UnifyWeaver

Chapter 5: The Semantic Playbook

The Semantic Playbook pattern is the recommended way to structure UnifyWeaver applications. It clearly separates Definition (Prolog) from Execution (Python).

5.1 The Playbook Structure

A typical playbook (semantic_playbook.pl) has three sections:

  1. Logic Definition: The semantic rules (Indexing, Searching, Summarizing).
  2. Compilation Helpers: Predicates that generate the Python scripts.
  3. Main Entry Point: The orchestrator.

5.2 Example Walkthrough

Let’s dissect examples/semantic_playbook.pl.

Section 1: Logic

% Indexing: Crawl the RDF file
index_pt(_) :-
    crawler_run(['data.rdf'], 1).

% RAG: Search and Summarize
summarize_topic(Topic, Summary) :-
    % 1. Graph Search (Vector + Links)
    graph_search(Topic, 3, 1, Context),
    
    % 2. LLM Synthesis
    Prompt = 'Summarize this context.',
    llm_ask(Prompt, Context, Summary).

Section 2: Compilation Helpers

These predicates tell the compiler what to build.

compile_summarizer(Topic) :-
    % Create a specialized query for the specific topic
    Term = (gen_summary(S) :- summarize_topic(Topic, S)),
    assertz(Term),
    
    % Compile it to a script
    compile_predicate_to_python(gen_summary/1, [mode(procedural)], Code),
    write_file('run_summary.py', Code).

Section 3: Orchestration

main :-
    compile_indexer,
    compile_searcher('hacktivism'),
    compile_summarizer('hacktivism').

5.3 Running the Playbook

  1. Generate: Run the Prolog playbook to create the Python agents. 
    swipl -g main -t halt semantic_playbook.pl
  2. Execute: Run the generated Python scripts. 
    # Index the data
python3 run_index.py < inputs.jsonl
    
# Run the RAG agent
python3 run_summary.py < inputs.jsonl

This “Meta-Programming” approach is powerful. You can write a Prolog script that generates hundreds of specialized, optimized Python micro-services, each hard-coded for a specific task or query.

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