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* add common llm wrapper, rename openai module to openai_api
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,156 @@ | ||
| { | ||
| "cells": [ | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "# Common Chat Completion\n", | ||
| "\n", | ||
| "This notebook covers not-again-ai's common abstraction around multiple chat completion model providers. \n", | ||
| "\n", | ||
| "Currently the supported providers are the [OpenAI API](https://openai.com/api) and [Ollama](https://github.com/ollama/ollama)." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Choosing a Client" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 1, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "from not_again_ai.llm.ollama.ollama_client import ollama_client\n", | ||
| "from not_again_ai.llm.openai_api.openai_client import openai_client\n", | ||
| "\n", | ||
| "client_openai = openai_client()\n", | ||
| "client_ollama = ollama_client()" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Define common variables we can try sending to different provider/model combinations." | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 2, | ||
| "metadata": {}, | ||
| "outputs": [], | ||
| "source": [ | ||
| "messages = [\n", | ||
| " {\"role\": \"system\", \"content\": \"You are a helpful assistant.\"},\n", | ||
| " {\n", | ||
| " \"role\": \"user\",\n", | ||
| " \"content\": \"Generate a random number between 0 and 100 and structure the response in using JSON.\",\n", | ||
| " },\n", | ||
| "]\n", | ||
| "\n", | ||
| "max_tokens = 200\n", | ||
| "temperature = 2\n", | ||
| "json_mode = True" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "## Use the OpenAI Client" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "{'message': {'randomNumber': 57},\n", | ||
| " 'completion_tokens': 10,\n", | ||
| " 'extras': {'prompt_tokens': 35, 'finish_reason': 'stop'}}" | ||
| ] | ||
| }, | ||
| "execution_count": 3, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "from not_again_ai.llm.chat_completion import chat_completion\n", | ||
| "\n", | ||
| "chat_completion(\n", | ||
| " messages=messages,\n", | ||
| " model=\"gpt-3.5-turbo\",\n", | ||
| " client=client_openai,\n", | ||
| " max_tokens=max_tokens,\n", | ||
| " temperature=temperature,\n", | ||
| " json_mode=json_mode,\n", | ||
| ")" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "markdown", | ||
| "metadata": {}, | ||
| "source": [ | ||
| "Use the Ollama Client" | ||
| ] | ||
| }, | ||
| { | ||
| "cell_type": "code", | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "outputs": [ | ||
| { | ||
| "data": { | ||
| "text/plain": [ | ||
| "{'message': {'random_number': 47},\n", | ||
| " 'completion_tokens': None,\n", | ||
| " 'extras': {'response_duration': None}}" | ||
| ] | ||
| }, | ||
| "execution_count": 4, | ||
| "metadata": {}, | ||
| "output_type": "execute_result" | ||
| } | ||
| ], | ||
| "source": [ | ||
| "chat_completion(\n", | ||
| " messages=messages,\n", | ||
| " model=\"phi3\",\n", | ||
| " client=client_ollama,\n", | ||
| " max_tokens=max_tokens,\n", | ||
| " temperature=temperature,\n", | ||
| " json_mode=json_mode,\n", | ||
| ")" | ||
| ] | ||
| } | ||
| ], | ||
| "metadata": { | ||
| "kernelspec": { | ||
| "display_name": ".venv", | ||
| "language": "python", | ||
| "name": "python3" | ||
| }, | ||
| "language_info": { | ||
| "codemirror_mode": { | ||
| "name": "ipython", | ||
| "version": 3 | ||
| }, | ||
| "file_extension": ".py", | ||
| "mimetype": "text/x-python", | ||
| "name": "python", | ||
| "nbconvert_exporter": "python", | ||
| "pygments_lexer": "ipython3", | ||
| "version": "3.11.4" | ||
| } | ||
| }, | ||
| "nbformat": 4, | ||
| "nbformat_minor": 2 | ||
| } |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,6 +1,6 @@ | ||
| [tool.poetry] | ||
| name = "not-again-ai" | ||
| version = "0.7.0" | ||
| version = "0.8.0" | ||
| description = "Designed to once and for all collect all the little things that come up over and over again in AI projects and put them in one place." | ||
| authors = ["DaveCoDev <[email protected]>"] | ||
| license = "MIT" | ||
|
|
||
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,76 @@ | ||
| from typing import Any | ||
|
|
||
| from ollama import Client | ||
| from openai import OpenAI | ||
|
|
||
| from not_again_ai.llm.ollama import chat_completion as chat_completion_ollama | ||
| from not_again_ai.llm.openai_api import chat_completion as chat_completion_openai | ||
|
|
||
|
|
||
| def chat_completion( | ||
| messages: list[dict[str, Any]], | ||
| model: str, | ||
| client: OpenAI | Client, | ||
| max_tokens: int | None = None, | ||
| temperature: float = 0.7, | ||
| json_mode: bool = False, | ||
| seed: int | None = None, | ||
| **kwargs: Any, | ||
| ) -> dict[str, Any]: | ||
| """Creates a common wrapper around chat completion models from different providers. | ||
| Currently supports the OpenAI API and Ollama local models. | ||
| All input parameters are supported by all providers in similar ways and the output is standardized. | ||
| Args: | ||
| messages (list[dict[str, Any]]): A list of messages to send to the model. | ||
| model (str): The model name to use. | ||
| client (OpenAI | Client): The client object to use for chat completion. | ||
| max_tokens (int, optional): The maximum number of tokens to generate. | ||
| temperature (float, optional): The temperature of the model. Increasing the temperature will make the model answer more creatively. | ||
| json_mode (bool, optional): This will structure the response as a valid JSON object. | ||
| seed (int, optional): The seed to use for the model for reproducible outputs. | ||
| Returns: | ||
| dict[str, Any]: A dictionary with the following keys | ||
| message (str | dict): The content of the generated assistant message. | ||
| If json_mode is True, this will be a dictionary. | ||
| completion_tokens (int): The number of tokens used by the model to generate the completion. | ||
| extras (dict): This will contain any additional fields returned by corresponding provider. | ||
| """ | ||
| # Determine which chat_completion function to call based on the client type | ||
| if isinstance(client, OpenAI): | ||
| response = chat_completion_openai.chat_completion( | ||
| messages=messages, | ||
| model=model, | ||
| client=client, | ||
| max_tokens=max_tokens, | ||
| temperature=temperature, | ||
| json_mode=json_mode, | ||
| seed=seed, | ||
| **kwargs, | ||
| ) | ||
| elif isinstance(client, Client): | ||
| response = chat_completion_ollama.chat_completion( | ||
| messages=messages, | ||
| model=model, | ||
| client=client, | ||
| max_tokens=max_tokens, | ||
| temperature=temperature, | ||
| json_mode=json_mode, | ||
| seed=seed, | ||
| **kwargs, | ||
| ) | ||
| else: | ||
| raise ValueError("Invalid client type") | ||
|
|
||
| # Parse the responses to be consistent | ||
| response_data = {} | ||
| response_data["message"] = response.get("message", None) | ||
| response_data["completion_tokens"] = response.get("completion_tokens", None) | ||
|
|
||
| # Return any additional fields from the response in an "extras" dictionary | ||
| extras = {k: v for k, v in response.items() if k not in response_data} | ||
| if extras: | ||
| response_data["extras"] = extras | ||
|
|
||
| return response_data |
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...again_ai/llm/openai/context_management.py → ...n_ai/llm/openai_api/context_management.py
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