langchain/docs/extras/integrations/text_embedding/gpt4all.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "d63d56c2",
   "metadata": {},
   "source": [
    "# GPT4All\n",
    "\n",
    "This notebook explains how to use [GPT4All embeddings](https://docs.gpt4all.io/gpt4all_python_embedding.html#gpt4all.gpt4all.Embed4All) with LangChain."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "cdd68231",
   "metadata": {},
   "outputs": [],
   "source": [
    "! pip install gpt4all"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "08f267d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from langchain.embeddings import GPT4AllEmbeddings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "0120e939",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "100%|████████████████████████| 45.5M/45.5M [00:02<00:00, 18.5MiB/s]\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model downloaded at:  /Users/rlm/.cache/gpt4all/ggml-all-MiniLM-L6-v2-f16.bin\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "objc[45711]: Class GGMLMetalClass is implemented in both /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libreplit-mainline-metal.dylib (0x29fe18208) and /Users/rlm/anaconda3/envs/lcn2/lib/python3.9/site-packages/gpt4all/llmodel_DO_NOT_MODIFY/build/libllamamodel-mainline-metal.dylib (0x2a0244208). One of the two will be used. Which one is undefined.\n"
     ]
    }
   ],
   "source": [
    "gpt4all_embd = GPT4AllEmbeddings()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "53134a38",
   "metadata": {},
   "outputs": [],
   "source": [
    "text = \"This is a test document.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "a55adf9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "query_result = gpt4all_embd.embed_query(text)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "6ebd42d7",
   "metadata": {},
   "outputs": [],
   "source": [
    "doc_result = gpt4all_embd.embed_documents([text])"
   ]
  }
 ],
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