4 KiB
4 KiB
Memify
Semantic enrichment of existing knowledge graphs with derived facts
What is the memify operation
The .memify operation enriches existing knowledge graphs by extracting derived facts and creating new associations from your already-processed data. Unlike Add and Cognify, memify works on existing graph structures to add semantic understanding and deeper contextual relationships.
- Graph enrichment: operates on existing knowledge graphs created by Cognify
- Derived facts: creates new nodes and edges from existing context without re-ingesting data
- Semantic enhancement: adds coding rules, associations, and other derived knowledge
- Pipeline-based: uses extraction and enrichment tasks to process subgraphs
- Incremental: can be run multiple times to add new derived facts as needed
Where memify fits
Use .memify after you've completed the Add → Cognify workflow:
- Prerequisites: requires an existing knowledge graph with chunks, embeddings, and graph structure
- Enhancement phase: adds semantic understanding and derived facts to your existing data
- Optional enrichment: not required for basic search, but adds valuable context and associations
What happens under the hood
The .memify pipeline processes your existing knowledge graph through two main phases:
- Extraction phase — pulls relevant subgraphs or chunks from your existing knowledge graph
- Enrichment phase — applies enrichment tasks to create new nodes and edges from existing context
The default memify tasks include:
- Extract subgraph chunks: identifies relevant portions of your graph for processing
- Add rule associations: creates coding rules and other derived facts from the extracted context
After memify finishes
When .memify completes:
- New derived facts are added to your knowledge graph as additional nodes and edges
- Enhanced searchability: specialized search types like
SearchType.CODING_RULESbecome available - Richer context: your existing data now includes semantic associations and derived knowledge
- No data re-ingestion: all enrichment happens on your existing graph structure
Examples and details
* **Extraction**: `extract_subgraph_chunks` - pulls relevant chunks from your graph * **Enrichment**: `add_rule_associations` - creates coding rules and associations * **Output**: new nodes and edges added to your existing knowledge graph * You can specify custom extraction and enrichment tasks * Extraction tasks determine what parts of the graph to process * Enrichment tasks define what derived facts to create * Tasks can be chained together for complex enrichment workflows * Enriched graphs support specialized search types * `SearchType.CODING_RULES` for finding coding guidelines * Other search modes can leverage the new derived facts * Enhanced context improves answer quality and relevance * Can be run multiple times on the same dataset * Only processes new or updated graph elements by default * Safe to re-run as it adds rather than replaces existing data Build the knowledge graph that memify enriches Query the enriched graph with specialized search types Learn how to create custom memify tasksTo find navigation and other pages in this documentation, fetch the llms.txt file at: https://docs.cognee.ai/llms.txt