GPT4ALL
Author: Do Woung Kong
Peer Review : Sun Hyoung Lee, Yongdam Kim
Proofread : Youngjun cho
This is a part of LangChain Open Tutorial
Overview
GPT4All
is a local execution-based privacy chatbot that can be used for free.
No GPU or internet connection is required, and GPT4All
offers popular models such as GPT4All Falcon, Wizard, and its own models.
This notebook explains how to use GPT4Allembeddings
with LangChain
.
Table of Contents
References
Environment Setup
Set up the environment. You may refer to Environment Setup for more details.
[Note]
langchain-opentutorial
is a package that provides a set of easy-to-use environment setup, useful functions and utilities for tutorials.You can checkout the
langchain-opentutorial
for more details.
%%capture --no-stderr
!pip install langchain-opentutorial
# Install required packages
from langchain_opentutorial import package
package.install(
["langchain_community"],
verbose=False,
upgrade=False,
)
Install Python Binding for GPT4All
Before diving into the practical exercises, you need to install the Python bindings for GPT4All
.
Python bindings allow a Python program to interface with external libraries or tools, enabling seamless integration and usage of functionalities provided by those external resources.
To install the Python bindings for GPT4All
, run the following command:
%pip install --upgrade --quiet gpt4all > /dev/null
Note: you may need to restart the kernel to use updated packages.
Import the GPT4AllEmbeddings
class from the langchain_community.embeddings
module.
The GPT4AllEmbeddings
class provides functionality to embed text data into vectors using the GPT4All model.
This class implements the embedding interface of the LangChain framework, allowing it to be used seamlessly with LangChain's various features.
from langchain_community.embeddings import GPT4AllEmbeddings
GPT4All supports the generation of high-quality embeddings for text documents of arbitrary length using a contrastive learning sentence transformer optimized for CPUs. These embeddings offer a quality comparable to many tasks using OpenAI models.
An instance of the GPT4AllEmbeddings
class is created.
The
GPT4AllEmbeddings
class is an embedding model that uses the GPT4All model to transform text data into vectors.In this code, the
gpt4all_embd
variable is assigned an instance ofGPT4AllEmbeddings
.You can then use
gpt4all_embd
to convert text data into vectors.
# Create a GPT4All embedding object
gpt4all_embd = GPT4AllEmbeddings()
Assign the string "This is a sample sentence for testing embeddings." to the text
variable.
# Define a sample document text for testing
text = "This is a sample sentence for testing embeddings."
Embed the Textual Data
The process of embedding text data is as follows:
First, the text data is tokenized and converted into numerical form.
During this step, a pre-trained tokenizer is used to split the text into tokens and map each token to a unique integer.
Next, the tokenized data is input into an embedding layer, where it is transformed into high-dimensional dense vectors.
In this process, each token is represented as a vector of real numbers that capture the token's meaning and context.
Finally, the embedded vectors can be used in various natural language processing tasks.
For example, they can serve as input data for tasks such as document classification, sentiment analysis, and machine translation, enhancing model performance.
This process of text data embedding plays a crucial role in natural language processing, making it essential for efficiently processing and analyzing large amounts of text data.
Use the embed_query
method of the gpt4all_embd
object to embed the given text (text
).
The
text
variable stores the text to be embedded.The
gpt4all_embd
object uses the GPT4All model to perform text embedding.The
embed_query
method converts the given text into a vector format and returns it.The embedding result is stored in the
query_result
variable.
# Generate query embeddings for the given text.
query_result = gpt4all_embd.embed_query(text)
# Check the dimensions of the embedded space.
len(query_result)
384
You can use the embed_documents
function to embed multiple text fragments.
Use the embed_documents
method of the gpt4all_embd
object to embed the text
document.
Wrap the
text
document in a list and pass it as an argument to theembed_documents
method.The
embed_documents
method calculates and returns the embedding vector of the document.The resulting embedding vector is stored in the
doc_result
variable.
Additionally, these embeddings can be mapped with Nomic's Atlas (https://docs.nomic.ai/index.html) to visualize the data.
# Generate query embeddings for the given text.
doc_result = gpt4all_embd.embed_documents([text])
# Check the dimensions of the embedded space.
len(doc_result[0])
384
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