feat: Add memify coding agent example

cognee/api/v1/cognify/memify.py

@@ -4,7 +4,7 @@ from uuid import UUID
 from cognee.shared.logging_utils import get_logger
 
 from cognee.modules.retrieval.utils.brute_force_triplet_search import get_memory_fragment
-from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
+
 from cognee.modules.engine.models.node_set import NodeSet
 from cognee.modules.pipelines import run_pipeline
 from cognee.modules.pipelines.tasks.task import Task
@@ -18,7 +18,6 @@ from cognee.modules.pipelines.layers.reset_dataset_pipeline_run_status import (
 from cognee.modules.engine.operations.setup import setup
 
 from cognee.tasks.memify.extract_subgraph import extract_subgraph
-from cognee.tasks.codingagents.coding_rule_associations import add_rule_associations
 from cognee.modules.pipelines.layers.pipeline_execution_mode import get_pipeline_executor
 
 logger = get_logger("memify")
@@ -65,14 +64,7 @@ async def memify(
 
     memify_tasks = [
         Task(extract_subgraph),
-        Task(CogneeGraph.resolve_edges_to_text, task_config={"batch_size": 10}),
-        Task(
-            add_rule_associations,
-            rules_nodeset_name="coding_agent_rules",
-            user_prompt_location="memify_coding_rule_association_agent_user.txt",
-            system_prompt_location="memify_coding_rule_association_agent_system.txt",
-        ),
-        # *tasks,  # Unpack tasks provided to memify pipeline
+        *tasks,  # Unpack tasks provided to memify pipeline
     ]
 
     await setup()

examples/python/memify_coding_agent_example.py

@@ -0,0 +1,76 @@
+import asyncio
+import cognee
+from cognee.shared.logging_utils import setup_logging, ERROR
+from cognee.api.v1.search import SearchType
+
+# Prerequisites:
+# 1. Copy `.env.template` and rename it to `.env`.
+# 2. Add your OpenAI API key to the `.env` file in the `LLM_API_KEY` field:
+#    LLM_API_KEY = "your_key_here"
+
+
+async def main():
+    # Create a clean slate for cognee -- reset data and system state
+    print("Resetting cognee data...")
+    await cognee.prune.prune_data()
+    await cognee.prune.prune_system(metadata=True)
+    print("Data reset complete.\n")
+
+    # cognee knowledge graph will be created based on this text
+    text = """
+    Natural language processing (NLP) is an interdisciplinary
+    subfield of computer science and information retrieval.
+    """
+
+    coding_rules_text = """
+    Code must be formatted by PEP8 standards.
+    Typing and Docstrings must be added.
+    """
+
+    print("Adding text to cognee:")
+    print(text.strip())
+    # Add the text, and make it available for cognify
+    await cognee.add(text)
+    await cognee.add(coding_rules_text, node_set=["coding_rules"])
+    print("Text added successfully.\n")
+
+    # Use LLMs and cognee to create knowledge graph
+    await cognee.cognify()
+    print("Cognify process complete.\n")
+
+    from cognee.api.v1.cognify.memify import memify
+
+    from cognee.modules.graph.cognee_graph.CogneeGraph import CogneeGraph
+    from cognee.tasks.codingagents.coding_rule_associations import add_rule_associations
+    from cognee.modules.pipelines.tasks.task import Task
+
+    memify_tasks = [
+        Task(CogneeGraph.resolve_edges_to_text, task_config={"batch_size": 10}),
+        Task(
+            add_rule_associations,
+            rules_nodeset_name="coding_agent_rules",
+            user_prompt_location="memify_coding_rule_association_agent_user.txt",
+            system_prompt_location="memify_coding_rule_association_agent_system.txt",
+        ),
+    ]
+
+    await memify(tasks=memify_tasks, node_name=["coding_rules"])
+
+    import os
+    import pathlib
+    from cognee.api.v1.visualize.visualize import visualize_graph
+
+    file_path = os.path.join(
+        pathlib.Path(__file__).parent, ".artifacts", "graph_visualization.html"
+    )
+    await visualize_graph(file_path)
+
+
+if __name__ == "__main__":
+    logger = setup_logging(log_level=ERROR)
+    loop = asyncio.new_event_loop()
+    asyncio.set_event_loop(loop)
+    try:
+        loop.run_until_complete(main())
+    finally:
+        loop.run_until_complete(loop.shutdown_asyncgens())