How-To Guides

Contents:

• How to use MongoDB with LinkML-Store
  • Creating a client and attaching to a database
  • Creating a collection
  • Preparing data to load
  • Inserting objects
  • Queries
  • Facet counts
  • Creating an LLM embedding index
• How to use Semantic Search
  • Background
  • Obtaining upstream files
  • Storing the JSON
  • Creating an LLMIndexer
  • Searching using the index
  • How it works
• How to query the Monarch-KG
• How to query Solr using the Command Line
  • Specifying a Solr backend on the command line:
  • Using a configuration
  • Search
• How to use Neo4J with LinkML-Store
  • Running this how-to guide interactively.
  • Using a schema
  • Create Node and Edge collections
  • Adding Data
  • Visualization
    • Exploring in Neo4j
  • Validation
    • Validating an existing database
    • Prospective Validation
  • Loading Data from a file
  • Queries
  • Facet Counts
  • Indexing via LLM embeddings
• How to Check Referential Integrity
  • Inserting invalid data
• Example: Storing an ontology
• How to index Phenopackets with LinkML-Store
  • Use pystow to download phenopackets
  • Creating a client and attaching to a database
  • Creating a collection
  • Inserting objects into the store
  • Check contents
  • Query faceting
  • Semantic Search
  • Semantic Search
  • Validation
  • Command Line Usage
  • Inference
• How to index GO-CAMs with LinkML-Store
  • Creating a client and attaching to a database
  • Creating a collection
  • Inserting objects into the store
  • Check contents
  • Query faceting
  • Semantic Search
  • Semantic Search
  • Validation
  • Command Line Usage
• How to predict missing data
  • The Infer Command
  • Training and Inference
  • Saving the Model
  • Exporting models to explainable visualizations
  • Generating a rule-based model
  • More advanced ML models
• Perform RAG Inference
  • Loading the data into duckdb
  • Inferring a specific field
  • Inferring a whole object
  • Inferring from multiple fields
  • RAG configuration - using a different model
  • Persisting the RAG model
  • Evaluation
  • How RAG indexing works under the hood
  • Configuring the training/test split
  • Extraction tasks