forked from Archives/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>fix_agent_callbacks
Zander Chase
1 year ago
committed by
GitHub
parent
f0a4bbb8e2
commit
c4cb55a0c5
@ -1,173 +1,173 @@
|
||||
{
|
||||
"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": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pip install pyllamacpp > /dev/null"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"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": 2,
|
||||
"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. For more info, visit https://github.com/nomic-ai/pyllamacpp\n",
|
||||
"\n",
|
||||
"For full installation instructions go [here](https://gpt4all.io/index.html).\n",
|
||||
"\n",
|
||||
"The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
|
||||
"\n",
|
||||
"Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_path = './models/gpt4all-lora-quantized-ggml.bin' # replace with your desired local file path"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Uncomment the below block to download a model. You may want to update `url` to a new version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 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/pyllamacpp for the latest models.\n",
|
||||
"# url = 'https://the-eye.eu/public/AI/models/nomic-ai/gpt4all/gpt4all-lora-quantized-ggml.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",
|
||||
"# Verbose is required to pass to the callback manager\n",
|
||||
"llm = GPT4All(model=local_path, 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)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.6"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
"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": []
|
||||
},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Note: you may need to restart the kernel to use updated packages.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"%pip install pygpt4all > /dev/null"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"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": 2,
|
||||
"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. For more info, visit https://github.com/nomic-ai/pygpt4all\n",
|
||||
"\n",
|
||||
"For full installation instructions go [here](https://gpt4all.io/index.html).\n",
|
||||
"\n",
|
||||
"The GPT4All Chat installer needs to decompress a 3GB LLM model during the installation process!\n",
|
||||
"\n",
|
||||
"Note that new models are uploaded regularly - check the link above for the most recent `.bin` URL"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"local_path = './models/ggml-gpt4all-l13b-snoozy.bin' # replace with your desired local file path"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"Uncomment the below block to download a model. You may want to update `url` to a new version."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"# 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/pygpt4all 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",
|
||||
"# Verbose is required to pass to the callback manager\n",
|
||||
"llm = GPT4All(model=local_path, 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)"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 4
|
||||
}
|
||||
|
||||
Loading…
Reference in New Issue