ollama-python 0.1.2

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Ollama Python Library

The ollama python library provides the easiest way to integrate your python project with Ollama

Getting Started

This requires a python version of 3.9 or higher

pip install ollama-python

The python package splits the functionality into three core endpoints

1. Model Management Endpoints: This includes the ability to create, delete, pull, push and list models amongst others
2. Generate Endpoint: This includes the generate and chat endpoints in Ollama
3. Embedding Endpoint: This includes the ability to generate embeddings for a given text

Pydantic is used to verify user input and Responses from the server are parsed into pydantic models

Example Usage

Generate Endpoint

Completions (Generate)

Without Streaming

from ollama_python.endpoints import GenerateAPI

api = GenerateAPI(base_url="http://localhost:8000", model="mistral")
result = api.generate(prompt="Hello World", options=dict(num_tokens=10), format="json")

With Streaming

from ollama_python.endpoints import GenerateAPI

api = GenerateAPI(base_url="http://localhost:8000", model="mistral")
for res in api.generate(prompt="Hello World", options=dict(num_tokens=10), format="json", stream=True):
    print(res.response)

Chat Completions

Without Streaming

from ollama_python.endpoints import GenerateAPI

api = GenerateAPI(base_url="http://localhost:8000", model="mistral")
messages = [{'role': 'user', 'content': 'Why is the sky blue?'}]

result = api.generate_chat_completion(messages=messages, options=dict(num_tokens=10), format="json")

With Streaming

from ollama_python.endpoints import GenerateAPI

api = GenerateAPI(base_url="http://localhost:8000", model="mistral")
messages = [{'role': 'user', 'content': 'Why is the sky blue?'}]

for res in api.generate_chat_completion(messages=messages, options=dict(num_tokens=10), format="json", stream=True):
    print(res.message)

Chat request with images

from ollama_python.endpoints import GenerateAPI

api = GenerateAPI(base_url="http://localhost:8000", model="llava")
messages = [{'role': 'user', 'content': 'What is in this image', 'image': 'iVBORw0KGgoAAAANSUhEUgAAAG0AAABmCAYAAADBPx+VAAAACXBIWXMAAAsTAAALEwEAmp'}]

result = api.generate_chat_completion(messages=messages, options=dict(num_tokens=10), format="json")
print(result.message)

Embeddings Endpoint

Generate Embeddings

from ollama_python.endpoints import EmbeddingAPI

api = EmbeddingAPI(base_url="http://localhost:8000", model="mistral")
result = api.get_embedding(prompt="Hello World", options=dict(seed=10))

Model Management Endpoints

Create a model

Without Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.create(name="test_model", model_file="random model_file")

With Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
for res in api.create(name="test_model", model_file="random model_file", stream=True):
    print(res.status)

Check if a blob exists

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.check_blob_exists(digest="sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2")

Create a blob

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.create_blob(digest="sha256:29fdb92e57cf0827ded04ae6461b5931d01fa595843f55d36f5b275a52087dd2")

List local models

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.list_local_models()

print(result.models)

Show model information

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.show(name="mistral")

print(result.details)

Copy a model

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.copy(source="mistral", destination="mistral_copy")

Delete a model

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
api.delete(name="mistral_copy")

Pull a model

Without Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.pull(name="mistral")
print(result.status)

With Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
for res in api.pull(name="mistral", stream=True):
    print(res.status)

Push a model

Without Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
result = api.push(name="mistral")
print(result.status)

With Streaming

from ollama_python.endpoints import ModelManagementAPI

api = ModelManagementAPI(base_url="http://localhost:8000")
for res in api.push(name="mistral", stream=True):
    print(res.status)

Valid Options/Parameters

Parameter	Description	Value Type	Example Usage
mirostat	Enable Mirostat sampling for controlling perplexity. (default: 0, 0 = disabled, 1 = Mirostat, 2 = Mirostat 2.0)	int	mirostat 0
mirostat_eta	Influences how quickly the algorithm responds to feedback from the generated text. A lower learning rate will result in slower adjustments, while a higher learning rate will make the algorithm more responsive. (Default: 0.1)	float	mirostat_eta 0.1
mirostat_tau	Controls the balance between coherence and diversity of the output. A lower value will result in more focused and coherent text. (Default: 5.0)	float	mirostat_tau 5.0
num_ctx	Sets the size of the context window used to generate the next token. (Default: 2048)	int	num_ctx 4096
num_gqa	The number of GQA groups in the transformer layer. Required for some models, for example it is 8 for llama2:70b	int	num_gqa 1
num_gpu	The number of layers to send to the GPU(s). On macOS it defaults to 1 to enable metal support, 0 to disable.	int	num_gpu 50
num_thread	Sets the number of threads to use during computation. By default, Ollama will detect this for optimal performance. It is recommended to set this value to the number of physical CPU cores your system has (as opposed to the logical number of cores).	int	num_thread 8
repeat_last_n	Sets how far back for the model to look back to prevent repetition. (Default: 64, 0 = disabled, -1 = num_ctx)	int	repeat_last_n 64
repeat_penalty	Sets how strongly to penalize repetitions. A higher value (e.g., 1.5) will penalize repetitions more strongly, while a lower value (e.g., 0.9) will be more lenient. (Default: 1.1)	float	repeat_penalty 1.1
temperature	The temperature of the model. Increasing the temperature will make the model answer more creatively. (Default: 0.8)	float	temperature 0.7
seed	Sets the random number seed to use for generation. Setting this to a specific number will make the model generate the same text for the same prompt. (Default: 0)	int	seed 42
stop	Sets the stop sequences to use. When this pattern is encountered the LLM will stop generating text and return. Multiple stop patterns may be set by specifying multiple separate stop parameters in a modelfile.	string	stop "AI assistant:"
tfs_z	Tail free sampling is used to reduce the impact of less probable tokens from the output. A higher value (e.g., 2.0) will reduce the impact more, while a value of 1.0 disables this setting. (default: 1)	float	tfs_z 1
num_predict	Maximum number of tokens to predict when generating text. (Default: 128, -1 = infinite generation, -2 = fill context)	int	num_predict 42
top_k	Reduces the probability of generating nonsense. A higher value (e.g. 100) will give more diverse answers, while a lower value (e.g. 10) will be more conservative. (Default: 40)	int	top_k 40
top_p	Works together with top-k. A higher value (e.g., 0.95) will lead to more diverse text, while a lower value (e.g., 0.5) will generate more focused and conservative text. (Default: 0.9)	float	top_p 0.9

Todo

Add support for Asynchronous version of the library

To Contribute

1. Clone the repo
2. Run poetry install
3. Run pre-commit install

Then you're ready to contribute to the repo