposition = "merge",
)
send_telemetry( "COGNEE_ADD")
send_telemetry("cognee.add")
return run_info

0
cognee/infrastructure/pipeline/models/_init_.py → cognee/api/v1/cognify/__init__.py
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32
cognee/api/v1/cognify/cognify.py
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@ -3,6 +3,7 @@ from uuid import uuid4
from typing import List, Union
import logging
import instructor
import nltk
from openai import OpenAI
from nltk.corpus import stopwords
from cognee.config import Config
@ -40,8 +41,13 @@ logger = logging.getLogger("cognify")
async def cognify(datasets: Union[str, List[str]] = None):
"""This function is responsible for the cognitive processing of the content."""
# Has to be loaded in advance, multithreading doesn't work without it.
nltk.download('stopwords', quiet=True)
stopwords.ensure_loaded()
graph_db_type = infrastructure_config.get_config()["graph_engine"]
graph_client = await get_graph_client(graph_db_type)
db_engine = infrastructure_config.get_config()["database_engine"]
if datasets is None or len(datasets) == 0:
@ -67,12 +73,6 @@ async def cognify(datasets: Union[str, List[str]] = None):
if dataset_name in added_dataset:
dataset_files.append((added_dataset, db_engine.get_files_metadata(added_dataset)))
awaitables = []
graph_db_type = infrastructure_config.get_config()["graph_engine"]
graph_client = await get_graph_client(graph_db_type)
# await initialize_graph(USER_ID, graph_data_model, graph_client)
data_chunks = {}
@ -163,6 +163,24 @@ async def process_text(chunk_collection: str, chunk_id: str, input_text: str, fi
score_threshold = infrastructure_config.get_config()["intra_layer_score_treshold"]
)
send_telemetry( "COGNEE_COGNIFY")
send_telemetry("cognee.cognify")
print(f"Chunk ({chunk_id}) cognified.")
if __name__ == "__main__":
async def test():
from cognee.api.v1.add import add
await add(["A large language model (LLM) is a language model notable for its ability to achieve general-purpose language generation and other natural language processing tasks such as classification"], "test")
graph = await cognify()
from cognee.utils import render_graph
await render_graph(graph, include_color=True, include_nodes=True, include_size=True)
import asyncio
asyncio.run(test())

0
cognee/api/v1/datasets/__init__.py Normal file
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2
cognee/api/v1/search/search.py
View file

@ -70,7 +70,7 @@ async def specific_search(query_params: List[SearchParameters]) -> List:
# Update the results set with the results from all tasks
results.extend(search_results)
send_telemetry( "COGNEE_SEARCH")
send_telemetry("cognee.search")
return results

6
cognee/config.py
View file

@ -34,9 +34,9 @@ class Config:
vectordb: str = os.getenv("VECTORDB", "weaviate")
qdrant_path: str = os.getenv("QDRANT_PATH")
qdrant_url: str = os.getenv("QDRANT_URL")
qdrant_api_key: str = os.getenv("QDRANT_API_KEY")
qdrant_path: str = os.getenv("QDRANT_PATH", None)
qdrant_url: str = os.getenv("QDRANT_URL", None)
qdrant_api_key: str = os.getenv("QDRANT_API_KEY", None)
db_path = str = os.getenv("COGNEE_DB_PATH", "databases")
db_name: str = os.getenv("DB_NAME", "cognee.db")

3
cognee/infrastructure/InfrastructureConfig.py
View file

@ -94,9 +94,6 @@ class InfrastructureConfig():
embedding_engine = self.embedding_engine
)
except (EnvironmentError, ModuleNotFoundError):
if config.qdrant_url is None and config.qdrant_api_key is None:
raise EnvironmentError("Qdrant is not configured!")
self.vector_engine = QDrantAdapter(
qdrant_url = config.qdrant_url,
qdrant_api_key = config.qdrant_api_key,

0
cognee/infrastructure/data/chunking/__init__.py Normal file
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0
cognee/infrastructure/data/utils/__init__.py Normal file
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6
cognee/infrastructure/databases/graph/get_graph_client.py
View file

@ -27,5 +27,9 @@ async def get_graph_client(graph_type: GraphDBType, graph_file_name: str = None)
pass
graph_client = NetworkXAdapter(filename = graph_file_path)
await graph_client.load_graph_from_file()
if (graph_client.graph is None):
await graph_client.load_graph_from_file()
return graph_client

27
cognee/infrastructure/databases/graph/networkx/adapter.py
View file

@ -12,7 +12,8 @@ from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInte
logger = logging.getLogger("NetworkXAdapter")
class NetworkXAdapter(GraphDBInterface):
_instance = None # Class variable to store the singleton instance
_instance = None
graph = None # Class variable to store the singleton instance
def __new__(cls, filename):
if cls._instance is None:
@ -22,10 +23,8 @@ class NetworkXAdapter(GraphDBInterface):
def __init__(self, filename = "cognee_graph.pkl"):
self.filename = filename
self.graph = nx.MultiDiGraph()
async def graph(self):
return self.graph
async def add_node(
self,
@ -96,6 +95,7 @@ class NetworkXAdapter(GraphDBInterface):
async def extract_node(self, node_id: str) -> dict:
if self.graph.has_node(node_id):
return self.graph.nodes[node_id]
return None
@ -113,6 +113,9 @@ class NetworkXAdapter(GraphDBInterface):
async def load_graph_from_file(self, file_path: str = None):
"""Asynchronously load the graph from a file in JSON format."""
if file_path == self.filename:
return
if not file_path:
file_path = self.filename
try:
@ -120,24 +123,26 @@ class NetworkXAdapter(GraphDBInterface):
async with aiofiles.open(file_path, "r") as file:
graph_data = json.loads(await file.read())
self.graph = nx.readwrite.json_graph.node_link_graph(graph_data)
return self.graph
else:
# Log that the file does not exist and an empty graph is initialized
logger.warning("File %s not found. Initializing an empty graph.", file_path)
self.graph = nx.MultiDiGraph() # Use MultiDiGraph to keep it consistent with __init__
return self.graph
await self.save_graph_to_file(file_path)
except Exception as error:
logger.error("Failed to load graph from file: %s", file_path)
# Initialize an empty graph in case of error
self.graph = nx.MultiDiGraph()
return self.graph
await self.save_graph_to_file(file_path)
async def delete_graph(self, path: str = None):
async def delete_graph(self, file_path: str = None):
"""Asynchronously delete the graph file from the filesystem."""
if path is None:
path = self.filename # Assuming self.filename is defined elsewhere and holds the default graph file path
if file_path is None:
file_path = self.filename # Assuming self.filename is defined elsewhere and holds the default graph file path
try:
await aiofiles_os.remove(path)
if os.path.exists(file_path):
await aiofiles_os.remove(file_path)
self.graph = None
logger.info("Graph deleted successfully.")
except Exception as error:
logger.error("Failed to delete graph: %s", error)

0
cognee/infrastructure/databases/relational/duckdb/__init__.py Normal file
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0
cognee/infrastructure/databases/vector/embeddings/__init__.py Normal file
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74
cognee/infrastructure/databases/vector/lancedb/LanceDBAdapter.py Normal file
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@ -0,0 +1,74 @@
import asyncio
import lancedb
from typing import List, Optional
import asyncio
import lancedb
from pathlib import Path
import tempfile
class LanceDBAdapter:
def __init__(self, uri: Optional[str] = None, api_key: Optional[str] = None):
if uri:
self.uri = uri
else:
# Create a temporary directory for the LanceDB 'in-memory' simulation
self.temp_dir = tempfile.mkdtemp(suffix='.lancedb')
self.uri = f"file://{self.temp_dir}"
self.api_key = api_key
self.db = None
async def connect(self):
# Asynchronously connect to a LanceDB database, effectively in-memory if no URI is provided
self.db = await lancedb.connect_async(self.uri, api_key=self.api_key)
async def disconnect(self):
# Disconnect and clean up the database if it was set up as temporary
await self.db.close()
if hasattr(self, 'temp_dir'):
Path(self.temp_dir).unlink(missing_ok=True) # Remove the temporary directory
async def create_table(self, table_name: str, schema=None, data=None):
if not await self.table_exists(table_name):
return await self.db.create_table(name=table_name, schema=schema, data=data)
else:
raise ValueError(f"Table {table_name} already exists")
async def table_exists(self, table_name: str) -> bool:
table_names = await self.db.table_names()
return table_name in table_names
async def insert_data(self, table_name: str, data_points: List[dict]):
table = await self.db.open_table(table_name)
await table.add(data_points)
async def query_data(self, table_name: str, query=None, limit=10):
# Asynchronously query data from a table
table = await self.db.open_table(table_name)
if query:
query_result = await table.query().where(query).limit(limit).to_pandas()
else:
query_result = await table.query().limit(limit).to_pandas()
return query_result
async def vector_search(self, table_name: str, query_vector: List[float], limit=10):
# Perform an asynchronous vector search
table = await self.db.open_table(table_name)
query_result = await table.vector_search().nearest_to(query_vector).limit(limit).to_pandas()
return query_result
async def main():
# Example without providing a URI, simulates in-memory behavior
adapter = LanceDBAdapter()
await adapter.connect()
try:
await adapter.create_table("my_table")
data_points = [{"id": 1, "text": "example", "vector": [0.1, 0.2, 0.3]}]
await adapter.insert_data("my_table", data_points)
finally:
await adapter.disconnect()
if __name__ == "__main__":
asyncio.run(main())

0
cognee/infrastructure/databases/vector/models/__init__.py Normal file
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0
cognee/infrastructure/databases/vector/pinecone/__init__.py Normal file
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35
cognee/infrastructure/databases/vector/qdrant/QDrantAdapter.py
View file

@ -43,12 +43,13 @@ class QDrantAdapter(VectorDBInterface):
def get_qdrant_client(self) -> AsyncQdrantClient:
if self.qdrant_path is not None:
return AsyncQdrantClient(
path = self.qdrant_path,
path = self.qdrant_path, port=6333
)
elif self.qdrant_url is not None:
return AsyncQdrantClient(
url = self.qdrant_url,
api_key = self.qdrant_api_key,
port = 6333
)
return AsyncQdrantClient(
@ -58,13 +59,19 @@ class QDrantAdapter(VectorDBInterface):
async def embed_data(self, data: List[str]) -> List[float]:
return await self.embedding_engine.embed_text(data)
async def collection_exists(self, collection_name: str) -> bool:
client = self.get_qdrant_client()
result = await client.collection_exists(collection_name)
await client.close()
return result
async def create_collection(
self,
collection_name: str,
):
client = self.get_qdrant_client()
return await client.create_collection(
result = await client.create_collection(
collection_name = collection_name,
vectors_config = {
"text": models.VectorParams(
@ -74,6 +81,10 @@ class QDrantAdapter(VectorDBInterface):
}
)
await client.close()
return result
async def create_data_points(self, collection_name: str, data_points: List[DataPoint]):
client = self.get_qdrant_client()
@ -90,13 +101,19 @@ class QDrantAdapter(VectorDBInterface):
points = [convert_to_qdrant_point(point) for point in data_points]
return await client.upload_points(
result = await client.upload_points(
collection_name = collection_name,
points = points
)
await client.close()
return result
async def retrieve(self, collection_name: str, data_id: str):
results = await self.get_qdrant_client().retrieve(collection_name, [data_id], with_payload = True)
client = self.get_qdrant_client()
results = await client.retrieve(collection_name, [data_id], with_payload = True)
await client.close()
return results[0] if len(results) > 0 else None
async def search(
@ -112,7 +129,7 @@ class QDrantAdapter(VectorDBInterface):
client = self.get_qdrant_client()
return await client.search(
result = await client.search(
collection_name = collection_name,
query_vector = models.NamedVector(
name = "text",
@ -122,6 +139,10 @@ class QDrantAdapter(VectorDBInterface):
with_vectors = with_vector
)
await client.close()
return result
async def batch_search(self, collection_name: str, query_texts: List[str], limit: int = None, with_vectors: bool = False):
"""
@ -159,6 +180,8 @@ class QDrantAdapter(VectorDBInterface):
requests = requests
)
await client.close()
return [filter(lambda result: result.score > 0.9, result_group) for result_group in results]
async def prune(self):
@ -168,3 +191,5 @@ class QDrantAdapter(VectorDBInterface):
for collection in response.collections:
await client.delete_collection(collection.name)
await client.close()

8
cognee/infrastructure/databases/vector/weaviate_db/WeaviateAdapter.py
View file

@ -30,6 +30,14 @@ class WeaviateAdapter(VectorDBInterface):
async def embed_data(self, data: List[str]) -> List[float]:
return await self.embedding_engine.embed_text(data)
async def collection_exists(self, collection_name: str) -> bool:
event_loop = asyncio.get_event_loop()
def sync_collection_exists():
return self.client.collections.exists(collection_name)
return await event_loop.run_in_executor(None, sync_collection_exists)
async def create_collection(self, collection_name: str):
import weaviate.classes.config as wvcc

0
cognee/infrastructure/llm/generic_llm_api/__init__.py Normal file
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0
cognee/infrastructure/pipeline/models/__init__.py Normal file
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4
cognee/modules/cognify/graph/add_classification_nodes.py
View file

@ -2,8 +2,8 @@ from typing import List
async def add_classification_nodes(graph_client, parent_node_id: str, categories: List) -> None:
for category in categories:
data_type = category["data_type"].upper().replace(' ', '_')
category_name = category["category_name"].upper().replace(' ', '_').replace("'", "").replace("/", "_")
data_type = category["data_type"].upper().replace(" ", "_")
category_name = category["category_name"].upper().replace(" ", "_").replace("'", "").replace("/", "_")
data_type_node_id = data_type

13
cognee/modules/cognify/graph/add_cognitive_layers.py
View file

@ -1,4 +1,6 @@
""" This module adds cognitive layers to the graph. """
from typing import List
import re
async def add_cognitive_layers(graph_client, parent_node_id: str, cognitive_layers: List):
cognitive_layer_nodes = [(
@ -20,5 +22,14 @@ async def add_cognitive_layers(graph_client, parent_node_id: str, cognitive_laye
return cognitive_layer_nodes
def fix_layer_name(layer_name):
sanitized_name = re.sub(r'[^a-zA-Z0-9_]', '', layer_name)
return sanitized_name
def generate_cognitive_layer_id(layer_id: str) -> str:
return layer_id.upper().replace(" ", "_").replace("'", "").replace("/", "_")
layer = f"COGNITIVE_LAYER__{layer_id.upper().replace(' ', '_')}".replace("'", "").replace("/", "_")
return fix_layer_name(layer)

37
cognee/modules/cognify/graph/add_data_chunks.py
View file

@ -1,3 +1,4 @@
import logging
from typing import TypedDict
from cognee.infrastructure import infrastructure_config
from cognee.infrastructure.databases.vector import DataPoint
@ -14,6 +15,8 @@ async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
for (dataset_name, chunks) in dataset_data_chunks.items():
try:
# if not await vector_client.collection_exists(dataset_name):
# logging.error(f"Creating collection {str(dataset_name)}")
await vector_client.create_collection(dataset_name)
except Exception:
pass
@ -29,15 +32,29 @@ async def add_data_chunks(dataset_data_chunks: dict[str, list[TextChunk]]):
identified_chunks.extend(dataset_chunks)
await vector_client.create_data_points(
dataset_name,
[
DataPoint(
id = chunk["chunk_id"],
payload = dict(text = chunk["text"]),
embed_field = "text"
) for chunk in dataset_chunks
],
)
# if not await vector_client.collection_exists(dataset_name):
try:
logging.error("Collection still not found. Creating collection again.")
await vector_client.create_collection(dataset_name)
except:
pass
async def create_collection_retry(dataset_name, dataset_chunks):
await vector_client.create_data_points(
dataset_name,
[
DataPoint(
id = chunk["chunk_id"],
payload = dict(text = chunk["text"]),
embed_field = "text"
) for chunk in dataset_chunks
],
)
try:
await create_collection_retry(dataset_name, dataset_chunks)
except Exception:
logging.error("Collection not found in create data points.")
await create_collection_retry(dataset_name, dataset_chunks)
return identified_chunks

4
cognee/modules/cognify/graph/add_document_node.py
View file

@ -4,6 +4,10 @@ from cognee.infrastructure.databases.graph.graph_db_interface import GraphDBInte
async def add_document_node(graph_client: GraphDBInterface, parent_node_id, document_metadata):
document_id = f"DOCUMENT__{document_metadata['id']}"
document = await graph_client.extract_node(document_id)
if not document:

0
cognee/modules/data/__init__.py Normal file
View file

0
cognee/modules/data/extraction/__init__.py Normal file
View file

0
cognee/modules/data/extraction/knowledge_graph/__init__.py Normal file
View file

2
cognee/modules/data/extraction/knowledge_graph/extract_knowledge_graph_module.py
View file

@ -46,7 +46,7 @@ class ExtractKnowledgeGraph(dspy.Module):
self.lm = lm
dspy.settings.configure(lm=self.lm)
self.generate_graph = dspy.TypedChainOfThought(GraphFromText)
nltk.download('stopwords')
nltk.download("stopwords", quiet = True)
def forward(self, context: str, question: str):
context = remove_stop_words(context)

0
cognee/tests/__init__.py Normal file
View file

0
.test_data/Natural language processing.txt → cognee/tests/test_data/Natural_language_processing.txt
View file

35
cognee/tests/test_library.py Normal file → Executable file
View file

@ -1,21 +1,23 @@
async def main():
from os import path
from cognee import config, prune
data_directory_path = path.abspath("../.data")
config.data_root_directory(data_directory_path)
cognee_directory_path = path.abspath("../.cognee_system")
config.system_root_directory(cognee_directory_path)
await prune.prune_system()
from os import path
import pathlib
import cognee
dataset_name = "explanations"
await cognee.add([path.abspath("../cognee/.test_data/Natural language processing.txt")], dataset_name)
print("Working dir: ", str(pathlib.Path(__file__).parent))
data_directory_path = str(pathlib.Path(path.join(pathlib.Path(__file__).parent, "../../.data")).resolve())
print("Data dir: ", data_directory_path)
cognee.config.data_root_directory(data_directory_path)
cognee_directory_path = str(pathlib.Path(path.join(pathlib.Path(__file__).parent, "../../.cognee_system")).resolve())
print("System dir: ", cognee_directory_path)
cognee.config.system_root_directory(cognee_directory_path)
await cognee.prune.prune_system()
dataset_name = "cs_explanations"
explanation_file_path = path.join(pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt")
await cognee.add([explanation_file_path], dataset_name)
dataset_name = "short_stories"
# data_directory_path is defined above
await cognee.add("data://" + data_directory_path, dataset_name)
@ -34,7 +36,8 @@ async def main():
Some notable LLMs are OpenAI's GPT series of models (e.g., GPT-3.5 and GPT-4, used in ChatGPT and Microsoft Copilot), Google's PaLM and Gemini (the latter of which is currently used in the chatbot of the same name), xAI's Grok, Meta's LLaMA family of open-source models, Anthropic's Claude models, Mistral AI's open source models, and Databricks' open source DBRX.
"""
dataset_name = "explanations"
dataset_name = "cs_explanations"
await cognee.add(
[
text_1,
@ -43,11 +46,9 @@ async def main():
dataset_name
)
graph = await cognee.cognify(dataset_name)
await cognee.cognify(["short_stories", "cs_explanations"])
if __name__ == "__main__":
import asyncio
asyncio.run(main())

32
cognee/utils.py
View file

@ -1,6 +1,8 @@
""" This module contains utility functions for the cognee. """
import os
import uuid
import datetime
import graphistry
import networkx as nx
import numpy as np
@ -11,31 +13,35 @@ import nltk
from posthog import Posthog
from cognee.config import Config
import uuid
import datetime
config = Config()
config.load()
def send_telemetry( posthog, event_name="COGNEE_ADD"):
def send_telemetry(event_name: str):
if os.getenv("TELEMETRY_DISABLED"):
return
posthog = Posthog(project_api_key='phc_bbR86N876kwub62Lr3dhQ7zIeRyMMMm0fxXqxPqzLm3',
host='https://eu.i.posthog.com')
env = os.getenv("ENV")
if env in ["local", "test", "dev"]:
return
posthog = Posthog(
project_api_key = "phc_bbR86N876kwub62Lr3dhQ7zIeRyMMMm0fxXqxPqzLm3",
host="https://eu.i.posthog.com"
)
user_id = str(uuid.uuid4())
current_time = datetime.datetime.now()
properties = {
"time": current_time.strftime('%m/%d/%Y')
"time": current_time.strftime("%m/%d/%Y")
}
try:
posthog.capture(user_id, event_name, properties)
except Exception as e:
print('ERROR sending telemetric data to Posthog. See exception: %s', e)
print("ERROR sending telemetric data to Posthog. See exception: %s", e)
def get_document_names(doc_input):
"""
Get a list of document names.
@ -214,7 +220,7 @@ def sanitize_df(df):
def get_entities(tagged_tokens):
nltk.download("maxent_ne_chunker")
nltk.download("maxent_ne_chunker", quiet=True)
from nltk.chunk import ne_chunk
return ne_chunk(tagged_tokens)
@ -223,9 +229,9 @@ def extract_pos_tags(sentence):
"""Extract Part-of-Speech (POS) tags for words in a sentence."""
# Ensure that the necessary NLTK resources are downloaded
nltk.download("words")
nltk.download("punkt")
nltk.download("averaged_perceptron_tagger")
nltk.download("words", quiet=True)
nltk.download("punkt", quiet=True)
nltk.download("averaged_perceptron_tagger", quiet=True)
from nltk.tag import pos_tag
from nltk.tokenize import word_tokenize
@ -262,7 +268,7 @@ def extract_sentiment_vader(text):
"""
from nltk.sentiment import SentimentIntensityAnalyzer
nltk.download("vader_lexicon")
nltk.download("vader_lexicon", quiet=True)
# Initialize the VADER Sentiment Intensity Analyzer
sia = SentimentIntensityAnalyzer()

550
poetry.lock generated
View file

@ -508,33 +508,33 @@ lxml = ["lxml"]
[[package]]
name = "black"
version = "24.4.0"
version = "24.4.1"
description = "The uncompromising code formatter."
optional = false
python-versions = ">=3.8"
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]
[package.dependencies]
@ -572,17 +572,17 @@ css = ["tinycss2 (>=1.1.0,<1.3)"]
[[package]]
name = "boto3"
version = "1.34.88"
version = "1.34.91"
description = "The AWS SDK for Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "boto3-1.34.88-py3-none-any.whl", hash = "sha256:1bd4cef11b7c5f293cede50f3d33ca89fe3413c51f1864f40163c56a732dd6b3"},
{file = "boto3-1.34.88.tar.gz", hash = "sha256:168894499578a9d69d6f7deb5811952bf4171c51b95749a9aef32cf67bc71f87"},
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{file = "boto3-1.34.91.tar.gz", hash = "sha256:5077917041adaaae15eeca340289547ef905ca7e11516e9bd22d394fb5057d2a"},
]
[package.dependencies]
botocore = ">=1.34.88,<1.35.0"
botocore = ">=1.34.91,<1.35.0"
jmespath = ">=0.7.1,<2.0.0"
s3transfer = ">=0.10.0,<0.11.0"
@ -591,13 +591,13 @@ crt = ["botocore[crt] (>=1.21.0,<2.0a0)"]
[[package]]
name = "botocore"
version = "1.34.88"
version = "1.34.91"
description = "Low-level, data-driven core of boto 3."
optional = false
python-versions = ">=3.8"
files = [
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{file = "botocore-1.34.88.tar.gz", hash = "sha256:36f2e9e8dfa856e55dbbe703aea601f134db3fddc3615f1020a755b27fd26a5e"},
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]
[package.dependencies]
@ -611,6 +611,17 @@ urllib3 = [
[package.extras]
crt = ["awscrt (==0.20.9)"]
[[package]]
name = "cachetools"
version = "5.3.3"
description = "Extensible memoizing collections and decorators"
optional = false
python-versions = ">=3.7"
files = [
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{file = "cachetools-5.3.3.tar.gz", hash = "sha256:ba29e2dfa0b8b556606f097407ed1aa62080ee108ab0dc5ec9d6a723a007d105"},
]
[[package]]
name = "cairocffi"
version = "1.6.1"
@ -967,63 +978,63 @@ test-no-images = ["pytest", "pytest-cov", "pytest-xdist", "wurlitzer"]
[[package]]
name = "coverage"
version = "7.4.4"
version = "7.5.0"
description = "Code coverage measurement for Python"
optional = false
python-versions = ">=3.8"
files = [
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{file = "pylance-0.10.12-cp38-abi3-macosx_11_0_arm64.whl", hash = "sha256:e558163ff6035d518706cc66848497219ccc755e2972b8f3b1706a3e1fd800fd"},
{file = "pylance-0.10.12-cp38-abi3-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:75afb39f71d7f12429f9b4d380eb6cf6aed179ae5a1c5d16cc768373a1521f87"},
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{file = "pylance-0.10.12-cp38-abi3-manylinux_2_28_x86_64.whl", hash = "sha256:34a5278b90f4cbcf21261353976127aa2ffbbd7d068810f0a2b0c1aa0334022a"},
{file = "pylance-0.10.12-cp38-abi3-win_amd64.whl", hash = "sha256:6cef5975d513097fd2c22692296c9a5a138928f38d02cd34ab63a7369abc1463"},
]
[package.dependencies]
numpy = ">=1.22"
pyarrow = ">=12,<15.0.1"
[package.extras]
benchmarks = ["pytest-benchmark"]
dev = ["ruff (==0.2.2)"]
ray = ["ray[data]"]
tests = ["boto3", "datasets", "duckdb", "h5py (<3.11)", "ml-dtypes", "pandas", "pillow", "polars[pandas,pyarrow]", "pytest", "tensorflow", "tqdm"]
torch = ["torch"]
[[package]]
name = "pylint"
version = "3.1.0"
@ -5264,13 +5380,13 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
[[package]]
name = "qdrant-client"
version = "1.8.2"
version = "1.9.0"
description = "Client library for the Qdrant vector search engine"
optional = false
python-versions = ">=3.8"
files = [
{file = "qdrant_client-1.8.2-py3-none-any.whl", hash = "sha256:ee5341c0486d09e4346b0f5ef7781436e6d8cdbf1d5ecddfde7adb3647d353a8"},
{file = "qdrant_client-1.8.2.tar.gz", hash = "sha256:65078d5328bc0393f42a46a31cd319a989b8285bf3958360acf1dffffdf4cc4e"},
{file = "qdrant_client-1.9.0-py3-none-any.whl", hash = "sha256:ee02893eab1f642481b1ac1e38eb68ec30bab0f673bef7cc05c19fa5d2cbf43e"},
{file = "qdrant_client-1.9.0.tar.gz", hash = "sha256:7b1792f616651a6f0a76312f945c13d088e9451726795b82ce0350f7df3b7981"},
]
[package.dependencies]
@ -5283,17 +5399,31 @@ pydantic = ">=1.10.8"
urllib3 = ">=1.26.14,<3"
[package.extras]
fastembed = ["fastembed (==0.2.5)"]
fastembed = ["fastembed (==0.2.6)"]
[[package]]
name = "ratelimiter"
version = "1.2.0.post0"
description = "Simple python rate limiting object"
optional = false
python-versions = "*"
files = [
{file = "ratelimiter-1.2.0.post0-py3-none-any.whl", hash = "sha256:a52be07bc0bb0b3674b4b304550f10c769bbb00fead3072e035904474259809f"},
{file = "ratelimiter-1.2.0.post0.tar.gz", hash = "sha256:5c395dcabdbbde2e5178ef3f89b568a3066454a6ddc223b76473dac22f89b4f7"},
]
[package.extras]
test = ["pytest (>=3.0)", "pytest-asyncio"]
[[package]]
name = "redis"
version = "5.0.3"
version = "5.0.4"
description = "Python client for Redis database and key-value store"
optional = false
python-versions = ">=3.7"
files = [
{file = "redis-5.0.3-py3-none-any.whl", hash = "sha256:5da9b8fe9e1254293756c16c008e8620b3d15fcc6dde6babde9541850e72a32d"},
{file = "redis-5.0.3.tar.gz", hash = "sha256:4973bae7444c0fbed64a06b87446f79361cb7e4ec1538c022d696ed7a5015580"},
{file = "redis-5.0.4-py3-none-any.whl", hash = "sha256:7adc2835c7a9b5033b7ad8f8918d09b7344188228809c98df07af226d39dec91"},
{file = "redis-5.0.4.tar.gz", hash = "sha256:ec31f2ed9675cc54c21ba854cfe0462e6faf1d83c8ce5944709db8a4700b9c61"},
]
[package.dependencies]
@ -5305,13 +5435,13 @@ ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==20.0.1)", "requests (>=2.26.0)"
[[package]]
name = "referencing"
version = "0.34.0"
version = "0.35.0"
description = "JSON Referencing + Python"
optional = false
python-versions = ">=3.8"
files = [
{file = "referencing-0.34.0-py3-none-any.whl", hash = "sha256:d53ae300ceddd3169f1ffa9caf2cb7b769e92657e4fafb23d34b93679116dfd4"},
{file = "referencing-0.34.0.tar.gz", hash = "sha256:5773bd84ef41799a5a8ca72dc34590c041eb01bf9aa02632b4a973fb0181a844"},
{file = "referencing-0.35.0-py3-none-any.whl", hash = "sha256:8080727b30e364e5783152903672df9b6b091c926a146a759080b62ca3126cd6"},
{file = "referencing-0.35.0.tar.gz", hash = "sha256:191e936b0c696d0af17ad7430a3dc68e88bc11be6514f4757dc890f04ab05889"},
]
[package.dependencies]
@ -5455,6 +5585,21 @@ files = [
[package.dependencies]
types-setuptools = ">=69.1.0"
[[package]]
name = "retry"
version = "0.9.2"
description = "Easy to use retry decorator."
optional = false
python-versions = "*"
files = [
{file = "retry-0.9.2-py2.py3-none-any.whl", hash = "sha256:ccddf89761fa2c726ab29391837d4327f819ea14d244c232a1d24c67a2f98606"},
{file = "retry-0.9.2.tar.gz", hash = "sha256:f8bfa8b99b69c4506d6f5bd3b0aabf77f98cdb17f3c9fc3f5ca820033336fba4"},
]
[package.dependencies]
decorator = ">=3.4.2"
py = ">=1.4.26,<2.0.0"
[[package]]
name = "rfc3339-validator"
version = "0.1.4"
@ -6218,13 +6363,13 @@ blobfile = ["blobfile (>=2)"]
[[package]]
name = "tinycss2"
version = "1.2.1"
version = "1.3.0"
description = "A tiny CSS parser"
optional = false
python-versions = ">=3.7"
python-versions = ">=3.8"
files = [
{file = "tinycss2-1.2.1-py3-none-any.whl", hash = "sha256:2b80a96d41e7c3914b8cda8bc7f705a4d9c49275616e886103dd839dfc847847"},
{file = "tinycss2-1.2.1.tar.gz", hash = "sha256:8cff3a8f066c2ec677c06dbc7b45619804a6938478d9d73c284b29d14ecb0627"},
{file = "tinycss2-1.3.0-py3-none-any.whl", hash = "sha256:54a8dbdffb334d536851be0226030e9505965bb2f30f21a4a82c55fb2a80fae7"},
{file = "tinycss2-1.3.0.tar.gz", hash = "sha256:152f9acabd296a8375fbca5b84c961ff95971fcfc32e79550c8df8e29118c54d"},
]
[package.dependencies]
@ -6232,7 +6377,7 @@ webencodings = ">=0.4"
[package.extras]
doc = ["sphinx", "sphinx_rtd_theme"]
test = ["flake8", "isort", "pytest"]
test = ["pytest", "ruff"]
[[package]]
name = "tokenizers"
@ -6479,13 +6624,13 @@ files = [
[[package]]
name = "types-setuptools"
version = "69.5.0.20240415"
version = "69.5.0.20240423"
description = "Typing stubs for setuptools"
optional = false
python-versions = ">=3.8"
files = [
{file = "types-setuptools-69.5.0.20240415.tar.gz", hash = "sha256:ea64af0a96a674f8c40ba34c09c254f3c70bc3f218c6bffa1d0912bd91584a2f"},
{file = "types_setuptools-69.5.0.20240415-py3-none-any.whl", hash = "sha256:637cdb24a0d48a6ab362c09cfe3b89ecaa1c10666a8ba9452924e9a0ae00fa4a"},
{file = "types-setuptools-69.5.0.20240423.tar.gz", hash = "sha256:a7ba908f1746c4337d13f027fa0f4a5bcad6d1d92048219ba792b3295c58586d"},
{file = "types_setuptools-69.5.0.20240423-py3-none-any.whl", hash = "sha256:a4381e041510755a6c9210e26ad55b1629bc10237aeb9cb8b6bd24996b73db48"},
]
[[package]]
@ -6651,26 +6796,15 @@ standard = ["colorama (>=0.4)", "httptools (>=0.5.0)", "python-dotenv (>=0.13)",
[[package]]
name = "validators"
version = "0.22.0"
version = "0.28.0"
description = "Python Data Validation for Humans™"
optional = false
python-versions = ">=3.8"
files = [
{file = "validators-0.22.0-py3-none-any.whl", hash = "sha256:61cf7d4a62bbae559f2e54aed3b000cea9ff3e2fdbe463f51179b92c58c9585a"},
{file = "validators-0.22.0.tar.gz", hash = "sha256:77b2689b172eeeb600d9605ab86194641670cdb73b60afd577142a9397873370"},
{file = "validators-0.28.0-py3-none-any.whl", hash = "sha256:e0184691dea3ba82b52c161ba81d3ec1d8be8da9609f0137d1430b395b366521"},
{file = "validators-0.28.0.tar.gz", hash = "sha256:85bc82511f6ccd0800f4c15d8c0dc546c15e369640c5ea1f24349ba0b3b17815"},
]
[package.extras]
docs-offline = ["myst-parser (>=2.0.0)", "pypandoc-binary (>=1.11)", "sphinx (>=7.1.1)"]
docs-online = ["mkdocs (>=1.5.2)", "mkdocs-git-revision-date-localized-plugin (>=1.2.0)", "mkdocs-material (>=9.2.6)", "mkdocstrings[python] (>=0.22.0)", "pyaml (>=23.7.0)"]
hooks = ["pre-commit (>=3.3.3)"]
package = ["build (>=1.0.0)", "twine (>=4.0.2)"]
runner = ["tox (>=4.11.1)"]
sast = ["bandit[toml] (>=1.7.5)"]
testing = ["pytest (>=7.4.0)"]
tooling = ["black (>=23.7.0)", "pyright (>=1.1.325)", "ruff (>=0.0.287)"]
tooling-extras = ["pyaml (>=23.7.0)", "pypandoc-binary (>=1.11)", "pytest (>=7.4.0)"]
[[package]]
name = "watchdog"
version = "4.0.0"
@ -6725,13 +6859,13 @@ files = [
[[package]]
name = "weaviate-client"
version = "4.5.5"
version = "4.5.6"
description = "A python native Weaviate client"
optional = false
python-versions = ">=3.8"
files = [
{file = "weaviate-client-4.5.5.tar.gz", hash = "sha256:69906588e8eda0a307ad2c5b3c7c7e0ae4b9d80202a5cc97bdd2af15293977e3"},
{file = "weaviate_client-4.5.5-py3-none-any.whl", hash = "sha256:70cbd139f8a230723eb2400b8a3fb495055ae8c0897bd837ab58994924de0413"},
{file = "weaviate_client-4.5.6-py3-none-any.whl", hash = "sha256:bdafbf94343f621ca68bc547b5c9a5272dc6ca7953ad6a228f5ad8179021de68"},
{file = "weaviate_client-4.5.6.tar.gz", hash = "sha256:32a2b328f0a6637228c064e04aa6004c4ba733469b81754ae4598750735a9624"},
]
[package.dependencies]
@ -6742,7 +6876,7 @@ grpcio-tools = ">=1.57.0,<2.0.0"
httpx = "0.27.0"
pydantic = ">=2.5.0,<3.0.0"
requests = ">=2.30.0,<3.0.0"
validators = "0.22.0"
validators = "0.28.0"
[[package]]
name = "webcolors"
@ -6772,17 +6906,17 @@ files = [
[[package]]
name = "websocket-client"
version = "1.7.0"
version = "1.8.0"
description = "WebSocket client for Python with low level API options"
optional = false
python-versions = ">=3.8"
files = [
{file = "websocket-client-1.7.0.tar.gz", hash = "sha256:10e511ea3a8c744631d3bd77e61eb17ed09304c413ad42cf6ddfa4c7787e8fe6"},
{file = "websocket_client-1.7.0-py3-none-any.whl", hash = "sha256:f4c3d22fec12a2461427a29957ff07d35098ee2d976d3ba244e688b8b4057588"},
{file = "websocket_client-1.8.0-py3-none-any.whl", hash = "sha256:17b44cc997f5c498e809b22cdf2d9c7a9e71c02c8cc2b6c56e7c2d1239bfa526"},
{file = "websocket_client-1.8.0.tar.gz", hash = "sha256:3239df9f44da632f96012472805d40a23281a991027ce11d2f45a6f24ac4c3da"},
]
[package.extras]
docs = ["Sphinx (>=6.0)", "sphinx-rtd-theme (>=1.1.0)"]
docs = ["Sphinx (>=6.0)", "myst-parser (>=2.0.0)", "sphinx-rtd-theme (>=1.1.0)"]
optional = ["python-socks", "wsaccel"]
test = ["websockets"]
@ -7110,4 +7244,4 @@ weaviate = ["weaviate-client"]
[metadata]
lock-version = "2.0"
python-versions = ">=3.9.0,<3.12"
content-hash = "d026e03ff7f204a4026f4f980876fca84a1ac48d7dbfdcfec2c5d7656ed8355d"
content-hash = "9609dfc41209efd1fd5e53fd1b77353cb5bce3244ffdb65f81eee686a68b6fc5"

5
pyproject.toml
View file

@ -1,6 +1,6 @@
[tool.poetry]
name = "cognee"
version = "0.1.3"
version = "0.1.6"
description = "Cognee - is a library for enriching LLM context with a semantic layer for better understanding and reasoning."
authors = ["Vasilije Markovic", "Boris Arzentar"]
readme = "README.md"
@ -44,7 +44,7 @@ dlt = "^0.4.7"
duckdb = {version = "^0.10.0", extras = ["dlt"]}
overrides = "^7.7.0"
aiofiles = "^23.2.1"
qdrant-client = "^1.8.0"
qdrant-client = "^1.9.0"
duckdb-engine = "^0.11.2"
graphistry = "^0.33.5"
tenacity = "^8.2.3"
@ -62,6 +62,7 @@ structlog = "^24.1.0"
tiktoken = "^0.6.0"
dspy-ai = "2.4.3"
posthog = "^3.5.0"
lancedb = "^0.6.10"
[tool.poetry.extras]

44
tools/check-package.sh
View file

@ -1,3 +1,33 @@
##!/usr/bin/env bash
#
#set -e
#set -u
#
#shopt -s dotglob
#shopt -s nullglob
## verifies if python packages have proper structure
## find all directories not containing __init__
#error=
#while IFS= read -r d; do
# myarray=(`find $d -maxdepth 1 -name "*.py"`)
# if [ ${#myarray[@]} -gt 0 ]; then
# if [[ $@ == *--fix* ]]; then
# echo Will create "$d/__init__.py"
# touch "$d/__init__.py"
# else
# echo Folder "$d" lacks __init__.py file
# error="yes"
# fi
# fi
#done < <(find . -mindepth 1 -not -path "./docs/website/node_modules*" -type d -regex "^./[^.^_].*" '!' -exec test -e "{}/__init__.py" ';' -print)
#
#if [ -z $error ]; then
# exit 0
#fi
#
## error in package
#exit 1
#!/usr/bin/env bash
set -e
@ -5,25 +35,27 @@ set -u
shopt -s dotglob
shopt -s nullglob
# verifies if python packages have proper structure
# find all directories not containing __init__
error=
while IFS= read -r d; do
myarray=(`find $d -maxdepth 1 -name "*.py"`)
myarray=(`find "$d" -maxdepth 1 -name "*.py"`)
if [ ${#myarray[@]} -gt 0 ]; then
if [[ $@ == *--fix* ]]; then
echo Will create "$d/__init__.py"
echo "Will create $d/__init__.py"
touch "$d/__init__.py"
else
echo Folder "$d" lacks __init__.py file
echo "Folder $d lacks __init__.py file"
error="yes"
fi
fi
done < <(find . -mindepth 1 -not -path "./docs/website/node_modules*" -type d -regex "^./[^.^_].*" '!' -exec test -e "{}/__init__.py" ';' -print)
done < <(find . -mindepth 1 -not \( -path "./docs*" -prune \) -not -path "./docs/website/node_modules*" -not -path "./tools*" -type d -regex "^./[^.^_].*" '!' -exec test -e "{}/__init__.py" ';' -print)
if [ -z $error ]; then
if [ -z "$error" ]; then
exit 0
fi
# error in package
exit 1
exit 1