Merge branch 'dev' into feat/make-authentication-optional
This commit is contained in:
Vasilije
GitHub
commit
ea0edc7056
68 changed files with 2439 additions and 364 deletions
224
.github/workflows/temporal_graph_tests.yml
vendored
Normal file
224
.github/workflows/temporal_graph_tests.yml
vendored
Normal file
|
|
@ -0,0 +1,224 @@
|
|||
name: Temporal Graph Tests
|
||||
|
||||
permissions:
|
||||
contents: read
|
||||
|
||||
on:
|
||||
workflow_call:
|
||||
inputs:
|
||||
databases:
|
||||
required: false
|
||||
type: string
|
||||
default: "all"
|
||||
description: "Which vector databases to test (comma-separated list or 'all')"
|
||||
|
||||
jobs:
|
||||
run_temporal_graph_kuzu_lance_sqlite:
|
||||
name: Temporal Graph test Kuzu (lancedb + sqlite)
|
||||
runs-on: ubuntu-22.04
|
||||
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/lance/sqlite') }}
|
||||
steps:
|
||||
- name: Check out
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Cognee Setup
|
||||
uses: ./.github/actions/cognee_setup
|
||||
with:
|
||||
python-version: ${{ inputs.python-version }}
|
||||
|
||||
- name: Dependencies already installed
|
||||
run: echo "Dependencies already installed in setup"
|
||||
|
||||
- name: Run Temporal Graph with Kuzu (lancedb + sqlite)
|
||||
env:
|
||||
ENV: 'dev'
|
||||
LLM_MODEL: ${{ secrets.LLM_MODEL }}
|
||||
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
|
||||
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
|
||||
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
|
||||
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
|
||||
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
|
||||
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
|
||||
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
|
||||
GRAPH_DATABASE_PROVIDER: 'kuzu'
|
||||
VECTOR_DB_PROVIDER: 'lancedb'
|
||||
DB_PROVIDER: 'sqlite'
|
||||
run: uv run python ./cognee/tests/test_temporal_graph.py
|
||||
|
||||
run_temporal_graph_neo4j_lance_sqlite:
|
||||
name: Temporal Graph test Neo4j (lancedb + sqlite)
|
||||
runs-on: ubuntu-22.04
|
||||
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/lance/sqlite') }}
|
||||
services:
|
||||
neo4j:
|
||||
image: neo4j:5.11
|
||||
env:
|
||||
NEO4J_AUTH: neo4j/pleaseletmein
|
||||
NEO4J_PLUGINS: '["apoc","graph-data-science"]'
|
||||
ports:
|
||||
- 7474:7474
|
||||
- 7687:7687
|
||||
options: >-
|
||||
--health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'"
|
||||
--health-interval=10s
|
||||
--health-timeout=5s
|
||||
--health-retries=5
|
||||
|
||||
steps:
|
||||
- name: Check out
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Cognee Setup
|
||||
uses: ./.github/actions/cognee_setup
|
||||
with:
|
||||
python-version: ${{ inputs.python-version }}
|
||||
|
||||
- name: Dependencies already installed
|
||||
run: echo "Dependencies already installed in setup"
|
||||
|
||||
- name: Run Temporal Graph with Neo4j (lancedb + sqlite)
|
||||
env:
|
||||
ENV: 'dev'
|
||||
LLM_MODEL: ${{ secrets.LLM_MODEL }}
|
||||
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
|
||||
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
|
||||
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
|
||||
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
|
||||
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
|
||||
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
|
||||
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
|
||||
GRAPH_DATABASE_PROVIDER: 'neo4j'
|
||||
VECTOR_DB_PROVIDER: 'lancedb'
|
||||
DB_PROVIDER: 'sqlite'
|
||||
GRAPH_DATABASE_URL: bolt://localhost:7687
|
||||
GRAPH_DATABASE_USERNAME: neo4j
|
||||
GRAPH_DATABASE_PASSWORD: pleaseletmein
|
||||
run: uv run python ./cognee/tests/test_temporal_graph.py
|
||||
|
||||
run_temporal_graph_kuzu_postgres_pgvector:
|
||||
name: Temporal Graph test Kuzu (postgres + pgvector)
|
||||
runs-on: ubuntu-22.04
|
||||
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'kuzu/pgvector/postgres') }}
|
||||
services:
|
||||
postgres:
|
||||
image: pgvector/pgvector:pg17
|
||||
env:
|
||||
POSTGRES_USER: cognee
|
||||
POSTGRES_PASSWORD: cognee
|
||||
POSTGRES_DB: cognee_db
|
||||
options: >-
|
||||
--health-cmd pg_isready
|
||||
--health-interval 10s
|
||||
--health-timeout 5s
|
||||
--health-retries 5
|
||||
ports:
|
||||
- 5432:5432
|
||||
steps:
|
||||
- name: Check out
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Cognee Setup
|
||||
uses: ./.github/actions/cognee_setup
|
||||
with:
|
||||
python-version: ${{ inputs.python-version }}
|
||||
extra-dependencies: "postgres"
|
||||
|
||||
- name: Dependencies already installed
|
||||
run: echo "Dependencies already installed in setup"
|
||||
|
||||
- name: Run Temporal Graph with Kuzu (postgres + pgvector)
|
||||
env:
|
||||
ENV: dev
|
||||
LLM_MODEL: ${{ secrets.LLM_MODEL }}
|
||||
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
|
||||
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
|
||||
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
|
||||
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
|
||||
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
|
||||
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
|
||||
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
|
||||
GRAPH_DATABASE_PROVIDER: 'kuzu'
|
||||
VECTOR_DB_PROVIDER: 'pgvector'
|
||||
DB_PROVIDER: 'postgres'
|
||||
DB_NAME: 'cognee_db'
|
||||
DB_HOST: '127.0.0.1'
|
||||
DB_PORT: 5432
|
||||
DB_USERNAME: cognee
|
||||
DB_PASSWORD: cognee
|
||||
run: uv run python ./cognee/tests/test_temporal_graph.py
|
||||
|
||||
run_temporal_graph_neo4j_postgres_pgvector:
|
||||
name: Temporal Graph test Neo4j (postgres + pgvector)
|
||||
runs-on: ubuntu-22.04
|
||||
if: ${{ inputs.databases == 'all' || contains(inputs.databases, 'neo4j/pgvector/postgres') }}
|
||||
services:
|
||||
neo4j:
|
||||
image: neo4j:5.11
|
||||
env:
|
||||
NEO4J_AUTH: neo4j/pleaseletmein
|
||||
NEO4J_PLUGINS: '["apoc","graph-data-science"]'
|
||||
ports:
|
||||
- 7474:7474
|
||||
- 7687:7687
|
||||
options: >-
|
||||
--health-cmd="cypher-shell -u neo4j -p pleaseletmein 'RETURN 1'"
|
||||
--health-interval=10s
|
||||
--health-timeout=5s
|
||||
--health-retries=5
|
||||
postgres:
|
||||
image: pgvector/pgvector:pg17
|
||||
env:
|
||||
POSTGRES_USER: cognee
|
||||
POSTGRES_PASSWORD: cognee
|
||||
POSTGRES_DB: cognee_db
|
||||
ports:
|
||||
- 5432:5432
|
||||
options: >-
|
||||
--health-cmd pg_isready
|
||||
--health-interval 10s
|
||||
--health-timeout 5s
|
||||
--health-retries=5
|
||||
steps:
|
||||
- name: Check out
|
||||
uses: actions/checkout@v4
|
||||
with:
|
||||
fetch-depth: 0
|
||||
|
||||
- name: Cognee Setup
|
||||
uses: ./.github/actions/cognee_setup
|
||||
with:
|
||||
python-version: ${{ inputs.python-version }}
|
||||
extra-dependencies: "postgres"
|
||||
|
||||
- name: Dependencies already installed
|
||||
run: echo "Dependencies already installed in setup"
|
||||
|
||||
- name: Run Temporal Graph with Neo4j (postgres + pgvector)
|
||||
env:
|
||||
ENV: dev
|
||||
LLM_MODEL: ${{ secrets.LLM_MODEL }}
|
||||
LLM_ENDPOINT: ${{ secrets.LLM_ENDPOINT }}
|
||||
LLM_API_KEY: ${{ secrets.LLM_API_KEY }}
|
||||
LLM_API_VERSION: ${{ secrets.LLM_API_VERSION }}
|
||||
EMBEDDING_MODEL: ${{ secrets.EMBEDDING_MODEL }}
|
||||
EMBEDDING_ENDPOINT: ${{ secrets.EMBEDDING_ENDPOINT }}
|
||||
EMBEDDING_API_KEY: ${{ secrets.EMBEDDING_API_KEY }}
|
||||
EMBEDDING_API_VERSION: ${{ secrets.EMBEDDING_API_VERSION }}
|
||||
GRAPH_DATABASE_PROVIDER: 'neo4j'
|
||||
VECTOR_DB_PROVIDER: 'pgvector'
|
||||
DB_PROVIDER: 'postgres'
|
||||
GRAPH_DATABASE_URL: bolt://localhost:7687
|
||||
GRAPH_DATABASE_USERNAME: neo4j
|
||||
GRAPH_DATABASE_PASSWORD: pleaseletmein
|
||||
DB_NAME: cognee_db
|
||||
DB_HOST: 127.0.0.1
|
||||
DB_PORT: 5432
|
||||
DB_USERNAME: cognee
|
||||
DB_PASSWORD: cognee
|
||||
run: uv run python ./cognee/tests/test_temporal_graph.py
|
||||
30
.github/workflows/test_openrouter.yml
vendored
Normal file
30
.github/workflows/test_openrouter.yml
vendored
Normal file
|
|
@ -0,0 +1,30 @@
|
|||
name: test | openrouter
|
||||
|
||||
on:
|
||||
workflow_call:
|
||||
|
||||
jobs:
|
||||
test-openrouter:
|
||||
name: Run OpenRouter Test
|
||||
runs-on: ubuntu-22.04
|
||||
steps:
|
||||
- name: Check out repository
|
||||
uses: actions/checkout@v4
|
||||
|
||||
- name: Cognee Setup
|
||||
uses: ./.github/actions/cognee_setup
|
||||
with:
|
||||
python-version: '3.11.x'
|
||||
|
||||
- name: Run OpenRouter Simple Example
|
||||
env:
|
||||
LLM_PROVIDER: "custom"
|
||||
LLM_API_KEY: ${{ secrets.OPENROUTER_API_KEY }}
|
||||
LLM_MODEL: "openrouter/x-ai/grok-code-fast-1"
|
||||
LLM_ENDPOINT: "https://openrouter.ai/api/v1"
|
||||
EMBEDDING_PROVIDER: "openai"
|
||||
EMBEDDING_API_KEY: ${{ secrets.OPENAI_API_KEY }}
|
||||
EMBEDDING_MODEL: "openai/text-embedding-3-large"
|
||||
EMBEDDING_DIMENSIONS: "3072"
|
||||
EMBEDDING_MAX_TOKENS: "8191"
|
||||
run: uv run python ./examples/python/simple_example.py
|
||||
15
.github/workflows/test_suites.yml
vendored
15
.github/workflows/test_suites.yml
vendored
|
|
@ -50,6 +50,12 @@ jobs:
|
|||
uses: ./.github/workflows/graph_db_tests.yml
|
||||
secrets: inherit
|
||||
|
||||
temporal-graph-tests:
|
||||
name: Temporal Graph Test
|
||||
needs: [ basic-tests, e2e-tests, cli-tests, graph-db-tests ]
|
||||
uses: ./.github/workflows/temporal_graph_tests.yml
|
||||
secrets: inherit
|
||||
|
||||
search-db-tests:
|
||||
name: Search Test on Different DBs
|
||||
needs: [basic-tests, e2e-tests, cli-tests, graph-db-tests]
|
||||
|
|
@ -115,6 +121,12 @@ jobs:
|
|||
uses: ./.github/workflows/test_gemini.yml
|
||||
secrets: inherit
|
||||
|
||||
openrouter-tests:
|
||||
name: OpenRouter Tests
|
||||
needs: [basic-tests, e2e-tests, cli-tests]
|
||||
uses: ./.github/workflows/test_openrouter.yml
|
||||
secrets: inherit
|
||||
|
||||
# Ollama tests moved to the end
|
||||
ollama-tests:
|
||||
name: Ollama Tests
|
||||
|
|
@ -128,6 +140,7 @@ jobs:
|
|||
vector-db-tests,
|
||||
example-tests,
|
||||
gemini-tests,
|
||||
openrouter-tests,
|
||||
mcp-test,
|
||||
relational-db-migration-tests,
|
||||
docker-compose-test,
|
||||
|
|
@ -150,6 +163,7 @@ jobs:
|
|||
db-examples-tests,
|
||||
mcp-test,
|
||||
gemini-tests,
|
||||
openrouter-tests,
|
||||
ollama-tests,
|
||||
relational-db-migration-tests,
|
||||
docker-compose-test,
|
||||
|
|
@ -171,6 +185,7 @@ jobs:
|
|||
"${{ needs.db-examples-tests.result }}" == "success" &&
|
||||
"${{ needs.relational-db-migration-tests.result }}" == "success" &&
|
||||
"${{ needs.gemini-tests.result }}" == "success" &&
|
||||
"${{ needs.openrouter-tests.result }}" == "success" &&
|
||||
"${{ needs.docker-compose-test.result }}" == "success" &&
|
||||
"${{ needs.docker-ci-test.result }}" == "success" &&
|
||||
"${{ needs.ollama-tests.result }}" == "success" ]]; then
|
||||
|
|
|
|||
|
|
@ -79,7 +79,9 @@ More on [use-cases](https://docs.cognee.ai/use-cases) and [evals](https://github
|
|||
|
||||
## Get Started
|
||||
|
||||
Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1jHbWVypDgCLwjE71GSXhRL3YxYhCZzG1?usp=sharing">notebook</a> , <a href="https://deepnote.com/workspace/cognee-382213d0-0444-4c89-8265-13770e333c02/project/cognee-demo-78ffacb9-5832-4611-bb1a-560386068b30/notebook/Notebook-1-75b24cda566d4c24ab348f7150792601?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=78ffacb9-5832-4611-bb1a-560386068b30">Deepnote notebook</a> or <a href="https://github.com/topoteretes/cognee-starter">starter repo</a>
|
||||
Get started quickly with a Google Colab <a href="https://colab.research.google.com/drive/1jHbWVypDgCLwjE71GSXhRL3YxYhCZzG1?usp=sharing">notebook</a> , <a href="https://deepnote.com/workspace/cognee-382213d0-0444-4c89-8265-13770e333c02/project/cognee-demo-78ffacb9-5832-4611-bb1a-560386068b30/notebook/Notebook-1-75b24cda566d4c24ab348f7150792601?utm_source=share-modal&utm_medium=product-shared-content&utm_campaign=notebook&utm_content=78ffacb9-5832-4611-bb1a-560386068b30">Deepnote notebook</a> or <a href="https://github.com/topoteretes/cognee/tree/main/cognee-starter-kit">starter repo</a>
|
||||
|
||||
|
||||
|
||||
|
||||
## Contributing
|
||||
|
|
|
|||
|
|
@ -8,7 +8,7 @@ requires-python = ">=3.10"
|
|||
dependencies = [
|
||||
# For local cognee repo usage remove comment bellow and add absolute path to cognee. Then run `uv sync --reinstall` in the mcp folder on local cognee changes.
|
||||
# "cognee[postgres,codegraph,gemini,huggingface,docs,neo4j] @ file:/Users/vasilije/Projects/tiktok/cognee",
|
||||
"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.2.3",
|
||||
"cognee[postgres,codegraph,gemini,huggingface,docs,neo4j]==0.2.4",
|
||||
"fastmcp>=2.10.0,<3.0.0",
|
||||
"mcp>=1.12.0,<2.0.0",
|
||||
"uv>=0.6.3,<1.0.0",
|
||||
|
|
|
|||
|
|
@ -21,16 +21,16 @@ from cognee.shared.data_models import KnowledgeGraph
|
|||
from cognee.modules.storage.utils import JSONEncoder
|
||||
|
||||
|
||||
# try:
|
||||
# from codingagents.coding_rule_associations import (
|
||||
# add_rule_associations,
|
||||
# get_existing_rules,
|
||||
# )
|
||||
# except ModuleNotFoundError:
|
||||
# from .codingagents.coding_rule_associations import (
|
||||
# add_rule_associations,
|
||||
# get_existing_rules,
|
||||
# )
|
||||
try:
|
||||
from codingagents.coding_rule_associations import (
|
||||
add_rule_associations,
|
||||
get_existing_rules,
|
||||
)
|
||||
except ModuleNotFoundError:
|
||||
from .codingagents.coding_rule_associations import (
|
||||
add_rule_associations,
|
||||
get_existing_rules,
|
||||
)
|
||||
|
||||
|
||||
mcp = FastMCP("Cognee")
|
||||
|
|
@ -310,7 +310,7 @@ async def save_interaction(data: str) -> list:
|
|||
logger.info("Save interaction process finished.")
|
||||
logger.info("Generating associated rules from interaction data.")
|
||||
|
||||
# await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules")
|
||||
await add_rule_associations(data=data, rules_nodeset_name="coding_agent_rules")
|
||||
|
||||
logger.info("Associated rules generated from interaction data.")
|
||||
|
||||
|
|
@ -572,10 +572,8 @@ async def get_developer_rules() -> list:
|
|||
async def fetch_rules_from_cognee() -> str:
|
||||
"""Collect all developer rules from Cognee"""
|
||||
with redirect_stdout(sys.stderr):
|
||||
note = "This is broken in 0.2.2"
|
||||
return note
|
||||
# developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules")
|
||||
# return developer_rules
|
||||
developer_rules = await get_existing_rules(rules_nodeset_name="coding_agent_rules")
|
||||
return developer_rules
|
||||
|
||||
rules_text = await fetch_rules_from_cognee()
|
||||
|
||||
|
|
|
|||
71
cognee-mcp/uv.lock
generated
71
cognee-mcp/uv.lock
generated
|
|
@ -332,6 +332,21 @@ wheels = [
|
|||
{ url = "https://files.pythonhosted.org/packages/df/73/b6e24bd22e6720ca8ee9a85a0c4a2971af8497d8f3193fa05390cbd46e09/backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8", size = 15148, upload-time = "2022-10-05T19:19:30.546Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "baml-py"
|
||||
version = "0.201.0"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
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|
|||
{ url = "https://files.pythonhosted.org/packages/6c/73/9f872cb81fc5c3bb48f7227872c28975f998f3e7c2b1c16e95e6432bbb90/python_magic-0.4.27-py2.py3-none-any.whl", hash = "sha256:c212960ad306f700aa0d01e5d7a325d20548ff97eb9920dcd29513174f0294d3", size = 13840, upload-time = "2022-06-07T20:16:57.763Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "python-magic-bin"
|
||||
version = "0.4.14"
|
||||
source = { registry = "https://pypi.org/simple" }
|
||||
wheels = [
|
||||
{ url = "https://files.pythonhosted.org/packages/5a/5d/10b9ac745d9fd2f7151a2ab901e6bb6983dbd70e87c71111f54859d1ca2e/python_magic_bin-0.4.14-py2.py3-none-win32.whl", hash = "sha256:34a788c03adde7608028203e2dbb208f1f62225ad91518787ae26d603ae68892", size = 397784, upload-time = "2017-10-02T16:30:15.806Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/07/c2/094e3d62b906d952537196603a23aec4bcd7c6126bf80eb14e6f9f4be3a2/python_magic_bin-0.4.14-py2.py3-none-win_amd64.whl", hash = "sha256:90be6206ad31071a36065a2fc169c5afb5e0355cbe6030e87641c6c62edc2b69", size = 409299, upload-time = "2017-10-02T16:30:18.545Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "python-multipart"
|
||||
version = "0.0.20"
|
||||
|
|
|
|||
|
|
@ -25,6 +25,7 @@ def get_add_router() -> APIRouter:
|
|||
data: List[UploadFile] = File(default=None),
|
||||
datasetName: Optional[str] = Form(default=None),
|
||||
datasetId: Union[UUID, Literal[""], None] = Form(default=None, examples=[""]),
|
||||
node_set: Optional[List[str]] = Form(default=[""], example=[""]),
|
||||
user: User = Depends(get_authenticated_user),
|
||||
):
|
||||
"""
|
||||
|
|
@ -41,6 +42,8 @@ def get_add_router() -> APIRouter:
|
|||
- Regular file uploads
|
||||
- **datasetName** (Optional[str]): Name of the dataset to add data to
|
||||
- **datasetId** (Optional[UUID]): UUID of an already existing dataset
|
||||
- **node_set** Optional[list[str]]: List of node identifiers for graph organization and access control.
|
||||
Used for grouping related data points in the knowledge graph.
|
||||
|
||||
Either datasetName or datasetId must be provided.
|
||||
|
||||
|
|
@ -65,9 +68,7 @@ def get_add_router() -> APIRouter:
|
|||
send_telemetry(
|
||||
"Add API Endpoint Invoked",
|
||||
user.id,
|
||||
additional_properties={
|
||||
"endpoint": "POST /v1/add",
|
||||
},
|
||||
additional_properties={"endpoint": "POST /v1/add", "node_set": node_set},
|
||||
)
|
||||
|
||||
from cognee.api.v1.add import add as cognee_add
|
||||
|
|
@ -76,34 +77,13 @@ def get_add_router() -> APIRouter:
|
|||
raise ValueError("Either datasetId or datasetName must be provided.")
|
||||
|
||||
try:
|
||||
if (
|
||||
isinstance(data, str)
|
||||
and data.startswith("http")
|
||||
and (os.getenv("ALLOW_HTTP_REQUESTS", "true").lower() == "true")
|
||||
):
|
||||
if "github" in data:
|
||||
# Perform git clone if the URL is from GitHub
|
||||
repo_name = data.split("/")[-1].replace(".git", "")
|
||||
subprocess.run(["git", "clone", data, f".data/{repo_name}"], check=True)
|
||||
# TODO: Update add call with dataset info
|
||||
await cognee_add(
|
||||
"data://.data/",
|
||||
f"{repo_name}",
|
||||
)
|
||||
else:
|
||||
# Fetch and store the data from other types of URL using curl
|
||||
response = requests.get(data)
|
||||
response.raise_for_status()
|
||||
add_run = await cognee_add(
|
||||
data, datasetName, user=user, dataset_id=datasetId, node_set=node_set
|
||||
)
|
||||
|
||||
file_data = await response.content()
|
||||
# TODO: Update add call with dataset info
|
||||
return await cognee_add(file_data)
|
||||
else:
|
||||
add_run = await cognee_add(data, datasetName, user=user, dataset_id=datasetId)
|
||||
|
||||
if isinstance(add_run, PipelineRunErrored):
|
||||
return JSONResponse(status_code=420, content=add_run.model_dump(mode="json"))
|
||||
return add_run.model_dump()
|
||||
if isinstance(add_run, PipelineRunErrored):
|
||||
return JSONResponse(status_code=420, content=add_run.model_dump(mode="json"))
|
||||
return add_run.model_dump()
|
||||
except Exception as error:
|
||||
return JSONResponse(status_code=409, content={"error": str(error)})
|
||||
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
import os
|
||||
import pathlib
|
||||
import asyncio
|
||||
from typing import Optional
|
||||
from cognee.shared.logging_utils import get_logger, setup_logging
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
|
||||
|
|
@ -28,7 +29,12 @@ logger = get_logger("code_graph_pipeline")
|
|||
|
||||
|
||||
@observe
|
||||
async def run_code_graph_pipeline(repo_path, include_docs=False):
|
||||
async def run_code_graph_pipeline(
|
||||
repo_path,
|
||||
include_docs=False,
|
||||
excluded_paths: Optional[list[str]] = None,
|
||||
supported_languages: Optional[list[str]] = None,
|
||||
):
|
||||
import cognee
|
||||
from cognee.low_level import setup
|
||||
|
||||
|
|
@ -40,13 +46,12 @@ async def run_code_graph_pipeline(repo_path, include_docs=False):
|
|||
user = await get_default_user()
|
||||
detailed_extraction = True
|
||||
|
||||
# Multi-language support: allow passing supported_languages
|
||||
supported_languages = None # defer to task defaults
|
||||
tasks = [
|
||||
Task(
|
||||
get_repo_file_dependencies,
|
||||
detailed_extraction=detailed_extraction,
|
||||
supported_languages=supported_languages,
|
||||
excluded_paths=excluded_paths,
|
||||
),
|
||||
# Task(summarize_code, task_config={"batch_size": 500}), # This task takes a long time to complete
|
||||
Task(add_data_points, task_config={"batch_size": 30}),
|
||||
|
|
@ -95,7 +100,7 @@ if __name__ == "__main__":
|
|||
|
||||
async def main():
|
||||
async for run_status in run_code_graph_pipeline("REPO_PATH"):
|
||||
print(f"{run_status.pipeline_name}: {run_status.status}")
|
||||
print(f"{run_status.pipeline_run_id}: {run_status.status}")
|
||||
|
||||
file_path = os.path.join(
|
||||
pathlib.Path(__file__).parent, ".artifacts", "graph_visualization.html"
|
||||
|
|
|
|||
|
|
@ -22,6 +22,11 @@ from cognee.tasks.graph import extract_graph_from_data
|
|||
from cognee.tasks.storage import add_data_points
|
||||
from cognee.tasks.summarization import summarize_text
|
||||
from cognee.modules.pipelines.layers.pipeline_execution_mode import get_pipeline_executor
|
||||
from cognee.tasks.temporal_graph.extract_events_and_entities import extract_events_and_timestamps
|
||||
from cognee.tasks.temporal_graph.extract_knowledge_graph_from_events import (
|
||||
extract_knowledge_graph_from_events,
|
||||
)
|
||||
|
||||
|
||||
logger = get_logger("cognify")
|
||||
|
||||
|
|
@ -40,6 +45,7 @@ async def cognify(
|
|||
run_in_background: bool = False,
|
||||
incremental_loading: bool = True,
|
||||
custom_prompt: Optional[str] = None,
|
||||
temporal_cognify: bool = False,
|
||||
):
|
||||
"""
|
||||
Transform ingested data into a structured knowledge graph.
|
||||
|
|
@ -182,9 +188,12 @@ async def cognify(
|
|||
- LLM_RATE_LIMIT_ENABLED: Enable rate limiting (default: False)
|
||||
- LLM_RATE_LIMIT_REQUESTS: Max requests per interval (default: 60)
|
||||
"""
|
||||
tasks = await get_default_tasks(
|
||||
user, graph_model, chunker, chunk_size, ontology_file_path, custom_prompt
|
||||
)
|
||||
if temporal_cognify:
|
||||
tasks = await get_temporal_tasks(user, chunker, chunk_size)
|
||||
else:
|
||||
tasks = await get_default_tasks(
|
||||
user, graph_model, chunker, chunk_size, ontology_file_path, custom_prompt
|
||||
)
|
||||
|
||||
# By calling get pipeline executor we get a function that will have the run_pipeline run in the background or a function that we will need to wait for
|
||||
pipeline_executor_func = get_pipeline_executor(run_in_background=run_in_background)
|
||||
|
|
@ -233,3 +242,41 @@ async def get_default_tasks( # TODO: Find out a better way to do this (Boris's
|
|||
]
|
||||
|
||||
return default_tasks
|
||||
|
||||
|
||||
async def get_temporal_tasks(
|
||||
user: User = None, chunker=TextChunker, chunk_size: int = None
|
||||
) -> list[Task]:
|
||||
"""
|
||||
Builds and returns a list of temporal processing tasks to be executed in sequence.
|
||||
|
||||
The pipeline includes:
|
||||
1. Document classification.
|
||||
2. Dataset permission checks (requires "write" access).
|
||||
3. Document chunking with a specified or default chunk size.
|
||||
4. Event and timestamp extraction from chunks.
|
||||
5. Knowledge graph extraction from events.
|
||||
6. Batched insertion of data points.
|
||||
|
||||
Args:
|
||||
user (User, optional): The user requesting task execution, used for permission checks.
|
||||
chunker (Callable, optional): A text chunking function/class to split documents. Defaults to TextChunker.
|
||||
chunk_size (int, optional): Maximum token size per chunk. If not provided, uses system default.
|
||||
|
||||
Returns:
|
||||
list[Task]: A list of Task objects representing the temporal processing pipeline.
|
||||
"""
|
||||
temporal_tasks = [
|
||||
Task(classify_documents),
|
||||
Task(check_permissions_on_dataset, user=user, permissions=["write"]),
|
||||
Task(
|
||||
extract_chunks_from_documents,
|
||||
max_chunk_size=chunk_size or get_max_chunk_tokens(),
|
||||
chunker=chunker,
|
||||
),
|
||||
Task(extract_events_and_timestamps, task_config={"chunk_size": 10}),
|
||||
Task(extract_knowledge_graph_from_events),
|
||||
Task(add_data_points, task_config={"batch_size": 10}),
|
||||
]
|
||||
|
||||
return temporal_tasks
|
||||
|
|
|
|||
|
|
@ -38,7 +38,7 @@ class CognifyPayloadDTO(InDTO):
|
|||
dataset_ids: Optional[List[UUID]] = Field(default=None, examples=[[]])
|
||||
run_in_background: Optional[bool] = Field(default=False)
|
||||
custom_prompt: Optional[str] = Field(
|
||||
default=None, description="Custom prompt for entity extraction and graph generation"
|
||||
default="", description="Custom prompt for entity extraction and graph generation"
|
||||
)
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,9 +1,11 @@
|
|||
from uuid import UUID
|
||||
import pathlib
|
||||
from typing import Optional
|
||||
from datetime import datetime
|
||||
from pydantic import Field
|
||||
from fastapi import Depends, APIRouter
|
||||
from fastapi.responses import JSONResponse
|
||||
|
||||
from cognee.modules.search.types import SearchType
|
||||
from cognee.api.DTO import InDTO, OutDTO
|
||||
from cognee.modules.users.exceptions.exceptions import PermissionDeniedError
|
||||
|
|
@ -20,7 +22,12 @@ class SearchPayloadDTO(InDTO):
|
|||
datasets: Optional[list[str]] = Field(default=None)
|
||||
dataset_ids: Optional[list[UUID]] = Field(default=None, examples=[[]])
|
||||
query: str = Field(default="What is in the document?")
|
||||
system_prompt: Optional[str] = Field(
|
||||
default="Answer the question using the provided context. Be as brief as possible."
|
||||
)
|
||||
node_name: Optional[list[str]] = Field(default=None, example=[])
|
||||
top_k: Optional[int] = Field(default=10)
|
||||
only_context: bool = Field(default=False)
|
||||
|
||||
|
||||
def get_search_router() -> APIRouter:
|
||||
|
|
@ -79,7 +86,10 @@ def get_search_router() -> APIRouter:
|
|||
- **datasets** (Optional[List[str]]): List of dataset names to search within
|
||||
- **dataset_ids** (Optional[List[UUID]]): List of dataset UUIDs to search within
|
||||
- **query** (str): The search query string
|
||||
- **system_prompt** Optional[str]: System prompt to be used for Completion type searches in Cognee
|
||||
- **node_name** Optional[list[str]]: Filter results to specific node_sets defined in the add pipeline (for targeted search).
|
||||
- **top_k** (Optional[int]): Maximum number of results to return (default: 10)
|
||||
- **only_context** bool: Set to true to only return context Cognee will be sending to LLM in Completion type searches. This will be returned instead of LLM calls for completion type searches.
|
||||
|
||||
## Response
|
||||
Returns a list of search results containing relevant nodes from the graph.
|
||||
|
|
@ -102,7 +112,10 @@ def get_search_router() -> APIRouter:
|
|||
"datasets": payload.datasets,
|
||||
"dataset_ids": [str(dataset_id) for dataset_id in payload.dataset_ids or []],
|
||||
"query": payload.query,
|
||||
"system_prompt": payload.system_prompt,
|
||||
"node_name": payload.node_name,
|
||||
"top_k": payload.top_k,
|
||||
"only_context": payload.only_context,
|
||||
},
|
||||
)
|
||||
|
||||
|
|
@ -115,7 +128,10 @@ def get_search_router() -> APIRouter:
|
|||
user=user,
|
||||
datasets=payload.datasets,
|
||||
dataset_ids=payload.dataset_ids,
|
||||
system_prompt=payload.system_prompt,
|
||||
node_name=payload.node_name,
|
||||
top_k=payload.top_k,
|
||||
only_context=payload.only_context,
|
||||
)
|
||||
|
||||
return results
|
||||
|
|
|
|||
|
|
@ -1,6 +1,7 @@
|
|||
from uuid import UUID
|
||||
from typing import Union, Optional, List, Type
|
||||
|
||||
from cognee.modules.engine.models.node_set import NodeSet
|
||||
from cognee.modules.users.models import User
|
||||
from cognee.modules.search.types import SearchType
|
||||
from cognee.modules.users.methods import get_default_user
|
||||
|
|
@ -16,11 +17,13 @@ async def search(
|
|||
datasets: Optional[Union[list[str], str]] = None,
|
||||
dataset_ids: Optional[Union[list[UUID], UUID]] = None,
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
node_type: Optional[Type] = None,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: bool = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = False,
|
||||
) -> list:
|
||||
"""
|
||||
Search and query the knowledge graph for insights, information, and connections.
|
||||
|
|
@ -183,11 +186,13 @@ async def search(
|
|||
dataset_ids=dataset_ids if dataset_ids else datasets,
|
||||
user=user,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
|
||||
return filtered_search_results
|
||||
|
|
|
|||
|
|
@ -1,15 +1,24 @@
|
|||
import os
|
||||
from typing import Optional
|
||||
from functools import lru_cache
|
||||
from cognee.root_dir import get_absolute_path
|
||||
from cognee.root_dir import get_absolute_path, ensure_absolute_path
|
||||
from cognee.modules.observability.observers import Observer
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
import pydantic
|
||||
|
||||
|
||||
class BaseConfig(BaseSettings):
|
||||
data_root_directory: str = get_absolute_path(".data_storage")
|
||||
system_root_directory: str = get_absolute_path(".cognee_system")
|
||||
monitoring_tool: object = Observer.LANGFUSE
|
||||
|
||||
@pydantic.model_validator(mode="after")
|
||||
def validate_paths(self):
|
||||
# Require absolute paths for root directories
|
||||
self.data_root_directory = ensure_absolute_path(self.data_root_directory)
|
||||
self.system_root_directory = ensure_absolute_path(self.system_root_directory)
|
||||
return self
|
||||
|
||||
langfuse_public_key: Optional[str] = os.getenv("LANGFUSE_PUBLIC_KEY")
|
||||
langfuse_secret_key: Optional[str] = os.getenv("LANGFUSE_SECRET_KEY")
|
||||
langfuse_host: Optional[str] = os.getenv("LANGFUSE_HOST")
|
||||
|
|
|
|||
|
|
@ -6,6 +6,7 @@ from pydantic_settings import BaseSettings, SettingsConfigDict
|
|||
import pydantic
|
||||
from pydantic import Field
|
||||
from cognee.base_config import get_base_config
|
||||
from cognee.root_dir import ensure_absolute_path
|
||||
from cognee.shared.data_models import KnowledgeGraph
|
||||
|
||||
|
||||
|
|
@ -51,15 +52,20 @@ class GraphConfig(BaseSettings):
|
|||
@pydantic.model_validator(mode="after")
|
||||
def fill_derived(cls, values):
|
||||
provider = values.graph_database_provider.lower()
|
||||
base_config = get_base_config()
|
||||
|
||||
# Set default filename if no filename is provided
|
||||
if not values.graph_filename:
|
||||
values.graph_filename = f"cognee_graph_{provider}"
|
||||
|
||||
# Set file path based on graph database provider if no file path is provided
|
||||
if not values.graph_file_path:
|
||||
base_config = get_base_config()
|
||||
|
||||
# Handle graph file path
|
||||
if values.graph_file_path:
|
||||
# Check if absolute path is provided
|
||||
values.graph_file_path = ensure_absolute_path(
|
||||
os.path.join(values.graph_file_path, values.graph_filename)
|
||||
)
|
||||
else:
|
||||
# Default path
|
||||
databases_directory_path = os.path.join(base_config.system_root_directory, "databases")
|
||||
values.graph_file_path = os.path.join(databases_directory_path, values.graph_filename)
|
||||
|
||||
|
|
|
|||
|
|
@ -21,6 +21,8 @@ from cognee.infrastructure.databases.graph.graph_db_interface import (
|
|||
)
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.storage.utils import JSONEncoder
|
||||
from cognee.modules.engine.utils.generate_timestamp_datapoint import date_to_int
|
||||
from cognee.tasks.temporal_graph.models import Timestamp
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
|
@ -106,6 +108,18 @@ class KuzuAdapter(GraphDBInterface):
|
|||
|
||||
self.db.init_database()
|
||||
self.connection = Connection(self.db)
|
||||
|
||||
try:
|
||||
self.connection.execute("INSTALL JSON;")
|
||||
except Exception as e:
|
||||
logger.info(f"JSON extension already installed or not needed: {e}")
|
||||
|
||||
try:
|
||||
self.connection.execute("LOAD EXTENSION JSON;")
|
||||
logger.info("Loaded JSON extension")
|
||||
except Exception as e:
|
||||
logger.info(f"JSON extension already loaded or unavailable: {e}")
|
||||
|
||||
# Create node table with essential fields and timestamp
|
||||
self.connection.execute("""
|
||||
CREATE NODE TABLE IF NOT EXISTS Node(
|
||||
|
|
@ -1693,3 +1707,124 @@ class KuzuAdapter(GraphDBInterface):
|
|||
SET r.properties = $props
|
||||
"""
|
||||
await self.query(update_query, {"node_id": node_id, "props": new_props})
|
||||
|
||||
async def collect_events(self, ids: List[str]) -> Any:
|
||||
"""
|
||||
Collect all Event-type nodes reachable within 1..2 hops
|
||||
from the given node IDs.
|
||||
|
||||
Args:
|
||||
graph_engine: Object exposing an async .query(str) -> Any
|
||||
ids: List of node IDs (strings)
|
||||
|
||||
Returns:
|
||||
List of events
|
||||
"""
|
||||
|
||||
event_collection_cypher = """UNWIND [{quoted}] AS uid
|
||||
MATCH (start {{id: uid}})
|
||||
MATCH (start)-[*1..2]-(event)
|
||||
WHERE event.type = 'Event'
|
||||
WITH DISTINCT event
|
||||
RETURN collect(event) AS events;
|
||||
"""
|
||||
|
||||
query = event_collection_cypher.format(quoted=ids)
|
||||
result = await self.query(query)
|
||||
events = []
|
||||
for node in result[0][0]:
|
||||
props = json.loads(node["properties"])
|
||||
|
||||
event = {
|
||||
"id": node["id"],
|
||||
"name": node["name"],
|
||||
"description": props.get("description"),
|
||||
}
|
||||
|
||||
if props.get("location"):
|
||||
event["location"] = props["location"]
|
||||
|
||||
events.append(event)
|
||||
|
||||
return [{"events": events}]
|
||||
|
||||
async def collect_time_ids(
|
||||
self,
|
||||
time_from: Optional[Timestamp] = None,
|
||||
time_to: Optional[Timestamp] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Collect IDs of Timestamp nodes between time_from and time_to.
|
||||
|
||||
Args:
|
||||
graph_engine: Object exposing an async .query(query, params) -> list[dict]
|
||||
time_from: Lower bound int (inclusive), optional
|
||||
time_to: Upper bound int (inclusive), optional
|
||||
|
||||
Returns:
|
||||
A string of quoted IDs: "'id1', 'id2', 'id3'"
|
||||
(ready for use in a Cypher UNWIND clause).
|
||||
"""
|
||||
|
||||
ids: List[str] = []
|
||||
|
||||
if time_from and time_to:
|
||||
time_from = date_to_int(time_from)
|
||||
time_to = date_to_int(time_to)
|
||||
|
||||
cypher = f"""
|
||||
MATCH (n:Node)
|
||||
WHERE n.type = 'Timestamp'
|
||||
// Extract time_at from the JSON string and cast to INT64
|
||||
WITH n, json_extract(n.properties, '$.time_at') AS t_str
|
||||
WITH n,
|
||||
CASE
|
||||
WHEN t_str IS NULL OR t_str = '' THEN NULL
|
||||
ELSE CAST(t_str AS INT64)
|
||||
END AS t
|
||||
WHERE t >= {time_from}
|
||||
AND t <= {time_to}
|
||||
RETURN n.id as id
|
||||
"""
|
||||
|
||||
elif time_from:
|
||||
time_from = date_to_int(time_from)
|
||||
|
||||
cypher = f"""
|
||||
MATCH (n:Node)
|
||||
WHERE n.type = 'Timestamp'
|
||||
// Extract time_at from the JSON string and cast to INT64
|
||||
WITH n, json_extract(n.properties, '$.time_at') AS t_str
|
||||
WITH n,
|
||||
CASE
|
||||
WHEN t_str IS NULL OR t_str = '' THEN NULL
|
||||
ELSE CAST(t_str AS INT64)
|
||||
END AS t
|
||||
WHERE t >= {time_from}
|
||||
RETURN n.id as id
|
||||
"""
|
||||
|
||||
elif time_to:
|
||||
time_to = date_to_int(time_to)
|
||||
|
||||
cypher = f"""
|
||||
MATCH (n:Node)
|
||||
WHERE n.type = 'Timestamp'
|
||||
// Extract time_at from the JSON string and cast to INT64
|
||||
WITH n, json_extract(n.properties, '$.time_at') AS t_str
|
||||
WITH n,
|
||||
CASE
|
||||
WHEN t_str IS NULL OR t_str = '' THEN NULL
|
||||
ELSE CAST(t_str AS INT64)
|
||||
END AS t
|
||||
WHERE t <= {time_to}
|
||||
RETURN n.id as id
|
||||
"""
|
||||
|
||||
else:
|
||||
return ids
|
||||
|
||||
time_nodes = await self.query(cypher)
|
||||
time_ids_list = [item[0] for item in time_nodes]
|
||||
|
||||
return ", ".join(f"'{uid}'" for uid in time_ids_list)
|
||||
|
|
|
|||
|
|
@ -11,6 +11,8 @@ from contextlib import asynccontextmanager
|
|||
from typing import Optional, Any, List, Dict, Type, Tuple
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.engine.utils.generate_timestamp_datapoint import date_to_int
|
||||
from cognee.tasks.temporal_graph.models import Timestamp
|
||||
from cognee.shared.logging_utils import get_logger, ERROR
|
||||
from cognee.infrastructure.databases.graph.graph_db_interface import (
|
||||
GraphDBInterface,
|
||||
|
|
@ -1371,3 +1373,90 @@ class Neo4jAdapter(GraphDBInterface):
|
|||
query,
|
||||
params={"weight": float(weight), "node_ids": list(node_ids)},
|
||||
)
|
||||
|
||||
async def collect_events(self, ids: List[str]) -> Any:
|
||||
"""
|
||||
Collect all Event-type nodes reachable within 1..2 hops
|
||||
from the given node IDs.
|
||||
|
||||
Args:
|
||||
graph_engine: Object exposing an async .query(str) -> Any
|
||||
ids: List of node IDs (strings)
|
||||
|
||||
Returns:
|
||||
List of events
|
||||
"""
|
||||
|
||||
event_collection_cypher = """UNWIND [{quoted}] AS uid
|
||||
MATCH (start {{id: uid}})
|
||||
MATCH (start)-[*1..2]-(event)
|
||||
WHERE event.type = 'Event'
|
||||
WITH DISTINCT event
|
||||
RETURN collect(event) AS events;
|
||||
"""
|
||||
|
||||
query = event_collection_cypher.format(quoted=ids)
|
||||
return await self.query(query)
|
||||
|
||||
async def collect_time_ids(
|
||||
self,
|
||||
time_from: Optional[Timestamp] = None,
|
||||
time_to: Optional[Timestamp] = None,
|
||||
) -> str:
|
||||
"""
|
||||
Collect IDs of Timestamp nodes between time_from and time_to.
|
||||
|
||||
Args:
|
||||
graph_engine: Object exposing an async .query(query, params) -> list[dict]
|
||||
time_from: Lower bound int (inclusive), optional
|
||||
time_to: Upper bound int (inclusive), optional
|
||||
|
||||
Returns:
|
||||
A string of quoted IDs: "'id1', 'id2', 'id3'"
|
||||
(ready for use in a Cypher UNWIND clause).
|
||||
"""
|
||||
|
||||
ids: List[str] = []
|
||||
|
||||
if time_from and time_to:
|
||||
time_from = date_to_int(time_from)
|
||||
time_to = date_to_int(time_to)
|
||||
|
||||
cypher = """
|
||||
MATCH (n)
|
||||
WHERE n.type = 'Timestamp'
|
||||
AND n.time_at >= $time_from
|
||||
AND n.time_at <= $time_to
|
||||
RETURN n.id AS id
|
||||
"""
|
||||
params = {"time_from": time_from, "time_to": time_to}
|
||||
|
||||
elif time_from:
|
||||
time_from = date_to_int(time_from)
|
||||
|
||||
cypher = """
|
||||
MATCH (n)
|
||||
WHERE n.type = 'Timestamp'
|
||||
AND n.time_at >= $time_from
|
||||
RETURN n.id AS id
|
||||
"""
|
||||
params = {"time_from": time_from}
|
||||
|
||||
elif time_to:
|
||||
time_to = date_to_int(time_to)
|
||||
|
||||
cypher = """
|
||||
MATCH (n)
|
||||
WHERE n.type = 'Timestamp'
|
||||
AND n.time_at <= $time_to
|
||||
RETURN n.id AS id
|
||||
"""
|
||||
params = {"time_to": time_to}
|
||||
|
||||
else:
|
||||
return ids
|
||||
|
||||
time_nodes = await self.query(cypher, params)
|
||||
time_ids_list = [item["id"] for item in time_nodes if "id" in item]
|
||||
|
||||
return ", ".join(f"'{uid}'" for uid in time_ids_list)
|
||||
|
|
|
|||
|
|
@ -1,9 +1,11 @@
|
|||
import os
|
||||
import pydantic
|
||||
from pathlib import Path
|
||||
from functools import lru_cache
|
||||
from pydantic_settings import BaseSettings, SettingsConfigDict
|
||||
|
||||
from cognee.base_config import get_base_config
|
||||
from cognee.root_dir import ensure_absolute_path
|
||||
|
||||
|
||||
class VectorConfig(BaseSettings):
|
||||
|
|
@ -11,11 +13,9 @@ class VectorConfig(BaseSettings):
|
|||
Manage the configuration settings for the vector database.
|
||||
|
||||
Public methods:
|
||||
|
||||
- to_dict: Convert the configuration to a dictionary.
|
||||
|
||||
Instance variables:
|
||||
|
||||
- vector_db_url: The URL of the vector database.
|
||||
- vector_db_port: The port for the vector database.
|
||||
- vector_db_key: The key for accessing the vector database.
|
||||
|
|
@ -30,10 +30,17 @@ class VectorConfig(BaseSettings):
|
|||
model_config = SettingsConfigDict(env_file=".env", extra="allow")
|
||||
|
||||
@pydantic.model_validator(mode="after")
|
||||
def fill_derived(cls, values):
|
||||
# Set file path based on graph database provider if no file path is provided
|
||||
if not values.vector_db_url:
|
||||
base_config = get_base_config()
|
||||
def validate_paths(cls, values):
|
||||
base_config = get_base_config()
|
||||
|
||||
# If vector_db_url is provided and is not a path skip checking if path is absolute (as it can also be a url)
|
||||
if values.vector_db_url and Path(values.vector_db_url).exists():
|
||||
# Relative path to absolute
|
||||
values.vector_db_url = ensure_absolute_path(
|
||||
values.vector_db_url,
|
||||
)
|
||||
else:
|
||||
# Default path
|
||||
databases_directory_path = os.path.join(base_config.system_root_directory, "databases")
|
||||
values.vector_db_url = os.path.join(databases_directory_path, "cognee.lancedb")
|
||||
|
||||
|
|
|
|||
|
|
@ -33,6 +33,7 @@ def get_embedding_engine() -> EmbeddingEngine:
|
|||
config.embedding_api_version,
|
||||
config.huggingface_tokenizer,
|
||||
llm_config.llm_api_key,
|
||||
llm_config.llm_provider,
|
||||
)
|
||||
|
||||
|
||||
|
|
@ -47,6 +48,7 @@ def create_embedding_engine(
|
|||
embedding_api_version,
|
||||
huggingface_tokenizer,
|
||||
llm_api_key,
|
||||
llm_provider,
|
||||
):
|
||||
"""
|
||||
Create and return an embedding engine based on the specified provider.
|
||||
|
|
@ -99,7 +101,8 @@ def create_embedding_engine(
|
|||
|
||||
return LiteLLMEmbeddingEngine(
|
||||
provider=embedding_provider,
|
||||
api_key=embedding_api_key or llm_api_key,
|
||||
api_key=embedding_api_key
|
||||
or (embedding_api_key if llm_provider == "custom" else llm_api_key),
|
||||
endpoint=embedding_endpoint,
|
||||
api_version=embedding_api_version,
|
||||
model=embedding_model,
|
||||
|
|
|
|||
|
|
@ -144,3 +144,21 @@ class LLMGateway:
|
|||
)
|
||||
|
||||
return extract_summary(content=content, response_model=response_model)
|
||||
|
||||
@staticmethod
|
||||
def extract_event_graph(content: str, response_model: Type[BaseModel]) -> Coroutine:
|
||||
# TODO: Add BAML version of category and extraction and update function (consulted with Igor)
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.extraction import (
|
||||
extract_event_graph,
|
||||
)
|
||||
|
||||
return extract_event_graph(content=content, response_model=response_model)
|
||||
|
||||
@staticmethod
|
||||
def extract_event_entities(content: str, response_model: Type[BaseModel]) -> Coroutine:
|
||||
# TODO: Add BAML version of category and extraction and update function (consulted with Igor)
|
||||
from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.extraction import (
|
||||
extract_event_entities,
|
||||
)
|
||||
|
||||
return extract_event_entities(content=content, response_model=response_model)
|
||||
|
|
|
|||
|
|
@ -52,6 +52,8 @@ class LLMConfig(BaseSettings):
|
|||
|
||||
transcription_model: str = "whisper-1"
|
||||
graph_prompt_path: str = "generate_graph_prompt.txt"
|
||||
temporal_graph_prompt_path: str = "generate_event_graph_prompt.txt"
|
||||
event_entity_prompt_path: str = "generate_event_entity_prompt.txt"
|
||||
llm_rate_limit_enabled: bool = False
|
||||
llm_rate_limit_requests: int = 60
|
||||
llm_rate_limit_interval: int = 60 # in seconds (default is 60 requests per minute)
|
||||
|
|
|
|||
15
cognee/infrastructure/llm/prompts/extract_query_time.txt
Normal file
15
cognee/infrastructure/llm/prompts/extract_query_time.txt
Normal file
|
|
@ -0,0 +1,15 @@
|
|||
For the purposes of identifying timestamps in a query, you are tasked with extracting relevant timestamps from the query.
|
||||
## Timestamp requirements
|
||||
- If the query contains interval extrack both starts_at and ends_at properties
|
||||
- If the query contains an instantaneous timestamp, starts_at and ends_at should be the same
|
||||
- If the query its open-ended (before 2009 or after 2009), the corresponding non defined end of the time should be none
|
||||
-For example: "before 2009" -- starts_at: None, ends_at: 2009 or "after 2009" -- starts_at: 2009, ends_at: None
|
||||
- Put always the data that comes first in time as starts_at and the timestamps that comes second in time as ends_at
|
||||
- If starts_at or ends_at cannot be extracted both of them has to be None
|
||||
## Output Format
|
||||
Your reply should be a JSON: list of dictionaries with the following structure:
|
||||
```python
|
||||
class QueryInterval(BaseModel):
|
||||
starts_at: Optional[Timestamp] = None
|
||||
ends_at: Optional[Timestamp] = None
|
||||
```
|
||||
|
|
@ -0,0 +1,25 @@
|
|||
For the purposes of building event-based knowledge graphs, you are tasked with extracting highly granular entities from events text. An entity is any distinct, identifiable thing, person, place, object, organization, concept, or phenomenon that can be named, referenced, or described in the event context. This includes but is not limited to: people, places, objects, organizations, concepts, events, processes, states, conditions, properties, attributes, roles, functions, and any other meaningful referents that contribute to understanding the event.
|
||||
**Temporal Entity Exclusion**: Do not extract timestamp-like entities (dates, times, durations) as these are handled separately. However, extract named temporal periods, eras, historical epochs, and culturally significant time references
|
||||
## Input Format
|
||||
The input will be a list of dictionaries, each containing:
|
||||
- `event_name`: The name of the event
|
||||
- `description`: The description of the event
|
||||
## Task
|
||||
For each event, extract all entities mentioned in the event description and determine their relationship to the event.
|
||||
## Output Format
|
||||
Return the same enriched JSON with an additional key in each dictionary: `attributes`.
|
||||
The `attributes` should be a list of dictionaries, each containing:
|
||||
- `entity`: The name of the entity
|
||||
- `entity_type`: The type/category of the entity (person, place, organization, object, concept, etc.)
|
||||
- `relationship`: A concise description of how the entity relates to the event
|
||||
## Requirements
|
||||
- **Be extremely thorough** - extract EVERY non-temporal entity mentioned, no matter how small, obvious, or seemingly insignificant
|
||||
- **After you are done with obvious entities, every noun, pronoun, proper noun, and named reference = one entity**
|
||||
- We expect rich entity networks from any event, easily reaching a dozens of entities per event
|
||||
- Granularity and richness of the entity extraction is key to our success and is of utmost importance
|
||||
- **Do not skip any entities** - if you're unsure whether something is an entity, extract it anyway
|
||||
- Use the event name for context when determining relationships
|
||||
- Relationships should be technical with one or at most two words. If two words, use underscore camelcase style
|
||||
- Relationships could imply general meaning like: subject, object, participant, recipient, agent, instrument, tool, source, cause, effect, purpose, manner, resource, etc.
|
||||
- You can combine two words to form a relationship name: subject_role, previous_owner, etc.
|
||||
- Focus on how the entity specifically relates to the event
|
||||
|
|
@ -0,0 +1,30 @@
|
|||
For the purposes of building event-based knowledge graphs, you are tasked with extracting highly granular stream events from a text. The events are defined as follows:
|
||||
## Event Definition
|
||||
- Anything with a date or a timestamp is an event
|
||||
- Anything that took place in time (even if the time is unknown) is an event
|
||||
- Anything that lasted over a period of time, or happened in an instant is an event: from historical milestones (wars, presidencies, olympiads) to personal milestones (birth, death, employment, etc.), to mundane actions (a walk, a conversation, etc.)
|
||||
- **ANY action or verb represents an event** - this is the most important rule
|
||||
- Every single verb in the text corresponds to an event that must be extracted
|
||||
- This includes: thinking, feeling, seeing, hearing, moving, speaking, writing, reading, eating, sleeping, working, playing, studying, traveling, meeting, calling, texting, buying, selling, creating, destroying, building, breaking, starting, stopping, beginning, ending, etc.
|
||||
- Even the most mundane or obvious actions are events: "he walked", "she sat", "they talked", "I thought", "we waited"
|
||||
## Requirements
|
||||
- **Be extremely thorough** - extract EVERY event mentioned, no matter how small or obvious
|
||||
- **Timestamped first" - every time stamp, or date should have atleast one event
|
||||
- **Verbs/actions = one event** - After you are done with timestamped events -- every verb that is an action should have a corresponding event.
|
||||
- We expect long streams of events from any piece of text, easily reaching a hundred events
|
||||
- Granularity and richness of the stream is key to our success and is of utmost importance
|
||||
- Not all events will have timestamps, add timestamps only to known events
|
||||
- For events that were instantaneous, just attach the time_from or time_to property don't create both
|
||||
- **Do not skip any events** - if you're unsure whether something is an event, extract it anyway
|
||||
- **Quantity over filtering** - it's better to extract too many events than to miss any
|
||||
- **Descriptions** - Always include the event description together with entities (Who did what, what happened? What is the event?). If you can include the corresponding part from the text.
|
||||
## Output Format
|
||||
Your reply should be a JSON: list of dictionaries with the following structure:
|
||||
```python
|
||||
class Event(BaseModel):
|
||||
name: str [concise]
|
||||
description: Optional[str] = None
|
||||
time_from: Optional[Timestamp] = None
|
||||
time_to: Optional[Timestamp] = None
|
||||
location: Optional[str] = None
|
||||
```
|
||||
|
|
@ -1,3 +1,5 @@
|
|||
from .knowledge_graph.extract_content_graph import extract_content_graph
|
||||
from .knowledge_graph.extract_event_graph import extract_event_graph
|
||||
from .extract_categories import extract_categories
|
||||
from .extract_summary import extract_summary, extract_code_summary
|
||||
from .extract_event_entities import extract_event_entities
|
||||
|
|
|
|||
|
|
@ -0,0 +1,44 @@
|
|||
import os
|
||||
from typing import List, Type
|
||||
from pydantic import BaseModel
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
from cognee.infrastructure.llm.config import (
|
||||
get_llm_config,
|
||||
)
|
||||
|
||||
|
||||
async def extract_event_entities(content: str, response_model: Type[BaseModel]):
|
||||
"""
|
||||
Extracts event-related entities from the given content using an LLM with structured output.
|
||||
|
||||
This function loads an event entity extraction prompt from the LLM configuration,
|
||||
renders it into a system prompt, and queries the LLM to produce structured entities
|
||||
that conform to the specified response model.
|
||||
|
||||
Args:
|
||||
content (str): The input text from which to extract event entities.
|
||||
response_model (Type[BaseModel]): A Pydantic model defining the structure of the expected output.
|
||||
|
||||
Returns:
|
||||
BaseModel: An instance of the response_model populated with extracted event entities.
|
||||
"""
|
||||
llm_config = get_llm_config()
|
||||
|
||||
prompt_path = llm_config.event_entity_prompt_path
|
||||
|
||||
# Check if the prompt path is an absolute path or just a filename
|
||||
if os.path.isabs(prompt_path):
|
||||
# directory containing the file
|
||||
base_directory = os.path.dirname(prompt_path)
|
||||
# just the filename itself
|
||||
prompt_path = os.path.basename(prompt_path)
|
||||
else:
|
||||
base_directory = None
|
||||
|
||||
system_prompt = LLMGateway.render_prompt(prompt_path, {}, base_directory=base_directory)
|
||||
|
||||
content_graph = await LLMGateway.acreate_structured_output(
|
||||
content, system_prompt, response_model
|
||||
)
|
||||
|
||||
return content_graph
|
||||
|
|
@ -1 +1,2 @@
|
|||
from .extract_content_graph import extract_content_graph
|
||||
from .extract_event_graph import extract_event_graph
|
||||
|
|
|
|||
|
|
@ -0,0 +1,46 @@
|
|||
import os
|
||||
from pydantic import BaseModel
|
||||
from typing import Type
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
|
||||
from cognee.infrastructure.llm.config import (
|
||||
get_llm_config,
|
||||
)
|
||||
|
||||
|
||||
async def extract_event_graph(content: str, response_model: Type[BaseModel]):
|
||||
"""
|
||||
Extracts an event graph from the given content using an LLM with a structured output format.
|
||||
|
||||
This function loads a temporal graph extraction prompt from the LLM configuration,
|
||||
renders it as a system prompt, and queries the LLM to produce a structured event
|
||||
graph matching the specified response model.
|
||||
|
||||
Args:
|
||||
content (str): The input text from which to extract the event graph.
|
||||
response_model (Type[BaseModel]): A Pydantic model defining the structure of the expected output.
|
||||
|
||||
Returns:
|
||||
BaseModel: An instance of the response_model populated with the extracted event graph.
|
||||
"""
|
||||
|
||||
llm_config = get_llm_config()
|
||||
|
||||
prompt_path = llm_config.temporal_graph_prompt_path
|
||||
|
||||
# Check if the prompt path is an absolute path or just a filename
|
||||
if os.path.isabs(prompt_path):
|
||||
# directory containing the file
|
||||
base_directory = os.path.dirname(prompt_path)
|
||||
# just the filename itself
|
||||
prompt_path = os.path.basename(prompt_path)
|
||||
else:
|
||||
base_directory = None
|
||||
|
||||
system_prompt = LLMGateway.render_prompt(prompt_path, {}, base_directory=base_directory)
|
||||
|
||||
content_graph = await LLMGateway.acreate_structured_output(
|
||||
content, system_prompt, response_model
|
||||
)
|
||||
|
||||
return content_graph
|
||||
|
|
@ -23,9 +23,12 @@ from cognee.infrastructure.llm.structured_output_framework.litellm_instructor.ll
|
|||
sleep_and_retry_sync,
|
||||
)
|
||||
from cognee.modules.observability.get_observe import get_observe
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
|
||||
observe = get_observe()
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class OpenAIAdapter(LLMInterface):
|
||||
"""
|
||||
|
|
@ -129,6 +132,7 @@ class OpenAIAdapter(LLMInterface):
|
|||
api_version=self.api_version,
|
||||
response_model=response_model,
|
||||
max_retries=self.MAX_RETRIES,
|
||||
extra_body={"reasoning_effort": "minimal"},
|
||||
)
|
||||
except (
|
||||
ContentFilterFinishReasonError,
|
||||
|
|
@ -139,7 +143,27 @@ class OpenAIAdapter(LLMInterface):
|
|||
isinstance(error, InstructorRetryException)
|
||||
and "content management policy" not in str(error).lower()
|
||||
):
|
||||
raise error
|
||||
logger.debug(
|
||||
"LLM Model does not support reasoning_effort parameter, trying call without the parameter."
|
||||
)
|
||||
return await self.aclient.chat.completions.create(
|
||||
model=self.model,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": f"""{text_input}""",
|
||||
},
|
||||
{
|
||||
"role": "system",
|
||||
"content": system_prompt,
|
||||
},
|
||||
],
|
||||
api_key=self.api_key,
|
||||
api_base=self.endpoint,
|
||||
api_version=self.api_version,
|
||||
response_model=response_model,
|
||||
max_retries=self.MAX_RETRIES,
|
||||
)
|
||||
|
||||
if not (self.fallback_model and self.fallback_api_key):
|
||||
raise ContentPolicyFilterError(
|
||||
|
|
|
|||
|
|
@ -1,8 +1,9 @@
|
|||
from typing import List
|
||||
from typing import List, Union
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
from cognee.modules.engine.models import Entity
|
||||
from cognee.tasks.temporal_graph.models import Event
|
||||
|
||||
|
||||
class DocumentChunk(DataPoint):
|
||||
|
|
@ -20,7 +21,7 @@ class DocumentChunk(DataPoint):
|
|||
- chunk_index: The index of the chunk in the original document.
|
||||
- cut_type: The type of cut that defined this chunk.
|
||||
- is_part_of: The document to which this chunk belongs.
|
||||
- contains: A list of entities contained within the chunk (default is None).
|
||||
- contains: A list of entities or events contained within the chunk (default is None).
|
||||
- metadata: A dictionary to hold meta information related to the chunk, including index
|
||||
fields.
|
||||
"""
|
||||
|
|
@ -30,6 +31,6 @@ class DocumentChunk(DataPoint):
|
|||
chunk_index: int
|
||||
cut_type: str
|
||||
is_part_of: Document
|
||||
contains: List[Entity] = None
|
||||
contains: List[Union[Entity, Event]] = None
|
||||
|
||||
metadata: dict = {"index_fields": ["text"]}
|
||||
|
|
|
|||
|
|
@ -3,7 +3,7 @@ from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_eng
|
|||
from cognee.infrastructure.databases.relational import get_relational_engine
|
||||
|
||||
|
||||
async def prune_system(graph=True, vector=True, metadata=False):
|
||||
async def prune_system(graph=True, vector=True, metadata=True):
|
||||
if graph:
|
||||
graph_engine = await get_graph_engine()
|
||||
await graph_engine.delete_graph()
|
||||
|
|
|
|||
16
cognee/modules/engine/models/Event.py
Normal file
16
cognee/modules/engine/models/Event.py
Normal file
|
|
@ -0,0 +1,16 @@
|
|||
from typing import Optional, Any
|
||||
from pydantic import SkipValidation
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.engine.models.Timestamp import Timestamp
|
||||
from cognee.modules.engine.models.Interval import Interval
|
||||
|
||||
|
||||
class Event(DataPoint):
|
||||
name: str
|
||||
description: Optional[str] = None
|
||||
at: Optional[Timestamp] = None
|
||||
during: Optional[Interval] = None
|
||||
location: Optional[str] = None
|
||||
attributes: SkipValidation[Any] = None
|
||||
|
||||
metadata: dict = {"index_fields": ["name"]}
|
||||
8
cognee/modules/engine/models/Interval.py
Normal file
8
cognee/modules/engine/models/Interval.py
Normal file
|
|
@ -0,0 +1,8 @@
|
|||
from pydantic import Field
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.engine.models.Timestamp import Timestamp
|
||||
|
||||
|
||||
class Interval(DataPoint):
|
||||
time_from: Timestamp = Field(...)
|
||||
time_to: Timestamp = Field(...)
|
||||
13
cognee/modules/engine/models/Timestamp.py
Normal file
13
cognee/modules/engine/models/Timestamp.py
Normal file
|
|
@ -0,0 +1,13 @@
|
|||
from pydantic import Field
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
|
||||
|
||||
class Timestamp(DataPoint):
|
||||
time_at: int = Field(...)
|
||||
year: int = Field(...)
|
||||
month: int = Field(...)
|
||||
day: int = Field(...)
|
||||
hour: int = Field(...)
|
||||
minute: int = Field(...)
|
||||
second: int = Field(...)
|
||||
timestamp_str: str = Field(...)
|
||||
|
|
@ -4,3 +4,6 @@ from .TableRow import TableRow
|
|||
from .TableType import TableType
|
||||
from .node_set import NodeSet
|
||||
from .ColumnValue import ColumnValue
|
||||
from .Timestamp import Timestamp
|
||||
from .Interval import Interval
|
||||
from .Event import Event
|
||||
|
|
|
|||
|
|
@ -1,3 +1,5 @@
|
|||
from .generate_node_id import generate_node_id
|
||||
from .generate_node_name import generate_node_name
|
||||
from .generate_edge_name import generate_edge_name
|
||||
from .generate_event_datapoint import generate_event_datapoint
|
||||
from .generate_timestamp_datapoint import generate_timestamp_datapoint
|
||||
|
|
|
|||
46
cognee/modules/engine/utils/generate_event_datapoint.py
Normal file
46
cognee/modules/engine/utils/generate_event_datapoint.py
Normal file
|
|
@ -0,0 +1,46 @@
|
|||
from cognee.modules.engine.models import Interval, Event
|
||||
from cognee.modules.engine.utils.generate_timestamp_datapoint import generate_timestamp_datapoint
|
||||
|
||||
|
||||
def generate_event_datapoint(event) -> Event:
|
||||
"""
|
||||
Generates an Event datapoint from a given event model, including temporal metadata if available.
|
||||
|
||||
The function maps the basic attributes (name, description, location) from the input event
|
||||
and enriches them with temporal information. If start and end times are provided, an
|
||||
Interval is created. If only one timestamp is available, it is added directly. Temporal
|
||||
information is also appended to the event description for context.
|
||||
|
||||
Args:
|
||||
event: An event model instance containing attributes such as name, description,
|
||||
location, time_from, and time_to.
|
||||
|
||||
Returns:
|
||||
Event: A structured Event object with name, description, location, and enriched
|
||||
temporal details.
|
||||
"""
|
||||
# Base event data
|
||||
event_data = {
|
||||
"name": event.name,
|
||||
"description": event.description,
|
||||
"location": event.location,
|
||||
}
|
||||
|
||||
# Create timestamps if they exist
|
||||
time_from = generate_timestamp_datapoint(event.time_from) if event.time_from else None
|
||||
time_to = generate_timestamp_datapoint(event.time_to) if event.time_to else None
|
||||
|
||||
# Add temporal information
|
||||
if time_from and time_to:
|
||||
event_data["during"] = Interval(time_from=time_from, time_to=time_to)
|
||||
# Enrich description with temporal info
|
||||
temporal_info = f"\n---\nTime data: {time_from.timestamp_str} to {time_to.timestamp_str}"
|
||||
event_data["description"] = (event_data["description"] or "Event") + temporal_info
|
||||
elif time_from or time_to:
|
||||
timestamp = time_from or time_to
|
||||
event_data["at"] = timestamp
|
||||
# Enrich description with temporal info
|
||||
temporal_info = f"\n---\nTime data: {timestamp.timestamp_str}"
|
||||
event_data["description"] = (event_data["description"] or "Event") + temporal_info
|
||||
|
||||
return Event(**event_data)
|
||||
51
cognee/modules/engine/utils/generate_timestamp_datapoint.py
Normal file
51
cognee/modules/engine/utils/generate_timestamp_datapoint.py
Normal file
|
|
@ -0,0 +1,51 @@
|
|||
from datetime import datetime, timezone
|
||||
from cognee.modules.engine.models import Interval, Timestamp, Event
|
||||
from cognee.modules.engine.utils import generate_node_id
|
||||
|
||||
|
||||
def generate_timestamp_datapoint(ts: Timestamp) -> Timestamp:
|
||||
"""
|
||||
Generates a normalized Timestamp datapoint from a given Timestamp model.
|
||||
|
||||
The function converts the provided timestamp into an integer representation,
|
||||
constructs a human-readable string format, and creates a new Timestamp object
|
||||
with a unique identifier.
|
||||
|
||||
Args:
|
||||
ts (Timestamp): The input Timestamp model containing date and time components.
|
||||
|
||||
Returns:
|
||||
Timestamp: A new Timestamp object with a generated ID, integer representation,
|
||||
original components, and formatted string.
|
||||
"""
|
||||
|
||||
time_at = date_to_int(ts)
|
||||
timestamp_str = (
|
||||
f"{ts.year:04d}-{ts.month:02d}-{ts.day:02d} {ts.hour:02d}:{ts.minute:02d}:{ts.second:02d}"
|
||||
)
|
||||
return Timestamp(
|
||||
id=generate_node_id(str(time_at)),
|
||||
time_at=time_at,
|
||||
year=ts.year,
|
||||
month=ts.month,
|
||||
day=ts.day,
|
||||
hour=ts.hour,
|
||||
minute=ts.minute,
|
||||
second=ts.second,
|
||||
timestamp_str=timestamp_str,
|
||||
)
|
||||
|
||||
|
||||
def date_to_int(ts: Timestamp) -> int:
|
||||
"""
|
||||
Converts a Timestamp model into an integer representation in milliseconds since the Unix epoch (UTC).
|
||||
|
||||
Args:
|
||||
ts (Timestamp): The input Timestamp model containing year, month, day, hour, minute, and second.
|
||||
|
||||
Returns:
|
||||
int: The UTC timestamp in milliseconds since January 1, 1970.
|
||||
"""
|
||||
dt = datetime(ts.year, ts.month, ts.day, ts.hour, ts.minute, ts.second, tzinfo=timezone.utc)
|
||||
time = int(dt.timestamp() * 1000)
|
||||
return time
|
||||
|
|
@ -76,7 +76,7 @@ class CogneeGraph(CogneeAbstractGraph):
|
|||
start_time = time.time()
|
||||
|
||||
# Determine projection strategy
|
||||
if node_type is not None and node_name is not None:
|
||||
if node_type is not None and node_name not in [None, []]:
|
||||
nodes_data, edges_data = await adapter.get_nodeset_subgraph(
|
||||
node_type=node_type, node_name=node_name
|
||||
)
|
||||
|
|
|
|||
|
|
@ -94,7 +94,15 @@ class CodeRetriever(BaseRetriever):
|
|||
{"id": res.id, "score": res.score, "payload": res.payload}
|
||||
)
|
||||
|
||||
existing_collection = []
|
||||
for collection in self.classes_and_functions_collections:
|
||||
if await vector_engine.has_collection(collection):
|
||||
existing_collection.append(collection)
|
||||
|
||||
if not existing_collection:
|
||||
raise RuntimeError("No collection found for code retriever")
|
||||
|
||||
for collection in existing_collection:
|
||||
logger.debug(f"Searching {collection} collection with general query")
|
||||
search_results_code = await vector_engine.search(
|
||||
collection, query, limit=self.top_k
|
||||
|
|
|
|||
|
|
@ -23,12 +23,16 @@ class CompletionRetriever(BaseRetriever):
|
|||
self,
|
||||
user_prompt_path: str = "context_for_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: str = None,
|
||||
top_k: Optional[int] = 1,
|
||||
only_context: bool = False,
|
||||
):
|
||||
"""Initialize retriever with optional custom prompt paths."""
|
||||
self.user_prompt_path = user_prompt_path
|
||||
self.system_prompt_path = system_prompt_path
|
||||
self.top_k = top_k if top_k is not None else 1
|
||||
self.system_prompt = system_prompt
|
||||
self.only_context = only_context
|
||||
|
||||
async def get_context(self, query: str) -> str:
|
||||
"""
|
||||
|
|
@ -88,6 +92,11 @@ class CompletionRetriever(BaseRetriever):
|
|||
context = await self.get_context(query)
|
||||
|
||||
completion = await generate_completion(
|
||||
query, context, self.user_prompt_path, self.system_prompt_path
|
||||
query=query,
|
||||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
system_prompt=self.system_prompt,
|
||||
only_context=self.only_context,
|
||||
)
|
||||
return [completion]
|
||||
|
|
|
|||
|
|
@ -26,10 +26,12 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
|
|||
self,
|
||||
user_prompt_path: str = "graph_context_for_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: Optional[int] = 5,
|
||||
node_type: Optional[Type] = None,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: bool = False,
|
||||
only_context: bool = False,
|
||||
):
|
||||
super().__init__(
|
||||
user_prompt_path=user_prompt_path,
|
||||
|
|
@ -38,10 +40,15 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
)
|
||||
|
||||
async def get_completion(
|
||||
self, query: str, context: Optional[Any] = None, context_extension_rounds=4
|
||||
self,
|
||||
query: str,
|
||||
context: Optional[Any] = None,
|
||||
context_extension_rounds=4,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Extends the context for a given query by retrieving related triplets and generating new
|
||||
|
|
@ -86,6 +93,7 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
|
|||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
system_prompt=self.system_prompt,
|
||||
)
|
||||
|
||||
triplets += await self.get_triplets(completion)
|
||||
|
|
@ -112,6 +120,8 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
|
|||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
system_prompt=self.system_prompt,
|
||||
only_context=self.only_context,
|
||||
)
|
||||
|
||||
if self.save_interaction and context and triplets and completion:
|
||||
|
|
@ -119,4 +129,7 @@ class GraphCompletionContextExtensionRetriever(GraphCompletionRetriever):
|
|||
question=query, answer=completion, context=context, triplets=triplets
|
||||
)
|
||||
|
||||
return [completion]
|
||||
if self.only_context:
|
||||
return [context]
|
||||
else:
|
||||
return [completion]
|
||||
|
|
|
|||
|
|
@ -32,14 +32,18 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
|
|||
validation_system_prompt_path: str = "cot_validation_system_prompt.txt",
|
||||
followup_system_prompt_path: str = "cot_followup_system_prompt.txt",
|
||||
followup_user_prompt_path: str = "cot_followup_user_prompt.txt",
|
||||
system_prompt: str = None,
|
||||
top_k: Optional[int] = 5,
|
||||
node_type: Optional[Type] = None,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: bool = False,
|
||||
only_context: bool = False,
|
||||
):
|
||||
super().__init__(
|
||||
user_prompt_path=user_prompt_path,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
|
|
@ -51,7 +55,10 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
|
|||
self.followup_user_prompt_path = followup_user_prompt_path
|
||||
|
||||
async def get_completion(
|
||||
self, query: str, context: Optional[Any] = None, max_iter=4
|
||||
self,
|
||||
query: str,
|
||||
context: Optional[Any] = None,
|
||||
max_iter=4,
|
||||
) -> List[str]:
|
||||
"""
|
||||
Generate completion responses based on a user query and contextual information.
|
||||
|
|
@ -92,6 +99,7 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
|
|||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
system_prompt=self.system_prompt,
|
||||
)
|
||||
logger.info(f"Chain-of-thought: round {round_idx} - answer: {completion}")
|
||||
if round_idx < max_iter:
|
||||
|
|
@ -128,4 +136,7 @@ class GraphCompletionCotRetriever(GraphCompletionRetriever):
|
|||
question=query, answer=completion, context=context, triplets=triplets
|
||||
)
|
||||
|
||||
return [completion]
|
||||
if self.only_context:
|
||||
return [context]
|
||||
else:
|
||||
return [completion]
|
||||
|
|
|
|||
|
|
@ -36,15 +36,19 @@ class GraphCompletionRetriever(BaseRetriever):
|
|||
self,
|
||||
user_prompt_path: str = "graph_context_for_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: str = None,
|
||||
top_k: Optional[int] = 5,
|
||||
node_type: Optional[Type] = None,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: bool = False,
|
||||
only_context: bool = False,
|
||||
):
|
||||
"""Initialize retriever with prompt paths and search parameters."""
|
||||
self.save_interaction = save_interaction
|
||||
self.user_prompt_path = user_prompt_path
|
||||
self.system_prompt_path = system_prompt_path
|
||||
self.system_prompt = system_prompt
|
||||
self.only_context = only_context
|
||||
self.top_k = top_k if top_k is not None else 5
|
||||
self.node_type = node_type
|
||||
self.node_name = node_name
|
||||
|
|
@ -151,7 +155,11 @@ class GraphCompletionRetriever(BaseRetriever):
|
|||
|
||||
return context, triplets
|
||||
|
||||
async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
|
||||
async def get_completion(
|
||||
self,
|
||||
query: str,
|
||||
context: Optional[Any] = None,
|
||||
) -> Any:
|
||||
"""
|
||||
Generates a completion using graph connections context based on a query.
|
||||
|
||||
|
|
@ -177,6 +185,8 @@ class GraphCompletionRetriever(BaseRetriever):
|
|||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
system_prompt=self.system_prompt,
|
||||
only_context=self.only_context,
|
||||
)
|
||||
|
||||
if self.save_interaction and context and triplets and completion:
|
||||
|
|
|
|||
|
|
@ -21,6 +21,7 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever):
|
|||
user_prompt_path: str = "graph_context_for_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
summarize_prompt_path: str = "summarize_search_results.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: Optional[int] = 5,
|
||||
node_type: Optional[Type] = None,
|
||||
node_name: Optional[List[str]] = None,
|
||||
|
|
@ -34,6 +35,7 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever):
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
)
|
||||
self.summarize_prompt_path = summarize_prompt_path
|
||||
|
||||
|
|
@ -57,4 +59,4 @@ class GraphSummaryCompletionRetriever(GraphCompletionRetriever):
|
|||
- str: A summary string representing the content of the retrieved edges.
|
||||
"""
|
||||
direct_text = await super().resolve_edges_to_text(retrieved_edges)
|
||||
return await summarize_text(direct_text, self.summarize_prompt_path)
|
||||
return await summarize_text(direct_text, self.summarize_prompt_path, self.system_prompt)
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ class SummariesRetriever(BaseRetriever):
|
|||
logger.info(f"Returning {len(summary_payloads)} summary payloads")
|
||||
return summary_payloads
|
||||
|
||||
async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
|
||||
async def get_completion(self, query: str, context: Optional[Any] = None, **kwargs) -> Any:
|
||||
"""
|
||||
Generates a completion using summaries context.
|
||||
|
||||
|
|
|
|||
149
cognee/modules/retrieval/temporal_retriever.py
Normal file
149
cognee/modules/retrieval/temporal_retriever.py
Normal file
|
|
@ -0,0 +1,149 @@
|
|||
import os
|
||||
from typing import Any, Optional, List, Type
|
||||
|
||||
|
||||
from operator import itemgetter
|
||||
from cognee.infrastructure.databases.vector import get_vector_engine
|
||||
from cognee.modules.retrieval.utils.completion import generate_completion
|
||||
from cognee.infrastructure.databases.graph import get_graph_engine
|
||||
from cognee.infrastructure.llm import LLMGateway
|
||||
from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
|
||||
|
||||
from cognee.tasks.temporal_graph.models import QueryInterval
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
|
||||
class TemporalRetriever(GraphCompletionRetriever):
|
||||
"""
|
||||
Handles graph completion by generating responses based on a series of interactions with
|
||||
a language model. This class extends from GraphCompletionRetriever and is designed to
|
||||
manage the retrieval and validation process for user queries, integrating follow-up
|
||||
questions based on reasoning. The public methods are:
|
||||
|
||||
- get_completion
|
||||
|
||||
Instance variables include:
|
||||
- validation_system_prompt_path
|
||||
- validation_user_prompt_path
|
||||
- followup_system_prompt_path
|
||||
- followup_user_prompt_path
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
user_prompt_path: str = "graph_context_for_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
time_extraction_prompt_path: str = "extract_query_time.txt",
|
||||
top_k: Optional[int] = 5,
|
||||
node_type: Optional[Type] = None,
|
||||
node_name: Optional[List[str]] = None,
|
||||
):
|
||||
super().__init__(
|
||||
user_prompt_path=user_prompt_path,
|
||||
system_prompt_path=system_prompt_path,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
)
|
||||
self.user_prompt_path = user_prompt_path
|
||||
self.system_prompt_path = system_prompt_path
|
||||
self.time_extraction_prompt_path = time_extraction_prompt_path
|
||||
self.top_k = top_k if top_k is not None else 5
|
||||
self.node_type = node_type
|
||||
self.node_name = node_name
|
||||
|
||||
def descriptions_to_string(self, results):
|
||||
descs = []
|
||||
for entry in results:
|
||||
d = entry.get("description")
|
||||
if d:
|
||||
descs.append(d.strip())
|
||||
return "\n#####################\n".join(descs)
|
||||
|
||||
async def extract_time_from_query(self, query: str):
|
||||
prompt_path = self.time_extraction_prompt_path
|
||||
|
||||
if os.path.isabs(prompt_path):
|
||||
base_directory = os.path.dirname(prompt_path)
|
||||
prompt_path = os.path.basename(prompt_path)
|
||||
else:
|
||||
base_directory = None
|
||||
|
||||
system_prompt = LLMGateway.render_prompt(prompt_path, {}, base_directory=base_directory)
|
||||
|
||||
interval = await LLMGateway.acreate_structured_output(query, system_prompt, QueryInterval)
|
||||
|
||||
time_from = interval.starts_at
|
||||
time_to = interval.ends_at
|
||||
|
||||
return time_from, time_to
|
||||
|
||||
async def filter_top_k_events(self, relevant_events, scored_results):
|
||||
# Build a score lookup from vector search results
|
||||
score_lookup = {res.payload["id"]: res.score for res in scored_results}
|
||||
|
||||
events_with_scores = []
|
||||
for event in relevant_events[0]["events"]:
|
||||
score = score_lookup.get(event["id"], float("inf"))
|
||||
events_with_scores.append({**event, "score": score})
|
||||
|
||||
events_with_scores.sort(key=itemgetter("score"))
|
||||
|
||||
return events_with_scores[: self.top_k]
|
||||
|
||||
async def get_context(self, query: str) -> Any:
|
||||
"""Retrieves context based on the query."""
|
||||
|
||||
time_from, time_to = await self.extract_time_from_query(query)
|
||||
|
||||
graph_engine = await get_graph_engine()
|
||||
|
||||
if time_from and time_to:
|
||||
ids = await graph_engine.collect_time_ids(time_from=time_from, time_to=time_to)
|
||||
elif time_from:
|
||||
ids = await graph_engine.collect_time_ids(time_from=time_from)
|
||||
elif time_to:
|
||||
ids = await graph_engine.collect_time_ids(time_to=time_to)
|
||||
else:
|
||||
logger.info(
|
||||
"No timestamps identified based on the query, performing retrieval using triplet search on events and entities."
|
||||
)
|
||||
triplets = await self.get_triplets(query)
|
||||
return await self.resolve_edges_to_text(triplets)
|
||||
|
||||
if ids:
|
||||
relevant_events = await graph_engine.collect_events(ids=ids)
|
||||
else:
|
||||
logger.info(
|
||||
"No events identified based on timestamp filtering, performing retrieval using triplet search on events and entities."
|
||||
)
|
||||
triplets = await self.get_triplets(query)
|
||||
return await self.resolve_edges_to_text(triplets)
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
query_vector = (await vector_engine.embedding_engine.embed_text([query]))[0]
|
||||
|
||||
vector_search_results = await vector_engine.search(
|
||||
collection_name="Event_name", query_vector=query_vector, limit=0
|
||||
)
|
||||
|
||||
top_k_events = await self.filter_top_k_events(relevant_events, vector_search_results)
|
||||
|
||||
return self.descriptions_to_string(top_k_events)
|
||||
|
||||
async def get_completion(self, query: str, context: Optional[Any] = None) -> Any:
|
||||
"""Generates a response using the query and optional context."""
|
||||
|
||||
context = await self.get_context(query=query)
|
||||
|
||||
completion = await generate_completion(
|
||||
query=query,
|
||||
context=context,
|
||||
user_prompt_path=self.user_prompt_path,
|
||||
system_prompt_path=self.system_prompt_path,
|
||||
)
|
||||
|
||||
return [completion]
|
||||
|
|
@ -1,3 +1,4 @@
|
|||
from typing import Optional
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
|
||||
|
||||
|
|
@ -6,25 +7,35 @@ async def generate_completion(
|
|||
context: str,
|
||||
user_prompt_path: str,
|
||||
system_prompt_path: str,
|
||||
system_prompt: Optional[str] = None,
|
||||
only_context: bool = False,
|
||||
) -> str:
|
||||
"""Generates a completion using LLM with given context and prompts."""
|
||||
args = {"question": query, "context": context}
|
||||
user_prompt = LLMGateway.render_prompt(user_prompt_path, args)
|
||||
system_prompt = LLMGateway.read_query_prompt(system_prompt_path)
|
||||
|
||||
return await LLMGateway.acreate_structured_output(
|
||||
text_input=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
response_model=str,
|
||||
system_prompt = (
|
||||
system_prompt if system_prompt else LLMGateway.read_query_prompt(system_prompt_path)
|
||||
)
|
||||
|
||||
if only_context:
|
||||
return context
|
||||
else:
|
||||
return await LLMGateway.acreate_structured_output(
|
||||
text_input=user_prompt,
|
||||
system_prompt=system_prompt,
|
||||
response_model=str,
|
||||
)
|
||||
|
||||
|
||||
async def summarize_text(
|
||||
text: str,
|
||||
prompt_path: str = "summarize_search_results.txt",
|
||||
system_prompt_path: str = "summarize_search_results.txt",
|
||||
system_prompt: str = None,
|
||||
) -> str:
|
||||
"""Summarizes text using LLM with the specified prompt."""
|
||||
system_prompt = LLMGateway.read_query_prompt(prompt_path)
|
||||
system_prompt = (
|
||||
system_prompt if system_prompt else LLMGateway.read_query_prompt(system_prompt_path)
|
||||
)
|
||||
|
||||
return await LLMGateway.acreate_structured_output(
|
||||
text_input=text,
|
||||
|
|
|
|||
|
|
@ -4,6 +4,7 @@ import asyncio
|
|||
from uuid import UUID
|
||||
from typing import Callable, List, Optional, Type, Union
|
||||
|
||||
from cognee.modules.engine.models.node_set import NodeSet
|
||||
from cognee.modules.retrieval.user_qa_feedback import UserQAFeedback
|
||||
from cognee.modules.search.exceptions import UnsupportedSearchTypeError
|
||||
from cognee.context_global_variables import set_database_global_context_variables
|
||||
|
|
@ -12,6 +13,7 @@ from cognee.modules.retrieval.insights_retriever import InsightsRetriever
|
|||
from cognee.modules.retrieval.summaries_retriever import SummariesRetriever
|
||||
from cognee.modules.retrieval.completion_retriever import CompletionRetriever
|
||||
from cognee.modules.retrieval.graph_completion_retriever import GraphCompletionRetriever
|
||||
from cognee.modules.retrieval.temporal_retriever import TemporalRetriever
|
||||
from cognee.modules.retrieval.graph_summary_completion_retriever import (
|
||||
GraphSummaryCompletionRetriever,
|
||||
)
|
||||
|
|
@ -37,11 +39,13 @@ async def search(
|
|||
dataset_ids: Union[list[UUID], None],
|
||||
user: User,
|
||||
system_prompt_path="answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
node_type: Optional[Type] = None,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: Optional[bool] = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = False,
|
||||
):
|
||||
"""
|
||||
|
||||
|
|
@ -61,28 +65,34 @@ async def search(
|
|||
# Use search function filtered by permissions if access control is enabled
|
||||
if os.getenv("ENABLE_BACKEND_ACCESS_CONTROL", "false").lower() == "true":
|
||||
return await authorized_search(
|
||||
query_text=query_text,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=user,
|
||||
dataset_ids=dataset_ids,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
|
||||
query = await log_query(query_text, query_type.value, user.id)
|
||||
|
||||
search_results = await specific_search(
|
||||
query_type,
|
||||
query_text,
|
||||
user,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=user,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
|
||||
await log_result(
|
||||
|
|
@ -98,21 +108,26 @@ async def search(
|
|||
|
||||
async def specific_search(
|
||||
query_type: SearchType,
|
||||
query: str,
|
||||
query_text: str,
|
||||
user: User,
|
||||
system_prompt_path="answer_simple_question.txt",
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
node_type: Optional[Type] = None,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: Optional[bool] = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = None,
|
||||
) -> list:
|
||||
search_tasks: dict[SearchType, Callable] = {
|
||||
SearchType.SUMMARIES: SummariesRetriever(top_k=top_k).get_completion,
|
||||
SearchType.INSIGHTS: InsightsRetriever(top_k=top_k).get_completion,
|
||||
SearchType.CHUNKS: ChunksRetriever(top_k=top_k).get_completion,
|
||||
SearchType.RAG_COMPLETION: CompletionRetriever(
|
||||
system_prompt_path=system_prompt_path, top_k=top_k
|
||||
system_prompt_path=system_prompt_path,
|
||||
top_k=top_k,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
).get_completion,
|
||||
SearchType.GRAPH_COMPLETION: GraphCompletionRetriever(
|
||||
system_prompt_path=system_prompt_path,
|
||||
|
|
@ -120,6 +135,8 @@ async def specific_search(
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
).get_completion,
|
||||
SearchType.GRAPH_COMPLETION_COT: GraphCompletionCotRetriever(
|
||||
system_prompt_path=system_prompt_path,
|
||||
|
|
@ -127,6 +144,8 @@ async def specific_search(
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
).get_completion,
|
||||
SearchType.GRAPH_COMPLETION_CONTEXT_EXTENSION: GraphCompletionContextExtensionRetriever(
|
||||
system_prompt_path=system_prompt_path,
|
||||
|
|
@ -134,6 +153,8 @@ async def specific_search(
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
only_context=only_context,
|
||||
).get_completion,
|
||||
SearchType.GRAPH_SUMMARY_COMPLETION: GraphSummaryCompletionRetriever(
|
||||
system_prompt_path=system_prompt_path,
|
||||
|
|
@ -141,16 +162,18 @@ async def specific_search(
|
|||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
system_prompt=system_prompt,
|
||||
).get_completion,
|
||||
SearchType.CODE: CodeRetriever(top_k=top_k).get_completion,
|
||||
SearchType.CYPHER: CypherSearchRetriever().get_completion,
|
||||
SearchType.NATURAL_LANGUAGE: NaturalLanguageRetriever().get_completion,
|
||||
SearchType.FEEDBACK: UserQAFeedback(last_k=last_k).add_feedback,
|
||||
SearchType.TEMPORAL: TemporalRetriever(top_k=top_k).get_completion,
|
||||
}
|
||||
|
||||
# If the query type is FEELING_LUCKY, select the search type intelligently
|
||||
if query_type is SearchType.FEELING_LUCKY:
|
||||
query_type = await select_search_type(query)
|
||||
query_type = await select_search_type(query_text)
|
||||
|
||||
search_task = search_tasks.get(query_type)
|
||||
|
||||
|
|
@ -159,7 +182,7 @@ async def specific_search(
|
|||
|
||||
send_telemetry("cognee.search EXECUTION STARTED", user.id)
|
||||
|
||||
results = await search_task(query)
|
||||
results = await search_task(query_text)
|
||||
|
||||
send_telemetry("cognee.search EXECUTION COMPLETED", user.id)
|
||||
|
||||
|
|
@ -167,14 +190,18 @@ async def specific_search(
|
|||
|
||||
|
||||
async def authorized_search(
|
||||
query_text: str,
|
||||
query_type: SearchType,
|
||||
user: User = None,
|
||||
query_text: str,
|
||||
user: User,
|
||||
dataset_ids: Optional[list[UUID]] = None,
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
save_interaction: bool = False,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: Optional[bool] = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = None,
|
||||
) -> list:
|
||||
"""
|
||||
Verifies access for provided datasets or uses all datasets user has read access for and performs search per dataset.
|
||||
|
|
@ -188,14 +215,18 @@ async def authorized_search(
|
|||
|
||||
# Searches all provided datasets and handles setting up of appropriate database context based on permissions
|
||||
search_results = await specific_search_by_context(
|
||||
search_datasets,
|
||||
query_text,
|
||||
query_type,
|
||||
user,
|
||||
system_prompt_path,
|
||||
top_k,
|
||||
save_interaction,
|
||||
search_datasets=search_datasets,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=user,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
|
||||
await log_result(query.id, json.dumps(search_results, cls=JSONEncoder), user.id)
|
||||
|
|
@ -205,13 +236,17 @@ async def authorized_search(
|
|||
|
||||
async def specific_search_by_context(
|
||||
search_datasets: list[Dataset],
|
||||
query_text: str,
|
||||
query_type: SearchType,
|
||||
query_text: str,
|
||||
user: User,
|
||||
system_prompt_path: str,
|
||||
top_k: int,
|
||||
save_interaction: bool = False,
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: Optional[bool] = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = None,
|
||||
):
|
||||
"""
|
||||
Searches all provided datasets and handles setting up of appropriate database context based on permissions.
|
||||
|
|
@ -219,18 +254,33 @@ async def specific_search_by_context(
|
|||
"""
|
||||
|
||||
async def _search_by_context(
|
||||
dataset, user, query_type, query_text, system_prompt_path, top_k, last_k
|
||||
dataset: Dataset,
|
||||
query_type: SearchType,
|
||||
query_text: str,
|
||||
user: User,
|
||||
system_prompt_path: str = "answer_simple_question.txt",
|
||||
system_prompt: Optional[str] = None,
|
||||
top_k: int = 10,
|
||||
node_type: Optional[Type] = NodeSet,
|
||||
node_name: Optional[List[str]] = None,
|
||||
save_interaction: Optional[bool] = False,
|
||||
last_k: Optional[int] = None,
|
||||
only_context: bool = None,
|
||||
):
|
||||
# Set database configuration in async context for each dataset user has access for
|
||||
await set_database_global_context_variables(dataset.id, dataset.owner_id)
|
||||
search_results = await specific_search(
|
||||
query_type,
|
||||
query_text,
|
||||
user,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=user,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
return {
|
||||
"search_result": search_results,
|
||||
|
|
@ -243,7 +293,18 @@ async def specific_search_by_context(
|
|||
for dataset in search_datasets:
|
||||
tasks.append(
|
||||
_search_by_context(
|
||||
dataset, user, query_type, query_text, system_prompt_path, top_k, last_k
|
||||
dataset=dataset,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=user,
|
||||
system_prompt_path=system_prompt_path,
|
||||
system_prompt=system_prompt,
|
||||
top_k=top_k,
|
||||
node_type=node_type,
|
||||
node_name=node_name,
|
||||
save_interaction=save_interaction,
|
||||
last_k=last_k,
|
||||
only_context=only_context,
|
||||
)
|
||||
)
|
||||
|
||||
|
|
|
|||
|
|
@ -15,3 +15,4 @@ class SearchType(Enum):
|
|||
GRAPH_COMPLETION_CONTEXT_EXTENSION = "GRAPH_COMPLETION_CONTEXT_EXTENSION"
|
||||
FEELING_LUCKY = "FEELING_LUCKY"
|
||||
FEEDBACK = "FEEDBACK"
|
||||
TEMPORAL = "TEMPORAL"
|
||||
|
|
|
|||
|
|
@ -1,4 +1,5 @@
|
|||
from pathlib import Path
|
||||
from typing import Optional
|
||||
|
||||
ROOT_DIR = Path(__file__).resolve().parent
|
||||
|
||||
|
|
@ -6,3 +7,21 @@ ROOT_DIR = Path(__file__).resolve().parent
|
|||
def get_absolute_path(path_from_root: str) -> str:
|
||||
absolute_path = ROOT_DIR / path_from_root
|
||||
return str(absolute_path.resolve())
|
||||
|
||||
|
||||
def ensure_absolute_path(path: str) -> str:
|
||||
"""Ensures a path is absolute.
|
||||
|
||||
Args:
|
||||
path: The path to validate.
|
||||
|
||||
Returns:
|
||||
Absolute path as string
|
||||
"""
|
||||
if path is None:
|
||||
raise ValueError("Path cannot be None")
|
||||
path_obj = Path(path).expanduser()
|
||||
if path_obj.is_absolute():
|
||||
return str(path_obj.resolve())
|
||||
|
||||
raise ValueError(f"Path must be absolute. Got relative path: {path}")
|
||||
|
|
|
|||
|
|
@ -1,24 +1,48 @@
|
|||
import asyncio
|
||||
import math
|
||||
import os
|
||||
|
||||
# from concurrent.futures import ProcessPoolExecutor
|
||||
from typing import AsyncGenerator
|
||||
from pathlib import Path
|
||||
from typing import Set
|
||||
from typing import AsyncGenerator, Optional, List
|
||||
from uuid import NAMESPACE_OID, uuid5
|
||||
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.shared.CodeGraphEntities import CodeFile, Repository
|
||||
|
||||
# constant, declared only once
|
||||
EXCLUDED_DIRS: Set[str] = {
|
||||
".venv",
|
||||
"venv",
|
||||
"env",
|
||||
".env",
|
||||
"site-packages",
|
||||
"node_modules",
|
||||
"dist",
|
||||
"build",
|
||||
".git",
|
||||
"tests",
|
||||
"test",
|
||||
}
|
||||
|
||||
async def get_source_code_files(repo_path, language_config: dict[str, list[str]] | None = None):
|
||||
|
||||
async def get_source_code_files(
|
||||
repo_path,
|
||||
language_config: dict[str, list[str]] | None = None,
|
||||
excluded_paths: Optional[List[str]] = None,
|
||||
):
|
||||
"""
|
||||
Retrieve source code files from the specified repository path for multiple languages.
|
||||
Retrieve Python source code files from the specified repository path.
|
||||
|
||||
This function scans the given repository path for files that have the .py extension
|
||||
while excluding test files and files within a virtual environment. It returns a list of
|
||||
absolute paths to the source code files that are not empty.
|
||||
|
||||
Parameters:
|
||||
-----------
|
||||
- repo_path: The file path to the repository to search for source files.
|
||||
- language_config: dict mapping language names to file extensions, e.g.,
|
||||
- repo_path: Root path of the repository to search
|
||||
- language_config: dict mapping language names to file extensions, e.g.,
|
||||
{'python': ['.py'], 'javascript': ['.js', '.jsx'], ...}
|
||||
- excluded_paths: Optional list of path fragments or glob patterns to exclude
|
||||
|
||||
Returns:
|
||||
--------
|
||||
|
|
@ -54,28 +78,23 @@ async def get_source_code_files(repo_path, language_config: dict[str, list[str]]
|
|||
lang = _get_language_from_extension(file, language_config)
|
||||
if lang is None:
|
||||
continue
|
||||
# Exclude tests and common build/venv directories
|
||||
excluded_dirs = {
|
||||
".venv",
|
||||
"venv",
|
||||
"env",
|
||||
".env",
|
||||
"site-packages",
|
||||
"node_modules",
|
||||
"dist",
|
||||
"build",
|
||||
".git",
|
||||
"tests",
|
||||
"test",
|
||||
}
|
||||
root_parts = set(os.path.normpath(root).split(os.sep))
|
||||
# Exclude tests, common build/venv directories and files provided in exclude_paths
|
||||
excluded_dirs = EXCLUDED_DIRS
|
||||
excluded_paths = {Path(p).resolve() for p in (excluded_paths or [])} # full paths
|
||||
|
||||
root_path = Path(root).resolve()
|
||||
root_parts = set(root_path.parts) # same as before
|
||||
base_name, _ext = os.path.splitext(file)
|
||||
if (
|
||||
base_name.startswith("test_")
|
||||
or base_name.endswith("_test") # catches Go's *_test.go and similar
|
||||
or base_name.endswith("_test")
|
||||
or ".test." in file
|
||||
or ".spec." in file
|
||||
or (excluded_dirs & root_parts)
|
||||
or (excluded_dirs & root_parts) # name match
|
||||
or any(
|
||||
root_path.is_relative_to(p) # full-path match
|
||||
for p in excluded_paths
|
||||
)
|
||||
):
|
||||
continue
|
||||
file_path = os.path.abspath(os.path.join(root, file))
|
||||
|
|
@ -115,7 +134,10 @@ def run_coroutine(coroutine_func, *args, **kwargs):
|
|||
|
||||
|
||||
async def get_repo_file_dependencies(
|
||||
repo_path: str, detailed_extraction: bool = False, supported_languages: list = None
|
||||
repo_path: str,
|
||||
detailed_extraction: bool = False,
|
||||
supported_languages: list = None,
|
||||
excluded_paths: Optional[List[str]] = None,
|
||||
) -> AsyncGenerator[DataPoint, None]:
|
||||
"""
|
||||
Generate a dependency graph for source files (multi-language) in the given repository path.
|
||||
|
|
@ -150,6 +172,7 @@ async def get_repo_file_dependencies(
|
|||
"go": [".go"],
|
||||
"rust": [".rs"],
|
||||
"cpp": [".cpp", ".c", ".h", ".hpp"],
|
||||
"c": [".c", ".h"],
|
||||
}
|
||||
if supported_languages is not None:
|
||||
language_config = {
|
||||
|
|
@ -158,7 +181,9 @@ async def get_repo_file_dependencies(
|
|||
else:
|
||||
language_config = default_language_config
|
||||
|
||||
source_code_files = await get_source_code_files(repo_path, language_config=language_config)
|
||||
source_code_files = await get_source_code_files(
|
||||
repo_path, language_config=language_config, excluded_paths=excluded_paths
|
||||
)
|
||||
|
||||
repo = Repository(
|
||||
id=uuid5(NAMESPACE_OID, repo_path),
|
||||
|
|
|
|||
1
cognee/tasks/temporal_graph/__init__.py
Normal file
1
cognee/tasks/temporal_graph/__init__.py
Normal file
|
|
@ -0,0 +1 @@
|
|||
|
||||
85
cognee/tasks/temporal_graph/add_entities_to_event.py
Normal file
85
cognee/tasks/temporal_graph/add_entities_to_event.py
Normal file
|
|
@ -0,0 +1,85 @@
|
|||
from cognee.modules.engine.models import Event
|
||||
from cognee.tasks.temporal_graph.models import EventWithEntities
|
||||
from cognee.modules.engine.models.Entity import Entity
|
||||
from cognee.modules.engine.models.EntityType import EntityType
|
||||
from cognee.infrastructure.engine.models.Edge import Edge
|
||||
from cognee.modules.engine.utils import generate_node_id, generate_node_name
|
||||
|
||||
|
||||
def add_entities_to_event(event: Event, event_with_entities: EventWithEntities) -> None:
|
||||
"""
|
||||
Adds extracted entities to an Event object by populating its attributes field.
|
||||
|
||||
For each attribute in the provided EventWithEntities, the function ensures that
|
||||
the corresponding entity type exists, creates an Entity node with metadata, and
|
||||
links it to the event via an Edge representing the relationship. Entities are
|
||||
cached by type to avoid duplication.
|
||||
|
||||
Args:
|
||||
event (Event): The target Event object to enrich with entities.
|
||||
event_with_entities (EventWithEntities): An event model containing extracted
|
||||
attributes with entity, type, and relationship metadata.
|
||||
|
||||
Returns:
|
||||
None
|
||||
"""
|
||||
|
||||
if not event_with_entities.attributes:
|
||||
return
|
||||
|
||||
# Create entity types cache
|
||||
entity_types = {}
|
||||
|
||||
# Process each attribute
|
||||
for attribute in event_with_entities.attributes:
|
||||
# Get or create entity type
|
||||
entity_type = get_or_create_entity_type(entity_types, attribute.entity_type)
|
||||
|
||||
# Create entity
|
||||
entity_id = generate_node_id(attribute.entity)
|
||||
entity_name = generate_node_name(attribute.entity)
|
||||
entity = Entity(
|
||||
id=entity_id,
|
||||
name=entity_name,
|
||||
is_a=entity_type,
|
||||
description=f"Entity {attribute.entity} of type {attribute.entity_type}",
|
||||
ontology_valid=False,
|
||||
belongs_to_set=None,
|
||||
)
|
||||
|
||||
# Create edge
|
||||
edge = Edge(relationship_type=attribute.relationship)
|
||||
|
||||
# Add to event attributes
|
||||
if event.attributes is None:
|
||||
event.attributes = []
|
||||
event.attributes.append((edge, [entity]))
|
||||
|
||||
|
||||
def get_or_create_entity_type(entity_types: dict, entity_type_name: str) -> EntityType:
|
||||
"""
|
||||
Retrieves an existing EntityType from the cache or creates a new one if it does not exist.
|
||||
|
||||
If the given entity type name is not already in the cache, a new EntityType is generated
|
||||
with a unique ID, normalized name, and description, then added to the cache.
|
||||
|
||||
Args:
|
||||
entity_types (dict): A cache mapping entity type names to EntityType objects.
|
||||
entity_type_name (str): The name of the entity type to retrieve or create.
|
||||
|
||||
Returns:
|
||||
EntityType: The existing or newly created EntityType object.
|
||||
"""
|
||||
if entity_type_name not in entity_types:
|
||||
type_id = generate_node_id(entity_type_name)
|
||||
type_name = generate_node_name(entity_type_name)
|
||||
entity_type = EntityType(
|
||||
id=type_id,
|
||||
name=type_name,
|
||||
type=type_name,
|
||||
description=f"Type for {entity_type_name}",
|
||||
ontology_valid=False,
|
||||
)
|
||||
entity_types[entity_type_name] = entity_type
|
||||
|
||||
return entity_types[entity_type_name]
|
||||
34
cognee/tasks/temporal_graph/enrich_events.py
Normal file
34
cognee/tasks/temporal_graph/enrich_events.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from typing import List
|
||||
|
||||
from cognee.infrastructure.llm import LLMGateway
|
||||
from cognee.modules.engine.models import Event
|
||||
from cognee.tasks.temporal_graph.models import EventWithEntities, EventEntityList
|
||||
|
||||
|
||||
async def enrich_events(events: List[Event]) -> List[EventWithEntities]:
|
||||
"""
|
||||
Enriches a list of events by extracting entities using an LLM.
|
||||
|
||||
The function serializes event data into JSON, sends it to the LLM for
|
||||
entity extraction, and returns enriched events with associated entities.
|
||||
|
||||
Args:
|
||||
events (List[Event]): A list of Event objects to be enriched.
|
||||
|
||||
Returns:
|
||||
List[EventWithEntities]: A list of events augmented with extracted entities.
|
||||
"""
|
||||
|
||||
import json
|
||||
|
||||
# Convert events to JSON format for LLM processing
|
||||
events_json = [
|
||||
{"event_name": event.name, "description": event.description or ""} for event in events
|
||||
]
|
||||
|
||||
events_json_str = json.dumps(events_json)
|
||||
|
||||
# Extract entities from events
|
||||
entity_result = await LLMGateway.extract_event_entities(events_json_str, EventEntityList)
|
||||
|
||||
return entity_result.events
|
||||
32
cognee/tasks/temporal_graph/extract_events_and_entities.py
Normal file
32
cognee/tasks/temporal_graph/extract_events_and_entities.py
Normal file
|
|
@ -0,0 +1,32 @@
|
|||
import asyncio
|
||||
from typing import Type, List
|
||||
from cognee.infrastructure.llm.LLMGateway import LLMGateway
|
||||
from cognee.modules.chunking.models import DocumentChunk
|
||||
from cognee.tasks.temporal_graph.models import EventList
|
||||
from cognee.modules.engine.utils.generate_event_datapoint import generate_event_datapoint
|
||||
|
||||
|
||||
async def extract_events_and_timestamps(data_chunks: List[DocumentChunk]) -> List[DocumentChunk]:
|
||||
"""
|
||||
Extracts events and their timestamps from document chunks using an LLM.
|
||||
|
||||
Each document chunk is processed with the event graph extractor to identify events.
|
||||
The extracted events are converted into Event datapoints and appended to the
|
||||
chunk's `contains` list.
|
||||
|
||||
Args:
|
||||
data_chunks (List[DocumentChunk]): A list of document chunks containing text to process.
|
||||
|
||||
Returns:
|
||||
List[DocumentChunk]: The same list of document chunks, enriched with extracted Event datapoints.
|
||||
"""
|
||||
events = await asyncio.gather(
|
||||
*[LLMGateway.extract_event_graph(chunk.text, EventList) for chunk in data_chunks]
|
||||
)
|
||||
|
||||
for data_chunk, event_list in zip(data_chunks, events):
|
||||
for event in event_list.events:
|
||||
event_datapoint = generate_event_datapoint(event)
|
||||
data_chunk.contains.append(event_datapoint)
|
||||
|
||||
return data_chunks
|
||||
|
|
@ -0,0 +1,41 @@
|
|||
from typing import List
|
||||
from cognee.modules.chunking.models import DocumentChunk
|
||||
from cognee.modules.engine.models import Event
|
||||
from cognee.tasks.temporal_graph.enrich_events import enrich_events
|
||||
from cognee.tasks.temporal_graph.add_entities_to_event import add_entities_to_event
|
||||
|
||||
|
||||
async def extract_knowledge_graph_from_events(
|
||||
data_chunks: List[DocumentChunk],
|
||||
) -> List[DocumentChunk]:
|
||||
"""
|
||||
Extracts events from document chunks and enriches them with entities to form a knowledge graph.
|
||||
|
||||
The function collects all Event objects from the given document chunks,
|
||||
uses an LLM to extract and attach related entities, and updates the events
|
||||
with these enriched attributes.
|
||||
|
||||
Args:
|
||||
data_chunks (List[DocumentChunk]): A list of document chunks containing extracted events.
|
||||
|
||||
Returns:
|
||||
List[DocumentChunk]: The same list of document chunks, with their events enriched by entities.
|
||||
"""
|
||||
# Extract events from chunks
|
||||
all_events = []
|
||||
for chunk in data_chunks:
|
||||
for item in chunk.contains:
|
||||
if isinstance(item, Event):
|
||||
all_events.append(item)
|
||||
|
||||
if not all_events:
|
||||
return data_chunks
|
||||
|
||||
# Enrich events with entities
|
||||
enriched_events = await enrich_events(all_events)
|
||||
|
||||
# Add entities to events
|
||||
for event, enriched_event in zip(all_events, enriched_events):
|
||||
add_entities_to_event(event, enriched_event)
|
||||
|
||||
return data_chunks
|
||||
49
cognee/tasks/temporal_graph/models.py
Normal file
49
cognee/tasks/temporal_graph/models.py
Normal file
|
|
@ -0,0 +1,49 @@
|
|||
from typing import Optional, List
|
||||
from pydantic import BaseModel, Field
|
||||
|
||||
|
||||
class Timestamp(BaseModel):
|
||||
year: int = Field(..., ge=1, le=9999)
|
||||
month: int = Field(..., ge=1, le=12)
|
||||
day: int = Field(..., ge=1, le=31)
|
||||
hour: int = Field(..., ge=0, le=23)
|
||||
minute: int = Field(..., ge=0, le=59)
|
||||
second: int = Field(..., ge=0, le=59)
|
||||
|
||||
|
||||
class Interval(BaseModel):
|
||||
starts_at: Timestamp
|
||||
ends_at: Timestamp
|
||||
|
||||
|
||||
class QueryInterval(BaseModel):
|
||||
starts_at: Optional[Timestamp] = None
|
||||
ends_at: Optional[Timestamp] = None
|
||||
|
||||
|
||||
class Event(BaseModel):
|
||||
name: str
|
||||
description: Optional[str] = None
|
||||
time_from: Optional[Timestamp] = None
|
||||
time_to: Optional[Timestamp] = None
|
||||
location: Optional[str] = None
|
||||
|
||||
|
||||
class EventList(BaseModel):
|
||||
events: List[Event]
|
||||
|
||||
|
||||
class EntityAttribute(BaseModel):
|
||||
entity: str
|
||||
entity_type: str
|
||||
relationship: str
|
||||
|
||||
|
||||
class EventWithEntities(BaseModel):
|
||||
event_name: str
|
||||
description: Optional[str] = None
|
||||
attributes: List[EntityAttribute] = []
|
||||
|
||||
|
||||
class EventEntityList(BaseModel):
|
||||
events: List[EventWithEntities]
|
||||
167
cognee/tests/test_temporal_graph.py
Normal file
167
cognee/tests/test_temporal_graph.py
Normal file
|
|
@ -0,0 +1,167 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
from cognee.modules.retrieval.temporal_retriever import TemporalRetriever
|
||||
|
||||
from cognee.shared.logging_utils import setup_logging, INFO
|
||||
from cognee.tasks.temporal_graph.models import Timestamp
|
||||
from cognee.api.v1.search import SearchType
|
||||
from cognee.shared.logging_utils import get_logger
|
||||
from cognee.infrastructure.databases.graph.get_graph_engine import get_graph_engine
|
||||
from collections import Counter
|
||||
from cognee.modules.engine.utils.generate_timestamp_datapoint import date_to_int
|
||||
|
||||
logger = get_logger()
|
||||
|
||||
biography_1 = """
|
||||
Attaphol Buspakom Attaphol Buspakom ( ; ) , nicknamed Tak ( ; ) ; 1 October 1962 – 16 April 2015 ) was a Thai national and football coach . He was given the role at Muangthong United and Buriram United after TTM Samut Sakhon folded after the 2009 season . He played for the Thailand national football team , appearing in several FIFA World Cup qualifying matches .
|
||||
|
||||
Club career .
|
||||
Attaphol began his career as a player at Thai Port FC Authority of Thailand in 1985 . In his first year , he won his first championship with the club . He played for the club until 1989 and in 1987 also won the Queens Cup . He then moved to Malaysia for two seasons for Pahang FA , then return to Thailand to his former club . His time from 1991 to 1994 was marked by less success than in his first stay at Port Authority . From 1994 to 1996 he played for Pahang again and this time he was able to win with the club , the Malaysia Super League and also reached the final of the Malaysia Cup and the Malaysia FA Cup . Both cup finals but lost . Back in Thailand , he let end his playing career at FC Stock Exchange of Thailand , with which he once again runner‑up in 1996-97 . In 1998 , he finished his career .
|
||||
|
||||
International career .
|
||||
For the Thailand national football team Attaphol played between 1985 and 1998 a total of 85 games and scored 13 results . In 1992 , he participated with the team in the finals of the Asian Cup . He also stood in various cadres to qualifications to FIFA World Cup .
|
||||
|
||||
Coaching career .
|
||||
Bec Tero Sasana .
|
||||
In BEC Tero Sasana F.C . began his coaching career in 2001 for him , first as assistant coach . He took over the reigning champions of the Thai League T1 , after his predecessor Pichai Pituwong resigned from his post . It was his first coach station and he had the difficult task of leading the club through the new AFC Champions League . He could accomplish this task with flying colors and even led the club to the finals . The finale , then still played in home and away matches , was lost with 1:2 at the end against Al Ain FC . Attaphol is and was next to Charnwit Polcheewin the only coach who managed a club from Thailand to lead to the final of the AFC Champions League . 2002-03 and 2003-04 he won with the club also two runner‑up . In his team , which reached the final of the Champions League , were a number of exceptional players like Therdsak Chaiman , Worrawoot Srimaka , Dusit Chalermsan and Anurak Srikerd .
|
||||
|
||||
Geylang United / Krung Thai Bank .
|
||||
In 2006 , he went to Singapore in the S‑League to Geylang United He was released after a few months due to lack of success . In 2008 , he took over as coach at Krung Thai Bank F.C. , where he had almost a similar task , as a few years earlier by BEC‑Tero . As vice‑champion of the club was also qualified for the AFC Champions League . However , he failed to lead the team through the group stage of the season 2008 and beyond . With the Kashima Antlers of Japan and Beijing Guoan F.C . athletic competition was too great . One of the highlights was put under his leadership , yet the club . In the group match against the Vietnam club Nam Dinh F.C . his team won with 9-1 , but also lost four weeks later with 1-8 against Kashima Antlers . At the end of the National Football League season , he reached the Krung Thai 6th Table space . The Erstligalizenz the club was sold at the end of the season at the Bangkok Glass F.C. . Attaphol finished his coaching career with the club and accepted an offer of TTM Samutsakorn . After only a short time in office
|
||||
|
||||
Muangthong United .
|
||||
In 2009 , he received an offer from Muangthong United F.C. , which he accepted and changed . He can champion Muang Thong United for 2009 Thai Premier League and Attaphol won Coach of The year for Thai Premier League and he was able to lead Muang Thong United to play AFC Champions League qualifying play‑off for the first in the clubs history .
|
||||
|
||||
Buriram United .
|
||||
In 2010 Buspakom moved from Muangthong United to Buriram United F.C. . He received Coach of the Month in Thai Premier League 2 time in June and October . In 2011 , he led Buriram United win 2011 Thai Premier League second time for club and set a record with the most points in the Thai League T1 for 85 point and He led Buriram win 2011 Thai FA Cup by beat Muangthong United F.C . 1‑0 and he led Buriram win 2011 Thai League Cup by beat Thai Port F.C . 2‑0 . In 2012 , he led Buriram United to the 2012 AFC Champions League group stage . Buriram along with Guangzhou Evergrande F.C . from China , Kashiwa Reysol from Japan and Jeonbuk Hyundai Motors which are all champions from their country . In the first match of Buriram they beat Kashiwa 3‑2 and Second Match they beat Guangzhou 1‑2 at the Tianhe Stadium . Before losing to Jeonbuk 0‑2 and 3‑2 with lose Kashiwa and Guangzhou 1‑0 and 1‑2 respectively and Thai Premier League Attaphol lead Buriram end 4th for table with win 2012 Thai FA Cup and 2012 Thai League Cup .
|
||||
|
||||
Bangkok Glass .
|
||||
In 2013 , he moved from Buriram United to Bangkok Glass F.C. .
|
||||
|
||||
Individual
|
||||
- Thai Premier League Coach of the Year ( 3 ) : 2001-02 , 2009 , 2013
|
||||
"""
|
||||
|
||||
|
||||
biography_2 = """
|
||||
Arnulf Øverland Ole Peter Arnulf Øverland ( 27 April 1889 – 25 March 1968 ) was a Norwegian poet and artist . He is principally known for his poetry which served to inspire the Norwegian resistance movement during the German occupation of Norway during World War II .
|
||||
|
||||
Biography .
|
||||
Øverland was born in Kristiansund and raised in Bergen . His parents were Peter Anton Øverland ( 1852–1906 ) and Hanna Hage ( 1854–1939 ) . The early death of his father , left the family economically stressed . He was able to attend Bergen Cathedral School and in 1904 Kristiania Cathedral School . He graduated in 1907 and for a time studied philology at University of Kristiania . Øverland published his first collection of poems ( 1911 ) .
|
||||
|
||||
Øverland became a communist sympathizer from the early 1920s and became a member of Mot Dag . He also served as chairman of the Norwegian Students Society 1923–28 . He changed his stand in 1937 , partly as an expression of dissent against the ongoing Moscow Trials . He was an avid opponent of Nazism and in 1936 he wrote the poem Du må ikke sove which was printed in the journal Samtiden . It ends with . ( I thought: : Something is imminent . Our era is over – Europe’s on fire! ) . Probably the most famous line of the poem is ( You mustnt endure so well the injustice that doesnt affect you yourself! )
|
||||
|
||||
During the German occupation of Norway from 1940 in World War II , he wrote to inspire the Norwegian resistance movement . He wrote a series of poems which were clandestinely distributed , leading to the arrest of both him and his future wife Margrete Aamot Øverland in 1941 . Arnulf Øverland was held first in the prison camp of Grini before being transferred to Sachsenhausen concentration camp in Germany . He spent a four‑year imprisonment until the liberation of Norway in 1945 . His poems were later collected in Vi overlever alt and published in 1945 .
|
||||
|
||||
Øverland played an important role in the Norwegian language struggle in the post‑war era . He became a noted supporter for the conservative written form of Norwegian called Riksmål , he was president of Riksmålsforbundet ( an organization in support of Riksmål ) from 1947 to 1956 . In addition , Øverland adhered to the traditionalist style of writing , criticising modernist poetry on several occasions . His speech Tungetale fra parnasset , published in Arbeiderbladet in 1954 , initiated the so‑called Glossolalia debate .
|
||||
|
||||
Personal life .
|
||||
In 1918 he had married the singer Hildur Arntzen ( 1888–1957 ) . Their marriage was dissolved in 1939 . In 1940 , he married Bartholine Eufemia Leganger ( 1903–1995 ) . They separated shortly after , and were officially divorced in 1945 . Øverland was married to journalist Margrete Aamot Øverland ( 1913–1978 ) during June 1945 . In 1946 , the Norwegian Parliament arranged for Arnulf and Margrete Aamot Øverland to reside at the Grotten . He lived there until his death in 1968 and she lived there for another ten years until her death in 1978 . Arnulf Øverland was buried at Vår Frelsers Gravlund in Oslo . Joseph Grimeland designed the bust of Arnulf Øverland ( bronze , 1970 ) at his grave site .
|
||||
|
||||
Selected Works .
|
||||
- Den ensomme fest ( 1911 )
|
||||
- Berget det blå ( 1927 )
|
||||
- En Hustavle ( 1929 )
|
||||
- Den røde front ( 1937 )
|
||||
- Vi overlever alt ( 1945 )
|
||||
- Sverdet bak døren ( 1956 )
|
||||
- Livets minutter ( 1965 )
|
||||
|
||||
Awards .
|
||||
- Gyldendals Endowment ( 1935 )
|
||||
- Dobloug Prize ( 1951 )
|
||||
- Mads Wiel Nygaards legat ( 1961 )
|
||||
"""
|
||||
|
||||
|
||||
async def main():
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
await cognee.add([biography_1, biography_2])
|
||||
|
||||
await cognee.cognify(temporal_cognify=True)
|
||||
|
||||
graph_engine = await get_graph_engine()
|
||||
graph = await graph_engine.get_graph_data()
|
||||
|
||||
type_counts = Counter(node_data[1].get("type", {}) for node_data in graph[0])
|
||||
|
||||
edge_type_counts = Counter(edge_type[2] for edge_type in graph[1])
|
||||
|
||||
# Graph structure test
|
||||
assert type_counts.get("TextDocument", 0) == 2, (
|
||||
f"Expected exactly one TextDocument, but found {type_counts.get('TextDocument', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("DocumentChunk", 0) == 2, (
|
||||
f"Expected exactly one DocumentChunk, but found {type_counts.get('DocumentChunk', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("Entity", 0) >= 20, (
|
||||
f"Expected multiple entities (assert is set to 20), but found {type_counts.get('Entity', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("EntityType", 0) >= 2, (
|
||||
f"Expected multiple entity types, but found {type_counts.get('EntityType', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("Event", 0) >= 20, (
|
||||
f"Expected multiple events (assert is set to 20), but found {type_counts.get('Event', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("Timestamp", 0) >= 20, (
|
||||
f"Expected multiple timestamps (assert is set to 20), but found {type_counts.get('Timestamp', 0)}"
|
||||
)
|
||||
|
||||
assert type_counts.get("Interval", 0) >= 2, (
|
||||
f"Expected multiple intervals, but found {type_counts.get('Interval', 0)}"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("contains", 0) >= 20, (
|
||||
f"Expected multiple 'contains' edge, but found {edge_type_counts.get('contains', 0)}"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("is_a", 0) >= 20, (
|
||||
f"Expected multiple 'is_a' edge, but found {edge_type_counts.get('is_a', 0)}"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("during", 0) == type_counts.get("Interval", 0), (
|
||||
"Expected the same amount of during and interval objects in the graph"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("during", 0) == type_counts.get("Interval", 0), (
|
||||
"Expected the same amount of during and interval objects in the graph"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("time_from", 0) == type_counts.get("Interval", 0), (
|
||||
"Expected the same amount of time_from and interval objects in the graph"
|
||||
)
|
||||
|
||||
assert edge_type_counts.get("time_to", 0) == type_counts.get("Interval", 0), (
|
||||
"Expected the same amount of time_to and interval objects in the graph"
|
||||
)
|
||||
|
||||
retriever = TemporalRetriever()
|
||||
|
||||
result_before = await retriever.extract_time_from_query("What happened before 1890?")
|
||||
|
||||
assert result_before[0] is None
|
||||
|
||||
result_after = await retriever.extract_time_from_query("What happened after 1891?")
|
||||
|
||||
assert result_after[1] is None
|
||||
|
||||
result_between = await retriever.extract_time_from_query("What happened between 1890 and 1900?")
|
||||
|
||||
assert result_between[1]
|
||||
assert result_between[0]
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger = setup_logging(log_level=INFO)
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
loop.run_until_complete(main())
|
||||
finally:
|
||||
loop.run_until_complete(loop.shutdown_asyncgens())
|
||||
|
|
@ -1,7 +1,7 @@
|
|||
import os
|
||||
import pytest
|
||||
import pathlib
|
||||
|
||||
from typing import List
|
||||
import cognee
|
||||
from cognee.low_level import setup
|
||||
from cognee.tasks.storage import add_data_points
|
||||
|
|
@ -10,6 +10,20 @@ from cognee.modules.chunking.models import DocumentChunk
|
|||
from cognee.modules.data.processing.document_types import TextDocument
|
||||
from cognee.modules.retrieval.exceptions.exceptions import NoDataError
|
||||
from cognee.modules.retrieval.chunks_retriever import ChunksRetriever
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
from cognee.modules.engine.models import Entity
|
||||
|
||||
|
||||
class DocumentChunkWithEntities(DataPoint):
|
||||
text: str
|
||||
chunk_size: int
|
||||
chunk_index: int
|
||||
cut_type: str
|
||||
is_part_of: Document
|
||||
contains: List[Entity] = None
|
||||
|
||||
metadata: dict = {"index_fields": ["text"]}
|
||||
|
||||
|
||||
class TestChunksRetriever:
|
||||
|
|
@ -179,7 +193,9 @@ class TestChunksRetriever:
|
|||
await retriever.get_context("Christina Mayer")
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
await vector_engine.create_collection("DocumentChunk_text", payload_schema=DocumentChunk)
|
||||
await vector_engine.create_collection(
|
||||
"DocumentChunk_text", payload_schema=DocumentChunkWithEntities
|
||||
)
|
||||
|
||||
context = await retriever.get_context("Christina Mayer")
|
||||
assert len(context) == 0, "Found chunks when none should exist"
|
||||
|
|
|
|||
|
|
@ -1,7 +1,7 @@
|
|||
import os
|
||||
from typing import List
|
||||
import pytest
|
||||
import pathlib
|
||||
|
||||
import cognee
|
||||
from cognee.low_level import setup
|
||||
from cognee.tasks.storage import add_data_points
|
||||
|
|
@ -10,6 +10,20 @@ from cognee.modules.chunking.models import DocumentChunk
|
|||
from cognee.modules.data.processing.document_types import TextDocument
|
||||
from cognee.modules.retrieval.exceptions.exceptions import NoDataError
|
||||
from cognee.modules.retrieval.completion_retriever import CompletionRetriever
|
||||
from cognee.infrastructure.engine import DataPoint
|
||||
from cognee.modules.data.processing.document_types import Document
|
||||
from cognee.modules.engine.models import Entity
|
||||
|
||||
|
||||
class DocumentChunkWithEntities(DataPoint):
|
||||
text: str
|
||||
chunk_size: int
|
||||
chunk_index: int
|
||||
cut_type: str
|
||||
is_part_of: Document
|
||||
contains: List[Entity] = None
|
||||
|
||||
metadata: dict = {"index_fields": ["text"]}
|
||||
|
||||
|
||||
class TestRAGCompletionRetriever:
|
||||
|
|
@ -182,7 +196,9 @@ class TestRAGCompletionRetriever:
|
|||
await retriever.get_context("Christina Mayer")
|
||||
|
||||
vector_engine = get_vector_engine()
|
||||
await vector_engine.create_collection("DocumentChunk_text", payload_schema=DocumentChunk)
|
||||
await vector_engine.create_collection(
|
||||
"DocumentChunk_text", payload_schema=DocumentChunkWithEntities
|
||||
)
|
||||
|
||||
context = await retriever.get_context("Christina Mayer")
|
||||
assert context == "", "Returned context should be empty on an empty graph"
|
||||
|
|
|
|||
225
cognee/tests/unit/modules/retrieval/temporal_retriever_test.py
Normal file
225
cognee/tests/unit/modules/retrieval/temporal_retriever_test.py
Normal file
|
|
@ -0,0 +1,225 @@
|
|||
import asyncio
|
||||
from types import SimpleNamespace
|
||||
import pytest
|
||||
|
||||
from cognee.modules.retrieval.temporal_retriever import TemporalRetriever
|
||||
|
||||
|
||||
# Test TemporalRetriever initialization defaults and overrides
|
||||
def test_init_defaults_and_overrides():
|
||||
tr = TemporalRetriever()
|
||||
assert tr.top_k == 5
|
||||
assert tr.user_prompt_path == "graph_context_for_question.txt"
|
||||
assert tr.system_prompt_path == "answer_simple_question.txt"
|
||||
assert tr.time_extraction_prompt_path == "extract_query_time.txt"
|
||||
|
||||
tr2 = TemporalRetriever(
|
||||
top_k=3,
|
||||
user_prompt_path="u.txt",
|
||||
system_prompt_path="s.txt",
|
||||
time_extraction_prompt_path="t.txt",
|
||||
)
|
||||
assert tr2.top_k == 3
|
||||
assert tr2.user_prompt_path == "u.txt"
|
||||
assert tr2.system_prompt_path == "s.txt"
|
||||
assert tr2.time_extraction_prompt_path == "t.txt"
|
||||
|
||||
|
||||
# Test descriptions_to_string with basic and empty results
|
||||
def test_descriptions_to_string_basic_and_empty():
|
||||
tr = TemporalRetriever()
|
||||
|
||||
results = [
|
||||
{"description": " First "},
|
||||
{"nope": "no description"},
|
||||
{"description": "Second"},
|
||||
{"description": ""},
|
||||
{"description": " Third line "},
|
||||
]
|
||||
|
||||
s = tr.descriptions_to_string(results)
|
||||
assert s == "First\n#####################\nSecond\n#####################\nThird line"
|
||||
|
||||
assert tr.descriptions_to_string([]) == ""
|
||||
|
||||
|
||||
# Test filter_top_k_events sorts and limits correctly
|
||||
@pytest.mark.asyncio
|
||||
async def test_filter_top_k_events_sorts_and_limits():
|
||||
tr = TemporalRetriever(top_k=2)
|
||||
|
||||
relevant_events = [
|
||||
{
|
||||
"events": [
|
||||
{"id": "e1", "description": "E1"},
|
||||
{"id": "e2", "description": "E2"},
|
||||
{"id": "e3", "description": "E3 - not in vector results"},
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
scored_results = [
|
||||
SimpleNamespace(payload={"id": "e2"}, score=0.10),
|
||||
SimpleNamespace(payload={"id": "e1"}, score=0.20),
|
||||
]
|
||||
|
||||
top = await tr.filter_top_k_events(relevant_events, scored_results)
|
||||
|
||||
assert [e["id"] for e in top] == ["e2", "e1"]
|
||||
assert all("score" in e for e in top)
|
||||
assert top[0]["score"] == 0.10
|
||||
assert top[1]["score"] == 0.20
|
||||
|
||||
|
||||
# Test filter_top_k_events handles unknown ids as infinite scores
|
||||
@pytest.mark.asyncio
|
||||
async def test_filter_top_k_events_includes_unknown_as_infinite_but_not_in_top_k():
|
||||
tr = TemporalRetriever(top_k=2)
|
||||
|
||||
relevant_events = [
|
||||
{
|
||||
"events": [
|
||||
{"id": "known1", "description": "Known 1"},
|
||||
{"id": "unknown", "description": "Unknown"},
|
||||
{"id": "known2", "description": "Known 2"},
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
scored_results = [
|
||||
SimpleNamespace(payload={"id": "known2"}, score=0.05),
|
||||
SimpleNamespace(payload={"id": "known1"}, score=0.50),
|
||||
]
|
||||
|
||||
top = await tr.filter_top_k_events(relevant_events, scored_results)
|
||||
assert [e["id"] for e in top] == ["known2", "known1"]
|
||||
assert all(e["score"] != float("inf") for e in top)
|
||||
|
||||
|
||||
# Test descriptions_to_string with unicode and newlines
|
||||
def test_descriptions_to_string_unicode_and_newlines():
|
||||
tr = TemporalRetriever()
|
||||
results = [
|
||||
{"description": "Line A\nwith newline"},
|
||||
{"description": "This is a description"},
|
||||
]
|
||||
s = tr.descriptions_to_string(results)
|
||||
assert "Line A\nwith newline" in s
|
||||
assert "This is a description" in s
|
||||
assert s.count("#####################") == 1
|
||||
|
||||
|
||||
# Test filter_top_k_events when top_k is larger than available events
|
||||
@pytest.mark.asyncio
|
||||
async def test_filter_top_k_events_limits_when_top_k_exceeds_events():
|
||||
tr = TemporalRetriever(top_k=10)
|
||||
relevant_events = [{"events": [{"id": "a"}, {"id": "b"}]}]
|
||||
scored_results = [
|
||||
SimpleNamespace(payload={"id": "a"}, score=0.1),
|
||||
SimpleNamespace(payload={"id": "b"}, score=0.2),
|
||||
]
|
||||
out = await tr.filter_top_k_events(relevant_events, scored_results)
|
||||
assert [e["id"] for e in out] == ["a", "b"]
|
||||
|
||||
|
||||
# Test filter_top_k_events when scored_results is empty
|
||||
@pytest.mark.asyncio
|
||||
async def test_filter_top_k_events_handles_empty_scored_results():
|
||||
tr = TemporalRetriever(top_k=2)
|
||||
relevant_events = [{"events": [{"id": "x"}, {"id": "y"}]}]
|
||||
scored_results = []
|
||||
out = await tr.filter_top_k_events(relevant_events, scored_results)
|
||||
assert [e["id"] for e in out] == ["x", "y"]
|
||||
assert all(e["score"] == float("inf") for e in out)
|
||||
|
||||
|
||||
# Test filter_top_k_events error handling for missing structure
|
||||
@pytest.mark.asyncio
|
||||
async def test_filter_top_k_events_error_handling():
|
||||
tr = TemporalRetriever(top_k=2)
|
||||
with pytest.raises((KeyError, TypeError)):
|
||||
await tr.filter_top_k_events([{}], [])
|
||||
|
||||
|
||||
class _FakeRetriever(TemporalRetriever):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super().__init__(*args, **kwargs)
|
||||
self._calls = []
|
||||
|
||||
async def extract_time_from_query(self, query: str):
|
||||
if "both" in query:
|
||||
return "2024-01-01", "2024-12-31"
|
||||
if "from_only" in query:
|
||||
return "2024-01-01", None
|
||||
if "to_only" in query:
|
||||
return None, "2024-12-31"
|
||||
return None, None
|
||||
|
||||
async def get_triplets(self, query: str):
|
||||
self._calls.append(("get_triplets", query))
|
||||
return [{"s": "a", "p": "b", "o": "c"}]
|
||||
|
||||
async def resolve_edges_to_text(self, triplets):
|
||||
self._calls.append(("resolve_edges_to_text", len(triplets)))
|
||||
return "edges->text"
|
||||
|
||||
async def _fake_graph_collect_ids(self, **kwargs):
|
||||
return ["e1", "e2"]
|
||||
|
||||
async def _fake_graph_collect_events(self, ids):
|
||||
return [
|
||||
{
|
||||
"events": [
|
||||
{"id": "e1", "description": "E1"},
|
||||
{"id": "e2", "description": "E2"},
|
||||
{"id": "e3", "description": "E3"},
|
||||
]
|
||||
}
|
||||
]
|
||||
|
||||
async def _fake_vector_embed(self, texts):
|
||||
assert isinstance(texts, list) and texts
|
||||
return [[0.0, 1.0, 2.0]]
|
||||
|
||||
async def _fake_vector_search(self, **kwargs):
|
||||
return [
|
||||
SimpleNamespace(payload={"id": "e2"}, score=0.05),
|
||||
SimpleNamespace(payload={"id": "e1"}, score=0.10),
|
||||
]
|
||||
|
||||
async def get_context(self, query: str):
|
||||
time_from, time_to = await self.extract_time_from_query(query)
|
||||
|
||||
if not (time_from or time_to):
|
||||
triplets = await self.get_triplets(query)
|
||||
return await self.resolve_edges_to_text(triplets)
|
||||
|
||||
ids = await self._fake_graph_collect_ids(time_from=time_from, time_to=time_to)
|
||||
relevant_events = await self._fake_graph_collect_events(ids)
|
||||
|
||||
_ = await self._fake_vector_embed([query])
|
||||
vector_search_results = await self._fake_vector_search(
|
||||
collection_name="Event_name", query_vector=[0.0], limit=0
|
||||
)
|
||||
top_k_events = await self.filter_top_k_events(relevant_events, vector_search_results)
|
||||
return self.descriptions_to_string(top_k_events)
|
||||
|
||||
|
||||
# Test get_context fallback to triplets when no time is extracted
|
||||
@pytest.mark.asyncio
|
||||
async def test_fake_get_context_falls_back_to_triplets_when_no_time():
|
||||
tr = _FakeRetriever(top_k=2)
|
||||
ctx = await tr.get_context("no_time")
|
||||
assert ctx == "edges->text"
|
||||
assert tr._calls[0][0] == "get_triplets"
|
||||
assert tr._calls[1][0] == "resolve_edges_to_text"
|
||||
|
||||
|
||||
# Test get_context when time is extracted and vector ranking is applied
|
||||
@pytest.mark.asyncio
|
||||
async def test_fake_get_context_with_time_filters_and_vector_ranking():
|
||||
tr = _FakeRetriever(top_k=2)
|
||||
ctx = await tr.get_context("both time")
|
||||
assert ctx.startswith("E2")
|
||||
assert "#####################" in ctx
|
||||
assert "E1" in ctx and "E3" not in ctx
|
||||
|
|
@ -3,8 +3,8 @@ import uuid
|
|||
from unittest.mock import AsyncMock, MagicMock, patch
|
||||
|
||||
import pytest
|
||||
from pylint.checkers.utils import node_type
|
||||
|
||||
from cognee.modules.engine.models.node_set import NodeSet
|
||||
from cognee.modules.search.exceptions import UnsupportedSearchTypeError
|
||||
from cognee.modules.search.methods.search import search, specific_search
|
||||
from cognee.modules.search.types import SearchType
|
||||
|
|
@ -58,15 +58,17 @@ async def test_search(
|
|||
# Verify
|
||||
mock_log_query.assert_called_once_with(query_text, query_type.value, mock_user.id)
|
||||
mock_specific_search.assert_called_once_with(
|
||||
query_type,
|
||||
query_text,
|
||||
mock_user,
|
||||
query_type=query_type,
|
||||
query_text=query_text,
|
||||
user=mock_user,
|
||||
system_prompt_path="answer_simple_question.txt",
|
||||
system_prompt=None,
|
||||
top_k=10,
|
||||
node_type=None,
|
||||
node_type=NodeSet,
|
||||
node_name=None,
|
||||
save_interaction=False,
|
||||
last_k=None,
|
||||
only_context=False,
|
||||
)
|
||||
|
||||
# Verify result logging
|
||||
|
|
@ -201,7 +203,10 @@ async def test_specific_search_feeling_lucky(
|
|||
|
||||
if retriever_name == "CompletionRetriever":
|
||||
mock_retriever_class.assert_called_once_with(
|
||||
system_prompt_path="answer_simple_question.txt", top_k=top_k
|
||||
system_prompt_path="answer_simple_question.txt",
|
||||
top_k=top_k,
|
||||
system_prompt=None,
|
||||
only_context=None,
|
||||
)
|
||||
else:
|
||||
mock_retriever_class.assert_called_once_with(top_k=top_k)
|
||||
|
|
|
|||
|
|
@ -4,8 +4,9 @@ import pytest
|
|||
from unittest.mock import patch, mock_open
|
||||
from io import BytesIO
|
||||
from uuid import uuid4
|
||||
from pathlib import Path
|
||||
|
||||
|
||||
from cognee.root_dir import ensure_absolute_path
|
||||
from cognee.infrastructure.files.utils.get_file_content_hash import get_file_content_hash
|
||||
from cognee.shared.utils import get_anonymous_id
|
||||
|
||||
|
|
@ -52,3 +53,21 @@ async def test_get_file_content_hash_stream():
|
|||
expected_hash = hashlib.md5(b"test_data").hexdigest()
|
||||
result = await get_file_content_hash(stream)
|
||||
assert result == expected_hash
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_root_dir_absolute_paths():
|
||||
"""Test absolute path handling in root_dir.py"""
|
||||
# Test with absolute path
|
||||
abs_path = "C:/absolute/path" if os.name == "nt" else "/absolute/path"
|
||||
result = ensure_absolute_path(abs_path)
|
||||
assert result == str(Path(abs_path).resolve())
|
||||
|
||||
# Test with relative path (should fail)
|
||||
rel_path = "relative/path"
|
||||
with pytest.raises(ValueError, match="must be absolute"):
|
||||
ensure_absolute_path(rel_path)
|
||||
|
||||
# Test with None path
|
||||
with pytest.raises(ValueError, match="cannot be None"):
|
||||
ensure_absolute_path(None)
|
||||
|
|
|
|||
100
examples/python/temporal_example.py
Normal file
100
examples/python/temporal_example.py
Normal file
|
|
@ -0,0 +1,100 @@
|
|||
import asyncio
|
||||
import cognee
|
||||
from cognee.shared.logging_utils import setup_logging, INFO
|
||||
from cognee.api.v1.search import SearchType
|
||||
|
||||
|
||||
biography_1 = """
|
||||
Attaphol Buspakom Attaphol Buspakom ( ; ) , nicknamed Tak ( ; ) ; 1 October 1962 – 16 April 2015 ) was a Thai national and football coach . He was given the role at Muangthong United and Buriram United after TTM Samut Sakhon folded after the 2009 season . He played for the Thailand national football team , appearing in several FIFA World Cup qualifying matches .
|
||||
|
||||
Club career .
|
||||
Attaphol began his career as a player at Thai Port FC Authority of Thailand in 1985 . In his first year , he won his first championship with the club . He played for the club until 1989 and in 1987 also won the Queens Cup . He then moved to Malaysia for two seasons for Pahang FA , then return to Thailand to his former club . His time from 1991 to 1994 was marked by less success than in his first stay at Port Authority . From 1994 to 1996 he played for Pahang again and this time he was able to win with the club , the Malaysia Super League and also reached the final of the Malaysia Cup and the Malaysia FA Cup . Both cup finals but lost . Back in Thailand , he let end his playing career at FC Stock Exchange of Thailand , with which he once again runner-up in 1996-97 . In 1998 , he finished his career .
|
||||
|
||||
International career .
|
||||
For the Thailand national football team Attaphol played between 1985 and 1998 a total of 85 games and scored 13 results . In 1992 , he participated with the team in the finals of the Asian Cup . He also stood in various cadres to qualifications to FIFA World Cup .
|
||||
|
||||
Coaching career .
|
||||
Bec Tero Sasana .
|
||||
In BEC Tero Sasana F.C . began his coaching career in 2001 for him , first as assistant coach . He took over the reigning champions of the Thai League T1 , after his predecessor Pichai Pituwong resigned from his post . It was his first coach station and he had the difficult task of leading the club through the new AFC Champions League . He could accomplish this task with flying colors and even led the club to the finals . The finale , then still played in home and away matches , was lost with 1:2 at the end against Al Ain FC . Attaphol is and was next to Charnwit Polcheewin the only coach who managed a club from Thailand to lead to the final of the AFC Champions League . 2002-03 and 2003-04 he won with the club also two runner-up . In his team , which reached the final of the Champions League , were a number of exceptional players like Therdsak Chaiman , Worrawoot Srimaka , Dusit Chalermsan and Anurak Srikerd .
|
||||
|
||||
Geylang United / Krung Thai Bank .
|
||||
In 2006 , he went to Singapore in the S-League to Geylang United He was released after a few months due to lack of success . In 2008 , he took over as coach at Krung Thai Bank F.C. , where he had almost a similar task , as a few years earlier by BEC-Tero . As vice-champion of the club was also qualified for the AFC Champions League . However , he failed to lead the team through the group stage of the season 2008 and beyond . With the Kashima Antlers of Japan and Beijing Guoan F.C . athletic competition was too great . One of the highlights was put under his leadership , yet the club . In the group match against the Vietnam club Nam Dinh F.C . his team won with 9-1 , but also lost four weeks later with 1-8 against Kashima Antlers . At the end of the National Football League season , he reached the Krung Thai 6th Table space . The Erstligalizenz the club was sold at the end of the season at the Bangkok Glass F.C. . Attaphol finished his coaching career with the club and accepted an offer of TTM Samutsakorn . After only a short time in office
|
||||
|
||||
Muangthong United .
|
||||
In 2009 , he received an offer from Muangthong United F.C. , which he accepted and changed . He can champion Muang Thong United for 2009 Thai Premier League and Attaphol won Coach of The year for Thai Premier League and he was able to lead Muang Thong United to play AFC Champions League qualifying play-off for the first in the clubs history .
|
||||
|
||||
Buriram United .
|
||||
In 2010 Buspakom moved from Muangthong United to Buriram United F.C. . He received Coach of the Month in Thai Premier League 2 time in June and October . In 2011 , he led Buriram United win 2011 Thai Premier League second time for club and set a record with the most points in the Thai League T1 for 85 point and He led Buriram win 2011 Thai FA Cup by beat Muangthong United F.C . 1-0 and he led Buriram win 2011 Thai League Cup by beat Thai Port F.C . 2-0 . In 2012 , he led Buriram United to the 2012 AFC Champions League group stage . Buriram along with Guangzhou Evergrande F.C . from China , Kashiwa Reysol from Japan and Jeonbuk Hyundai Motors which are all champions from their country . In the first match of Buriram they beat Kashiwa 3-2 and Second Match they beat Guangzhou 1-2 at the Tianhe Stadium . Before losing to Jeonbuk 0-2 and 3-2 with lose Kashiwa and Guangzhou 1-0 and 1-2 respectively and Thai Premier League Attaphol lead Buriram end 4th for table with win 2012 Thai FA Cup and 2012 Thai League Cup .
|
||||
|
||||
Bangkok Glass .
|
||||
In 2013 , he moved from Buriram United to Bangkok Glass F.C. .
|
||||
|
||||
Individual
|
||||
- Thai Premier League Coach of the Year ( 3 ) : 2001-02 , 2009 , 2013
|
||||
"""
|
||||
|
||||
biography_2 = """
|
||||
Arnulf Øverland Ole Peter Arnulf Øverland ( 27 April 1889 – 25 March 1968 ) was a Norwegian poet and artist . He is principally known for his poetry which served to inspire the Norwegian resistance movement during the German occupation of Norway during World War II .
|
||||
|
||||
Biography .
|
||||
Øverland was born in Kristiansund and raised in Bergen . His parents were Peter Anton Øverland ( 1852–1906 ) and Hanna Hage ( 1854–1939 ) . The early death of his father , left the family economically stressed . He was able to attend Bergen Cathedral School and in 1904 Kristiania Cathedral School . He graduated in 1907 and for a time studied philology at University of Kristiania . Øverland published his first collection of poems ( 1911 ) .
|
||||
|
||||
Øverland became a communist sympathizer from the early 1920s and became a member of Mot Dag . He also served as chairman of the Norwegian Students Society 1923–28 . He changed his stand in 1937 , partly as an expression of dissent against the ongoing Moscow Trials . He was an avid opponent of Nazism and in 1936 he wrote the poem Du må ikke sove which was printed in the journal Samtiden . It ends with . ( I thought: : Something is imminent . Our era is over – Europe’s on fire! ) . Probably the most famous line of the poem is ( You mustnt endure so well the injustice that doesnt affect you yourself! )
|
||||
|
||||
During the German occupation of Norway from 1940 in World War II , he wrote to inspire the Norwegian resistance movement . He wrote a series of poems which were clandestinely distributed , leading to the arrest of both him and his future wife Margrete Aamot Øverland in 1941 . Arnulf Øverland was held first in the prison camp of Grini before being transferred to Sachsenhausen concentration camp in Germany . He spent a four-year imprisonment until the liberation of Norway in 1945 . His poems were later collected in Vi overlever alt and published in 1945 .
|
||||
|
||||
Øverland played an important role in the Norwegian language struggle in the post-war era . He became a noted supporter for the conservative written form of Norwegian called Riksmål , he was president of Riksmålsforbundet ( an organization in support of Riksmål ) from 1947 to 1956 . In addition , Øverland adhered to the traditionalist style of writing , criticising modernist poetry on several occasions . His speech Tungetale fra parnasset , published in Arbeiderbladet in 1954 , initiated the so-called Glossolalia debate .
|
||||
|
||||
Personal life .
|
||||
In 1918 he had married the singer Hildur Arntzen ( 1888–1957 ) . Their marriage was dissolved in 1939 . In 1940 , he married Bartholine Eufemia Leganger ( 1903–1995 ) . They separated shortly after , and were officially divorced in 1945 . Øverland was married to journalist Margrete Aamot Øverland ( 1913–1978 ) during June 1945 . In 1946 , the Norwegian Parliament arranged for Arnulf and Margrete Aamot Øverland to reside at the Grotten . He lived there until his death in 1968 and she lived there for another ten years until her death in 1978 . Arnulf Øverland was buried at Vår Frelsers Gravlund in Oslo . Joseph Grimeland designed the bust of Arnulf Øverland ( bronze , 1970 ) at his grave site .
|
||||
|
||||
Selected Works .
|
||||
- Den ensomme fest ( 1911 )
|
||||
- Berget det blå ( 1927 )
|
||||
- En Hustavle ( 1929 )
|
||||
- Den røde front ( 1937 )
|
||||
- Vi overlever alt ( 1945 )
|
||||
- Sverdet bak døren ( 1956 )
|
||||
- Livets minutter ( 1965 )
|
||||
|
||||
Awards .
|
||||
- Gyldendals Endowment ( 1935 )
|
||||
- Dobloug Prize ( 1951 )
|
||||
- Mads Wiel Nygaards legat ( 1961 )
|
||||
"""
|
||||
|
||||
|
||||
async def main():
|
||||
await cognee.prune.prune_data()
|
||||
await cognee.prune.prune_system(metadata=True)
|
||||
|
||||
await cognee.add([biography_1, biography_2])
|
||||
await cognee.cognify(temporal_cognify=True)
|
||||
|
||||
queries = [
|
||||
"What happened before 1980?",
|
||||
"What happened after 2010?",
|
||||
"What happened between 2000 and 2006?",
|
||||
"What happened between 1903 and 1995, I am interested in the Selected Works of Arnulf Øverland Ole Peter Arnulf Øverland?",
|
||||
"Who is Attaphol Buspakom Attaphol Buspakom?",
|
||||
]
|
||||
|
||||
for query_text in queries:
|
||||
search_results = await cognee.search(
|
||||
query_type=SearchType.TEMPORAL,
|
||||
query_text=query_text,
|
||||
top_k=15,
|
||||
)
|
||||
print(f"Query: {query_text}")
|
||||
print(f"Results: {search_results}\n")
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
logger = setup_logging(log_level=INFO)
|
||||
|
||||
loop = asyncio.new_event_loop()
|
||||
asyncio.set_event_loop(loop)
|
||||
try:
|
||||
loop.run_until_complete(main())
|
||||
finally:
|
||||
loop.run_until_complete(loop.shutdown_asyncgens())
|
||||
198
poetry.lock
generated
198
poetry.lock
generated
|
|
@ -7123,15 +7123,15 @@ twisted = ["twisted"]
|
|||
|
||||
[[package]]
|
||||
name = "prompt-toolkit"
|
||||
version = "3.0.51"
|
||||
version = "3.0.52"
|
||||
description = "Library for building powerful interactive command lines in Python"
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"notebook\" or extra == \"dev\""
|
||||
files = [
|
||||
{file = "prompt_toolkit-3.0.51-py3-none-any.whl", hash = "sha256:52742911fde84e2d423e2f9a4cf1de7d7ac4e51958f648d9540e0fb8db077b07"},
|
||||
{file = "prompt_toolkit-3.0.51.tar.gz", hash = "sha256:931a162e3b27fc90c86f1b48bb1fb2c528c2761475e57c9c06de13311c7b54ed"},
|
||||
{file = "prompt_toolkit-3.0.52-py3-none-any.whl", hash = "sha256:9aac639a3bbd33284347de5ad8d68ecc044b91a762dc39b7c21095fcd6a19955"},
|
||||
{file = "prompt_toolkit-3.0.52.tar.gz", hash = "sha256:28cde192929c8e7321de85de1ddbe736f1375148b02f2e17edd840042b1be855"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -8707,107 +8707,105 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "rapidfuzz"
|
||||
version = "3.13.0"
|
||||
version = "3.14.0"
|
||||
description = "rapid fuzzy string matching"
|
||||
optional = true
|
||||
python-versions = ">=3.9"
|
||||
python-versions = ">=3.10"
|
||||
groups = ["main"]
|
||||
markers = "extra == \"docs\""
|
||||
files = [
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:aafc42a1dc5e1beeba52cd83baa41372228d6d8266f6d803c16dbabbcc156255"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:85c9a131a44a95f9cac2eb6e65531db014e09d89c4f18c7b1fa54979cb9ff1f3"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:7d7cec4242d30dd521ef91c0df872e14449d1dffc2a6990ede33943b0dae56c3"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:e297c09972698c95649e89121e3550cee761ca3640cd005e24aaa2619175464e"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:ef0f5f03f61b0e5a57b1df7beafd83df993fd5811a09871bad6038d08e526d0d"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:d8cf5f7cd6e4d5eb272baf6a54e182b2c237548d048e2882258336533f3f02b7"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9256218ac8f1a957806ec2fb9a6ddfc6c32ea937c0429e88cf16362a20ed8602"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-musllinux_1_2_aarch64.whl", hash = "sha256:e1bdd2e6d0c5f9706ef7595773a81ca2b40f3b33fd7f9840b726fb00c6c4eb2e"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-musllinux_1_2_i686.whl", hash = "sha256:5280be8fd7e2bee5822e254fe0a5763aa0ad57054b85a32a3d9970e9b09bbcbf"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-musllinux_1_2_ppc64le.whl", hash = "sha256:fd742c03885db1fce798a1cd87a20f47f144ccf26d75d52feb6f2bae3d57af05"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-musllinux_1_2_s390x.whl", hash = "sha256:5435fcac94c9ecf0504bf88a8a60c55482c32e18e108d6079a0089c47f3f8cf6"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-musllinux_1_2_x86_64.whl", hash = "sha256:93a755266856599be4ab6346273f192acde3102d7aa0735e2f48b456397a041f"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-win32.whl", hash = "sha256:3abe6a4e8eb4cfc4cda04dd650a2dc6d2934cbdeda5def7e6fd1c20f6e7d2a0b"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-win_amd64.whl", hash = "sha256:e8ddb58961401da7d6f55f185512c0d6bd24f529a637078d41dd8ffa5a49c107"},
|
||||
{file = "rapidfuzz-3.13.0-cp310-cp310-win_arm64.whl", hash = "sha256:c523620d14ebd03a8d473c89e05fa1ae152821920c3ff78b839218ff69e19ca3"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d395a5cad0c09c7f096433e5fd4224d83b53298d53499945a9b0e5a971a84f3a"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b7b3eda607a019169f7187328a8d1648fb9a90265087f6903d7ee3a8eee01805"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:98e0bfa602e1942d542de077baf15d658bd9d5dcfe9b762aff791724c1c38b70"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:bef86df6d59667d9655905b02770a0c776d2853971c0773767d5ef8077acd624"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:fedd316c165beed6307bf754dee54d3faca2c47e1f3bcbd67595001dfa11e969"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:5158da7f2ec02a930be13bac53bb5903527c073c90ee37804090614cab83c29e"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3b6f913ee4618ddb6d6f3e387b76e8ec2fc5efee313a128809fbd44e65c2bbb2"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-musllinux_1_2_aarch64.whl", hash = "sha256:d25fdbce6459ccbbbf23b4b044f56fbd1158b97ac50994eaae2a1c0baae78301"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-musllinux_1_2_i686.whl", hash = "sha256:25343ccc589a4579fbde832e6a1e27258bfdd7f2eb0f28cb836d6694ab8591fc"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-musllinux_1_2_ppc64le.whl", hash = "sha256:a9ad1f37894e3ffb76bbab76256e8a8b789657183870be11aa64e306bb5228fd"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-musllinux_1_2_s390x.whl", hash = "sha256:5dc71ef23845bb6b62d194c39a97bb30ff171389c9812d83030c1199f319098c"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-musllinux_1_2_x86_64.whl", hash = "sha256:b7f4c65facdb94f44be759bbd9b6dda1fa54d0d6169cdf1a209a5ab97d311a75"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-win32.whl", hash = "sha256:b5104b62711565e0ff6deab2a8f5dbf1fbe333c5155abe26d2cfd6f1849b6c87"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-win_amd64.whl", hash = "sha256:9093cdeb926deb32a4887ebe6910f57fbcdbc9fbfa52252c10b56ef2efb0289f"},
|
||||
{file = "rapidfuzz-3.13.0-cp311-cp311-win_arm64.whl", hash = "sha256:f70f646751b6aa9d05be1fb40372f006cc89d6aad54e9d79ae97bd1f5fce5203"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-macosx_10_13_x86_64.whl", hash = "sha256:4a1a6a906ba62f2556372282b1ef37b26bca67e3d2ea957277cfcefc6275cca7"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:2fd0975e015b05c79a97f38883a11236f5a24cca83aa992bd2558ceaa5652b26"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5d4e13593d298c50c4f94ce453f757b4b398af3fa0fd2fde693c3e51195b7f69"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:ed6f416bda1c9133000009d84d9409823eb2358df0950231cc936e4bf784eb97"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:1dc82b6ed01acb536b94a43996a94471a218f4d89f3fdd9185ab496de4b2a981"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:e9d824de871daa6e443b39ff495a884931970d567eb0dfa213d234337343835f"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:2d18228a2390375cf45726ce1af9d36ff3dc1f11dce9775eae1f1b13ac6ec50f"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-musllinux_1_2_aarch64.whl", hash = "sha256:9f5fe634c9482ec5d4a6692afb8c45d370ae86755e5f57aa6c50bfe4ca2bdd87"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:694eb531889f71022b2be86f625a4209c4049e74be9ca836919b9e395d5e33b3"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:11b47b40650e06147dee5e51a9c9ad73bb7b86968b6f7d30e503b9f8dd1292db"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:98b8107ff14f5af0243f27d236bcc6e1ef8e7e3b3c25df114e91e3a99572da73"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:b836f486dba0aceb2551e838ff3f514a38ee72b015364f739e526d720fdb823a"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-win32.whl", hash = "sha256:4671ee300d1818d7bdfd8fa0608580d7778ba701817216f0c17fb29e6b972514"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-win_amd64.whl", hash = "sha256:6e2065f68fb1d0bf65adc289c1bdc45ba7e464e406b319d67bb54441a1b9da9e"},
|
||||
{file = "rapidfuzz-3.13.0-cp312-cp312-win_arm64.whl", hash = "sha256:65cc97c2fc2c2fe23586599686f3b1ceeedeca8e598cfcc1b7e56dc8ca7e2aa7"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-macosx_10_13_x86_64.whl", hash = "sha256:09e908064d3684c541d312bd4c7b05acb99a2c764f6231bd507d4b4b65226c23"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-macosx_11_0_arm64.whl", hash = "sha256:57c390336cb50d5d3bfb0cfe1467478a15733703af61f6dffb14b1cd312a6fae"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0da54aa8547b3c2c188db3d1c7eb4d1bb6dd80baa8cdaeaec3d1da3346ec9caa"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:df8e8c21e67afb9d7fbe18f42c6111fe155e801ab103c81109a61312927cc611"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:461fd13250a2adf8e90ca9a0e1e166515cbcaa5e9c3b1f37545cbbeff9e77f6b"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:c2b3dd5d206a12deca16870acc0d6e5036abeb70e3cad6549c294eff15591527"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:1343d745fbf4688e412d8f398c6e6d6f269db99a54456873f232ba2e7aeb4939"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-musllinux_1_2_aarch64.whl", hash = "sha256:b1b065f370d54551dcc785c6f9eeb5bd517ae14c983d2784c064b3aa525896df"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-musllinux_1_2_i686.whl", hash = "sha256:11b125d8edd67e767b2295eac6eb9afe0b1cdc82ea3d4b9257da4b8e06077798"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-musllinux_1_2_ppc64le.whl", hash = "sha256:c33f9c841630b2bb7e69a3fb5c84a854075bb812c47620978bddc591f764da3d"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-musllinux_1_2_s390x.whl", hash = "sha256:ae4574cb66cf1e85d32bb7e9ec45af5409c5b3970b7ceb8dea90168024127566"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-musllinux_1_2_x86_64.whl", hash = "sha256:e05752418b24bbd411841b256344c26f57da1148c5509e34ea39c7eb5099ab72"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-win32.whl", hash = "sha256:0e1d08cb884805a543f2de1f6744069495ef527e279e05370dd7c83416af83f8"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-win_amd64.whl", hash = "sha256:9a7c6232be5f809cd39da30ee5d24e6cadd919831e6020ec6c2391f4c3bc9264"},
|
||||
{file = "rapidfuzz-3.13.0-cp313-cp313-win_arm64.whl", hash = "sha256:3f32f15bacd1838c929b35c84b43618481e1b3d7a61b5ed2db0291b70ae88b53"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:cc64da907114d7a18b5e589057e3acaf2fec723d31c49e13fedf043592a3f6a7"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:4d9d7f84c8e992a8dbe5a3fdbea73d733da39bf464e62c912ac3ceba9c0cff93"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1a79a2f07786a2070669b4b8e45bd96a01c788e7a3c218f531f3947878e0f956"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:9f338e71c45b69a482de8b11bf4a029993230760120c8c6e7c9b71760b6825a1"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:adb40ca8ddfcd4edd07b0713a860be32bdf632687f656963bcbce84cea04b8d8"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:48719f7dcf62dfb181063b60ee2d0a39d327fa8ad81b05e3e510680c44e1c078"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:9327a4577f65fc3fb712e79f78233815b8a1c94433d0c2c9f6bc5953018b3565"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:200030dfc0a1d5d6ac18e993c5097c870c97c41574e67f227300a1fb74457b1d"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:cc269e74cad6043cb8a46d0ce580031ab642b5930562c2bb79aa7fbf9c858d26"},
|
||||
{file = "rapidfuzz-3.13.0-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:e62779c6371bd2b21dbd1fdce89eaec2d93fd98179d36f61130b489f62294a92"},
|
||||
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{ url = "https://files.pythonhosted.org/packages/e6/d9/9332a39587a2478470a54218d5f85b5a29b6b3eb02b2310689b59ad3da11/rapidfuzz-3.14.0-pp311-pypy311_pp73-macosx_11_0_arm64.whl", hash = "sha256:c053cad08ab872df4e201daacb66d7fd04b5b4c395baebb193b9910c63ed22ec", size = 1363908, upload-time = "2025-08-27T13:41:26.463Z" },
|
||||
{ url = "https://files.pythonhosted.org/packages/21/7f/c90f55402b5b43fd5cff42a8dab60373345b8f2697a7b83515eb62666913/rapidfuzz-3.14.0-pp311-pypy311_pp73-win_amd64.whl", hash = "sha256:7e52ac8a458b2f09291fa968b23192d6664c7568a43607de2a51a088d016152d", size = 1555592, upload-time = "2025-08-27T13:41:28.583Z" },
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
|
|||
Loading…
Add table
Reference in a new issue