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

LangChain simplifies the complexity of managing document insertion and embeddings generation using vector stores by providing streamlined methods for these tasks.

Here's how you can initialize Postgres Vector with LangChain:

// 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();

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:

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.

Initialize Postgres Vector Store

Generate Embeddings with OpenAI

Stream Chat Completions with OpenAI

Starter apps

AI chatbot (OpenAI + LangChain)

RAG chatbot (OpenAI + LangChain)

Semantic search chatbot (OpenAI + LangChain)

Chat with PDF (OpenAI + LangChain)

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