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chore: remove memgraph from cognee repo

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This commit is contained in:
Daulet Amirkhanov 2025-10-15 17:32:48 +01:00
parent c73e8964a1
commit 38406a0ab1
4 changed files with 42 additions and 1263 deletions
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2
cognee/infrastructure/databases/graph/get_graph_engine.py
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@ -162,5 +162,5 @@ def create_graph_engine(
raise EnvironmentError(
f"Unsupported graph database provider: {graph_database_provider}. "
f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'memgraph', 'neptune', 'neptune_analytics'])}"
f"Supported providers are: {', '.join(list(supported_databases.keys()) + ['neo4j', 'kuzu', 'kuzu-remote', 'neptune', 'neptune_analytics'])}"
)

1116
cognee/infrastructure/databases/graph/memgraph/memgraph_adapter.py
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File diff suppressed because it is too large Load diff

105
cognee/tests/test_memgraph.py
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@ -1,105 +0,0 @@
import os
import pathlib
import cognee
from cognee.infrastructure.files.storage import get_storage_config
from cognee.modules.search.operations import get_history
from cognee.modules.users.methods import get_default_user
from cognee.shared.logging_utils import get_logger
from cognee.modules.search.types import SearchType
logger = get_logger()
async def main():
cognee.config.set_graph_database_provider("memgraph")
data_directory_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".data_storage/test_memgraph")
).resolve()
)
cognee.config.data_root_directory(data_directory_path)
cognee_directory_path = str(
pathlib.Path(
os.path.join(pathlib.Path(__file__).parent, ".cognee_system/test_memgraph")
).resolve()
)
cognee.config.system_root_directory(cognee_directory_path)
await cognee.prune.prune_data()
await cognee.prune.prune_system(metadata=True)
dataset_name = "cs_explanations"
explanation_file_path_nlp = os.path.join(
pathlib.Path(__file__).parent, "test_data/Natural_language_processing.txt"
)
await cognee.add([explanation_file_path_nlp], dataset_name)
explanation_file_path_quantum = os.path.join(
pathlib.Path(__file__).parent, "test_data/Quantum_computers.txt"
)
await cognee.add([explanation_file_path_quantum], dataset_name)
await cognee.cognify([dataset_name])
from cognee.infrastructure.databases.vector import get_vector_engine
vector_engine = get_vector_engine()
random_node = (await vector_engine.search("Entity_name", "Quantum computer"))[0]
random_node_name = random_node.payload["text"]
search_results = await cognee.search(
query_type=SearchType.GRAPH_COMPLETION, query_text=random_node_name
)
assert len(search_results) != 0, "The search results list is empty."
print("\n\nExtracted sentences are:\n")
for result in search_results:
print(f"{result}\n")
search_results = await cognee.search(query_type=SearchType.CHUNKS, query_text=random_node_name)
assert len(search_results) != 0, "The search results list is empty."
print("\n\nExtracted chunks are:\n")
for result in search_results:
print(f"{result}\n")
search_results = await cognee.search(
query_type=SearchType.SUMMARIES, query_text=random_node_name
)
assert len(search_results) != 0, "Query related summaries don't exist."
print("\nExtracted results are:\n")
for result in search_results:
print(f"{result}\n")
search_results = await cognee.search(
query_type=SearchType.NATURAL_LANGUAGE,
query_text=f"Find nodes connected to node with name {random_node_name}",
)
assert len(search_results) != 0, "Query related natural language don't exist."
print("\nExtracted results are:\n")
for result in search_results:
print(f"{result}\n")
user = await get_default_user()
history = await get_history(user.id)
assert len(history) == 8, "Search history is not correct."
await cognee.prune.prune_data()
data_root_directory = get_storage_config()["data_root_directory"]
assert not os.path.isdir(data_root_directory), "Local data files are not deleted"
await cognee.prune.prune_system(metadata=True)
from cognee.infrastructure.databases.graph import get_graph_engine
graph_engine = await get_graph_engine()
nodes, edges = await graph_engine.get_graph_data()
assert len(nodes) == 0 and len(edges) == 0, "Memgraph graph database is not empty"
if __name__ == "__main__":
import asyncio
asyncio.run(main())

82
notebooks/neptune-analytics-example.ipynb vendored
View file

@ -83,16 +83,16 @@
]
},
{
"metadata": {},
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import os\n",
"import pathlib\n",
"from cognee import config, add, cognify, search, SearchType, prune, visualize_graph\n",
"from dotenv import load_dotenv"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@ -106,7 +106,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# load environment variables from file .env\n",
"load_dotenv()\n",
@ -145,9 +147,7 @@
" \"vector_db_url\": f\"neptune-graph://{graph_identifier}\", # Neptune Analytics endpoint with the format neptune-graph://<GRAPH_ID>\n",
" }\n",
")"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@ -159,19 +159,19 @@
]
},
{
"metadata": {},
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Prune data and system metadata before running, only if we want \"fresh\" state.\n",
"await prune.prune_data()\n",
"await prune.prune_system(metadata=True)"
],
"outputs": [],
"execution_count": null
]
},
{
"metadata": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## Setup data and cognify\n",
"\n",
@ -180,7 +180,9 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Add sample text to the dataset\n",
"sample_text_1 = \"\"\"Neptune Analytics is a memory-optimized graph database engine for analytics. With Neptune\n",
@ -205,9 +207,7 @@
"\n",
"# Cognify the text data.\n",
"await cognify([dataset_name])"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@ -215,14 +215,16 @@
"source": [
"## Graph Memory visualization\n",
"\n",
"Initialize Memgraph as a Graph Memory store and save to .artefacts/graph_visualization.html\n",
"Initialize Neptune as a Graph Memory store and save to .artefacts/graph_visualization.html\n",
"\n",
"![visualization](./neptune_analytics_demo.png)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Get a graphistry url (Register for a free account at https://www.graphistry.com)\n",
"# url = await render_graph()\n",
@ -235,9 +237,7 @@
" ).resolve()\n",
")\n",
"await visualize_graph(graph_file_path)"
],
"outputs": [],
"execution_count": null
]
},
{
"cell_type": "markdown",
@ -250,19 +250,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Completion query that uses graph data to form context.\n",
"graph_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.GRAPH_COMPLETION)\n",
"print(\"\\nGraph completion result is:\")\n",
"print(graph_completion)"
],
"outputs": [],
"execution_count": null
]
},
{
"metadata": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## SEARCH: RAG Completion\n",
"\n",
@ -271,19 +271,19 @@
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Completion query that uses document chunks to form context.\n",
"rag_completion = await search(query_text=\"What is Neptune Analytics?\", query_type=SearchType.RAG_COMPLETION)\n",
"print(\"\\nRAG Completion result is:\")\n",
"print(rag_completion)"
],
"outputs": [],
"execution_count": null
]
},
{
"metadata": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## SEARCH: Graph Insights\n",
"\n",
@ -291,8 +291,10 @@
]
},
{
"metadata": {},
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Search graph insights\n",
"insights_results = await search(query_text=\"Neptune Analytics\", query_type=SearchType.GRAPH_COMPLETION)\n",
@ -302,13 +304,11 @@
" tgt_node = result[2].get(\"name\", result[2][\"type\"])\n",
" relationship = result[1].get(\"relationship_name\", \"__relationship__\")\n",
" print(f\"- {src_node} -[{relationship}]-> {tgt_node}\")"
],
"outputs": [],
"execution_count": null
]
},
{
"metadata": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## SEARCH: Entity Summaries\n",
"\n",
@ -316,8 +316,10 @@
]
},
{
"metadata": {},
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Query all summaries related to query.\n",
"summaries = await search(query_text=\"Neptune Analytics\", query_type=SearchType.SUMMARIES)\n",
@ -326,13 +328,11 @@
" type = summary[\"type\"]\n",
" text = summary[\"text\"]\n",
" print(f\"- {type}: {text}\")"
],
"outputs": [],
"execution_count": null
]
},
{
"metadata": {},
"cell_type": "markdown",
"metadata": {},
"source": [
"## SEARCH: Chunks\n",
"\n",
@ -340,8 +340,10 @@
]
},
{
"metadata": {},
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"chunks = await search(query_text=\"Neptune Analytics\", query_type=SearchType.CHUNKS)\n",
"print(\"\\nChunk results are:\")\n",
@ -349,9 +351,7 @@
" type = chunk[\"type\"]\n",
" text = chunk[\"text\"]\n",
" print(f\"- {type}: {text}\")"
],
"outputs": [],
"execution_count": null
]
}
],
"metadata": {