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

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# GPT4All\n",
"\n",
"[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",
"\n",
"This example goes over how to use LangChain to interact with `GPT4All` models."
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"tags": []
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"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Note: you may need to restart the kernel to use updated packages.\n"
]
}
],
"source": [
"%pip install gpt4all > /dev/null"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"from langchain import PromptTemplate, LLMChain\n",
"from langchain.llms import GPT4All\n",
"from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"tags": []
},
"outputs": [],
"source": [
"template = \"\"\"Question: {question}\n",
"\n",
"Answer: Let's think step by step.\"\"\"\n",
"\n",
"prompt = PromptTemplate(template=template, input_variables=[\"question\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Specify Model\n",
"\n",
"To run locally, download a compatible ggml-formatted model. \n",
" \n",
"**Download option 1**: The [gpt4all page](https://gpt4all.io/index.html) has a useful `Model Explorer` section:\n",
"\n",
"* Select a model of interest\n",
"* Download using the UI and move the `.bin` to the `local_path` (noted below)\n",
"\n",
"For more info, visit https://github.com/nomic-ai/gpt4all.\n",
"\n",
"--- \n",
"\n",
"**Download option 2**: Uncomment the below block to download a model. \n",
"\n",
"* You may want to update `url` to a new version, whih can be browsed using the [gpt4all page](https://gpt4all.io/index.html)."
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"local_path = (\n",
" \"./models/ggml-gpt4all-l13b-snoozy.bin\" # replace with your desired local file path\n",
")\n",
"\n",
"# import requests\n",
"\n",
"# from pathlib import Path\n",
"# from tqdm import tqdm\n",
"\n",
"# Path(local_path).parent.mkdir(parents=True, exist_ok=True)\n",
"\n",
"# # Example model. Check https://github.com/nomic-ai/gpt4all for the latest models.\n",
"# url = 'http://gpt4all.io/models/ggml-gpt4all-l13b-snoozy.bin'\n",
"\n",
"# # send a GET request to the URL to download the file. Stream since it's large\n",
"# response = requests.get(url, stream=True)\n",
"\n",
"# # open the file in binary mode and write the contents of the response to it in chunks\n",
"# # This is a large file, so be prepared to wait.\n",
"# with open(local_path, 'wb') as f:\n",
"# for chunk in tqdm(response.iter_content(chunk_size=8192)):\n",
"# if chunk:\n",
"# f.write(chunk)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"# Callbacks support token-wise streaming\n",
"callbacks = [StreamingStdOutCallbackHandler()]\n",
"\n",
"# Verbose is required to pass to the callback manager\n",
"llm = GPT4All(model=local_path, callbacks=callbacks, verbose=True)\n",
"\n",
"# If you want to use a custom model add the backend parameter\n",
"# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends\n",
"llm = GPT4All(model=local_path, backend=\"gptj\", callbacks=callbacks, verbose=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"llm_chain = LLMChain(prompt=prompt, llm=llm)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"question = \"What NFL team won the Super Bowl in the year Justin Bieber was born?\"\n",
"\n",
"llm_chain.run(question)"
]
}
],
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