Dexter is a set of mature LLM tools used in production at Dexa, with a focus on real-world RAG (Retrieval Augmented Generation).
If you're a TypeScript AI engineer, check it out! 😊
- production-quality RAG
- extremely fast and minimal
- handles caching, throttling, and batching for ingesting large datasets
- optional hybrid search w/ SPLADE embeddings
- minimal TS package w/ full typing
- uses
fetcheverywhere - supports Node.js 18+, Deno, Cloudflare Workers, Vercel edge functions, etc
- full docs
npm install @dexaai/dexterThis package requires node >= 18 or an environment with fetch support.
This package exports ESM. If your project uses CommonJS, consider switching to ESM or use the dynamic import() function.
This is a basic example using OpenAI's text-embedding-ada-002 embedding model and a Pinecone datastore to index and query a set of documents.
import 'dotenv/config';
import { EmbeddingModel } from '@dexaai/dexter/model';
import { PineconeDatastore } from '@dexaai/dexter/datastore/pinecone';
async function example() {
const embeddingModel = new EmbeddingModel({
params: { model: 'text-embedding-ada-002' },
});
const store = new PineconeDatastore({
contentKey: 'content',
embeddingModel,
});
await store.upsert([
{ id: '1', metadata: { content: 'cat' } },
{ id: '2', metadata: { content: 'dog' } },
{ id: '3', metadata: { content: 'whale' } },
{ id: '4', metadata: { content: 'shark' } },
{ id: '5', metadata: { content: 'computer' } },
{ id: '6', metadata: { content: 'laptop' } },
{ id: '7', metadata: { content: 'phone' } },
{ id: '8', metadata: { content: 'tablet' } },
]);
const result = await store.query({ query: 'dolphin' });
console.log(result);
}See the docs for a full usage guide and API reference.
To run the included examples, clone this repo, run pnpm install, set up your .env file, and then run an example file using tsx.
Environment variables required to run the examples:
OPENAI_API_KEY- OpenAI API keyPINECONE_API_KEY- Pinecone API keyPINECONE_BASE_URL- Pinecone index's base URL- You should be able to use a free-tier "starter" index for most of the examples, but you'll need to upgrade to a paid index to run the any of the hybrid search examples
- Note that Pinecone's free starter index doesn't support namespaces,
deleteAll, or hybrid search :sigh:
SPLADE_SERVICE_URL- optional; only used for the chatbot hybrid search example
npx tsx examples/basic.tsnpx tsx examples/caching.tsThis example requires a valid REDIS_URL env var.
npx tsx examples/caching-redis.tsThis example shows how to use createAIFunction to handle function and tool_calls with the OpenAI chat completions API and Zod.
npx tsx examples/ai-function.tsThis example shows how to use createAIRunner to easily invoke a chain of OpenAI chat completion calls, resolving tool / function calls, retrying when necessary, and optionally validating the resulting output via Zod.
Note that createAIRunner takes in a functions array of AIFunction objects created by createAIFunction, as the two utility functions are meant to used together.
npx tsx examples/ai-runner.tsThis is a more involved example of a chatbot using RAG. It indexes 100 transcript chunks from the Huberman Lab Podcast into a hybrid Pinecone datastore using OpenAI ada-002 embeddings for the dense vectors and a HuggingFace SPLADE model for the sparse embeddings.
You'll need the following environment variables to run this example:
OPENAI_API_KEYPINECONE_API_KEYPINECONE_BASE_URL- Note: Pinecone's free starter indexes don't seem to support namespaces or hybrid search, so unfortunately you'll need to upgrade to a paid plan to run this example. See Pinecone's hybrid docs for details on setting up a hybrid index, and make sure it is using the
dotproductmetric.
- Note: Pinecone's free starter indexes don't seem to support namespaces or hybrid search, so unfortunately you'll need to upgrade to a paid plan to run this example. See Pinecone's hybrid docs for details on setting up a hybrid index, and make sure it is using the
SPLADE_SERVICE_URL
npx tsx examples/chatbot/ingest.tsnpx tsx examples/chatbot/cli.tsMIT © Dexa
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