test: remove docs tests. add trigger to docs repo

.github/workflows/docs_tests.yml

@@ -1,280 +0,0 @@
-name: Docs Tests
-
-permissions:
-  contents: read
-
-on:
-  workflow_dispatch:
-  workflow_call:
-    secrets:
-      LLM_PROVIDER:
-        required: true
-      LLM_MODEL:
-        required: true
-      LLM_ENDPOINT:
-        required: true
-      LLM_API_KEY:
-        required: true
-      LLM_API_VERSION:
-        required: true
-      EMBEDDING_PROVIDER:
-        required: true
-      EMBEDDING_MODEL:
-        required: true
-      EMBEDDING_ENDPOINT:
-        required: true
-      EMBEDDING_API_KEY:
-        required: true
-      EMBEDDING_API_VERSION:
-        required: true
-
-env:
-  ENV: 'dev'
-
-jobs:
-  test-search-basics:
-    name: Test Search Basics
-    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 Search Basics Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/search_basics.py
-
-  test-temporal-cognify:
-    name: Test Temporal Cognify
-    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 Temporal Cognify Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/temporal_cognify.py
-
-  test-ontology-quickstart:
-    name: Test Temporal Cognify
-    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 Temporal Cognify Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/temporal_cognify.py
-
-  test-s3-storage:
-    name: Test S3 Docs Guide
-    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'
-          extra-dependencies: "aws"
-
-      - name: Run S3 Docs Guide Test
-        env:
-          ENABLE_BACKEND_ACCESS_CONTROL: True
-          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 }}
-          STORAGE_BACKEND: s3
-          AWS_REGION: eu-west-1
-          AWS_ENDPOINT_URL: https://s3-eu-west-1.amazonaws.com
-          AWS_ACCESS_KEY_ID: ${{ secrets.AWS_S3_DEV_USER_KEY_ID }}
-          AWS_SECRET_ACCESS_KEY: ${{ secrets.AWS_S3_DEV_USER_SECRET_KEY }}
-        run: uv run python ./cognee/tests/docs/guides/s3_storage.py
-
-  test-graph-visualization:
-    name: Test Graph Visualization
-    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 Graph Visualization Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/graph_visualization.py
-
-  test-low-level-llm:
-    name: Test Low Level LLM
-    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 Low Level LLM Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/low_level_llm.py
-
-  test-memify-quickstart:
-    name: Test Memify Quickstart
-    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 Memify Quickstart Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/memify_quickstart.py
-
-  test-custom-data-models:
-    name: Test Custom Data Models
-    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 Custom Data Models Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/custom_data_models.py
-
-  test-custom-tasks-and-pipelines:
-    name: Test Custom Tasks and Pipelines
-    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 Custom Tasks and Pipelines Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/custom_tasks_and_pipelines.py
-
-  test-custom-prompts:
-    name: Test Custom Prompts
-    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 Custom Prompts Test
-        env:
-          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 }}
-        run: uv run python ./cognee/tests/docs/guides/custom_prompts.py

.github/workflows/release_test.yml

@@ -5,18 +5,27 @@ permissions:
   contents: read
 
 on:
+  push:
+    branches:
+      - feature/cog-3213-docs-set-up-guide-script-tests
   workflow_dispatch:
   pull_request:
     branches:
       - main
 
 jobs:
-  load-tests:
+#  load-tests:
-    name: Load Tests
+#    name: Load Tests
-    uses: ./.github/workflows/load_tests.yml
+#    uses: ./.github/workflows/load_tests.yml
-    secrets: inherit
+#    secrets: inherit
 
   docs-tests:
-    name: Docs Tests
+    runs-on: ubuntu-22.04
-    uses: ./.github/workflows/docs_tests.yml
+    steps:
-    secrets: inherit
+      - name: Trigger docs tests
+        run: |
+          curl -sS -X POST \
+            -H "Accept: application/vnd.github+json" \
+            -H "Authorization: Bearer ${{ secrets.DOCS_REPO_PAT_TOKEN }}" \
+            https://api.github.com/repos/your-org/repo-b/dispatches \
+            -d '{"event_type":"new-main-release","client_payload":{"caller_repo":"'"${GITHUB_REPOSITORY}"'"}}'

cognee/tests/docs/guides/custom_data_models.py

@@ -1,38 +0,0 @@
-import asyncio
-from typing import Any
-from pydantic import SkipValidation
-
-import cognee
-from cognee.infrastructure.engine import DataPoint
-from cognee.infrastructure.engine.models.Edge import Edge
-from cognee.tasks.storage import add_data_points
-
-
-class Person(DataPoint):
-    name: str
-    # Keep it simple for forward refs / mixed values
-    knows: SkipValidation[Any] = None  # single Person or list[Person]
-    # Recommended: specify which fields to index for search
-    metadata: dict = {"index_fields": ["name"]}
-
-
-async def main():
-    # Start clean (optional in your app)
-    await cognee.prune.prune_data()
-    await cognee.prune.prune_system(metadata=True)
-
-    alice = Person(name="Alice")
-    bob = Person(name="Bob")
-    charlie = Person(name="Charlie")
-
-    # Create relationships - field name becomes edge label
-    alice.knows = bob
-    # You can also do lists: alice.knows = [bob, charlie]
-
-    # Optional: add weights and custom relationship types
-    bob.knows = (Edge(weight=0.9, relationship_type="friend_of"), charlie)
-
-    await add_data_points([alice, bob, charlie])
-
-
-asyncio.run(main())

cognee/tests/docs/guides/custom_prompts.py

@@ -1,30 +0,0 @@
-import asyncio
-import cognee
-from cognee.api.v1.search import SearchType
-
-custom_prompt = """
-Extract only people and cities as entities.
-Connect people to cities with the relationship "lives_in".
-Ignore all other entities.
-"""
-
-
-async def main():
-    await cognee.add(
-        [
-            "Alice moved to Paris in 2010, while Bob has always lived in New York.",
-            "Andreas was born in Venice, but later settled in Lisbon.",
-            "Diana and Tom were born and raised in Helsingy. Diana currently resides in Berlin, while Tom never moved.",
-        ]
-    )
-    await cognee.cognify(custom_prompt=custom_prompt)
-
-    res = await cognee.search(
-        query_type=SearchType.GRAPH_COMPLETION,
-        query_text="Where does Alice live?",
-    )
-    print(res)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

cognee/tests/docs/guides/custom_tasks_and_pipelines.py

@@ -1,53 +0,0 @@
-import asyncio
-from typing import Any, Dict, List
-from pydantic import BaseModel, SkipValidation
-
-import cognee
-from cognee.modules.engine.operations.setup import setup
-from cognee.infrastructure.llm.LLMGateway import LLMGateway
-from cognee.infrastructure.engine import DataPoint
-from cognee.tasks.storage import add_data_points
-from cognee.modules.pipelines import Task, run_pipeline
-
-
-class Person(DataPoint):
-    name: str
-    # Optional relationships (we'll let the LLM populate this)
-    knows: List["Person"] = []
-    # Make names searchable in the vector store
-    metadata: Dict[str, Any] = {"index_fields": ["name"]}
-
-
-class People(BaseModel):
-    persons: List[Person]
-
-
-async def extract_people(text: str) -> List[Person]:
-    system_prompt = (
-        "Extract people mentioned in the text. "
-        "Return as `persons: Person[]` with each Person having `name` and optional `knows` relations. "
-        "If the text says someone knows someone set `knows` accordingly. "
-        "Only include facts explicitly stated."
-    )
-    people = await LLMGateway.acreate_structured_output(text, system_prompt, People)
-    return people.persons
-
-
-async def main():
-    await cognee.prune.prune_data()
-    await cognee.prune.prune_system(metadata=True)
-    await setup()
-
-    text = "Alice knows Bob."
-
-    tasks = [
-        Task(extract_people),  # input: text -> output: list[Person]
-        Task(add_data_points),  # input: list[Person] -> output: list[Person]
-    ]
-
-    async for _ in run_pipeline(tasks=tasks, data=text, datasets=["people_demo"]):
-        pass
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

cognee/tests/docs/guides/graph_visualization.py

@@ -1,13 +0,0 @@
-import asyncio
-import cognee
-from cognee.api.v1.visualize.visualize import visualize_graph
-
-
-async def main():
-    await cognee.add(["Alice knows Bob.", "NLP is a subfield of CS."])
-    await cognee.cognify()
-
-    await visualize_graph("./graph_after_cognify.html")
-
-
-asyncio.run(main())

cognee/tests/docs/guides/low_level_llm.py

@@ -1,31 +0,0 @@
-import asyncio
-
-from pydantic import BaseModel
-from typing import List
-from cognee.infrastructure.llm.LLMGateway import LLMGateway
-
-
-class MiniEntity(BaseModel):
-    name: str
-    type: str
-
-
-class MiniGraph(BaseModel):
-    nodes: List[MiniEntity]
-
-
-async def main():
-    system_prompt = (
-        "Extract entities as nodes with name and type. "
-        "Use concise, literal values present in the text."
-    )
-
-    text = "Apple develops iPhone; Audi produces the R8."
-
-    result = await LLMGateway.acreate_structured_output(text, system_prompt, MiniGraph)
-    print(result)
-    # MiniGraph(nodes=[MiniEntity(name='Apple', type='Organization'), ...])
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

cognee/tests/docs/guides/memify_quickstart.py

@@ -1,29 +0,0 @@
-import asyncio
-import cognee
-from cognee import SearchType
-
-
-async def main():
-    # 1) Add two short chats and build a graph
-    await cognee.add(
-        [
-            "We follow PEP8. Add type hints and docstrings.",
-            "Releases should not be on Friday. Susan must review PRs.",
-        ],
-        dataset_name="rules_demo",
-    )
-    await cognee.cognify(datasets=["rules_demo"])  # builds graph
-
-    # 2) Enrich the graph (uses default memify tasks)
-    await cognee.memify(dataset="rules_demo")
-
-    # 3) Query the new coding rules
-    rules = await cognee.search(
-        query_type=SearchType.CODING_RULES,
-        query_text="List coding rules",
-        node_name=["coding_agent_rules"],
-    )
-    print("Rules:", rules)
-
-
-asyncio.run(main())

cognee/tests/docs/guides/ontology_quickstart.py

@@ -1,30 +0,0 @@
-import asyncio
-import cognee
-
-
-async def main():
-    texts = ["Audi produces the R8 and e-tron.", "Apple develops iPhone and MacBook."]
-
-    await cognee.add(texts)
-    # or: await cognee.add("/path/to/folder/of/files")
-
-    import os
-    from cognee.modules.ontology.ontology_config import Config
-    from cognee.modules.ontology.rdf_xml.RDFLibOntologyResolver import RDFLibOntologyResolver
-
-    ontology_path = os.path.join(
-        os.path.dirname(os.path.abspath(__file__)), "ontology_input_example/basic_ontology.owl"
-    )
-
-    # Create full config structure manually
-    config: Config = {
-        "ontology_config": {
-            "ontology_resolver": RDFLibOntologyResolver(ontology_file=ontology_path)
-        }
-    }
-
-    await cognee.cognify(config=config)
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

cognee/tests/docs/guides/s3_storage.py

@@ -1,25 +0,0 @@
-import asyncio
-import cognee
-
-
-async def main():
-    # Single file
-    await cognee.add("s3://cognee-temp/2024-11-04.md")
-
-    # Folder/prefix (recursively expands)
-    await cognee.add("s3://cognee-temp")
-
-    # Mixed list
-    await cognee.add(
-        [
-            "s3://cognee-temp/2024-11-04.md",
-            "Some inline text to ingest",
-        ]
-    )
-
-    # Process the data
-    await cognee.cognify()
-
-
-if __name__ == "__main__":
-    asyncio.run(main())

cognee/tests/docs/guides/search_basics.py

@@ -1,58 +0,0 @@
-import asyncio
-import cognee
-
-from cognee.modules.search.types import SearchType, CombinedSearchResult
-
-
-async def main():
-    await cognee.prune.prune_data()
-    await cognee.prune.prune_system(metadata=True)
-
-    text = """
-        Natural language processing (NLP) is an interdisciplinary
-        subfield of computer science and information retrieval.
-        First rule of coding: Do not talk about coding.
-        """
-
-    text2 = """
-    Sandwiches are best served toasted with cheese, ham, mayo,
-    lettuce, mustard, and salt & pepper.
-    """
-
-    await cognee.add(text, dataset_name="NLP_coding")
-    await cognee.add(text2, dataset_name="Sandwiches")
-    await cognee.add(text2)
-
-    await cognee.cognify()
-
-    # Make sure you've already run cognee.cognify(...) so the graph has content
-    answers = await cognee.search(query_text="What are the main themes in my data?")
-    assert len(answers) > 0
-
-    answers = await cognee.search(
-        query_text="List coding guidelines",
-        query_type=SearchType.CODING_RULES,
-    )
-    assert len(answers) > 0
-
-    answers = await cognee.search(
-        query_text="Give me a confident answer: What is NLP?",
-        system_prompt="Answer succinctly and state confidence at the end.",
-    )
-    assert len(answers) > 0
-
-    answers = await cognee.search(
-        query_text="Tell me about NLP",
-        only_context=True,
-    )
-    assert len(answers) > 0
-
-    answers = await cognee.search(
-        query_text="Quarterly financial highlights",
-        datasets=["NLP_coding", "Sandwiches"],
-        use_combined_context=True,
-    )
-    assert isinstance(answers, CombinedSearchResult)
-
-
-asyncio.run(main())

cognee/tests/docs/guides/temporal_cognify.py

@@ -1,57 +0,0 @@
-import asyncio
-import cognee
-
-
-async def main():
-    text = """
-    In 1998 the project launched. In 2001 version 1.0 shipped. In 2004 the team merged
-    with another group. In 2010 support for v1 ended.
-    """
-
-    await cognee.add(text, dataset_name="timeline_demo")
-
-    await cognee.cognify(datasets=["timeline_demo"], temporal_cognify=True)
-
-    from cognee.api.v1.search import SearchType
-
-    # Before / after queries
-    result = await cognee.search(
-        query_type=SearchType.TEMPORAL, query_text="What happened before 2000?", top_k=10
-    )
-
-    assert result != []
-
-    result = await cognee.search(
-        query_type=SearchType.TEMPORAL, query_text="What happened after 2010?", top_k=10
-    )
-
-    assert result != []
-
-    # Between queries
-    result = await cognee.search(
-        query_type=SearchType.TEMPORAL, query_text="Events between 2001 and 2004", top_k=10
-    )
-
-    assert result != []
-
-    # Scoped descriptions
-    result = await cognee.search(
-        query_type=SearchType.TEMPORAL,
-        query_text="Key project milestones between 1998 and 2010",
-        top_k=10,
-    )
-
-    assert result != []
-
-    result = await cognee.search(
-        query_type=SearchType.TEMPORAL,
-        query_text="What happened after 2004?",
-        datasets=["timeline_demo"],
-        top_k=10,
-    )
-
-    assert result != []
-
-
-if __name__ == "__main__":
-    asyncio.run(main())