LangChain

LangChain is a popular framework for working with AI, Vectors, and embeddings. LangChain supports using Neon as a vector store, using the pgvector extension. Initialize Postgres Vector Store. LangChai...

LangChain is a popular framework for working with AI, Vectors, and embeddings. LangChain supports using Neon as a vector store, using the pgvector extension.

Initialize Postgres Vector Store

LangChain handles document insertion and embedding generation through its vector store methods.

Here's how you can initialize Postgres Vector with LangChain. OpenAIEmbeddings works against either OpenAI directly or the Neon AI Gateway, which serves embedding models on an OpenAI-compatible endpoint using the same Neon credential as the rest of your project:

TSX

// File: vectorStore.ts

import { NeonPostgres } from '@langchain/community/vectorstores/neon';
import { OpenAIEmbeddings } from '@langchain/openai';

const embeddings = new OpenAIEmbeddings({
  model: 'qwen3-embedding-0-6b', // 1024-dimensional; supports the `dimensions` parameter
  dimensions: 512,
  apiKey: process.env.NEON_AI_GATEWAY_TOKEN,
  configuration: {
    baseURL: `${process.env.NEON_AI_GATEWAY_BASE_URL}/v1`,
  },
});

export async function loadVectorStore() {
  return await NeonPostgres.initialize(embeddings, {
    connectionString: process.env.POSTGRES_URL as string,
  });
}

// Use in your code (say, in API routes)
const vectorStore = await loadVectorStore();

TSX

// File: vectorStore.ts

import { NeonPostgres } from '@langchain/community/vectorstores/neon';
import { OpenAIEmbeddings } from '@langchain/openai';

const embeddings = new OpenAIEmbeddings({
  dimensions: 512,
  model: 'text-embedding-3-small',
});

export async function loadVectorStore() {
  return await NeonPostgres.initialize(embeddings, {
    connectionString: process.env.POSTGRES_URL as string,
  });
}

// Use in your code (say, in API routes)
const vectorStore = await loadVectorStore();

Note: Set NEON_AI_GATEWAY_TOKEN and NEON_AI_GATEWAY_BASE_URL from your branch (neon env pull, or the Neon Console's Connect panel). On the openai JavaScript SDK v6, embedding calls need encoding_format: 'float', or upgrade to v7 or later. See Embeddings for model IDs, dimensions, and this SDK caveat.

Generate Embeddings with OpenAI

LangChain handles embedding generation internally while adding vectors to the Postgres database, simplifying the process for users. For more detailed control over embeddings, refer to the respective JavaScript and Python documentation.

Stream Chat Completions with OpenAI

LangChain can find similar documents to the user's latest query and invoke the OpenAI API to power chat completion responses, providing a seamless integration for creating dynamic interactions.

Here's how you can power chat completions in an API route:

TSX

import { loadVectorStore } from './vectorStore';

import { pull } from 'langchain/hub';
import { ChatOpenAI } from '@langchain/openai';
import { createRetrievalChain } from 'langchain/chains/retrieval';
import type { ChatPromptTemplate } from '@langchain/core/prompts';
import { AIMessage, HumanMessage } from '@langchain/core/messages';
import { createStuffDocumentsChain } from 'langchain/chains/combine_documents';

const topK = 3;

export async function POST(request: Request) {
  const llm = new ChatOpenAI();
  const encoder = new TextEncoder();
  const vectorStore = await loadVectorStore();
  const { messages = [] } = await request.json();
  const userMessages = messages.filter((i) => i.role === 'user');
  const input = userMessages[userMessages.length - 1].content;
  const retrievalQAChatPrompt = await pull<ChatPromptTemplate>('langchain-ai/retrieval-qa-chat');
  const retriever = vectorStore.asRetriever({ k: topK, searchType: 'similarity' });
  const combineDocsChain = await createStuffDocumentsChain({
    llm,
    prompt: retrievalQAChatPrompt,
  });
  const retrievalChain = await createRetrievalChain({
    retriever,
    combineDocsChain,
  });
  const customReadable = new ReadableStream({
    async start(controller) {
      const stream = await retrievalChain.stream({
        input,
        chat_history: messages.map((i) =>
          i.role === 'user' ? new HumanMessage(i.content) : new AIMessage(i.content)
        ),
      });
      for await (const chunk of stream) {
        controller.enqueue(encoder.encode(chunk.answer));
      }
      controller.close();
    },
  });
  return new Response(customReadable, {
    headers: {
      Connection: 'keep-alive',
      'Content-Encoding': 'none',
      'Cache-Control': 'no-cache, no-transform',
      'Content-Type': 'text/plain; charset=utf-8',
    },
  });
}

Starter apps

Hackable, fully-featured, pre-built starter apps to get you up and running with LlamaIndex and Postgres.

AI chatbot (OpenAI + LangChain)

A Next.js AI chatbot starter app built with OpenAI and LangChain

RAG chatbot (OpenAI + LangChain)

A Next.js RAG chatbot starter app built with OpenAI and LangChain

Semantic search chatbot (OpenAI + LangChain)

A Next.js Semantic Search chatbot starter app built with OpenAI and LangChain

Chat with PDF (OpenAI + LangChain)

A Next.js Chat with PDF chatbot starter app built with OpenAI and LangChain