gpt4all/gpt4all-bindings/python/docs/old/gpt4all_python_embedding.md

Embeddings

GPT4All supports generating high quality embeddings of arbitrary length text using any embedding model supported by llama.cpp.

An embedding is a vector representation of a piece of text. Embeddings are useful for tasks such as retrieval for question answering (including retrieval augmented generation or RAG), semantic similarity search, classification, and topic clustering.

Supported Embedding Models

The following models have built-in support in Embed4All:

Name	Embed4All model_name	Context Length	Embedding Length	File Size
SBert	all‑MiniLM‑L6‑v2.gguf2.f16.gguf	512	384	44 MiB
Nomic Embed v1	nomic‑embed‑text‑v1.f16.gguf	2048	768	262 MiB
Nomic Embed v1.5	nomic‑embed‑text‑v1.5.f16.gguf	2048	64-768	262 MiB

The context length is the maximum number of word pieces, or tokens, that a model can embed at once. Embedding texts longer than a model's context length requires some kind of strategy; see Embedding Longer Texts for more information.

The embedding length is the size of the vector returned by Embed4All.embed.

Quickstart

pip install gpt4all

Generating Embeddings

By default, embeddings will be generated on the CPU using all-MiniLM-L6-v2.

=== "Embed4All Example" py from gpt4all import Embed4All text = 'The quick brown fox jumps over the lazy dog' embedder = Embed4All() output = embedder.embed(text) print(output) === "Output" [0.034696947783231735, -0.07192722707986832, 0.06923297047615051, ...]

You can also use the GPU to accelerate the embedding model by specifying the device parameter. See the GPT4All constructor for more information.

=== "GPU Example" py from gpt4all import Embed4All text = 'The quick brown fox jumps over the lazy dog' embedder = Embed4All(device='gpu') output = embedder.embed(text) print(output) === "Output" [0.034696947783231735, -0.07192722707986832, 0.06923297047615051, ...]

Nomic Embed

Embed4All has built-in support for Nomic's open-source embedding model, Nomic Embed. When using this model, you must specify the task type using the prefix argument. This may be one of search_query, search_document, classification, or clustering. For retrieval applications, you should prepend search_document for all of your documents and search_query for your queries. See the Nomic Embedding Guide for more info.

=== "Nomic Embed Example" py from gpt4all import Embed4All text = 'Who is Laurens van der Maaten?' embedder = Embed4All('nomic-embed-text-v1.f16.gguf') output = embedder.embed(text, prefix='search_query') print(output) === "Output" [-0.013357644900679588, 0.027070969343185425, -0.0232995692640543, ...]

Embedding Longer Texts

Embed4All accepts a parameter called long_text_mode. This controls the behavior of Embed4All for texts longer than the context length of the embedding model.

In the default mode of "mean", Embed4All will break long inputs into chunks and average their embeddings to compute the final result.

To change this behavior, you can set the long_text_mode parameter to "truncate", which will truncate the input to the sequence length of the model before generating a single embedding.

=== "Truncation Example" py from gpt4all import Embed4All text = 'The ' * 512 + 'The quick brown fox jumps over the lazy dog' embedder = Embed4All() output = embedder.embed(text, long_text_mode="mean") print(output) print() output = embedder.embed(text, long_text_mode="truncate") print(output) === "Output" ``` [0.0039850445464253426, 0.04558328539133072, 0.0035536508075892925, ...]

[-0.009771130047738552, 0.034792833030223846, -0.013273917138576508, ...]
```

Batching

You can send multiple texts to Embed4All in a single call. This can give faster results when individual texts are significantly smaller than n_ctx tokens. (n_ctx defaults to 2048.)

=== "Batching Example" py from gpt4all import Embed4All texts = ['The quick brown fox jumps over the lazy dog', 'Foo bar baz'] embedder = Embed4All() output = embedder.embed(texts) print(output[0]) print() print(output[1]) === "Output" ``` [0.03551332652568817, 0.06137588247656822, 0.05281158909201622, ...]

[-0.03879690542817116, 0.00013223080895841122, 0.023148687556385994, ...]
```

The number of texts that can be embedded in one pass of the model is proportional to the n_ctx parameter of Embed4All. Increasing it may increase batched embedding throughput if you have a fast GPU, at the cost of VRAM.

embedder = Embed4All(n_ctx=4096, device='gpu')

Resizable Dimensionality

The embedding dimension of Nomic Embed v1.5 can be resized using the dimensionality parameter. This parameter supports any value between 64 and 768.

Shorter embeddings use less storage, memory, and bandwidth with a small performance cost. See the blog post for more info.

=== "Matryoshka Example" py from gpt4all import Embed4All text = 'The quick brown fox jumps over the lazy dog' embedder = Embed4All('nomic-embed-text-v1.5.f16.gguf') output = embedder.embed(text, dimensionality=64) print(len(output)) print(output) === "Output" 64 [-0.03567073494195938, 0.1301717758178711, -0.4333043396472931, ...]

API documentation

::: gpt4all.gpt4all.Embed4All