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
- 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_RULES
become 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
Default behavior
Default behavior
- 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
Custom tasks
Custom tasks
- 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
Search integration
Search integration
- 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
Incremental processing
Incremental processing
- 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