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docs: Update docstring for memify

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This commit is contained in:
Igor Ilic 2025-09-04 14:44:13 +02:00
parent 0e3a10d925
commit 3c50ef4d6f
1 changed files with 13 additions and 16 deletions
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29
cognee/api/v1/cognify/memify.py
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@ -33,24 +33,24 @@ async def memify(
user: User = None,
node_type: Optional[Type] = NodeSet,
node_name: Optional[List[str]] = None,
cypher_query: Optional[str] = None,
vector_db_config: dict = None,
graph_db_config: dict = None,
vector_db_config: Optional[dict] = None,
graph_db_config: Optional[dict] = None,
run_in_background: bool = False,
):
"""
Prerequisites:
- **LLM_API_KEY**: Must be configured (required for entity extraction and graph generation)
- **Data Added**: Must have data previously added via `cognee.add()` and `cognee.cognify()`
- **Vector Database**: Must be accessible for embeddings storage
- **Graph Database**: Must be accessible for relationship storage
Args:
datasets: Dataset name(s) or dataset uuid to process. Processes all available data if None.
extraction_tasks: List of Cognee Tasks to execute for graph/data extraction.
enrichment_tasks: List of Cognee Tasks to handle enrichment of provided graph/data from extraction tasks.
data: The data to ingest. Can be anything when custom extraction and enrichment tasks are used.
Data provided here will be forwarded to the first extraction task in the pipeline as input.
If no data is provided the whole graph (or subgraph if node_name/node_type is specified) will be forwarded
datasets: Dataset name(s) or dataset uuid to process. Processes all available datasets if None.
- Single dataset: "my_dataset"
- Multiple datasets: ["docs", "research", "reports"]
- None: Process all datasets for the user
user: User context for authentication and data access. Uses default if None.
node_type: Filter graph to specific entity types (for advanced filtering). Used when no data is provided.
node_name: Filter graph to specific named entities (for targeted search). Used when no data is provided.
vector_db_config: Custom vector database configuration for embeddings storage.
graph_db_config: Custom graph database configuration for relationship storage.
run_in_background: If True, starts processing asynchronously and returns immediately.
@ -60,12 +60,9 @@ async def memify(
"""
if not data:
if cypher_query:
pass
else:
memory_fragment = await get_memory_fragment(node_type=node_type, node_name=node_name)
# Subgraphs should be a single element in the list to represent one data item
data = [memory_fragment]
memory_fragment = await get_memory_fragment(node_type=node_type, node_name=node_name)
# Subgraphs should be a single element in the list to represent one data item
data = [memory_fragment]
memify_tasks = [
*extraction_tasks, # Unpack tasks provided to memify pipeline