mirror of https://github.com/hwchase17/langchain
[Breaking] Migrate GPT4All to use PyGPT4All (#3934)
Seems the pyllamacpp package is no longer the supported bindings from gpt4all. Tested that this works locally. Given that the older models weren't very performant, I think it's better to migrate now without trying to include a lot of try / except blocks --------- Co-authored-by: Nissan Pow <npow@users.noreply.github.com> Co-authored-by: Nissan Pow <pownissa@amazon.com>pull/3944/head
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GPT4All\n",
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"\n",
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"[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
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"\n",
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"This example goes over how to use LangChain to interact with `GPT4All` models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install pyllamacpp > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import PromptTemplate, LLMChain\n",
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"from langchain.llms import GPT4All\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Specify Model\n",
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"\n",
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"To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pyllamacpp\n",
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"\n",
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"For full installation instructions go [here](https://gpt4all.io/index.html).\n",
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"\n",
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"The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
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"\n",
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"Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"local_path = './models/gpt4all-lora-quantized-ggml.bin' # replace with your desired local file path"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Uncomment the below block to download a model. You may want to update `url` to a new version."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# import requests\n",
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"\n",
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"# from pathlib import Path\n",
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"# from tqdm import tqdm\n",
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"\n",
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"# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"# # Example model. Check https://github.com/nomic-ai/pyllamacpp for the latest models.\n",
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"# url = 'https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized-ggml.bin'\n",
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"\n",
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"# # send a GET request to the URL to download the file. Stream since it's large\n",
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"# response = requests.get(url, stream=True)\n",
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"\n",
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"# # open the file in binary mode and write the contents of the response to it in chunks\n",
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"# # This is a large file, so be prepared to wait.\n",
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"# with open(local_path, 'wb') as f:\n",
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"# for chunk in tqdm(response.iter_content(chunk_size=8192)):\n",
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"# if chunk:\n",
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"# f.write(chunk)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Callbacks support token-wise streaming\n",
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"callbacks = [StreamingStdOutCallbackHandler()]\n",
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"# Verbose is required to pass to the callback manager\n",
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"llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.6"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# GPT4All\n",
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"\n",
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"[GitHub:nomic-ai/gpt4all](https://github.com/nomic-ai/gpt4all) an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.\n",
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"\n",
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"This example goes over how to use LangChain to interact with `GPT4All` models."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"%pip install pygpt4all > /dev/null"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 1,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"from langchain import PromptTemplate, LLMChain\n",
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"from langchain.llms import GPT4All\n",
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"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {
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"tags": []
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},
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"outputs": [],
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"source": [
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"template = \"\"\"Question: {question}\n",
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"\n",
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"Answer: Let's think step by step.\"\"\"\n",
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"\n",
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"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Specify Model\n",
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"\n",
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"To run locally, download a compatible ggml-formatted model. For more info, visit https://github.com/nomic-ai/pygpt4all\n",
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"\n",
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"For full installation instructions go [here](https://gpt4all.io/index.html).\n",
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"\n",
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"The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
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"\n",
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"Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"local_path = './models/ggml-gpt4all-l13b-snoozy.bin' # replace with your desired local file path"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Uncomment the below block to download a model. You may want to update `url` to a new version."
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# import requests\n",
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"\n",
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"# from pathlib import Path\n",
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"# from tqdm import tqdm\n",
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"\n",
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"# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
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"\n",
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"# # Example model. Check https://github.com/nomic-ai/pygpt4all for the latest models.\n",
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"# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'\n",
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"\n",
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"# # send a GET request to the URL to download the file. Stream since it's large\n",
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"# response = requests.get(url, stream=True)\n",
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"\n",
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"# # open the file in binary mode and write the contents of the response to it in chunks\n",
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"# # This is a large file, so be prepared to wait.\n",
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"# with open(local_path, 'wb') as f:\n",
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"# for chunk in tqdm(response.iter_content(chunk_size=8192)):\n",
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"# if chunk:\n",
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"# f.write(chunk)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"# Callbacks support token-wise streaming\n",
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"callbacks = [StreamingStdOutCallbackHandler()]\n",
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"# Verbose is required to pass to the callback manager\n",
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"llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"llm_chain = LLMChain(prompt=prompt, llm=llm)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
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"\n",
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"llm_chain.run(question)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.2"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 4
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}
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