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97 lines
3.5 KiB
JSON
97 lines
3.5 KiB
JSON
{
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"name": "rag_sqlite_vec_demo",
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"description": "Zero-infrastructure RAG using the bundled SQLite + sqlite-vec vector store. Requires conductor.db.type=sqlite and conductor.integrations.ai.enabled=true, which auto-registers the 'default' vector DB instance. Embeddings are requested at 256 dimensions to match the default instance.",
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"version": 1,
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"schemaVersion": 2,
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"tasks": [
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{
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"name": "index_doc_1",
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"taskReferenceName": "index_doc_1_ref",
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"type": "LLM_INDEX_TEXT",
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"inputParameters": {
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"vectorDB": "default",
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"index": "demo_index",
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"namespace": "demo_docs",
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"docId": "intro-001",
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"text": "Conductor is a distributed workflow orchestration engine. It lets developers build complex stateful applications by orchestrating microservices and AI agents.",
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"embeddingModelProvider": "openai",
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"embeddingModel": "text-embedding-3-small",
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"dimensions": 256,
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"metadata": { "category": "introduction" }
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}
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},
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{
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"name": "index_doc_2",
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"taskReferenceName": "index_doc_2_ref",
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"type": "LLM_INDEX_TEXT",
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"inputParameters": {
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"vectorDB": "default",
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"index": "demo_index",
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"namespace": "demo_docs",
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"docId": "vectordb-002",
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"text": "Conductor supports several vector databases: PostgreSQL (pgvector), MongoDB Atlas, Pinecone, and an embedded SQLite backend powered by the sqlite-vec extension that needs no external server.",
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"embeddingModelProvider": "openai",
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"embeddingModel": "text-embedding-3-small",
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"dimensions": 256,
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"metadata": { "category": "features" }
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}
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},
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{
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"name": "index_doc_3",
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"taskReferenceName": "index_doc_3_ref",
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"type": "LLM_INDEX_TEXT",
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"inputParameters": {
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"vectorDB": "default",
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"index": "demo_index",
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"namespace": "demo_docs",
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"docId": "sqlite-003",
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"text": "When SQLite persistence and the AI integration are both enabled, Conductor bundles the sqlite-vec native extension and registers a default vector store automatically, so semantic search works out of the box.",
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"embeddingModelProvider": "openai",
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"embeddingModel": "text-embedding-3-small",
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"dimensions": 256,
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"metadata": { "category": "configuration" }
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}
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},
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{
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"name": "search_index",
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"taskReferenceName": "search_ref",
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"type": "LLM_SEARCH_INDEX",
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"inputParameters": {
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"vectorDB": "default",
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"index": "demo_index",
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"namespace": "demo_docs",
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"query": "${workflow.input.question}",
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"embeddingModelProvider": "openai",
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"embeddingModel": "text-embedding-3-small",
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"dimensions": 256,
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"maxResults": 3
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}
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},
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{
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"name": "generate_rag_answer",
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"taskReferenceName": "answer_ref",
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"type": "LLM_CHAT_COMPLETE",
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"inputParameters": {
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"llmProvider": "openai",
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"model": "gpt-4o-mini",
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"messages": [
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{
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"role": "system",
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"message": "You are a technical expert. Answer the question using only the provided context."
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},
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{
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"role": "user",
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"message": "Context:\n${search_ref.output.result}\n\nQuestion: ${workflow.input.question}"
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}
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],
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"temperature": 0.2
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}
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}
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],
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"inputParameters": ["question"],
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"outputParameters": {
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"search_results": "${search_ref.output.result}",
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"answer": "${answer_ref.output.result}"
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}
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}
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