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# Docs: compound ecosystem and integrations **Problem statement:** We have a big overlap between the References/Integrations and Ecosystem/LongChain Ecosystem pages. It confuses users. It creates a situation when new integration is added only on one of these pages, which creates even more confusion. - removed References/Integrations page (but move all its information into the individual integration pages - in the next PR). - renamed Ecosystem/LongChain Ecosystem into Integrations/Integrations. I like the Ecosystem term. It is more generic and semantically richer than the Integration term. But it mentally overloads users. The `integration` term is more concrete. UPDATE: after discussion, the Ecosystem is the term. Ecosystem/Integrations is the page (in place of Ecosystem/LongChain Ecosystem). As a result, a user gets a single place to start with the individual integration.
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GPT4All
This page covers how to use the GPT4All wrapper within LangChain. The tutorial is divided into two parts: installation and setup, followed by usage with an example.
Installation and Setup
- Install the Python package with
pip install pyllamacpp - Download a GPT4All model and place it in your desired directory
Usage
GPT4All
To use the GPT4All wrapper, you need to provide the path to the pre-trained model file and the model's configuration.
from langchain.llms import GPT4All
# Instantiate the model. Callbacks support token-wise streaming
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
# Generate text
response = model("Once upon a time, ")
You can also customize the generation parameters, such as n_predict, temp, top_p, top_k, and others.
To stream the model's predictions, add in a CallbackManager.
from langchain.llms import GPT4All
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
# There are many CallbackHandlers supported, such as
# from langchain.callbacks.streamlit import StreamlitCallbackHandler
callbacks = [StreamingStdOutCallbackHandler()]
model = GPT4All(model="./models/gpt4all-model.bin", n_ctx=512, n_threads=8)
# Generate text. Tokens are streamed through the callback manager.
model("Once upon a time, ", callbacks=callbacks)
Model File
You can find links to model file downloads in the pyllamacpp repository.
For a more detailed walkthrough of this, see this notebook