chore: remove memgraph from cognee repo (#1550)
<!-- .github/pull_request_template.md --> ## Description <!-- Please provide a clear, human-generated description of the changes in this PR. DO NOT use AI-generated descriptions. We want to understand your thought process and reasoning. --> Memgraph adapter has been moved to and being maintained in [`cognee-community`](https://github.com/topoteretes/cognee-community/tree/main) repo. This PR removes Memgraph, and updates any mentions of it in this repo. ## Type of Change <!-- Please check the relevant option --> - [ ] Bug fix (non-breaking change that fixes an issue) - [ ] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Code refactoring - [ ] Performance improvement - [ ] Other (please specify): ## Screenshots/Videos (if applicable) <!-- Add screenshots or videos to help explain your changes --> ## Pre-submission Checklist <!-- Please check all boxes that apply before submitting your PR --> - [ ] **I have tested my changes thoroughly before submitting this PR** - [ ] **This PR contains minimal changes necessary to address the issue/feature** - [ ] My code follows the project's coding standards and style guidelines - [ ] I have added tests that prove my fix is effective or that my feature works - [ ] I have added necessary documentation (if applicable) - [ ] All new and existing tests pass - [ ] I have searched existing PRs to ensure this change hasn't been submitted already - [ ] I have linked any relevant issues in the description - [ ] My commits have clear and descriptive messages ## DCO Affirmation I affirm that all code in every commit of this pull request conforms to the terms of the Topoteretes Developer Certificate of Origin.
This commit is contained in:
Vasilije
GitHub
commit
2ac15d4fff
4 changed files with 42 additions and 1263 deletions
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@ -162,5 +162,5 @@ def create_graph_engine(
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raise EnvironmentError(
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f"Unsupported graph database provider: {graph_database_provider}. "
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f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}"
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f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'neptune', 'neptune_analytics'])}"
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)
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File diff suppressed because it is too large
Load diff
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@ -1,105 +0,0 @@
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import os
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import pathlib
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import cognee
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from cognee.infrastructure.files.storage import get_storage_config
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from cognee.modules.search.operations import get_history
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from cognee.modules.users.methods import get_default_user
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from cognee.shared.logging_utils import get_logger
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from cognee.modules.search.types import SearchType
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logger = get_logger()
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async def main():
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cognee.config.set_graph_database_provider("memgraph")
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data_directory_path = str(
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pathlib.Path(
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os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_memgraph")
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).resolve()
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)
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cognee.config.data_root_directory(data_directory_path)
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cognee_directory_path = str(
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pathlib.Path(
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os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_memgraph")
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).resolve()
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)
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cognee.config.system_root_directory(cognee_directory_path)
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await cognee.prune.prune_data()
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await cognee.prune.prune_system(metadata=True)
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dataset_name = "cs_explanations"
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explanation_file_path_nlp = os.path.join(
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pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt"
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)
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await cognee.add([explanation_file_path_nlp], dataset_name)
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explanation_file_path_quantum = os.path.join(
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pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt"
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)
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await cognee.add([explanation_file_path_quantum], dataset_name)
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await cognee.cognify([dataset_name])
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from cognee.infrastructure.databases.vector import get_vector_engine
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vector_engine = get_vector_engine()
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random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
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random_node_name = random_node.payload["text"]
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search_results = await cognee.search(
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query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
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)
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assert len(search_results) != 0, "The search results list is empty."
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print("\n\nExtracted sentences are:\n")
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for result in search_results:
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print(f"{result}\n")
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search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=random_node_name)
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assert len(search_results) != 0, "The search results list is empty."
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print("\n\nExtracted chunks are:\n")
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for result in search_results:
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print(f"{result}\n")
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search_results = await cognee.search(
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query_type=SearchType.SUMMARIES, query_text=random_node_name
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)
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assert len(search_results) != 0, "Query related summaries don't exist."
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print("\nExtracted results are:\n")
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for result in search_results:
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print(f"{result}\n")
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search_results = await cognee.search(
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query_type=SearchType.NATURAL_LANGUAGE,
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query_text=f"Find nodes connected to node with name {random_node_name}",
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)
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assert len(search_results) != 0, "Query related natural language don't exist."
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print("\nExtracted results are:\n")
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for result in search_results:
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print(f"{result}\n")
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user = await get_default_user()
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history = await get_history(user.id)
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assert len(history) == 8, "Search history is not correct."
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await cognee.prune.prune_data()
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data_root_directory = get_storage_config()["data_root_directory"]
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assert not os.path.isdir(data_root_directory), "Local data files are not deleted"
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await cognee.prune.prune_system(metadata=True)
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from cognee.infrastructure.databases.graph import get_graph_engine
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graph_engine = await get_graph_engine()
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nodes, edges = await graph_engine.get_graph_data()
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assert len(nodes) == 0 and len(edges) == 0, "Memgraph graph database is not empty"
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if __name__ == "__main__":
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import asyncio
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asyncio.run(main())
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82
notebooks/neptune-analytics-example.ipynb
vendored
82
notebooks/neptune-analytics-example.ipynb
vendored
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@ -83,16 +83,16 @@
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import os\n",
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"import pathlib\n",
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"from cognee import config, add, cognify, search, SearchType, prune, visualize_graph\n",
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"from dotenv import load_dotenv"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"cell_type": "markdown",
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@ -106,7 +106,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# load environment variables from file .env\n",
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"load_dotenv()\n",
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@ -145,9 +147,7 @@
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" \"vector_db_url\": f\"neptune-graph://{graph_identifier}\", # Neptune Analytics endpoint with the format neptune-graph://<GRAPH_ID>\n",
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" }\n",
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")"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"cell_type": "markdown",
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@ -159,19 +159,19 @@
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Prune data and system metadata before running, only if we want \"fresh\" state.\n",
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"await prune.prune_data()\n",
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"await prune.prune_system(metadata=True)"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup data and cognify\n",
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"\n",
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@ -180,7 +180,9 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Add sample text to the dataset\n",
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"sample_text_1 = \"\"\"Neptune Analytics is a memory-optimized graph database engine for analytics. With Neptune\n",
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@ -205,9 +207,7 @@
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"\n",
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"# Cognify the text data.\n",
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"await cognify([dataset_name])"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"cell_type": "markdown",
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@ -215,14 +215,16 @@
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"source": [
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"## Graph Memory visualization\n",
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"\n",
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"Initialize Memgraph as a Graph Memory store and save to .artefacts/graph_visualization.html\n",
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"Initialize Neptune as a Graph Memory store and save to .artefacts/graph_visualization.html\n",
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"\n",
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""
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Get a graphistry url (Register for a free account at https://www.graphistry.com)\n",
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"# url = await render_graph()\n",
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@ -235,9 +237,7 @@
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" ).resolve()\n",
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")\n",
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"await visualize_graph(graph_file_path)"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"cell_type": "markdown",
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@ -250,19 +250,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Completion query that uses graph data to form context.\n",
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"graph_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.GRAPH_COMPLETION)\n",
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"print(\"\\nGraph completion result is:\")\n",
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"print(graph_completion)"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SEARCH: RAG Completion\n",
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"\n",
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@ -271,19 +271,19 @@
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Completion query that uses document chunks to form context.\n",
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"rag_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.RAG_COMPLETION)\n",
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"print(\"\\nRAG Completion result is:\")\n",
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"print(rag_completion)"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SEARCH: Graph Insights\n",
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"\n",
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@ -291,8 +291,10 @@
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Search graph insights\n",
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"insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n",
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@ -302,13 +304,11 @@
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" tgt_node = result[2].get(\"name\", result[2][\"type\"])\n",
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" relationship = result[1].get(\"relationship_name\", \"__relationship__\")\n",
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" print(f\"- {src_node} -[{relationship}]-> {tgt_node}\")"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SEARCH: Entity Summaries\n",
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"\n",
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@ -316,8 +316,10 @@
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Query all summaries related to query.\n",
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"summaries = await search(query_text=\"Neptune Analytics\", query_type=SearchType.SUMMARIES)\n",
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@ -326,13 +328,11 @@
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" type = summary[\"type\"]\n",
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" text = summary[\"text\"]\n",
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" print(f\"- {type}: {text}\")"
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],
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"outputs": [],
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"execution_count": null
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]
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},
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{
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"metadata": {},
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## SEARCH: Chunks\n",
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"\n",
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@ -340,8 +340,10 @@
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]
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},
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{
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"metadata": {},
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"chunks = await search(query_text=\"Neptune Analytics\", query_type=SearchType.CHUNKS)\n",
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"print(\"\\nChunk results are:\")\n",
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@ -349,9 +351,7 @@
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" type = chunk[\"type\"]\n",
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" text = chunk[\"text\"]\n",
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" print(f\"- {type}: {text}\")"
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],
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"outputs": [],
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"execution_count": null
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]
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}
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],
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"metadata": {
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