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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

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