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LangChain.js extension

We provide a LangChain.js compatible LLamaEmbeddings support since v0.0.28! We are not sure if it is accurate but it works :)

import { MemoryVectorStore } from "langchain/vectorstores/memory";
import { LLamaEmbeddings } from "llama-node/dist/extensions/langchain.js";
import { LLama } from "llama-node";
import { LLamaCpp, LoadConfig } from "llama-node/dist/llm/llama-cpp.js";
import path from "path";

const model = path.resolve(process.cwd(), "../ggml-vic7b-q5_1.bin");

const llama = new LLama(LLamaCpp);

const config: LoadConfig = {
path: model,
enableLogging: true,
nCtx: 1024,
nParts: -1,
seed: 0,
f16Kv: false,
logitsAll: false,
vocabOnly: false,
useMlock: false,
embedding: true,
useMmap: true,
};

llama.load(config);

const run = async () => {
// Load the docs into the vector store
const vectorStore = await MemoryVectorStore.fromTexts(
["Hello world", "Bye bye", "hello nice world"],
[{ id: 2 }, { id: 1 }, { id: 3 }],
new LLamaEmbeddings({ maxConcurrency: 1 }, llama)
);

// Search for the most similar document
const resultOne = await vectorStore.similaritySearch("hello world", 1);

console.log(resultOne);
};

run();