GPT4All

GitHub:nomic-ai/gpt4all an ecosystem of open-source chatbots trained on a massive collections of clean assistant data including code, stories and dialogue.

This example goes over how to use LangChain to interact with GPT4All models.

%pip install --upgrade --quiet langchain-community gpt4all

Import GPT4All

from langchain_community.llms import GPT4All
from langchain_core.prompts import PromptTemplate

API Reference:GPT4All | PromptTemplate

Set Up Question to pass to LLM

template = """Question: {question}

Answer: Let's think step by step."""

prompt = PromptTemplate.from_template(template)

Specify Model

To run locally, download a compatible ggml-formatted model.

The gpt4all page has a useful Model Explorer section:

• Select a model of interest
• Download using the UI and move the .bin to the local_path (noted below)

For more info, visit https://github.com/nomic-ai/gpt4all.

---

This integration does not yet support streaming in chunks via the .stream() method. The below example uses a callback handler with streaming=True:

local_path = (
    "./models/Meta-Llama-3-8B-Instruct.Q4_0.gguf"  # replace with your local file path
)

from langchain_core.callbacks import BaseCallbackHandler

count = 0


class MyCustomHandler(BaseCallbackHandler):
    def on_llm_new_token(self, token: str, **kwargs) -> None:
        global count
        if count < 10:
            print(f"Token: {token}")
            count += 1


# Verbose is required to pass to the callback manager
llm = GPT4All(model=local_path, callbacks=[MyCustomHandler()], streaming=True)

# If you want to use a custom model add the backend parameter
# Check https://docs.gpt4all.io/gpt4all_python.html for supported backends
# llm = GPT4All(model=local_path, backend="gptj", callbacks=callbacks, streaming=True)

chain = prompt | llm

question = "What NFL team won the Super Bowl in the year Justin Bieber was born?"

# Streamed tokens will be logged/aggregated via the passed callback
res = chain.invoke({"question": question})

API Reference:BaseCallbackHandler

Token:  Justin
Token:  Bieber
Token:  was
Token:  born
Token:  on
Token:  March
Token:  
Token: 1
Token: ,
Token:

Related

• LLM conceptual guide
• LLM how-to guides