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Prerequisites

  • smithers-orchestrator version 0.12.8 or later
  • An OpenAI API key (or another AI SDK-supported embedding provider)
No extra packages needed. The ai and @ai-sdk/openai dependencies are already included.

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Create a Vector Store

The vector store uses your workflow’s existing SQLite database: 
import { createSqliteVectorStore } from "smithers-orchestrator/rag";
import { createSmithers } from "smithers-orchestrator";
import { z } from "zod";

const { outputs, workflow } = createSmithers({
  answer: z.object({ text: z.string() }),
});

const store = createSqliteVectorStore(workflow.db);

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Build a Pipeline

Wire together chunking, embedding, and storage: 
import { createRagPipeline } from "smithers-orchestrator/rag";
import { openai } from "@ai-sdk/openai";

const pipeline = createRagPipeline({
  vectorStore: store,
  embeddingModel: openai.embedding("text-embedding-3-small"),
  chunkOptions: { strategy: "markdown", size: 1000, overlap: 200 },
});

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Ingest Documents

Load and ingest files: 
await pipeline.ingestFile("./docs/api-reference.md");
await pipeline.ingestFile("./docs/architecture.md");
Or create documents from strings: 
import { createDocument } from "smithers-orchestrator/rag";

const doc = createDocument(
  "Smithers uses a unidirectional dataflow model...",
  { metadata: { source: "design-doc" } },
);
await pipeline.ingest([doc]);

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Query the Pipeline

const results = await pipeline.retrieve("How does the scheduler work?", {
  topK: 5,
});

for (const r of results) {
  console.log(`[${r.score.toFixed(3)}] ${r.chunk.content.slice(0, 100)}...`);
}

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Give Agents a RAG Tool

Create a tool that agents can call to search the knowledge base: 
import { createRagTool } from "smithers-orchestrator/rag";

const searchDocs = createRagTool(pipeline, {
  name: "search_docs",
  description: "Search project documentation for relevant context",
});
Use it in a workflow: 
import { Workflow, Task, OpenAIAgent } from "smithers-orchestrator";

const agent = new OpenAIAgent({
  model: "gpt-4o",
  tools: { search_docs: searchDocs },
});

export default (
  <Workflow>
    <Task id="answer" output={outputs.answer} agent={agent}>
      Answer the user's question using the search_docs tool.
    </Task>
  </Workflow>
);

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Use Namespaces

Keep different document collections separate: 
const apiPipeline = createRagPipeline({
  vectorStore: store,
  embeddingModel: openai.embedding("text-embedding-3-small"),
  chunkOptions: { strategy: "markdown", size: 1000, overlap: 200 },
  namespace: "api-docs",
});

const designPipeline = createRagPipeline({
  vectorStore: store,
  embeddingModel: openai.embedding("text-embedding-3-small"),
  chunkOptions: { strategy: "recursive", size: 800, overlap: 100 },
  namespace: "design-docs",
});

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CLI Usage

Ingest and query without writing code: 
# Ingest a markdown file
smithers rag ingest ./docs/api.md --workflow my-workflow.tsx --namespace api-docs

# Query the knowledge base
smithers rag query "authentication flow" --workflow my-workflow.tsx --namespace api-docs --top-k 3

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Next Steps