venice_create_embeddings
Generate text embeddings to enable semantic search and Retrieval-Augmented Generation (RAG) applications.
Instructions
Generate text embeddings for semantic search and RAG
Input Schema
TableJSON Schema
| Name | Required | Description | Default |
|---|---|---|---|
| input | Yes | Text or array of texts to embed | |
| model | No | Embedding model | text-embedding-ada-002 |
Implementation Reference
- src/tools/inference/index.ts:129-135 (handler)The handler function that calls the Venice API to create embeddings and returns the result summary.
async ({ input, model }) => { const response = await veniceAPI("/embeddings", { method: "POST", body: JSON.stringify({ input, model }) }); const data = await response.json() as EmbeddingsResponse; if (!response.ok) return { content: [{ type: "text" as const, text: `Error: ${data.error?.message || response.statusText}` }] }; const embeddings = data.data || []; return { content: [{ type: "text" as const, text: `Generated ${embeddings.length} embedding(s), dimensions: ${embeddings[0]?.embedding?.length || 0}` }] }; } - src/tools/inference/index.ts:126-128 (schema)Zod schema defining the input parameters for the venice_create_embeddings tool.
input: z.union([z.string(), z.array(z.string())]).describe("Text or array of texts to embed"), model: z.string().optional().default("text-embedding-ada-002").describe("Embedding model"), }, - src/tools/inference/index.ts:123-136 (registration)Registration of the venice_create_embeddings tool in the registerInferenceTools function.
"venice_create_embeddings", "Generate text embeddings for semantic search and RAG", { input: z.union([z.string(), z.array(z.string())]).describe("Text or array of texts to embed"), model: z.string().optional().default("text-embedding-ada-002").describe("Embedding model"), }, async ({ input, model }) => { const response = await veniceAPI("/embeddings", { method: "POST", body: JSON.stringify({ input, model }) }); const data = await response.json() as EmbeddingsResponse; if (!response.ok) return { content: [{ type: "text" as const, text: `Error: ${data.error?.message || response.statusText}` }] }; const embeddings = data.data || []; return { content: [{ type: "text" as const, text: `Generated ${embeddings.length} embedding(s), dimensions: ${embeddings[0]?.embedding?.length || 0}` }] }; } );