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documentation and cleanup
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# Python GPT4All
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This package contains a set of Python bindings that runs the `llmodel` C-API.
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This package contains a set of Python bindings around the `llmodel` C-API.
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# Local Installation Instructions
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TODO: Right now instructions in main README still depend on Qt6 setup. To setup Python bindings, we just need `llmodel` to be built which is much simpler. However, in the future, the below installation instructions should be sequentially organized such that we expect the main README's instructions were followed first.
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## Local Build Instructions
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1. Setup `llmodel`
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@ -23,7 +21,6 @@ Confirm that `libllmodel.*` exists in `gpt4all-backend/llmodel/build`.
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```
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cd ../../gpt4all-bindings/python
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pip3 install -r requirements.txt
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pip3 install -e .
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```
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# GPT4All
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In this package, we introduce Python bindings built around GPT4All's C/C++ ecosystem.
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In this package, we introduce Python bindings built around GPT4All's C/C++ model backends.
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## Quickstart
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@ -11,7 +11,7 @@ pip install gpt4all
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In Python, run the following commands to retrieve a GPT4All model and generate a response
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to a prompt.
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**Download Note*:*
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**Download Note*:**
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By default, models are stored in `~/.cache/gpt4all/` (you can change this with `model_path`). If the file already exists, model download will be skipped.
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```python
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@ -20,3 +20,13 @@ gptj = gpt4all.GPT4All("ggml-gpt4all-j-v1.3-groovy")
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messages = [{"role": "user", "content": "Name 3 colors"}]
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gptj.chat_completion(messages)
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```
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## Give it a try!
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[Google Colab Tutorial](https://colab.research.google.com/drive/1QRFHV5lj1Kb7_tGZZGZ-E6BfX6izpeMI?usp=sharing)
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## Best Practices
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GPT4All models are designed to run locally on your own CPU. Large prompts may require longer computation time and
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result in worse performance. Giving an instruction to the model will typically produce the best results.
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There are two methods to interface with the underlying language model, `chat_completion()` and `generate()`. Chat completion formats a user-provided message dictionary into a prompt template (see API documentation for more details and options). This will usually produce much better results and is the approach we recommend. You may also prompt the model with `generate()` which will just pass the raw input string to the model.
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DEFAULT_MODEL_DIRECTORY = os.path.join(str(Path.home()), ".cache", "gpt4all").replace("\\", "\\\\")
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class GPT4All():
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"""Python API for retrieving and interacting with GPT4All models
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"""Python API for retrieving and interacting with GPT4All models.
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Attribuies:
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model: Pointer to underlying C model.
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@ -50,7 +50,7 @@ class GPT4All():
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@staticmethod
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def list_models():
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"""
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Fetch model list from https://gpt4all.io/models/models.json
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Fetch model list from https://gpt4all.io/models/models.json.
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Returns:
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Model list in JSON format.
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@ -60,7 +60,7 @@ class GPT4All():
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return model_json
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@staticmethod
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def retrieve_model(model_name: str, model_path: str = None, allow_download = True):
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def retrieve_model(model_name: str, model_path: str = None, allow_download: bool = True) -> str:
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"""
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Find model file, and if it doesn't exist, download the model.
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raise ValueError("Invalid model directory")
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@staticmethod
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def download_model(model_filename, model_path):
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# TODO: Find good way of safely removing file that got interrupted.
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def download_model(model_filename: str, model_path: str) -> str:
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"""
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Download model from https://gpt4all.io.
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Args:
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model_filename: Filename of model (with .bin extension).
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model_path: Path to download model to.
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Returns:
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Model file destination.
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"""
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def get_download_url(model_filename):
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return f"https://gpt4all.io/models/{model_filename}"
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download_path = os.path.join(model_path, model_filename).replace("\\", "\\\\")
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download_url = get_download_url(model_filename)
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# TODO: Find good way of safely removing file that got interrupted.
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response = requests.get(download_url, stream=True)
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total_size_in_bytes = int(response.headers.get("content-length", 0))
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block_size = 1048576 # 1 MB
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print("Model downloaded at: " + download_path)
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return download_path
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def generate(self, prompt: str, **generate_kwargs):
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def generate(self, prompt: str, **generate_kwargs) -> str:
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"""
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Surfaced method of running generate without accessing model object.
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Args:
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prompt: Raw string to be passed to model.
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**generate_kwargs: Optional kwargs to pass to prompt context.
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Returns:
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Raw string of generated model response.
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"""
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return self.model.generate(prompt, **generate_kwargs)
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generated content.
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Args:
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messages: Each dictionary should have a "role" key
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messages: List of dictionaries. Each dictionary should have a "role" key
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with value of "system", "assistant", or "user" and a "content" key with a
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string value. Messages are organized such that "system" messages are at top of prompt,
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and "user" and "assistant" messages are displayed in order. Assistant messages get formatted as
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"Reponse: {content}".
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default_prompt_header: If True (default), add default prompt header after any user specified system messages and
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before user/assistant messages.
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default_prompt_header: If True (default), add default prompt header after any system role messages and
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before user/assistant role messages.
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default_prompt_footer: If True (default), add default footer at end of prompt.
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verbose: If True (default), print full prompt and generated response.
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@ -175,7 +193,6 @@ class GPT4All():
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generated tokens in response, and total tokens.
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"choices": List of message dictionary where "content" is generated response and "role" is set
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as "assistant". Right now, only one choice is returned by model.
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"""
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full_prompt = self._build_prompt(messages,
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@ -210,6 +227,7 @@ class GPT4All():
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def _build_prompt(messages: List[Dict],
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default_prompt_header=True,
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default_prompt_footer=False) -> str:
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# Helper method to format messages into prompt.
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full_prompt = ""
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for message in messages:
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@ -238,7 +256,7 @@ class GPT4All():
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@staticmethod
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def get_model_from_type(model_type: str) -> pyllmodel.LLModel:
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# This needs to be updated for each new model
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# This needs to be updated for each new model type
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# TODO: Might be worth converting model_type to enum
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if model_type == "gptj":
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analytics:
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provider: google
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property: G-NPXC8BYHJV
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#social:
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# - icon: fontawesome/brands/twitter
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# link: https://twitter.com/nomic_ai
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# - icon: material/fruit-pineapple
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# link: https://www.youtube.com/watch?v=628eVJgHD6I
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setup(
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name=package_name,
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version="0.1.9",
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version="0.2.0",
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description="Python bindings for GPT4All",
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author="Richard Guo",
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author_email="richard@nomic.ai",
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