chore: remove memgraph from cognee repo

cognee/infrastructure/databases/graph/get_graph_engine.py

@@ -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'])}"
     )

cognee/tests/test_memgraph.py

@@ -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())

notebooks/neptune-analytics-example.ipynb

@@ -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": {