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python: documentation update and typing improvements (#2129)

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Key changes:
* revert "python: tweak constructor docstrings"
* docs: update python GPT4All and Embed4All documentation
* breaking: require keyword args to GPT4All.generate

Signed-off-by: Jared Van Bortel <jared@nomic.ai>
This commit is contained in:
Jared Van Bortel
2024-03-19 17:25:22 -04:00
committed by GitHub
parent f30151491d
commit a1bb6084ed
9 changed files with 303 additions and 254 deletions
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8
gpt4all-backend/llamamodel.cpp
View File

@@ -692,9 +692,9 @@ void LLamaModel::embed(
return "unsupported dimensionality " + std::to_string(dimensionality) + " for model " + modelName;
};
if (!spec->matryoshkaCapable)
throw std::logic_error(msg() + " (supported: " + std::to_string(n_embd) + ")");
throw std::out_of_range(msg() + " (supported: " + std::to_string(n_embd) + ")");
if (dimensionality == 0 || dimensionality > n_embd)
throw std::logic_error(msg() + " (recommended: " + spec->recommendedDims + ")");
throw std::out_of_range(msg() + " (recommended: " + spec->recommendedDims + ")");
}
if (!prefix) {
@@ -709,7 +709,7 @@ void LLamaModel::embed(
{
std::stringstream ss;
ss << std::quoted(*prefix) << " is not a valid task type for model " << modelName;
throw std::logic_error(ss.str());
throw std::invalid_argument(ss.str());
}
embedInternal(texts, embeddings, *prefix, dimensionality, doMean, atlas, spec);
@@ -763,7 +763,7 @@ void LLamaModel::embedInternal(
tokenize(text, inp, false);
if (atlas && inp.size() > atlasMaxLength) {
if (doMean) {
throw std::logic_error(
throw std::length_error(
"length of text at index " + std::to_string(i) + " is " + std::to_string(inp.size()) +
" tokens which exceeds limit of " + std::to_string(atlasMaxLength)
);

2
gpt4all-bindings/python/docs/gpt4all_cli.md
View File

@@ -5,7 +5,7 @@ The GPT4All command-line interface (CLI) is a Python script which is built on to
package. The source code, README, and local build instructions can be found
[here][repo-bindings-cli].
[docs-bindings-python]: gpt4all_python.html
[docs-bindings-python]: gpt4all_python.md
[repo-bindings-python]: https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/python
[repo-bindings-cli]: https://github.com/nomic-ai/gpt4all/tree/main/gpt4all-bindings/cli
[typer]: https://typer.tiangolo.com/

34
gpt4all-bindings/python/docs/gpt4all_modal.md
View File

@@ -1,34 +0,0 @@
# GPT4All with Modal Labs
You can easily query any GPT4All model on [Modal Labs](https://modal.com/) infrastructure!
## Example
```python
import modal
def download_model():
import gpt4all
#you can use any model from https://gpt4all.io/models/models2.json
return gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy.bin")
image=modal.Image.debian_slim().pip_install("gpt4all").run_function(download_model)
stub = modal.Stub("gpt4all", image=image)
@stub.cls(keep_warm=1)
class GPT4All:
def __enter__(self):
print("Downloading model")
self.gptj = download_model()
print("Loaded model")
@modal.method()
def generate(self):
messages = [{"role": "user", "content": "Name 3 colors"}]
completion = self.gptj.chat_completion(messages)
print(f"Completion: {completion}")
@stub.local_entrypoint()
def main():
model = GPT4All()
for i in range(10):
model.generate.call()
```

177
gpt4all-bindings/python/docs/gpt4all_python.md
View File

@@ -8,30 +8,22 @@ The source code and local build instructions can be found [here](https://github.
pip install gpt4all
```
=== "GPT4All Example"
``` py
from gpt4all import GPT4All
model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
output = model.generate("The capital of France is ", max_tokens=3)
print(output)
```
=== "Output"
```
1. Paris
```
This will:
- Instantiate `GPT4All`, which is the primary public API to your large language model (LLM).
- Automatically download the given model to `~/.cache/gpt4all/` if not already present.
- Through `model.generate(...)` the model starts working on a response. There are various ways to
steer that process. Here, `max_tokens` sets an upper limit, i.e. a hard cut-off point to the output.
Read further to see how to chat with this model.
### Chatting with GPT4All
Local LLMs can be optimized for chat conversations by reusing previous computational history.
Use the GPT4All `chat_session` context manager to hold chat conversations with the model.
To start chatting with a local LLM, you will need to start a chat session. Within a chat session, the model will be
prompted with the appropriate template, and history will be preserved between successive calls to `generate()`.
=== "GPT4All Example"
``` py
@@ -72,15 +64,19 @@ Use the GPT4All `chat_session` context manager to hold chat conversations with t
]
```
When using GPT4All models in the `chat_session` context:
When using GPT4All models in the `chat_session()` context:
- Consecutive chat exchanges are taken into account and not discarded until the session ends; as long as the model has capacity.
- Internal K/V caches are preserved from previous conversation history, speeding up inference.
- The model is given a system and prompt template which make it chatty. Depending on `allow_download=True` (default),
it will obtain the latest version of [models2.json] from the repository, which contains specifically tailored templates
for models. Conversely, if it is not allowed to download, it falls back to default templates instead.
- A system prompt is inserted into the beginning of the model's context.
- Each prompt passed to `generate()` is wrapped in the appropriate prompt template. If you pass `allow_download=False`
to GPT4All or are using a model that is not from the official models list, you must pass a prompt template using the
`prompt_template` parameter of `chat_session()`.
[models2.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models2.json
NOTE: If you do not use `chat_session()`, calls to `generate()` will not be wrapped in a prompt template. This will
cause the model to *continue* the prompt instead of *answering* it. When in doubt, use a chat session, as many newer
models are designed to be used exclusively with a prompt template.
[models3.json]: https://github.com/nomic-ai/gpt4all/blob/main/gpt4all-chat/metadata/models3.json
### Streaming Generations
@@ -91,13 +87,14 @@ To interact with GPT4All responses as the model generates, use the `streaming=Tr
from gpt4all import GPT4All
model = GPT4All("orca-mini-3b-gguf2-q4_0.gguf")
tokens = []
for token in model.generate("The capital of France is", max_tokens=20, streaming=True):
with model.chat_session():
for token in model.generate("What is the capital of France?", streaming=True):
tokens.append(token)
print(tokens)
```
=== "Output"
```
[' Paris', ' is', ' a', ' city', ' that', ' has', ' been', ' a', ' major', ' cultural', ' and', ' economic', ' center', ' for', ' over', ' ', '2', ',', '0', '0']
[' The', ' capital', ' of', ' France', ' is', ' Paris', '.']
```
@@ -131,19 +128,10 @@ generation; be sure to review all their descriptions.
The model folder can be set with the `model_path` parameter when creating a `GPT4All` instance. The example below is
is the same as if it weren't provided; that is, `~/.cache/gpt4all/` is the default folder.
=== "GPT4All Model Folder Example"
``` py
from pathlib import Path
from gpt4all import GPT4All
model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf',
model_path=(Path.home() / '.cache' / 'gpt4all'),
allow_download=False)
response = model.generate('my favorite 3 fruits are:', temp=0)
print(response)
```
=== "Output"
```
My favorite three fruits are apples, bananas and oranges.
model = GPT4All(model_name='orca-mini-3b-gguf2-q4_0.gguf', model_path=Path.home() / '.cache' / 'gpt4all')
```
If you want to point it at the chat GUI's default folder, it should be:
@@ -179,22 +167,20 @@ Alternatively, you could also change the module's default model directory:
``` py
from pathlib import Path
import gpt4all.gpt4all
gpt4all.gpt4all.DEFAULT_MODEL_DIRECTORY = Path.home() / 'my' / 'models-directory'
from gpt4all import GPT4All
from gpt4all import GPT4All, gpt4all
gpt4all.DEFAULT_MODEL_DIRECTORY = Path.home() / 'my' / 'models-directory'
model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf')
...
```
### Managing Templates
Session templates can be customized when starting a `chat_session` context:
When using a `chat_session()`, you may customize the system prompt, and set the prompt template if necessary:
=== "GPT4All Custom Session Templates Example"
``` py
from gpt4all import GPT4All
model = GPT4All('wizardlm-13b-v1.2.Q4_0.gguf')
system_template = 'A chat between a curious user and an artificial intelligence assistant.'
system_template = 'A chat between a curious user and an artificial intelligence assistant.\n'
# many models use triple hash '###' for keywords, Vicunas are simpler:
prompt_template = 'USER: {0}\nASSISTANT: '
with model.chat_session(system_template, prompt_template):
@@ -218,111 +204,38 @@ Session templates can be customized when starting a `chat_session` context:
particles, making the sky appear blue to our eyes.
```
To do the same outside a session, the input has to be formatted manually. For example:
=== "GPT4All Templates Outside a Session Example"
``` py
model = GPT4All('wizardlm-13b-v1.2.Q4_0.gguf')
system_template = 'A chat between a curious user and an artificial intelligence assistant.'
prompt_template = 'USER: {0}\nASSISTANT: '
prompts = ['name 3 colors', 'now name 3 fruits', 'what were the 3 colors in your earlier response?']
first_input = system_template + prompt_template.format(prompts[0])
response = model.generate(first_input, temp=0)
print(response)
for prompt in prompts[1:]:
response = model.generate(prompt_template.format(prompt), temp=0)
print(response)
```
=== "Output"
```
1) Red
2) Blue
3) Green
1. Apple
2. Banana
3. Orange
The colors in my previous response are blue, green and red.
```
### Introspection
A less apparent feature is the capacity to log the final prompt that gets sent to the model. It relies on
[Python's logging facilities][py-logging] implemented in the `pyllmodel` module at the `INFO` level. You can activate it
for example with a `basicConfig`, which displays it on the standard error stream. It's worth mentioning that Python's
logging infrastructure offers [many more customization options][py-logging-cookbook].
[py-logging]: https://docs.python.org/3/howto/logging.html
[py-logging-cookbook]: https://docs.python.org/3/howto/logging-cookbook.html
=== "GPT4All Prompt Logging Example"
``` py
import logging
from gpt4all import GPT4All
logging.basicConfig(level=logging.INFO)
model = GPT4All('nous-hermes-llama2-13b.Q4_0.gguf')
with model.chat_session('You are a geography expert.\nBe terse.',
'### Instruction:\n{0}\n\n### Response:\n'):
response = model.generate('who are you?', temp=0)
print(response)
response = model.generate('what are your favorite 3 mountains?', temp=0)
print(response)
```
=== "Output"
```
INFO:gpt4all.pyllmodel:LLModel.prompt_model -- prompt:
You are a geography expert.
Be terse.
### Instruction:
who are you?
### Response:
===/LLModel.prompt_model -- prompt/===
I am an AI-powered chatbot designed to assist users with their queries related to geographical information.
INFO:gpt4all.pyllmodel:LLModel.prompt_model -- prompt:
### Instruction:
what are your favorite 3 mountains?
### Response:
===/LLModel.prompt_model -- prompt/===
1) Mount Everest - Located in the Himalayas, it is the highest mountain on Earth and a significant challenge for mountaineers.
2) Kangchenjunga - This mountain is located in the Himalayas and is the third-highest peak in the world after Mount Everest and K2.
3) Lhotse - Located in the Himalayas, it is the fourth highest mountain on Earth and offers a challenging climb for experienced mountaineers.
```
### Without Online Connectivity
To prevent GPT4All from accessing online resources, instantiate it with `allow_download=False`. This will disable both
downloading missing models and [models2.json], which contains information about them. As a result, predefined templates
are used instead of model-specific system and prompt templates:
To prevent GPT4All from accessing online resources, instantiate it with `allow_download=False`. When using this flag,
there will be no default system prompt by default, and you must specify the prompt template yourself.
=== "GPT4All Default Templates Example"
You can retrieve a model's default system prompt and prompt template with an online instance of GPT4All:
=== "Prompt Template Retrieval"
``` py
from gpt4all import GPT4All
model = GPT4All('ggml-mpt-7b-chat.bin', allow_download=False)
# when downloads are disabled, it will use the default templates:
print("default system template:", repr(model.config['systemPrompt']))
print("default prompt template:", repr(model.config['promptTemplate']))
print()
# even when inside a session:
with model.chat_session():
assert model.current_chat_session[0]['role'] == 'system'
print("session system template:", repr(model.current_chat_session[0]['content']))
print("session prompt template:", repr(model._current_prompt_template))
model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf')
print(repr(model.config['systemPrompt']))
print(repr(model.config['promptTemplate']))
```
=== "Output"
```
default system template: ''
default prompt template: '### Human:\n{0}\n\n### Assistant:\n'
session system template: ''
session prompt template: '### Human:\n{0}\n\n### Assistant:\n'
```py
'### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n'
'### User:\n{0}\n### Response:\n'
```
Then you can pass them explicitly when creating an offline instance:
``` py
from gpt4all import GPT4All
model = GPT4All('orca-mini-3b-gguf2-q4_0.gguf', allow_download=False)
system_prompt = '### System:\nYou are an AI assistant that follows instruction extremely well. Help as much as you can.\n\n'
prompt_template = '### User:\n{0}\n\n### Response:\n'
with model.chat_session(system_prompt=system_prompt, prompt_template=prompt_template):
...
```
### Interrupting Generation
The simplest way to stop generation is to set a fixed upper limit with the `max_tokens` parameter.

163
gpt4all-bindings/python/docs/gpt4all_python_embedding.md
View File

@@ -1,18 +1,41 @@
# Embeddings
GPT4All supports generating high quality embeddings of arbitrary length documents of text using a CPU optimized contrastively trained [Sentence Transformer](https://www.sbert.net/). These embeddings are comparable in quality for many tasks with OpenAI.
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&#x2011;MiniLM&#x2011;L6&#x2011;v2.gguf2.f16.gguf | 512 | 384 | 44 MiB |
| [Nomic Embed v1] | nomic&#x2011;embed&#x2011;text&#x2011;v1.f16.gguf | 2048 | 768 | 262 MiB |
| [Nomic Embed v1.5] | nomic&#x2011;embed&#x2011;text&#x2011;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`.
[SBert]: https://huggingface.co/sentence-transformers/all-MiniLM-L6-v2
[Nomic Embed v1]: https://huggingface.co/nomic-ai/nomic-embed-text-v1
[Nomic Embed v1.5]: https://huggingface.co/nomic-ai/nomic-embed-text-v1.5
[Embedding Longer Texts]: #embedding-longer-texts
## Quickstart
```bash
pip install gpt4all
```
### Generating embeddings
The embedding model will automatically be downloaded if not installed.
### Generating Embeddings
By default, embeddings will be generated on the CPU using all-MiniLM-L6-v2.
=== "Embed4All Example"
```py
from gpt4all import GPT4All, Embed4All
from gpt4all import Embed4All
text = 'The quick brown fox jumps over the lazy dog'
embedder = Embed4All()
output = embedder.embed(text)
@@ -22,13 +45,131 @@ The embedding model will automatically be downloaded if not installed.
```
[0.034696947783231735, -0.07192722707986832, 0.06923297047615051, ...]
```
### Speed of embedding generation
The following table lists the generation speed for text document captured on an Intel i913900HX CPU with DDR5 5600 running with 8 threads under stable load.
| Tokens | 128 | 512 | 2048 | 8129 | 16,384 |
| --------------- | ---- | ---- | ---- | ---- | ---- |
| Wall time (s) | .02 | .08 | .24 | .96 | 1.9 |
| Tokens / Second | 6508 | 6431 | 8622 | 8509 | 8369 |
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, ...]
```
[GPT4All constructor]: gpt4all_python.md#gpt4all.gpt4all.GPT4All.__init__
### 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, ...]
```
[Nomic Embed]: https://blog.nomic.ai/posts/nomic-embed-text-v1
[Nomic Embedding Guide]: https://docs.nomic.ai/atlas/guides/embeddings#embedding-task-types
### 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.
```py
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.
[blog post]: https://blog.nomic.ai/posts/nomic-embed-matryoshka
=== "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

15
gpt4all-bindings/python/gpt4all/_pyllmodel.py
View File

@@ -9,7 +9,7 @@ import sys
import threading
from enum import Enum
from queue import Queue
from typing import Callable, Iterable, overload
from typing import Any, Callable, Iterable, overload
if sys.version_info >= (3, 9):
import importlib.resources as importlib_resources
@@ -295,15 +295,20 @@ class LLModel:
) -> list[float]: ...
@overload
def generate_embeddings(
self, text: list[str], prefix: str, dimensionality: int, do_mean: bool, atlas: bool,
self, text: list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
) -> list[list[float]]: ...
@overload
def generate_embeddings(
self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
) -> Any: ...
def generate_embeddings(self, text, prefix, dimensionality, do_mean, atlas):
def generate_embeddings(
self, text: str | list[str], prefix: str | None, dimensionality: int, do_mean: bool, atlas: bool,
) -> Any:
if not text:
raise ValueError("text must not be None or empty")
single_text = isinstance(text, str)
if single_text:
if (single_text := isinstance(text, str)):
text = [text]
# prepare input

98
gpt4all-bindings/python/gpt4all/gpt4all.py
View File

@@ -10,7 +10,7 @@ import time
import warnings
from contextlib import contextmanager
from pathlib import Path
from typing import Any, Dict, Iterable, List, Optional, Union, overload
from typing import TYPE_CHECKING, Any, Iterable, Literal, overload
import requests
from requests.exceptions import ChunkedEncodingError
@@ -19,31 +19,35 @@ from urllib3.exceptions import IncompleteRead, ProtocolError
from . import _pyllmodel
if TYPE_CHECKING:
from typing import TypeAlias
# TODO: move to config
DEFAULT_MODEL_DIRECTORY = Path.home() / ".cache" / "gpt4all"
DEFAULT_PROMPT_TEMPLATE = "### Human:\n{0}\n\n### Assistant:\n"
ConfigType = Dict[str, str]
MessageType = Dict[str, str]
ConfigType: TypeAlias = 'dict[str, str]'
MessageType: TypeAlias = 'dict[str, str]'
class Embed4All:
"""
Python class that handles embeddings for GPT4All.
Args:
model_name: The name of the embedding model to use. Defaults to `all-MiniLM-L6-v2.gguf2.f16.gguf`.
All other arguments are passed to the GPT4All constructor. See its documentation for more info.
"""
MIN_DIMENSIONALITY = 64
def __init__(self, model_name: Optional[str] = None, **kwargs):
def __init__(self, model_name: str | None = None, n_threads: int | None = None, **kwargs):
"""
Constructor
Args:
n_threads: number of CPU threads used by GPT4All. Default is None, then the number of threads are determined automatically.
"""
if model_name is None:
model_name = 'all-MiniLM-L6-v2.gguf2.f16.gguf'
self.gpt4all = GPT4All(model_name, **kwargs)
self.gpt4all = GPT4All(model_name, n_threads=n_threads, **kwargs)
@overload
def embed(
@@ -56,7 +60,10 @@ class Embed4All:
atlas: bool = ...,
) -> list[list[float]]: ...
def embed(self, text, prefix=None, dimensionality=None, long_text_mode="mean", atlas=False):
def embed(
self, text: str | list[str], prefix: str | None = None, dimensionality: int | None = None,
long_text_mode: str = "mean", atlas: bool = False,
) -> list[Any]:
"""
Generate one or more embeddings.
@@ -92,6 +99,22 @@ class Embed4All:
class GPT4All:
"""
Python class that handles instantiation, downloading, generation and chat with GPT4All models.
"""
def __init__(
self,
model_name: str,
model_path: str | os.PathLike[str] | None = None,
model_type: str | None = None,
allow_download: bool = True,
n_threads: int | None = None,
device: str | None = "cpu",
n_ctx: int = 2048,
ngl: int = 100,
verbose: bool = False,
):
"""
Constructor
Args:
model_name: Name of GPT4All or custom model. Including ".gguf" file extension is optional but encouraged.
@@ -113,19 +136,6 @@ class GPT4All:
ngl: Number of GPU layers to use (Vulkan)
verbose: If True, print debug messages.
"""
def __init__(
self,
model_name: str,
model_path: Optional[Union[str, os.PathLike[str]]] = None,
model_type: Optional[str] = None,
allow_download: bool = True,
n_threads: Optional[int] = None,
device: Optional[str] = "cpu",
n_ctx: int = 2048,
ngl: int = 100,
verbose: bool = False,
):
self.model_type = model_type
# Retrieve model and download if allowed
self.config: ConfigType = self.retrieve_model(model_name, model_path=model_path, allow_download=allow_download, verbose=verbose)
@@ -142,10 +152,10 @@ class GPT4All:
@property
def current_chat_session(self) -> list[MessageType] | None:
return self._history
return None if self._history is None else list(self._history)
@staticmethod
def list_models() -> List[ConfigType]:
def list_models() -> list[ConfigType]:
"""
Fetch model list from https://gpt4all.io/models/models2.json.
@@ -161,7 +171,7 @@ class GPT4All:
def retrieve_model(
cls,
model_name: str,
model_path: Optional[Union[str, os.PathLike[str]]] = None,
model_path: str | os.PathLike[str] | None = None,
allow_download: bool = True,
verbose: bool = False,
) -> ConfigType:
@@ -225,7 +235,7 @@ class GPT4All:
model_filename: str,
model_path: str | os.PathLike[str],
verbose: bool = True,
url: Optional[str] = None,
url: str | None = None,
) -> str | os.PathLike[str]:
"""
Download model from https://gpt4all.io.
@@ -302,9 +312,29 @@ class GPT4All:
print(f"Model downloaded to {str(download_path)!r}", file=sys.stderr)
return download_path
@overload
def generate(
self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
n_predict: int | None = ..., streaming: Literal[False] = ..., callback: _pyllmodel.ResponseCallbackType = ...,
) -> str: ...
@overload
def generate(
self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
n_predict: int | None = ..., streaming: Literal[True], callback: _pyllmodel.ResponseCallbackType = ...,
) -> Iterable[str]: ...
@overload
def generate(
self, prompt: str, *, max_tokens: int = ..., temp: float = ..., top_k: int = ..., top_p: float = ...,
min_p: float = ..., repeat_penalty: float = ..., repeat_last_n: int = ..., n_batch: int = ...,
n_predict: int | None = ..., streaming: bool, callback: _pyllmodel.ResponseCallbackType = ...,
) -> Any: ...
def generate(
self,
prompt: str,
*,
max_tokens: int = 200,
temp: float = 0.7,
top_k: int = 40,
@@ -313,10 +343,10 @@ class GPT4All:
repeat_penalty: float = 1.18,
repeat_last_n: int = 64,
n_batch: int = 8,
n_predict: Optional[int] = None,
n_predict: int | None = None,
streaming: bool = False,
callback: _pyllmodel.ResponseCallbackType = _pyllmodel.empty_response_callback,
) -> Union[str, Iterable[str]]:
) -> Any:
"""
Generate outputs from any GPT4All model.
@@ -339,7 +369,7 @@ class GPT4All:
"""
# Preparing the model request
generate_kwargs: Dict[str, Any] = dict(
generate_kwargs: dict[str, Any] = dict(
temp=temp,
top_k=top_k,
top_p=top_p,
@@ -380,7 +410,7 @@ class GPT4All:
generate_kwargs["reset_context"] = True
# Prepare the callback, process the model response
output_collector: List[MessageType]
output_collector: list[MessageType]
output_collector = [
{"content": ""}
] # placeholder for the self._history if chat session is not activated
@@ -391,7 +421,7 @@ class GPT4All:
def _callback_wrapper(
callback: _pyllmodel.ResponseCallbackType,
output_collector: List[MessageType],
output_collector: list[MessageType],
) -> _pyllmodel.ResponseCallbackType:
def _callback(token_id: int, response: str) -> bool:
nonlocal callback, output_collector
@@ -458,7 +488,7 @@ class GPT4All:
def _format_chat_prompt_template(
self,
messages: List[MessageType],
messages: list[MessageType],
default_prompt_header: str = "",
default_prompt_footer: str = "",
) -> str:

8
gpt4all-bindings/python/gpt4all/tests/test_gpt4all.py
View File

@@ -28,12 +28,8 @@ def test_inference():
assert len(tokens) > 0
with model.chat_session():
tokens = list(model.generate(prompt='hello', top_k=1, streaming=True))
model.current_chat_session.append({'role': 'assistant', 'content': ''.join(tokens)})
tokens = list(model.generate(prompt='write me a poem about dogs', top_k=1, streaming=True))
model.current_chat_session.append({'role': 'assistant', 'content': ''.join(tokens)})
model.generate(prompt='hello', top_k=1, streaming=True)
model.generate(prompt='write me a poem about dogs', top_k=1, streaming=True)
print(model.current_chat_session)

6
gpt4all-bindings/python/mkdocs.yml
View File

@@ -16,8 +16,6 @@ nav:
- 'Embedding': 'gpt4all_python_embedding.md'
- 'GPT4ALL in NodeJs': 'gpt4all_nodejs.md'
- 'gpt4all_cli.md'
# - 'Tutorials':
# - 'gpt4all_modal.md'
- 'Wiki':
- 'gpt4all_faq.md'
@@ -44,8 +42,8 @@ markdown_extensions:
- pymdownx.tabbed:
alternate_style: true
- pymdownx.emoji:
emoji_index: !!python/name:materialx.emoji.twemoji
emoji_generator: !!python/name:materialx.emoji.to_svg
emoji_index: !!python/name:material.extensions.emoji.twemoji
emoji_generator: !!python/name:material.extensions.emoji.to_svg
options:
custom_icons:
- docs/overrides/.icons