forked from phoenix/litellm-mirror
Merge branch 'main' into main
This commit is contained in:
Vince Lwt
GitHub
commit
dcbf5e2444
48 changed files with 9611 additions and 239 deletions
|
|
@ -2,4 +2,5 @@
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|||
openai
|
||||
python-dotenv
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||||
openai
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||||
tiktoken
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||||
tiktoken
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||||
importlib_metadata
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||||
1
.gitignore
vendored
1
.gitignore
vendored
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@ -2,3 +2,4 @@
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|||
.env
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||||
litellm_uuid.txt
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||||
__pycache__/
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||||
bun.lockb
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||||
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@ -3,7 +3,6 @@
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|||
[](https://pypi.org/project/litellm/0.1.1/)
|
||||
[](https://dl.circleci.com/status-badge/redirect/gh/BerriAI/litellm/tree/main)
|
||||
|
||||
[](https://github.com/BerriAI/litellm)
|
||||
|
||||
[](https://discord.gg/wuPM9dRgDw)
|
||||
|
||||
|
|
@ -12,10 +11,11 @@ a light package to simplify calling OpenAI, Azure, Cohere, Anthropic, Huggingfac
|
|||
- guarantees [consistent output](https://litellm.readthedocs.io/en/latest/output/), text responses will always be available at `['choices'][0]['message']['content']`
|
||||
- exception mapping - common exceptions across providers are mapped to the [OpenAI exception types](https://help.openai.com/en/articles/6897213-openai-library-error-types-guidance)
|
||||
# usage
|
||||
<a href='https://docs.litellm.ai/docs/completion/supported' target="_blank"><img alt='None' src='https://img.shields.io/badge/Supported_LLMs-100000?style=for-the-badge&logo=None&logoColor=000000&labelColor=000000&color=8400EA'/></a>
|
||||
<a href='https://docs.litellm.ai/docs/completion/supported' target="_blank"><img alt='None' src='https://img.shields.io/badge/100+_Supported_LLMs_liteLLM-100000?style=for-the-badge&logo=None&logoColor=000000&labelColor=000000&color=8400EA'/></a>
|
||||
|
||||
Demo - https://litellm.ai/playground \
|
||||
Read the docs - https://docs.litellm.ai/docs/
|
||||
Demo - https://litellm.ai/playground
|
||||
Docs - https://docs.litellm.ai/docs/
|
||||
**Free** Dashboard - https://docs.litellm.ai/docs/debugging/hosted_debugging
|
||||
|
||||
## quick start
|
||||
```
|
||||
|
|
|
|||
251
cookbook/liteLLM_A121_Jurrasic_example.ipynb
Normal file
251
cookbook/liteLLM_A121_Jurrasic_example.ipynb
Normal file
|
|
@ -0,0 +1,251 @@
|
|||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
}
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# LiteLLM A121 Tutorial\n",
|
||||
"\n",
|
||||
"This walks through using A121 Jurassic models\n",
|
||||
"* j2-light\n",
|
||||
"* j2-mid\n",
|
||||
"* j2-ultra"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "LeFYo8iqcn5g"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "GslPQFmaZsp-"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"!pip install litellm"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"from litellm import completion\n",
|
||||
"import os"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "P3cKiqURZx7P"
|
||||
},
|
||||
"execution_count": 2,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## Set A121 Keys\n",
|
||||
"You can get a free key from https://studio.ai21.com/account/api-key"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "tmTvA1_GaNU4"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"os.environ[\"AI21_API_KEY\"] = \"\""
|
||||
],
|
||||
"metadata": {
|
||||
"id": "_xX8LmxAZ2vp"
|
||||
},
|
||||
"execution_count": 5,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# A121 Supported Models:\n",
|
||||
"https://studio.ai21.com/foundation-models"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Fx5ZfJTLbF0A"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"## J2-light Call"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "H0tl-0Z3bDaL"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"messages = [{ \"content\": \"Hello, how are you?\",\"role\": \"user\"}]\n",
|
||||
"response = completion(model=\"j2-light\", messages=messages)\n",
|
||||
"response"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "DZnApsJUZ_I2",
|
||||
"outputId": "b5707cbe-f67c-47f7-bac5-a7b8af1ba815"
|
||||
},
|
||||
"execution_count": 6,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<ModelResponse at 0x7b2c2902e610> JSON: {\n",
|
||||
" \"choices\": [\n",
|
||||
" {\n",
|
||||
" \"finish_reason\": \"stop\",\n",
|
||||
" \"index\": 0,\n",
|
||||
" \"message\": {\n",
|
||||
" \"content\": \" However, I have an important question to ask you\\nMy name is X, and I was wondering if you would be willing to help me.\",\n",
|
||||
" \"role\": \"assistant\"\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" \"created\": 1692761063.5189915,\n",
|
||||
" \"model\": \"j2-light\",\n",
|
||||
" \"usage\": {\n",
|
||||
" \"prompt_tokens\": null,\n",
|
||||
" \"completion_tokens\": null,\n",
|
||||
" \"total_tokens\": null\n",
|
||||
" }\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 6
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# J2-Mid"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "wCcnrYnnbMQA"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"messages = [{ \"content\": \"what model are you\",\"role\": \"user\"}]\n",
|
||||
"response = completion(model=\"j2-mid\", messages=messages)\n",
|
||||
"response"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "-5Sxf4blaeEl",
|
||||
"outputId": "6264a5e8-16d6-44a3-e167-9e0c59b6dbc4"
|
||||
},
|
||||
"execution_count": 7,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<ModelResponse at 0x7b2c2902f6a0> JSON: {\n",
|
||||
" \"choices\": [\n",
|
||||
" {\n",
|
||||
" \"finish_reason\": \"stop\",\n",
|
||||
" \"index\": 0,\n",
|
||||
" \"message\": {\n",
|
||||
" \"content\": \"\\nplease choose the model from the list below\\nModel view in Tekla Structures\",\n",
|
||||
" \"role\": \"assistant\"\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" \"created\": 1692761140.0017524,\n",
|
||||
" \"model\": \"j2-mid\",\n",
|
||||
" \"usage\": {\n",
|
||||
" \"prompt_tokens\": null,\n",
|
||||
" \"completion_tokens\": null,\n",
|
||||
" \"total_tokens\": null\n",
|
||||
" }\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 7
|
||||
}
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# J2-Ultra"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "wDARpjxtbUcg"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"messages = [{ \"content\": \"what model are you\",\"role\": \"user\"}]\n",
|
||||
"response = completion(model=\"j2-ultra\", messages=messages)\n",
|
||||
"response"
|
||||
],
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"base_uri": "https://localhost:8080/"
|
||||
},
|
||||
"id": "i228xwsYbSYo",
|
||||
"outputId": "3765ac56-5a9b-442e-b357-2e346d02e1df"
|
||||
},
|
||||
"execution_count": 8,
|
||||
"outputs": [
|
||||
{
|
||||
"output_type": "execute_result",
|
||||
"data": {
|
||||
"text/plain": [
|
||||
"<ModelResponse at 0x7b2c28fd4090> JSON: {\n",
|
||||
" \"choices\": [\n",
|
||||
" {\n",
|
||||
" \"finish_reason\": \"stop\",\n",
|
||||
" \"index\": 0,\n",
|
||||
" \"message\": {\n",
|
||||
" \"content\": \"\\nI am not a specific model, but I can provide information and assistance based on my training data. Please let me know if there is anything you\",\n",
|
||||
" \"role\": \"assistant\"\n",
|
||||
" }\n",
|
||||
" }\n",
|
||||
" ],\n",
|
||||
" \"created\": 1692761157.8675153,\n",
|
||||
" \"model\": \"j2-ultra\",\n",
|
||||
" \"usage\": {\n",
|
||||
" \"prompt_tokens\": null,\n",
|
||||
" \"completion_tokens\": null,\n",
|
||||
" \"total_tokens\": null\n",
|
||||
" }\n",
|
||||
"}"
|
||||
]
|
||||
},
|
||||
"metadata": {},
|
||||
"execution_count": 8
|
||||
}
|
||||
]
|
||||
}
|
||||
]
|
||||
}
|
||||
BIN
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|
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@ -77,6 +77,22 @@ Here are some examples of supported models:
|
|||
| [bigcode/starcoder](https://huggingface.co/bigcode/starcoder) | `completion(model="bigcode/starcoder", messages=messages, custom_llm_provider="huggingface")` | `os.environ['HUGGINGFACE_API_KEY']` |
|
||||
| [google/flan-t5-xxl](https://huggingface.co/google/flan-t5-xxl) | `completion(model="google/flan-t5-xxl", messages=messages, custom_llm_provider="huggingface")` | `os.environ['HUGGINGFACE_API_KEY']` |
|
||||
| [google/flan-t5-large](https://huggingface.co/google/flan-t5-large) | `completion(model="google/flan-t5-large", messages=messages, custom_llm_provider="huggingface")` | `os.environ['HUGGINGFACE_API_KEY']` |
|
||||
|
||||
### Replicate Models
|
||||
liteLLM supports all replicate LLMs. For replicate models ensure to add a `replicate` prefix to the `model` arg. liteLLM detects it using this arg.
|
||||
Below are examples on how to call replicate LLMs using liteLLM
|
||||
|
||||
Model Name | Function Call | Required OS Variables |
|
||||
-----------------------------|----------------------------------------------------------------|--------------------------------------|
|
||||
replicate/llama-2-70b-chat | `completion('replicate/replicate/llama-2-70b-chat', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
a16z-infra/llama-2-13b-chat| `completion('replicate/a16z-infra/llama-2-13b-chat', messages)`| `os.environ['REPLICATE_API_KEY']` |
|
||||
joehoover/instructblip-vicuna13b | `completion('replicate/joehoover/instructblip-vicuna13b', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
replicate/dolly-v2-12b | `completion('replicate/replicate/dolly-v2-12b', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
a16z-infra/llama-2-7b-chat | `completion('replicate/a16z-infra/llama-2-7b-chat', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
replicate/vicuna-13b | `completion('replicate/replicate/vicuna-13b', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
daanelson/flan-t5-large | `completion('replicate/daanelson/flan-t5-large', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
replit/replit-code-v1-3b | `completion('replicate/replit/replit-code-v1-3b', messages)` | `os.environ['REPLICATE_API_KEY']` |
|
||||
|
||||
### AI21 Models
|
||||
|
||||
| Model Name | Function Call | Required OS Variables |
|
||||
|
|
@ -112,9 +128,9 @@ Example Baseten Usage - Note: liteLLM supports all models deployed on Basten
|
|||
|
||||
| Model Name | Function Call | Required OS Variables |
|
||||
|------------------|--------------------------------------------|------------------------------------|
|
||||
| Falcon 7B | `completion(model='<your model version id>', messages=messages, custom_llm_provider="baseten")` | `os.environ['BASETEN_API_KEY']` |
|
||||
| Wizard LM | `completion(model='<your model version id>', messages=messages, custom_llm_provider="baseten")` | `os.environ['BASETEN_API_KEY']` |
|
||||
| MPT 7B Base | `completion(model='<your model version id>', messages=messages, custom_llm_provider="baseten")` | `os.environ['BASETEN_API_KEY']` |
|
||||
| Falcon 7B | `completion(model='baseten/qvv0xeq', messages=messages)` | `os.environ['BASETEN_API_KEY']` |
|
||||
| Wizard LM | `completion(model='baseten/q841o8w', messages=messages)` | `os.environ['BASETEN_API_KEY']` |
|
||||
| MPT 7B Base | `completion(model='baseten/31dxrj3', messages=messages)` | `os.environ['BASETEN_API_KEY']` |
|
||||
|
||||
|
||||
### OpenRouter Completion Models
|
||||
|
|
|
|||
40
docs/my-website/docs/debugging/hosted_debugging.md
Normal file
40
docs/my-website/docs/debugging/hosted_debugging.md
Normal file
|
|
@ -0,0 +1,40 @@
|
|||
import Image from '@theme/IdealImage';
|
||||
|
||||
# Debugging Dashboard
|
||||
LiteLLM offers a free UI to debug your calls + add new models at (https://admin.litellm.ai/). This is useful if you're testing your LiteLLM server and need to see if the API calls were made successfully **or** want to add new models without going into code.
|
||||
|
||||
**Needs litellm>=0.1.438***
|
||||
|
||||
## Setup
|
||||
|
||||
Once created, your dashboard is viewable at - `admin.litellm.ai/<your_email>` [👋 Tell us if you need better privacy controls](https://calendly.com/d/4mp-gd3-k5k/berriai-1-1-onboarding-litellm-hosted-version?month=2023-08)
|
||||
|
||||
You can set your user email in 2 ways.
|
||||
- By setting it on the module - `litellm.email=<your_email>`.
|
||||
- By setting it as an environment variable - `os.environ["LITELLM_EMAIL"] = "your_email"`.
|
||||
|
||||
<Image img={require('../../img/dashboard.png')} alt="Dashboard" />
|
||||
|
||||
See our live dashboard 👉 [admin.litellm.ai](https://admin.litellm.ai/)
|
||||
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
import litellm
|
||||
from litellm import embedding, completion
|
||||
|
||||
## Set your email
|
||||
litellm.email = "test_email@test.com"
|
||||
|
||||
user_message = "Hello, how are you?"
|
||||
messages = [{ "content": user_message,"role": "user"}]
|
||||
|
||||
|
||||
# openai call
|
||||
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
|
||||
|
||||
# bad request call
|
||||
response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
|
||||
```
|
||||
|
||||
|
|
@ -54,4 +54,10 @@ response = completion(model="gpt-3.5-turbo", messages=messages, logger_fn=my_cus
|
|||
|
||||
# cohere call
|
||||
response = completion("command-nightly", messages, logger_fn=my_custom_logging_fn)
|
||||
```
|
||||
```
|
||||
|
||||
## Still Seeing Issues?
|
||||
|
||||
Text us @ +17708783106 or Join the [Discord](https://discord.com/invite/wuPM9dRgDw).
|
||||
|
||||
We promise to help you in `lite`ning speed ❤️
|
||||
|
|
@ -1,4 +1,4 @@
|
|||
## liteLLM Together AI Tutorial
|
||||
# liteLLM Together AI Tutorial
|
||||
https://together.ai/
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -1,32 +0,0 @@
|
|||
# Debugging UI Tutorial
|
||||
LiteLLM offers a free hosted debugger UI for your api calls. Useful if you're testing your LiteLLM server and need to see if the API calls were made successfully.
|
||||
|
||||
You can enable this setting `lite_debugger` as a callback.
|
||||
|
||||
## Example Usage
|
||||
|
||||
```
|
||||
import litellm
|
||||
from litellm import embedding, completion
|
||||
|
||||
litellm.input_callback = ["lite_debugger"]
|
||||
litellm.success_callback = ["lite_debugger"]
|
||||
litellm.failure_callback = ["lite_debugger"]
|
||||
|
||||
litellm.set_verbose = True
|
||||
|
||||
user_message = "Hello, how are you?"
|
||||
messages = [{ "content": user_message,"role": "user"}]
|
||||
|
||||
|
||||
# openai call
|
||||
response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
|
||||
|
||||
# bad request call
|
||||
response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
|
||||
|
||||
```
|
||||
|
||||
## Requirements
|
||||
|
||||
## How to see the UI
|
||||
41
docs/my-website/docs/tutorials/fallbacks.md
Normal file
41
docs/my-website/docs/tutorials/fallbacks.md
Normal file
|
|
@ -0,0 +1,41 @@
|
|||
# Using completion() with Fallbacks for Reliability
|
||||
|
||||
This tutorial demonstrates how to employ the `completion()` function with model fallbacks to ensure reliability. LLM APIs can be unstable, completion() with fallbacks ensures you'll always get a response from your calls
|
||||
|
||||
## Usage
|
||||
To use fallback models with `completion()`, specify a list of models in the `fallbacks` parameter.
|
||||
|
||||
```python
|
||||
response = completion(model="bad-model", messages=messages, fallbacks=["gpt-3.5-turbo" "command-nightly"])
|
||||
```
|
||||
|
||||
### Output from calls
|
||||
```
|
||||
Completion with 'bad-model': got exception Unable to map your input to a model. Check your input - {'model': 'bad-model'
|
||||
|
||||
|
||||
|
||||
completion call gpt-3.5-turbo
|
||||
{
|
||||
"id": "chatcmpl-7qTmVRuO3m3gIBg4aTmAumV1TmQhB",
|
||||
"object": "chat.completion",
|
||||
"created": 1692741891,
|
||||
"model": "gpt-3.5-turbo-0613",
|
||||
"choices": [
|
||||
{
|
||||
"index": 0,
|
||||
"message": {
|
||||
"role": "assistant",
|
||||
"content": "I apologize, but as an AI, I do not have the capability to provide real-time weather updates. However, you can easily check the current weather in San Francisco by using a search engine or checking a weather website or app."
|
||||
},
|
||||
"finish_reason": "stop"
|
||||
}
|
||||
],
|
||||
"usage": {
|
||||
"prompt_tokens": 16,
|
||||
"completion_tokens": 46,
|
||||
"total_tokens": 62
|
||||
}
|
||||
}
|
||||
|
||||
```
|
||||
22
docs/my-website/docs/tutorials/finetuned_chat_gpt.md
Normal file
22
docs/my-website/docs/tutorials/finetuned_chat_gpt.md
Normal file
|
|
@ -0,0 +1,22 @@
|
|||
# Using Fine-Tuned gpt-3.5-turbo
|
||||
LiteLLM allows you to call `completion` with your fine-tuned gpt-3.5-turbo models
|
||||
If you're trying to create your custom finetuned gpt-3.5-turbo model following along on this tutorial: https://platform.openai.com/docs/guides/fine-tuning/preparing-your-dataset
|
||||
|
||||
Once you've created your fine tuned model, you can call it with `completion()`
|
||||
|
||||
## Usage
|
||||
```python
|
||||
import os
|
||||
from litellm import completion
|
||||
# set your OPENAI key in your .env as "OPENAI_API_KEY"
|
||||
|
||||
response = completion(
|
||||
model="ft:gpt-3.5-turbo:my-org:custom_suffix:id",
|
||||
messages=[
|
||||
{"role": "system", "content": "You are a helpful assistant."},
|
||||
{"role": "user", "content": "Hello!"}
|
||||
]
|
||||
)
|
||||
|
||||
print(response.choices[0].message)
|
||||
```
|
||||
|
|
@ -1,21 +0,0 @@
|
|||
import React from 'react';
|
||||
import Layout from '@theme/Layout';
|
||||
|
||||
export default function Hello() {
|
||||
return (
|
||||
<Layout title="Hello" description="Hello React Page">
|
||||
<div
|
||||
style={{
|
||||
display: 'flex',
|
||||
justifyContent: 'center',
|
||||
alignItems: 'center',
|
||||
height: '50vh',
|
||||
fontSize: '20px',
|
||||
}}>
|
||||
<p>
|
||||
Edit <code>pages/helloReact.js</code> and save to reload.
|
||||
</p>
|
||||
</div>
|
||||
</Layout>
|
||||
);
|
||||
}
|
||||
|
|
@ -27,6 +27,22 @@ const config = {
|
|||
locales: ['en'],
|
||||
},
|
||||
|
||||
plugins: [
|
||||
[
|
||||
'@docusaurus/plugin-ideal-image',
|
||||
{
|
||||
quality: 70,
|
||||
max: 1030, // max resized image's size.
|
||||
min: 640, // min resized image's size. if original is lower, use that size.
|
||||
steps: 2, // the max number of images generated between min and max (inclusive)
|
||||
disableInDev: false,
|
||||
},
|
||||
],
|
||||
[ require.resolve('docusaurus-lunr-search'), {
|
||||
languages: ['en'] // language codes
|
||||
}]
|
||||
],
|
||||
|
||||
presets: [
|
||||
[
|
||||
'classic',
|
||||
|
|
@ -72,7 +88,7 @@ const config = {
|
|||
items: [
|
||||
{
|
||||
label: 'Tutorial',
|
||||
to: '/docs/intro',
|
||||
to: '/docs/index',
|
||||
},
|
||||
],
|
||||
},
|
||||
|
|
|
|||
BIN
docs/my-website/img/dashboard.png
Normal file
BIN
docs/my-website/img/dashboard.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 523 KiB |
BIN
docs/my-website/img/favicon.png
Normal file
BIN
docs/my-website/img/favicon.png
Normal file
Binary file not shown.
|
After Width: | Height: | Size: 21 KiB |
439
docs/my-website/package-lock.json
generated
439
docs/my-website/package-lock.json
generated
|
|
@ -9,12 +9,14 @@
|
|||
"version": "0.0.0",
|
||||
"dependencies": {
|
||||
"@docusaurus/core": "2.4.1",
|
||||
"@docusaurus/plugin-ideal-image": "^2.4.1",
|
||||
"@docusaurus/preset-classic": "2.4.1",
|
||||
"@mdx-js/react": "^1.6.22",
|
||||
"clsx": "^1.2.1",
|
||||
"prism-react-renderer": "^1.3.5",
|
||||
"react": "^17.0.2",
|
||||
"react-dom": "^17.0.2"
|
||||
"react-dom": "^17.0.2",
|
||||
"uuid": "^9.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@docusaurus/module-type-aliases": "2.4.1"
|
||||
|
|
@ -2268,6 +2270,21 @@
|
|||
"node": ">=16.14"
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/lqip-loader": {
|
||||
"version": "2.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/lqip-loader/-/lqip-loader-2.4.1.tgz",
|
||||
"integrity": "sha512-XJ0z/xSx5HtAQ+/xBoAiRZ7DY9zEP6IImAKlAk6RxuFzyB4HT8eINWN+LwLnOsTh5boIj37JCX+T76bH0ieULA==",
|
||||
"dependencies": {
|
||||
"@docusaurus/logger": "2.4.1",
|
||||
"file-loader": "^6.2.0",
|
||||
"lodash": "^4.17.21",
|
||||
"sharp": "^0.30.7",
|
||||
"tslib": "^2.4.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=16.14"
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/mdx-loader": {
|
||||
"version": "2.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/mdx-loader/-/mdx-loader-2.4.1.tgz",
|
||||
|
|
@ -2474,6 +2491,37 @@
|
|||
"react-dom": "^16.8.4 || ^17.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/plugin-ideal-image": {
|
||||
"version": "2.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/plugin-ideal-image/-/plugin-ideal-image-2.4.1.tgz",
|
||||
"integrity": "sha512-jxvgCGPmHxdae2Y2uskzxIbMCA4WLTfzkufsLbD4mEAjCRIkt6yzux6q5kqKTrO+AxzpANVcJNGmaBtKZGv5aw==",
|
||||
"dependencies": {
|
||||
"@docusaurus/core": "2.4.1",
|
||||
"@docusaurus/lqip-loader": "2.4.1",
|
||||
"@docusaurus/responsive-loader": "^1.7.0",
|
||||
"@docusaurus/theme-translations": "2.4.1",
|
||||
"@docusaurus/types": "2.4.1",
|
||||
"@docusaurus/utils-validation": "2.4.1",
|
||||
"@endiliey/react-ideal-image": "^0.0.11",
|
||||
"react-waypoint": "^10.3.0",
|
||||
"sharp": "^0.30.7",
|
||||
"tslib": "^2.4.0",
|
||||
"webpack": "^5.73.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=16.14"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"jimp": "*",
|
||||
"react": "^16.8.4 || ^17.0.0",
|
||||
"react-dom": "^16.8.4 || ^17.0.0"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"jimp": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/plugin-sitemap": {
|
||||
"version": "2.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/plugin-sitemap/-/plugin-sitemap-2.4.1.tgz",
|
||||
|
|
@ -2536,6 +2584,29 @@
|
|||
"react": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/responsive-loader": {
|
||||
"version": "1.7.0",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/responsive-loader/-/responsive-loader-1.7.0.tgz",
|
||||
"integrity": "sha512-N0cWuVqTRXRvkBxeMQcy/OF2l7GN8rmni5EzR3HpwR+iU2ckYPnziceojcxvvxQ5NqZg1QfEW0tycQgHp+e+Nw==",
|
||||
"dependencies": {
|
||||
"loader-utils": "^2.0.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=12"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"jimp": "*",
|
||||
"sharp": "*"
|
||||
},
|
||||
"peerDependenciesMeta": {
|
||||
"jimp": {
|
||||
"optional": true
|
||||
},
|
||||
"sharp": {
|
||||
"optional": true
|
||||
}
|
||||
}
|
||||
},
|
||||
"node_modules/@docusaurus/theme-classic": {
|
||||
"version": "2.4.1",
|
||||
"resolved": "https://registry.npmjs.org/@docusaurus/theme-classic/-/theme-classic-2.4.1.tgz",
|
||||
|
|
@ -2734,6 +2805,20 @@
|
|||
"node": ">=16.14"
|
||||
}
|
||||
},
|
||||
"node_modules/@endiliey/react-ideal-image": {
|
||||
"version": "0.0.11",
|
||||
"resolved": "https://registry.npmjs.org/@endiliey/react-ideal-image/-/react-ideal-image-0.0.11.tgz",
|
||||
"integrity": "sha512-QxMjt/Gvur/gLxSoCy7VIyGGGrGmDN+VHcXkN3R2ApoWX0EYUE+hMgPHSW/PV6VVebZ1Nd4t2UnGRBDihu16JQ==",
|
||||
"engines": {
|
||||
"node": ">= 8.9.0",
|
||||
"npm": "> 3"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"prop-types": ">=15",
|
||||
"react": ">=0.14.x",
|
||||
"react-waypoint": ">=9.0.2"
|
||||
}
|
||||
},
|
||||
"node_modules/@hapi/hoek": {
|
||||
"version": "9.3.0",
|
||||
"resolved": "https://registry.npmjs.org/@hapi/hoek/-/hoek-9.3.0.tgz",
|
||||
|
|
@ -4180,6 +4265,25 @@
|
|||
"resolved": "https://registry.npmjs.org/base16/-/base16-1.0.0.tgz",
|
||||
"integrity": "sha512-pNdYkNPiJUnEhnfXV56+sQy8+AaPcG3POZAUnwr4EeqCUZFz4u2PePbo3e5Gj4ziYPCWGUZT9RHisvJKnwFuBQ=="
|
||||
},
|
||||
"node_modules/base64-js": {
|
||||
"version": "1.5.1",
|
||||
"resolved": "https://registry.npmjs.org/base64-js/-/base64-js-1.5.1.tgz",
|
||||
"integrity": "sha512-AKpaYlHn8t4SVbOHCy+b5+KKgvR4vrsD8vbvrbiQJps7fKDTkjkDry6ji0rUJjC0kzbNePLwzxq8iypo41qeWA==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/feross"
|
||||
},
|
||||
{
|
||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
|
||||
},
|
||||
{
|
||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
||||
}
|
||||
]
|
||||
},
|
||||
"node_modules/batch": {
|
||||
"version": "0.6.1",
|
||||
"resolved": "https://registry.npmjs.org/batch/-/batch-0.6.1.tgz",
|
||||
|
|
@ -4201,6 +4305,16 @@
|
|||
"node": ">=8"
|
||||
}
|
||||
},
|
||||
"node_modules/bl": {
|
||||
"version": "4.1.0",
|
||||
"resolved": "https://registry.npmjs.org/bl/-/bl-4.1.0.tgz",
|
||||
"integrity": "sha512-1W07cM9gS6DcLperZfFSj+bWLtaPGSOHWhPiGzXmvVJbRLdG82sH/Kn8EtW1VqWVA54AKf2h5k5BbnIbwF3h6w==",
|
||||
"dependencies": {
|
||||
"buffer": "^5.5.0",
|
||||
"inherits": "^2.0.4",
|
||||
"readable-stream": "^3.4.0"
|
||||
}
|
||||
},
|
||||
"node_modules/body-parser": {
|
||||
"version": "1.20.1",
|
||||
"resolved": "https://registry.npmjs.org/body-parser/-/body-parser-1.20.1.tgz",
|
||||
|
|
@ -4333,6 +4447,29 @@
|
|||
"node": "^6 || ^7 || ^8 || ^9 || ^10 || ^11 || ^12 || >=13.7"
|
||||
}
|
||||
},
|
||||
"node_modules/buffer": {
|
||||
"version": "5.7.1",
|
||||
"resolved": "https://registry.npmjs.org/buffer/-/buffer-5.7.1.tgz",
|
||||
"integrity": "sha512-EHcyIPBQ4BSGlvjB16k5KgAJ27CIsHY/2JBmCRReo48y9rQ3MaUzWX3KVlBa4U7MyX02HdVj0K7C3WaB3ju7FQ==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/feross"
|
||||
},
|
||||
{
|
||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
|
||||
},
|
||||
{
|
||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
||||
}
|
||||
],
|
||||
"dependencies": {
|
||||
"base64-js": "^1.3.1",
|
||||
"ieee754": "^1.1.13"
|
||||
}
|
||||
},
|
||||
"node_modules/buffer-from": {
|
||||
"version": "1.1.2",
|
||||
"resolved": "https://registry.npmjs.org/buffer-from/-/buffer-from-1.1.2.tgz",
|
||||
|
|
@ -4584,6 +4721,11 @@
|
|||
"fsevents": "~2.3.2"
|
||||
}
|
||||
},
|
||||
"node_modules/chownr": {
|
||||
"version": "1.1.4",
|
||||
"resolved": "https://registry.npmjs.org/chownr/-/chownr-1.1.4.tgz",
|
||||
"integrity": "sha512-jJ0bqzaylmJtVnNgzTeSOs8DPavpbYgEr/b0YL8/2GO3xJEhInFmhKMUnEJQjZumK7KXGFhUy89PrsJWlakBVg=="
|
||||
},
|
||||
"node_modules/chrome-trace-event": {
|
||||
"version": "1.0.3",
|
||||
"resolved": "https://registry.npmjs.org/chrome-trace-event/-/chrome-trace-event-1.0.3.tgz",
|
||||
|
|
@ -4709,6 +4851,18 @@
|
|||
"url": "https://github.com/sponsors/wooorm"
|
||||
}
|
||||
},
|
||||
"node_modules/color": {
|
||||
"version": "4.2.3",
|
||||
"resolved": "https://registry.npmjs.org/color/-/color-4.2.3.tgz",
|
||||
"integrity": "sha512-1rXeuUUiGGrykh+CeBdu5Ie7OJwinCgQY0bc7GCRxy5xVHy+moaqkpL/jqQq0MtQOeYcrqEz4abc5f0KtU7W4A==",
|
||||
"dependencies": {
|
||||
"color-convert": "^2.0.1",
|
||||
"color-string": "^1.9.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=12.5.0"
|
||||
}
|
||||
},
|
||||
"node_modules/color-convert": {
|
||||
"version": "2.0.1",
|
||||
"resolved": "https://registry.npmjs.org/color-convert/-/color-convert-2.0.1.tgz",
|
||||
|
|
@ -4725,6 +4879,15 @@
|
|||
"resolved": "https://registry.npmjs.org/color-name/-/color-name-1.1.4.tgz",
|
||||
"integrity": "sha512-dOy+3AuW3a2wNbZHIuMZpTcgjGuLU/uBL/ubcZF9OXbDo8ff4O8yVp5Bf0efS8uEoYo5q4Fx7dY9OgQGXgAsQA=="
|
||||
},
|
||||
"node_modules/color-string": {
|
||||
"version": "1.9.1",
|
||||
"resolved": "https://registry.npmjs.org/color-string/-/color-string-1.9.1.tgz",
|
||||
"integrity": "sha512-shrVawQFojnZv6xM40anx4CkoDP+fZsw/ZerEMsW/pyzsRbElpsL/DBVW7q3ExxwusdNXI3lXpuhEZkzs8p5Eg==",
|
||||
"dependencies": {
|
||||
"color-name": "^1.0.0",
|
||||
"simple-swizzle": "^0.2.2"
|
||||
}
|
||||
},
|
||||
"node_modules/colord": {
|
||||
"version": "2.9.3",
|
||||
"resolved": "https://registry.npmjs.org/colord/-/colord-2.9.3.tgz",
|
||||
|
|
@ -4853,6 +5016,11 @@
|
|||
"resolved": "https://registry.npmjs.org/consola/-/consola-2.15.3.tgz",
|
||||
"integrity": "sha512-9vAdYbHj6x2fLKC4+oPH0kFzY/orMZyG2Aj+kNylHxKGJ/Ed4dpNyAQYwJOdqO4zdM7XpVHmyejQDcQHrnuXbw=="
|
||||
},
|
||||
"node_modules/consolidated-events": {
|
||||
"version": "2.0.2",
|
||||
"resolved": "https://registry.npmjs.org/consolidated-events/-/consolidated-events-2.0.2.tgz",
|
||||
"integrity": "sha512-2/uRVMdRypf5z/TW/ncD/66l75P5hH2vM/GR8Jf8HLc2xnfJtmina6F6du8+v4Z2vTrMo7jC+W1tmEEuuELgkQ=="
|
||||
},
|
||||
"node_modules/content-disposition": {
|
||||
"version": "0.5.2",
|
||||
"resolved": "https://registry.npmjs.org/content-disposition/-/content-disposition-0.5.2.tgz",
|
||||
|
|
@ -5508,6 +5676,14 @@
|
|||
"url": "https://github.com/sponsors/wooorm"
|
||||
}
|
||||
},
|
||||
"node_modules/detect-libc": {
|
||||
"version": "2.0.2",
|
||||
"resolved": "https://registry.npmjs.org/detect-libc/-/detect-libc-2.0.2.tgz",
|
||||
"integrity": "sha512-UX6sGumvvqSaXgdKGUsgZWqcUyIXZ/vZTrlRT/iobiKhGL0zL4d3osHj3uqllWJK+i+sixDS/3COVEOFbupFyw==",
|
||||
"engines": {
|
||||
"node": ">=8"
|
||||
}
|
||||
},
|
||||
"node_modules/detect-node": {
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/detect-node/-/detect-node-2.1.0.tgz",
|
||||
|
|
@ -5936,6 +6112,14 @@
|
|||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/expand-template": {
|
||||
"version": "2.0.3",
|
||||
"resolved": "https://registry.npmjs.org/expand-template/-/expand-template-2.0.3.tgz",
|
||||
"integrity": "sha512-XYfuKMvj4O35f/pOXLObndIRvyQ+/+6AhODh+OKWj9S9498pHHn/IMszH+gt0fBCRWMNfk1ZSp5x3AifmnI2vg==",
|
||||
"engines": {
|
||||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/express": {
|
||||
"version": "4.18.2",
|
||||
"resolved": "https://registry.npmjs.org/express/-/express-4.18.2.tgz",
|
||||
|
|
@ -6389,6 +6573,11 @@
|
|||
"node": ">= 0.6"
|
||||
}
|
||||
},
|
||||
"node_modules/fs-constants": {
|
||||
"version": "1.0.0",
|
||||
"resolved": "https://registry.npmjs.org/fs-constants/-/fs-constants-1.0.0.tgz",
|
||||
"integrity": "sha512-y6OAwoSIf7FyjMIv94u+b5rdheZEjzR63GTyZJm5qh4Bi+2YgwLCcI/fPFZkL5PSixOt6ZNKm+w+Hfp/Bciwow=="
|
||||
},
|
||||
"node_modules/fs-extra": {
|
||||
"version": "10.1.0",
|
||||
"resolved": "https://registry.npmjs.org/fs-extra/-/fs-extra-10.1.0.tgz",
|
||||
|
|
@ -6468,6 +6657,11 @@
|
|||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/github-from-package": {
|
||||
"version": "0.0.0",
|
||||
"resolved": "https://registry.npmjs.org/github-from-package/-/github-from-package-0.0.0.tgz",
|
||||
"integrity": "sha512-SyHy3T1v2NUXn29OsWdxmK6RwHD+vkj3v8en8AOBZ1wBQ/hCAQ5bAQTD02kW4W9tUp/3Qh6J8r9EvntiyCmOOw=="
|
||||
},
|
||||
"node_modules/github-slugger": {
|
||||
"version": "1.5.0",
|
||||
"resolved": "https://registry.npmjs.org/github-slugger/-/github-slugger-1.5.0.tgz",
|
||||
|
|
@ -7115,6 +7309,25 @@
|
|||
"postcss": "^8.1.0"
|
||||
}
|
||||
},
|
||||
"node_modules/ieee754": {
|
||||
"version": "1.2.1",
|
||||
"resolved": "https://registry.npmjs.org/ieee754/-/ieee754-1.2.1.tgz",
|
||||
"integrity": "sha512-dcyqhDvX1C46lXZcVqCpK+FtMRQVdIMN6/Df5js2zouUsqG7I6sFxitIC+7KYK29KdXOLHdu9zL4sFnoVQnqaA==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/feross"
|
||||
},
|
||||
{
|
||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
|
||||
},
|
||||
{
|
||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
||||
}
|
||||
]
|
||||
},
|
||||
"node_modules/ignore": {
|
||||
"version": "5.2.4",
|
||||
"resolved": "https://registry.npmjs.org/ignore/-/ignore-5.2.4.tgz",
|
||||
|
|
@ -8133,6 +8346,11 @@
|
|||
"url": "https://github.com/sponsors/ljharb"
|
||||
}
|
||||
},
|
||||
"node_modules/mkdirp-classic": {
|
||||
"version": "0.5.3",
|
||||
"resolved": "https://registry.npmjs.org/mkdirp-classic/-/mkdirp-classic-0.5.3.tgz",
|
||||
"integrity": "sha512-gKLcREMhtuZRwRAfqP3RFW+TK4JqApVBtOIftVgjuABpAtpxhPGaDcfvbhNvD0B8iD1oUr/txX35NjcaY6Ns/A=="
|
||||
},
|
||||
"node_modules/mrmime": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/mrmime/-/mrmime-1.0.1.tgz",
|
||||
|
|
@ -8175,6 +8393,11 @@
|
|||
"node": "^10 || ^12 || ^13.7 || ^14 || >=15.0.1"
|
||||
}
|
||||
},
|
||||
"node_modules/napi-build-utils": {
|
||||
"version": "1.0.2",
|
||||
"resolved": "https://registry.npmjs.org/napi-build-utils/-/napi-build-utils-1.0.2.tgz",
|
||||
"integrity": "sha512-ONmRUqK7zj7DWX0D9ADe03wbwOBZxNAfF20PlGfCWQcD3+/MakShIHrMqx9YwPTfxDdF1zLeL+RGZiR9kGMLdg=="
|
||||
},
|
||||
"node_modules/negotiator": {
|
||||
"version": "0.6.3",
|
||||
"resolved": "https://registry.npmjs.org/negotiator/-/negotiator-0.6.3.tgz",
|
||||
|
|
@ -8197,6 +8420,22 @@
|
|||
"tslib": "^2.0.3"
|
||||
}
|
||||
},
|
||||
"node_modules/node-abi": {
|
||||
"version": "3.47.0",
|
||||
"resolved": "https://registry.npmjs.org/node-abi/-/node-abi-3.47.0.tgz",
|
||||
"integrity": "sha512-2s6B2CWZM//kPgwnuI0KrYwNjfdByE25zvAaEpq9IH4zcNsarH8Ihu/UuX6XMPEogDAxkuUFeZn60pXNHAqn3A==",
|
||||
"dependencies": {
|
||||
"semver": "^7.3.5"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=10"
|
||||
}
|
||||
},
|
||||
"node_modules/node-addon-api": {
|
||||
"version": "5.1.0",
|
||||
"resolved": "https://registry.npmjs.org/node-addon-api/-/node-addon-api-5.1.0.tgz",
|
||||
"integrity": "sha512-eh0GgfEkpnoWDq+VY8OyvYhFEzBk6jIYbRKdIlyTiAXIVJ8PyBaKb0rp7oDtoddbdoHWhq8wwr+XZ81F1rpNdA=="
|
||||
},
|
||||
"node_modules/node-emoji": {
|
||||
"version": "1.11.0",
|
||||
"resolved": "https://registry.npmjs.org/node-emoji/-/node-emoji-1.11.0.tgz",
|
||||
|
|
@ -9303,6 +9542,31 @@
|
|||
"postcss": "^8.2.15"
|
||||
}
|
||||
},
|
||||
"node_modules/prebuild-install": {
|
||||
"version": "7.1.1",
|
||||
"resolved": "https://registry.npmjs.org/prebuild-install/-/prebuild-install-7.1.1.tgz",
|
||||
"integrity": "sha512-jAXscXWMcCK8GgCoHOfIr0ODh5ai8mj63L2nWrjuAgXE6tDyYGnx4/8o/rCgU+B4JSyZBKbeZqzhtwtC3ovxjw==",
|
||||
"dependencies": {
|
||||
"detect-libc": "^2.0.0",
|
||||
"expand-template": "^2.0.3",
|
||||
"github-from-package": "0.0.0",
|
||||
"minimist": "^1.2.3",
|
||||
"mkdirp-classic": "^0.5.3",
|
||||
"napi-build-utils": "^1.0.1",
|
||||
"node-abi": "^3.3.0",
|
||||
"pump": "^3.0.0",
|
||||
"rc": "^1.2.7",
|
||||
"simple-get": "^4.0.0",
|
||||
"tar-fs": "^2.0.0",
|
||||
"tunnel-agent": "^0.6.0"
|
||||
},
|
||||
"bin": {
|
||||
"prebuild-install": "bin.js"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=10"
|
||||
}
|
||||
},
|
||||
"node_modules/prepend-http": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/prepend-http/-/prepend-http-2.0.0.tgz",
|
||||
|
|
@ -9820,6 +10084,25 @@
|
|||
"react": "^16.8.0 || ^17.0.0 || ^18.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/react-waypoint": {
|
||||
"version": "10.3.0",
|
||||
"resolved": "https://registry.npmjs.org/react-waypoint/-/react-waypoint-10.3.0.tgz",
|
||||
"integrity": "sha512-iF1y2c1BsoXuEGz08NoahaLFIGI9gTUAAOKip96HUmylRT6DUtpgoBPjk/Y8dfcFVmfVDvUzWjNXpZyKTOV0SQ==",
|
||||
"dependencies": {
|
||||
"@babel/runtime": "^7.12.5",
|
||||
"consolidated-events": "^1.1.0 || ^2.0.0",
|
||||
"prop-types": "^15.0.0",
|
||||
"react-is": "^17.0.1 || ^18.0.0"
|
||||
},
|
||||
"peerDependencies": {
|
||||
"react": "^15.3.0 || ^16.0.0 || ^17.0.0 || ^18.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/react-waypoint/node_modules/react-is": {
|
||||
"version": "18.2.0",
|
||||
"resolved": "https://registry.npmjs.org/react-is/-/react-is-18.2.0.tgz",
|
||||
"integrity": "sha512-xWGDIW6x921xtzPkhiULtthJHoJvBbF3q26fzloPCK0hsvxtPVelvftw3zjbHWSkR2km9Z+4uxbDDK/6Zw9B8w=="
|
||||
},
|
||||
"node_modules/readable-stream": {
|
||||
"version": "3.6.2",
|
||||
"resolved": "https://registry.npmjs.org/readable-stream/-/readable-stream-3.6.2.tgz",
|
||||
|
|
@ -10745,6 +11028,28 @@
|
|||
"resolved": "https://registry.npmjs.org/shallowequal/-/shallowequal-1.1.0.tgz",
|
||||
"integrity": "sha512-y0m1JoUZSlPAjXVtPPW70aZWfIL/dSP7AFkRnniLCrK/8MDKog3TySTBmckD+RObVxH0v4Tox67+F14PdED2oQ=="
|
||||
},
|
||||
"node_modules/sharp": {
|
||||
"version": "0.30.7",
|
||||
"resolved": "https://registry.npmjs.org/sharp/-/sharp-0.30.7.tgz",
|
||||
"integrity": "sha512-G+MY2YW33jgflKPTXXptVO28HvNOo9G3j0MybYAHeEmby+QuD2U98dT6ueht9cv/XDqZspSpIhoSW+BAKJ7Hig==",
|
||||
"hasInstallScript": true,
|
||||
"dependencies": {
|
||||
"color": "^4.2.3",
|
||||
"detect-libc": "^2.0.1",
|
||||
"node-addon-api": "^5.0.0",
|
||||
"prebuild-install": "^7.1.1",
|
||||
"semver": "^7.3.7",
|
||||
"simple-get": "^4.0.1",
|
||||
"tar-fs": "^2.1.1",
|
||||
"tunnel-agent": "^0.6.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=12.13.0"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://opencollective.com/libvips"
|
||||
}
|
||||
},
|
||||
"node_modules/shebang-command": {
|
||||
"version": "2.0.0",
|
||||
"resolved": "https://registry.npmjs.org/shebang-command/-/shebang-command-2.0.0.tgz",
|
||||
|
|
@ -10806,6 +11111,87 @@
|
|||
"resolved": "https://registry.npmjs.org/signal-exit/-/signal-exit-3.0.7.tgz",
|
||||
"integrity": "sha512-wnD2ZE+l+SPC/uoS0vXeE9L1+0wuaMqKlfz9AMUo38JsyLSBWSFcHR1Rri62LZc12vLr1gb3jl7iwQhgwpAbGQ=="
|
||||
},
|
||||
"node_modules/simple-concat": {
|
||||
"version": "1.0.1",
|
||||
"resolved": "https://registry.npmjs.org/simple-concat/-/simple-concat-1.0.1.tgz",
|
||||
"integrity": "sha512-cSFtAPtRhljv69IK0hTVZQ+OfE9nePi/rtJmw5UjHeVyVroEqJXP1sFztKUy1qU+xvz3u/sfYJLa947b7nAN2Q==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/feross"
|
||||
},
|
||||
{
|
||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
|
||||
},
|
||||
{
|
||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
||||
}
|
||||
]
|
||||
},
|
||||
"node_modules/simple-get": {
|
||||
"version": "4.0.1",
|
||||
"resolved": "https://registry.npmjs.org/simple-get/-/simple-get-4.0.1.tgz",
|
||||
"integrity": "sha512-brv7p5WgH0jmQJr1ZDDfKDOSeWWg+OVypG99A/5vYGPqJ6pxiaHLy8nxtFjBA7oMa01ebA9gfh1uMCFqOuXxvA==",
|
||||
"funding": [
|
||||
{
|
||||
"type": "github",
|
||||
"url": "https://github.com/sponsors/feross"
|
||||
},
|
||||
{
|
||||
"type": "patreon",
|
||||
"url": "https://www.patreon.com/feross"
|
||||
},
|
||||
{
|
||||
"type": "consulting",
|
||||
"url": "https://feross.org/support"
|
||||
}
|
||||
],
|
||||
"dependencies": {
|
||||
"decompress-response": "^6.0.0",
|
||||
"once": "^1.3.1",
|
||||
"simple-concat": "^1.0.0"
|
||||
}
|
||||
},
|
||||
"node_modules/simple-get/node_modules/decompress-response": {
|
||||
"version": "6.0.0",
|
||||
"resolved": "https://registry.npmjs.org/decompress-response/-/decompress-response-6.0.0.tgz",
|
||||
"integrity": "sha512-aW35yZM6Bb/4oJlZncMH2LCoZtJXTRxES17vE3hoRiowU2kWHaJKFkSBDnDR+cm9J+9QhXmREyIfv0pji9ejCQ==",
|
||||
"dependencies": {
|
||||
"mimic-response": "^3.1.0"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=10"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/simple-get/node_modules/mimic-response": {
|
||||
"version": "3.1.0",
|
||||
"resolved": "https://registry.npmjs.org/mimic-response/-/mimic-response-3.1.0.tgz",
|
||||
"integrity": "sha512-z0yWI+4FDrrweS8Zmt4Ej5HdJmky15+L2e6Wgn3+iK5fWzb6T3fhNFq2+MeTRb064c6Wr4N/wv0DzQTjNzHNGQ==",
|
||||
"engines": {
|
||||
"node": ">=10"
|
||||
},
|
||||
"funding": {
|
||||
"url": "https://github.com/sponsors/sindresorhus"
|
||||
}
|
||||
},
|
||||
"node_modules/simple-swizzle": {
|
||||
"version": "0.2.2",
|
||||
"resolved": "https://registry.npmjs.org/simple-swizzle/-/simple-swizzle-0.2.2.tgz",
|
||||
"integrity": "sha512-JA//kQgZtbuY83m+xT+tXJkmJncGMTFT+C+g2h2R9uxkYIrE2yy9sgmcLhCnw57/WSD+Eh3J97FPEDFnbXnDUg==",
|
||||
"dependencies": {
|
||||
"is-arrayish": "^0.3.1"
|
||||
}
|
||||
},
|
||||
"node_modules/simple-swizzle/node_modules/is-arrayish": {
|
||||
"version": "0.3.2",
|
||||
"resolved": "https://registry.npmjs.org/is-arrayish/-/is-arrayish-0.3.2.tgz",
|
||||
"integrity": "sha512-eVRqCvVlZbuw3GrM63ovNSNAeA1K16kaR/LRY/92w0zxQ5/1YzwblUX652i4Xs9RwAGjW9d9y6X88t8OaAJfWQ=="
|
||||
},
|
||||
"node_modules/sirv": {
|
||||
"version": "1.0.19",
|
||||
"resolved": "https://registry.npmjs.org/sirv/-/sirv-1.0.19.tgz",
|
||||
|
|
@ -10865,6 +11251,14 @@
|
|||
"websocket-driver": "^0.7.4"
|
||||
}
|
||||
},
|
||||
"node_modules/sockjs/node_modules/uuid": {
|
||||
"version": "8.3.2",
|
||||
"resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz",
|
||||
"integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==",
|
||||
"bin": {
|
||||
"uuid": "dist/bin/uuid"
|
||||
}
|
||||
},
|
||||
"node_modules/sort-css-media-queries": {
|
||||
"version": "2.1.0",
|
||||
"resolved": "https://registry.npmjs.org/sort-css-media-queries/-/sort-css-media-queries-2.1.0.tgz",
|
||||
|
|
@ -11217,6 +11611,32 @@
|
|||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/tar-fs": {
|
||||
"version": "2.1.1",
|
||||
"resolved": "https://registry.npmjs.org/tar-fs/-/tar-fs-2.1.1.tgz",
|
||||
"integrity": "sha512-V0r2Y9scmbDRLCNex/+hYzvp/zyYjvFbHPNgVTKfQvVrb6guiE/fxP+XblDNR011utopbkex2nM4dHNV6GDsng==",
|
||||
"dependencies": {
|
||||
"chownr": "^1.1.1",
|
||||
"mkdirp-classic": "^0.5.2",
|
||||
"pump": "^3.0.0",
|
||||
"tar-stream": "^2.1.4"
|
||||
}
|
||||
},
|
||||
"node_modules/tar-stream": {
|
||||
"version": "2.2.0",
|
||||
"resolved": "https://registry.npmjs.org/tar-stream/-/tar-stream-2.2.0.tgz",
|
||||
"integrity": "sha512-ujeqbceABgwMZxEJnk2HDY2DlnUZ+9oEcb1KzTVfYHio0UE6dG71n60d8D2I4qNvleWrrXpmjpt7vZeF1LnMZQ==",
|
||||
"dependencies": {
|
||||
"bl": "^4.0.3",
|
||||
"end-of-stream": "^1.4.1",
|
||||
"fs-constants": "^1.0.0",
|
||||
"inherits": "^2.0.3",
|
||||
"readable-stream": "^3.1.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": ">=6"
|
||||
}
|
||||
},
|
||||
"node_modules/terser": {
|
||||
"version": "5.19.2",
|
||||
"resolved": "https://registry.npmjs.org/terser/-/terser-5.19.2.tgz",
|
||||
|
|
@ -11413,6 +11833,17 @@
|
|||
"resolved": "https://registry.npmjs.org/tslib/-/tslib-2.6.1.tgz",
|
||||
"integrity": "sha512-t0hLfiEKfMUoqhG+U1oid7Pva4bbDPHYfJNiB7BiIjRkj1pyC++4N3huJfqY6aRH6VTB0rvtzQwjM4K6qpfOig=="
|
||||
},
|
||||
"node_modules/tunnel-agent": {
|
||||
"version": "0.6.0",
|
||||
"resolved": "https://registry.npmjs.org/tunnel-agent/-/tunnel-agent-0.6.0.tgz",
|
||||
"integrity": "sha512-McnNiV1l8RYeY8tBgEpuodCC1mLUdbSN+CYBL7kJsJNInOP8UjDDEwdk6Mw60vdLLrr5NHKZhMAOSrR2NZuQ+w==",
|
||||
"dependencies": {
|
||||
"safe-buffer": "^5.0.1"
|
||||
},
|
||||
"engines": {
|
||||
"node": "*"
|
||||
}
|
||||
},
|
||||
"node_modules/type-fest": {
|
||||
"version": "2.19.0",
|
||||
"resolved": "https://registry.npmjs.org/type-fest/-/type-fest-2.19.0.tgz",
|
||||
|
|
@ -11991,9 +12422,9 @@
|
|||
}
|
||||
},
|
||||
"node_modules/uuid": {
|
||||
"version": "8.3.2",
|
||||
"resolved": "https://registry.npmjs.org/uuid/-/uuid-8.3.2.tgz",
|
||||
"integrity": "sha512-+NYs2QeMWy+GWFOEm9xnn6HCDp0l7QBD7ml8zLUmJ+93Q5NF0NocErnwkTkXVFNiX3/fpC6afS8Dhb/gz7R7eg==",
|
||||
"version": "9.0.0",
|
||||
"resolved": "https://registry.npmjs.org/uuid/-/uuid-9.0.0.tgz",
|
||||
"integrity": "sha512-MXcSTerfPa4uqyzStbRoTgt5XIe3x5+42+q1sDuy3R5MDk66URdLMOZe5aPX/SQd+kuYAh0FdP/pO28IkQyTeg==",
|
||||
"bin": {
|
||||
"uuid": "dist/bin/uuid"
|
||||
}
|
||||
|
|
|
|||
|
|
@ -15,12 +15,16 @@
|
|||
},
|
||||
"dependencies": {
|
||||
"@docusaurus/core": "2.4.1",
|
||||
"@docusaurus/plugin-ideal-image": "^2.4.1",
|
||||
"@docusaurus/preset-classic": "2.4.1",
|
||||
"@mdx-js/react": "^1.6.22",
|
||||
"clsx": "^1.2.1",
|
||||
"docusaurus-lunr-search": "^2.4.1",
|
||||
"prism-react-renderer": "^1.3.5",
|
||||
"react": "^17.0.2",
|
||||
"react-dom": "^17.0.2"
|
||||
"react-dom": "^17.0.2",
|
||||
"sharp": "^0.32.5",
|
||||
"uuid": "^9.0.0"
|
||||
},
|
||||
"devDependencies": {
|
||||
"@docusaurus/module-type-aliases": "2.4.1"
|
||||
|
|
|
|||
|
|
@ -28,16 +28,13 @@ const sidebars = {
|
|||
label: "Embedding()",
|
||||
items: ["embedding/supported_embedding"],
|
||||
},
|
||||
"debugging/local_debugging",
|
||||
"completion/supported",
|
||||
'completion/supported',
|
||||
'debugging/local_debugging',
|
||||
'debugging/hosted_debugging',
|
||||
{
|
||||
type: "category",
|
||||
label: "Tutorials",
|
||||
items: [
|
||||
"tutorials/huggingface_tutorial",
|
||||
"tutorials/TogetherAI_liteLLM",
|
||||
"tutorials/debugging_tutorial",
|
||||
],
|
||||
type: 'category',
|
||||
label: 'Tutorials',
|
||||
items: ['tutorials/huggingface_tutorial', 'tutorials/TogetherAI_liteLLM', 'tutorials/fallbacks', 'tutorials/finetuned_chat_gpt'],
|
||||
},
|
||||
"token_usage",
|
||||
"stream",
|
||||
|
|
|
|||
8161
docs/my-website/yarn.lock
Normal file
8161
docs/my-website/yarn.lock
Normal file
File diff suppressed because it is too large
Load diff
|
|
@ -1,9 +1,11 @@
|
|||
import threading
|
||||
from typing import Callable, List, Optional
|
||||
|
||||
input_callback: List[str] = []
|
||||
success_callback: List[str] = []
|
||||
failure_callback: List[str] = []
|
||||
set_verbose = False
|
||||
email: Optional[str] = None # for hosted dashboard. Learn more - https://docs.litellm.ai/docs/debugging/hosted_debugging
|
||||
telemetry = True
|
||||
max_tokens = 256 # OpenAI Defaults
|
||||
retry = True
|
||||
|
|
@ -17,10 +19,10 @@ openrouter_key: Optional[str] = None
|
|||
huggingface_key: Optional[str] = None
|
||||
vertex_project: Optional[str] = None
|
||||
vertex_location: Optional[str] = None
|
||||
hugging_api_token: Optional[str] = None
|
||||
togetherai_api_key: Optional[str] = None
|
||||
caching = False
|
||||
caching_with_models = False # if you want the caching key to be model + prompt
|
||||
caching_with_models = False # if you want the caching key to be model + prompt
|
||||
debugger = False
|
||||
model_cost = {
|
||||
"gpt-3.5-turbo": {
|
||||
"max_tokens": 4000,
|
||||
|
|
@ -148,7 +150,15 @@ cohere_models = [
|
|||
anthropic_models = ["claude-2", "claude-instant-1", "claude-instant-1.2"]
|
||||
|
||||
replicate_models = [
|
||||
"replicate/"
|
||||
"replicate/",
|
||||
"replicate/llama-2-70b-chat:58d078176e02c219e11eb4da5a02a7830a283b14cf8f94537af893ccff5ee781",
|
||||
"a16z-infra/llama-2-13b-chat:2a7f981751ec7fdf87b5b91ad4db53683a98082e9ff7bfd12c8cd5ea85980a52",
|
||||
"joehoover/instructblip-vicuna13b:c4c54e3c8c97cd50c2d2fec9be3b6065563ccf7d43787fb99f84151b867178fe"
|
||||
"replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5",
|
||||
"a16z-infra/llama-2-7b-chat:7b0bfc9aff140d5b75bacbed23e91fd3c34b01a1e958d32132de6e0a19796e2c",
|
||||
"replicate/vicuna-13b:6282abe6a492de4145d7bb601023762212f9ddbbe78278bd6771c8b3b2f2a13b",
|
||||
"daanelson/flan-t5-large:ce962b3f6792a57074a601d3979db5839697add2e4e02696b3ced4c022d4767f",
|
||||
"replit/replit-code-v1-3b:b84f4c074b807211cd75e3e8b1589b6399052125b4c27106e43d47189e8415ad",
|
||||
] # placeholder, to make sure we accept any replicate model in our model_list
|
||||
|
||||
openrouter_models = [
|
||||
|
|
@ -185,6 +195,14 @@ huggingface_models = [
|
|||
|
||||
ai21_models = ["j2-ultra", "j2-mid", "j2-light"]
|
||||
|
||||
together_ai_models = [
|
||||
"togethercomputer/llama-2-70b-chat",
|
||||
"togethercomputer/Llama-2-7B-32K-Instruct",
|
||||
"togethercomputer/llama-2-7b",
|
||||
]
|
||||
|
||||
baseten_models = ["qvv0xeq", "q841o8w", "31dxrj3"] # FALCON 7B # WizardLM # Mosaic ML
|
||||
|
||||
model_list = (
|
||||
open_ai_chat_completion_models
|
||||
+ open_ai_text_completion_models
|
||||
|
|
@ -196,10 +214,13 @@ model_list = (
|
|||
+ vertex_chat_models
|
||||
+ vertex_text_models
|
||||
+ ai21_models
|
||||
+ together_ai_models
|
||||
+ baseten_models
|
||||
)
|
||||
|
||||
provider_list = [
|
||||
"openai",
|
||||
"azure",
|
||||
"cohere",
|
||||
"anthropic",
|
||||
"replicate",
|
||||
|
|
@ -208,7 +229,22 @@ provider_list = [
|
|||
"openrouter",
|
||||
"vertex_ai",
|
||||
"ai21",
|
||||
"baseten",
|
||||
]
|
||||
|
||||
models_by_provider = {
|
||||
"openai": open_ai_chat_completion_models + open_ai_text_completion_models,
|
||||
"cohere": cohere_models,
|
||||
"anthropic": anthropic_models,
|
||||
"replicate": replicate_models,
|
||||
"huggingface": huggingface_models,
|
||||
"together_ai": together_ai_models,
|
||||
"baseten": baseten_models,
|
||||
"openrouter": openrouter_models,
|
||||
"vertex_ai": vertex_chat_models + vertex_text_models,
|
||||
"ai21": ai21_models,
|
||||
}
|
||||
|
||||
####### EMBEDDING MODELS ###################
|
||||
open_ai_embedding_models = ["text-embedding-ada-002"]
|
||||
|
||||
|
|
@ -223,7 +259,9 @@ from .utils import (
|
|||
cost_per_token,
|
||||
completion_cost,
|
||||
get_litellm_params,
|
||||
Logging
|
||||
Logging,
|
||||
acreate,
|
||||
get_model_list,
|
||||
)
|
||||
from .main import * # type: ignore
|
||||
from .integrations import *
|
||||
|
|
|
|||
Binary file not shown.
Binary file not shown.
Binary file not shown.
5
litellm/_version.py
Normal file
5
litellm/_version.py
Normal file
|
|
@ -0,0 +1,5 @@
|
|||
import importlib_metadata
|
||||
try:
|
||||
version = importlib_metadata.version("litellm")
|
||||
except:
|
||||
pass
|
||||
Binary file not shown.
|
|
@ -1,40 +1,72 @@
|
|||
import requests, traceback, json
|
||||
import requests, traceback, json, os
|
||||
|
||||
|
||||
class LiteDebugger:
|
||||
def __init__(self):
|
||||
user_email = None
|
||||
dashboard_url = None
|
||||
|
||||
def __init__(self, email=None):
|
||||
self.api_url = "https://api.litellm.ai/debugger"
|
||||
self.validate_environment(email)
|
||||
pass
|
||||
|
||||
def input_log_event(self, model, messages, end_user, litellm_call_id, print_verbose):
|
||||
|
||||
def validate_environment(self, email):
|
||||
try:
|
||||
self.user_email = os.getenv("LITELLM_EMAIL") or email
|
||||
self.dashboard_url = "https://admin.litellm.ai/" + self.user_email
|
||||
print(f"Here's your free Dashboard 👉 {self.dashboard_url}")
|
||||
if self.user_email == None:
|
||||
raise Exception(
|
||||
"[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_EMAIL. Set it in your environment. Eg.: os.environ['LITELLM_EMAIL']= <your_email>"
|
||||
)
|
||||
except Exception as e:
|
||||
raise ValueError(
|
||||
"[Non-Blocking Error] LiteLLMDebugger: Missing LITELLM_EMAIL. Set it in your environment. Eg.: os.environ['LITELLM_EMAIL']= <your_email>"
|
||||
)
|
||||
|
||||
def input_log_event(
|
||||
self, model, messages, end_user, litellm_call_id, print_verbose
|
||||
):
|
||||
try:
|
||||
print_verbose(
|
||||
f"LiteLLMDebugger: Logging - Enters input logging function for model {model}"
|
||||
)
|
||||
litellm_data_obj = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"end_user": end_user,
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"end_user": end_user,
|
||||
"status": "initiated",
|
||||
"litellm_call_id": litellm_call_id
|
||||
"litellm_call_id": litellm_call_id,
|
||||
"user_email": self.user_email,
|
||||
}
|
||||
response = requests.post(url=self.api_url, headers={"content-type": "application/json"}, data=json.dumps(litellm_data_obj))
|
||||
response = requests.post(
|
||||
url=self.api_url,
|
||||
headers={"content-type": "application/json"},
|
||||
data=json.dumps(litellm_data_obj),
|
||||
)
|
||||
print_verbose(f"LiteDebugger: api response - {response.text}")
|
||||
except:
|
||||
print_verbose(f"LiteDebugger: Logging Error - {traceback.format_exc()}")
|
||||
print_verbose(
|
||||
f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}"
|
||||
)
|
||||
pass
|
||||
|
||||
def log_event(self, model,
|
||||
|
||||
def log_event(
|
||||
self,
|
||||
model,
|
||||
messages,
|
||||
end_user,
|
||||
response_obj,
|
||||
start_time,
|
||||
end_time,
|
||||
litellm_call_id,
|
||||
print_verbose,):
|
||||
print_verbose,
|
||||
):
|
||||
try:
|
||||
print_verbose(
|
||||
f"LiteLLMDebugger: Logging - Enters input logging function for model {model}"
|
||||
)
|
||||
total_cost = 0 # [TODO] implement cost tracking
|
||||
total_cost = 0 # [TODO] implement cost tracking
|
||||
response_time = (end_time - start_time).total_seconds()
|
||||
if "choices" in response_obj:
|
||||
litellm_data_obj = {
|
||||
|
|
@ -45,12 +77,17 @@ class LiteDebugger:
|
|||
"response": response_obj["choices"][0]["message"]["content"],
|
||||
"end_user": end_user,
|
||||
"litellm_call_id": litellm_call_id,
|
||||
"status": "success"
|
||||
"status": "success",
|
||||
"user_email": self.user_email,
|
||||
}
|
||||
print_verbose(
|
||||
f"LiteDebugger: Logging - final data object: {litellm_data_obj}"
|
||||
)
|
||||
response = requests.post(url=self.api_url, headers={"content-type": "application/json"}, data=json.dumps(litellm_data_obj))
|
||||
response = requests.post(
|
||||
url=self.api_url,
|
||||
headers={"content-type": "application/json"},
|
||||
data=json.dumps(litellm_data_obj),
|
||||
)
|
||||
elif "error" in response_obj:
|
||||
if "Unable to map your input to a model." in response_obj["error"]:
|
||||
total_cost = 0
|
||||
|
|
@ -62,13 +99,20 @@ class LiteDebugger:
|
|||
"error": response_obj["error"],
|
||||
"end_user": end_user,
|
||||
"litellm_call_id": litellm_call_id,
|
||||
"status": "failure"
|
||||
"status": "failure",
|
||||
"user_email": self.user_email,
|
||||
}
|
||||
print_verbose(
|
||||
f"LiteDebugger: Logging - final data object: {litellm_data_obj}"
|
||||
)
|
||||
response = requests.post(url=self.api_url, headers={"content-type": "application/json"}, data=json.dumps(litellm_data_obj))
|
||||
response = requests.post(
|
||||
url=self.api_url,
|
||||
headers={"content-type": "application/json"},
|
||||
data=json.dumps(litellm_data_obj),
|
||||
)
|
||||
print_verbose(f"LiteDebugger: api response - {response.text}")
|
||||
except:
|
||||
print_verbose(f"LiteDebugger: Logging Error - {traceback.format_exc()}")
|
||||
pass
|
||||
print_verbose(
|
||||
f"[Non-Blocking Error] LiteDebugger: Logging Error - {traceback.format_exc()}"
|
||||
)
|
||||
pass
|
||||
|
|
|
|||
|
|
@ -144,23 +144,25 @@ class Supabase:
|
|||
)
|
||||
return prompt_tokens_cost_usd_dollar, completion_tokens_cost_usd_dollar
|
||||
|
||||
def input_log_event(self, model, messages, end_user, litellm_call_id, print_verbose):
|
||||
def input_log_event(
|
||||
self, model, messages, end_user, litellm_call_id, print_verbose
|
||||
):
|
||||
try:
|
||||
print_verbose(
|
||||
f"Supabase Logging - Enters input logging function for model {model}"
|
||||
)
|
||||
supabase_data_obj = {
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"end_user": end_user,
|
||||
"model": model,
|
||||
"messages": messages,
|
||||
"end_user": end_user,
|
||||
"status": "initiated",
|
||||
"litellm_call_id": litellm_call_id
|
||||
"litellm_call_id": litellm_call_id,
|
||||
}
|
||||
data, count = (
|
||||
self.supabase_client.table(self.supabase_table_name)
|
||||
.insert(supabase_data_obj)
|
||||
.execute()
|
||||
)
|
||||
self.supabase_client.table(self.supabase_table_name)
|
||||
.insert(supabase_data_obj)
|
||||
.execute()
|
||||
)
|
||||
print(f"data: {data}")
|
||||
except:
|
||||
print_verbose(f"Supabase Logging Error - {traceback.format_exc()}")
|
||||
|
|
@ -200,7 +202,7 @@ class Supabase:
|
|||
"response": response_obj["choices"][0]["message"]["content"],
|
||||
"end_user": end_user,
|
||||
"litellm_call_id": litellm_call_id,
|
||||
"status": "success"
|
||||
"status": "success",
|
||||
}
|
||||
print_verbose(
|
||||
f"Supabase Logging - final data object: {supabase_data_obj}"
|
||||
|
|
@ -221,7 +223,7 @@ class Supabase:
|
|||
"error": response_obj["error"],
|
||||
"end_user": end_user,
|
||||
"litellm_call_id": litellm_call_id,
|
||||
"status": "failure"
|
||||
"status": "failure",
|
||||
}
|
||||
print_verbose(
|
||||
f"Supabase Logging - final data object: {supabase_data_obj}"
|
||||
|
|
|
|||
|
|
@ -21,7 +21,9 @@ class AnthropicError(Exception):
|
|||
|
||||
|
||||
class AnthropicLLM:
|
||||
def __init__(self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None):
|
||||
def __init__(
|
||||
self, encoding, default_max_tokens_to_sample, logging_obj, api_key=None
|
||||
):
|
||||
self.encoding = encoding
|
||||
self.default_max_tokens_to_sample = default_max_tokens_to_sample
|
||||
self.completion_url = "https://api.anthropic.com/v1/complete"
|
||||
|
|
@ -84,7 +86,11 @@ class AnthropicLLM:
|
|||
}
|
||||
|
||||
## LOGGING
|
||||
self.logging_obj.pre_call(input=prompt, api_key=self.api_key, additional_args={"complete_input_dict": data})
|
||||
self.logging_obj.pre_call(
|
||||
input=prompt,
|
||||
api_key=self.api_key,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
response = requests.post(
|
||||
self.completion_url, headers=self.headers, data=json.dumps(data)
|
||||
|
|
@ -93,7 +99,12 @@ class AnthropicLLM:
|
|||
return response.iter_lines()
|
||||
else:
|
||||
## LOGGING
|
||||
self.logging_obj.post_call(input=prompt, api_key=self.api_key, original_response=response.text, additional_args={"complete_input_dict": data})
|
||||
self.logging_obj.post_call(
|
||||
input=prompt,
|
||||
api_key=self.api_key,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
print_verbose(f"raw model_response: {response.text}")
|
||||
## RESPONSE OBJECT
|
||||
completion_response = response.json()
|
||||
|
|
|
|||
|
|
@ -72,12 +72,13 @@ class HuggingfaceRestAPILLM:
|
|||
if "max_tokens" in optional_params:
|
||||
value = optional_params.pop("max_tokens")
|
||||
optional_params["max_new_tokens"] = value
|
||||
data = {
|
||||
"inputs": prompt,
|
||||
"parameters": optional_params
|
||||
}
|
||||
data = {"inputs": prompt, "parameters": optional_params}
|
||||
## LOGGING
|
||||
self.logging_obj.pre_call(input=prompt, api_key=self.api_key, additional_args={"complete_input_dict": data})
|
||||
self.logging_obj.pre_call(
|
||||
input=prompt,
|
||||
api_key=self.api_key,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
response = requests.post(
|
||||
completion_url, headers=self.headers, data=json.dumps(data)
|
||||
|
|
@ -86,7 +87,12 @@ class HuggingfaceRestAPILLM:
|
|||
return response.iter_lines()
|
||||
else:
|
||||
## LOGGING
|
||||
self.logging_obj.post_call(input=prompt, api_key=self.api_key, original_response=response.text, additional_args={"complete_input_dict": data})
|
||||
self.logging_obj.post_call(
|
||||
input=prompt,
|
||||
api_key=self.api_key,
|
||||
original_response=response.text,
|
||||
additional_args={"complete_input_dict": data},
|
||||
)
|
||||
## RESPONSE OBJECT
|
||||
completion_response = response.json()
|
||||
print_verbose(f"response: {completion_response}")
|
||||
|
|
|
|||
199
litellm/main.py
199
litellm/main.py
|
|
@ -10,13 +10,14 @@ from litellm import ( # type: ignore
|
|||
timeout,
|
||||
get_optional_params,
|
||||
get_litellm_params,
|
||||
Logging
|
||||
Logging,
|
||||
)
|
||||
from litellm.utils import (
|
||||
get_secret,
|
||||
install_and_import,
|
||||
CustomStreamWrapper,
|
||||
read_config_args,
|
||||
completion_with_fallbacks,
|
||||
)
|
||||
from .llms.anthropic import AnthropicLLM
|
||||
from .llms.huggingface_restapi import HuggingfaceRestAPILLM
|
||||
|
|
@ -90,17 +91,24 @@ def completion(
|
|||
# used by text-bison only
|
||||
top_k=40,
|
||||
request_timeout=0, # unused var for old version of OpenAI API
|
||||
fallbacks=[],
|
||||
) -> ModelResponse:
|
||||
args = locals()
|
||||
try:
|
||||
if fallbacks != []:
|
||||
return completion_with_fallbacks(**args)
|
||||
model_response = ModelResponse()
|
||||
if azure: # this flag is deprecated, remove once notebooks are also updated.
|
||||
custom_llm_provider = "azure"
|
||||
elif model.split("/", 1)[0] in litellm.provider_list: # allow custom provider to be passed in via the model name "azure/chatgpt-test"
|
||||
elif (
|
||||
model.split("/", 1)[0] in litellm.provider_list
|
||||
): # allow custom provider to be passed in via the model name "azure/chatgpt-test"
|
||||
custom_llm_provider = model.split("/", 1)[0]
|
||||
model = model.split("/", 1)[1]
|
||||
if "replicate" == custom_llm_provider and "/" not in model: # handle the "replicate/llama2..." edge-case
|
||||
if (
|
||||
"replicate" == custom_llm_provider and "/" not in model
|
||||
): # handle the "replicate/llama2..." edge-case
|
||||
model = custom_llm_provider + "/" + model
|
||||
args = locals()
|
||||
# check if user passed in any of the OpenAI optional params
|
||||
optional_params = get_optional_params(
|
||||
functions=functions,
|
||||
|
|
@ -130,9 +138,14 @@ def completion(
|
|||
verbose=verbose,
|
||||
custom_llm_provider=custom_llm_provider,
|
||||
custom_api_base=custom_api_base,
|
||||
litellm_call_id=litellm_call_id
|
||||
litellm_call_id=litellm_call_id,
|
||||
)
|
||||
logging = Logging(
|
||||
model=model,
|
||||
messages=messages,
|
||||
optional_params=optional_params,
|
||||
litellm_params=litellm_params,
|
||||
)
|
||||
logging = Logging(model=model, messages=messages, optional_params=optional_params, litellm_params=litellm_params)
|
||||
if custom_llm_provider == "azure":
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
|
|
@ -153,7 +166,15 @@ def completion(
|
|||
# set key
|
||||
openai.api_key = api_key
|
||||
## LOGGING
|
||||
logging.pre_call(input=messages, api_key=openai.api_key, additional_args={"headers": litellm.headers, "api_version": openai.api_version, "api_base": openai.api_base})
|
||||
logging.pre_call(
|
||||
input=messages,
|
||||
api_key=openai.api_key,
|
||||
additional_args={
|
||||
"headers": litellm.headers,
|
||||
"api_version": openai.api_version,
|
||||
"api_base": openai.api_base,
|
||||
},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
if litellm.headers:
|
||||
response = openai.ChatCompletion.create(
|
||||
|
|
@ -164,13 +185,24 @@ def completion(
|
|||
)
|
||||
else:
|
||||
response = openai.ChatCompletion.create(
|
||||
model=model, messages=messages, **optional_params
|
||||
engine=model, messages=messages, **optional_params
|
||||
)
|
||||
|
||||
## LOGGING
|
||||
logging.post_call(input=messages, api_key=openai.api_key, original_response=response, additional_args={"headers": litellm.headers, "api_version": openai.api_version, "api_base": openai.api_base})
|
||||
logging.post_call(
|
||||
input=messages,
|
||||
api_key=openai.api_key,
|
||||
original_response=response,
|
||||
additional_args={
|
||||
"headers": litellm.headers,
|
||||
"api_version": openai.api_version,
|
||||
"api_base": openai.api_base,
|
||||
},
|
||||
)
|
||||
elif (
|
||||
model in litellm.open_ai_chat_completion_models
|
||||
or custom_llm_provider == "custom_openai"
|
||||
or "ft:gpt-3.5-turbo" in model # finetuned gpt-3.5-turbo
|
||||
): # allow user to make an openai call with a custom base
|
||||
openai.api_type = "openai"
|
||||
# note: if a user sets a custom base - we should ensure this works
|
||||
|
|
@ -192,7 +224,11 @@ def completion(
|
|||
openai.api_key = api_key
|
||||
|
||||
## LOGGING
|
||||
logging.pre_call(input=messages, api_key=api_key, additional_args={"headers": litellm.headers, "api_base": api_base})
|
||||
logging.pre_call(
|
||||
input=messages,
|
||||
api_key=api_key,
|
||||
additional_args={"headers": litellm.headers, "api_base": api_base},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
if litellm.headers:
|
||||
response = openai.ChatCompletion.create(
|
||||
|
|
@ -206,7 +242,12 @@ def completion(
|
|||
model=model, messages=messages, **optional_params
|
||||
)
|
||||
## LOGGING
|
||||
logging.post_call(input=messages, api_key=api_key, original_response=response, additional_args={"headers": litellm.headers})
|
||||
logging.post_call(
|
||||
input=messages,
|
||||
api_key=api_key,
|
||||
original_response=response,
|
||||
additional_args={"headers": litellm.headers},
|
||||
)
|
||||
elif model in litellm.open_ai_text_completion_models:
|
||||
openai.api_type = "openai"
|
||||
openai.api_base = (
|
||||
|
|
@ -227,7 +268,16 @@ def completion(
|
|||
openai.organization = litellm.organization
|
||||
prompt = " ".join([message["content"] for message in messages])
|
||||
## LOGGING
|
||||
logging.pre_call(input=prompt, api_key=api_key, additional_args={"openai_organization": litellm.organization, "headers": litellm.headers, "api_base": openai.api_base, "api_type": openai.api_type})
|
||||
logging.pre_call(
|
||||
input=prompt,
|
||||
api_key=api_key,
|
||||
additional_args={
|
||||
"openai_organization": litellm.organization,
|
||||
"headers": litellm.headers,
|
||||
"api_base": openai.api_base,
|
||||
"api_type": openai.api_type,
|
||||
},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
if litellm.headers:
|
||||
response = openai.Completion.create(
|
||||
|
|
@ -238,7 +288,17 @@ def completion(
|
|||
else:
|
||||
response = openai.Completion.create(model=model, prompt=prompt)
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=api_key, original_response=response, additional_args={"openai_organization": litellm.organization, "headers": litellm.headers, "api_base": openai.api_base, "api_type": openai.api_type})
|
||||
logging.post_call(
|
||||
input=prompt,
|
||||
api_key=api_key,
|
||||
original_response=response,
|
||||
additional_args={
|
||||
"openai_organization": litellm.organization,
|
||||
"headers": litellm.headers,
|
||||
"api_base": openai.api_base,
|
||||
"api_type": openai.api_type,
|
||||
},
|
||||
)
|
||||
## RESPONSE OBJECT
|
||||
completion_response = response["choices"][0]["text"]
|
||||
model_response["choices"][0]["message"]["content"] = completion_response
|
||||
|
|
@ -269,7 +329,14 @@ def completion(
|
|||
input["max_length"] = max_tokens # for t5 models
|
||||
input["max_new_tokens"] = max_tokens # for llama2 models
|
||||
## LOGGING
|
||||
logging.pre_call(input=prompt, api_key=replicate_key, additional_args={"complete_input_dict": input, "max_tokens": max_tokens})
|
||||
logging.pre_call(
|
||||
input=prompt,
|
||||
api_key=replicate_key,
|
||||
additional_args={
|
||||
"complete_input_dict": input,
|
||||
"max_tokens": max_tokens,
|
||||
},
|
||||
)
|
||||
## COMPLETION CALL
|
||||
output = replicate.run(model, input=input)
|
||||
if "stream" in optional_params and optional_params["stream"] == True:
|
||||
|
|
@ -282,7 +349,15 @@ def completion(
|
|||
response += item
|
||||
completion_response = response
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=replicate_key, original_response=completion_response, additional_args={"complete_input_dict": input, "max_tokens": max_tokens})
|
||||
logging.post_call(
|
||||
input=prompt,
|
||||
api_key=replicate_key,
|
||||
original_response=completion_response,
|
||||
additional_args={
|
||||
"complete_input_dict": input,
|
||||
"max_tokens": max_tokens,
|
||||
},
|
||||
)
|
||||
## USAGE
|
||||
prompt_tokens = len(encoding.encode(prompt))
|
||||
completion_tokens = len(encoding.encode(completion_response))
|
||||
|
|
@ -304,7 +379,7 @@ def completion(
|
|||
encoding=encoding,
|
||||
default_max_tokens_to_sample=litellm.max_tokens,
|
||||
api_key=anthropic_key,
|
||||
logging_obj = logging # model call logging done inside the class as we make need to modify I/O to fit anthropic's requirements
|
||||
logging_obj=logging, # model call logging done inside the class as we make need to modify I/O to fit anthropic's requirements
|
||||
)
|
||||
model_response = anthropic_client.completion(
|
||||
model=model,
|
||||
|
|
@ -368,7 +443,9 @@ def completion(
|
|||
**optional_params,
|
||||
)
|
||||
## LOGGING
|
||||
logging.post_call(input=messages, api_key=openai.api_key, original_response=response)
|
||||
logging.post_call(
|
||||
input=messages, api_key=openai.api_key, original_response=response
|
||||
)
|
||||
elif model in litellm.cohere_models:
|
||||
# import cohere/if it fails then pip install cohere
|
||||
install_and_import("cohere")
|
||||
|
|
@ -391,7 +468,9 @@ def completion(
|
|||
response = CustomStreamWrapper(response, model)
|
||||
return response
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=cohere_key, original_response=response)
|
||||
logging.post_call(
|
||||
input=prompt, api_key=cohere_key, original_response=response
|
||||
)
|
||||
## USAGE
|
||||
completion_response = response[0].text
|
||||
prompt_tokens = len(encoding.encode(prompt))
|
||||
|
|
@ -474,7 +553,9 @@ def completion(
|
|||
headers=headers,
|
||||
)
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=TOGETHER_AI_TOKEN, original_response=res.text)
|
||||
logging.post_call(
|
||||
input=prompt, api_key=TOGETHER_AI_TOKEN, original_response=res.text
|
||||
)
|
||||
# make this safe for reading, if output does not exist raise an error
|
||||
json_response = res.json()
|
||||
if "output" not in json_response:
|
||||
|
|
@ -515,7 +596,9 @@ def completion(
|
|||
completion_response = chat.send_message(prompt, **optional_params)
|
||||
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=None, original_response=completion_response)
|
||||
logging.post_call(
|
||||
input=prompt, api_key=None, original_response=completion_response
|
||||
)
|
||||
|
||||
## RESPONSE OBJECT
|
||||
model_response["choices"][0]["message"]["content"] = completion_response
|
||||
|
|
@ -540,7 +623,9 @@ def completion(
|
|||
completion_response = vertex_model.predict(prompt, **optional_params)
|
||||
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=None, original_response=completion_response)
|
||||
logging.post_call(
|
||||
input=prompt, api_key=None, original_response=completion_response
|
||||
)
|
||||
## RESPONSE OBJECT
|
||||
model_response["choices"][0]["message"]["content"] = completion_response
|
||||
model_response["created"] = time.time()
|
||||
|
|
@ -563,7 +648,11 @@ def completion(
|
|||
completion_response = ai21_response["completions"][0]["data"]["text"]
|
||||
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=ai21.api_key, original_response=completion_response)
|
||||
logging.post_call(
|
||||
input=prompt,
|
||||
api_key=ai21.api_key,
|
||||
original_response=completion_response,
|
||||
)
|
||||
|
||||
## RESPONSE OBJECT
|
||||
model_response["choices"][0]["message"]["content"] = completion_response
|
||||
|
|
@ -577,7 +666,9 @@ def completion(
|
|||
prompt = " ".join([message["content"] for message in messages])
|
||||
|
||||
## LOGGING
|
||||
logging.pre_call(input=prompt, api_key=None, additional_args={"endpoint": endpoint})
|
||||
logging.pre_call(
|
||||
input=prompt, api_key=None, additional_args={"endpoint": endpoint}
|
||||
)
|
||||
|
||||
generator = get_ollama_response_stream(endpoint, model, prompt)
|
||||
# assume all responses are streamed
|
||||
|
|
@ -604,7 +695,11 @@ def completion(
|
|||
completion_response = completion_response["generated_text"]
|
||||
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=base_ten_key, original_response=completion_response)
|
||||
logging.post_call(
|
||||
input=prompt,
|
||||
api_key=base_ten_key,
|
||||
original_response=completion_response,
|
||||
)
|
||||
|
||||
## RESPONSE OBJECT
|
||||
model_response["choices"][0]["message"]["content"] = completion_response
|
||||
|
|
@ -621,13 +716,22 @@ def completion(
|
|||
prompt = " ".join([message["content"] for message in messages])
|
||||
|
||||
## LOGGING
|
||||
logging.pre_call(input=prompt, api_key=None, additional_args={"url": url, "max_new_tokens": 100})
|
||||
logging.pre_call(
|
||||
input=prompt,
|
||||
api_key=None,
|
||||
additional_args={"url": url, "max_new_tokens": 100},
|
||||
)
|
||||
|
||||
response = requests.post(
|
||||
url, data={"inputs": prompt, "max_new_tokens": 100, "model": model}
|
||||
)
|
||||
## LOGGING
|
||||
logging.post_call(input=prompt, api_key=None, original_response=response.text, additional_args={"url": url, "max_new_tokens": 100})
|
||||
logging.post_call(
|
||||
input=prompt,
|
||||
api_key=None,
|
||||
original_response=response.text,
|
||||
additional_args={"url": url, "max_new_tokens": 100},
|
||||
)
|
||||
|
||||
completion_response = response.json()["outputs"]
|
||||
|
||||
|
|
@ -637,7 +741,6 @@ def completion(
|
|||
model_response["model"] = model
|
||||
response = model_response
|
||||
else:
|
||||
args = locals()
|
||||
raise ValueError(
|
||||
f"Unable to map your input to a model. Check your input - {args}"
|
||||
)
|
||||
|
|
@ -676,10 +779,22 @@ def batch_completion(*args, **kwargs):
|
|||
@timeout( # type: ignore
|
||||
60
|
||||
) ## set timeouts, in case calls hang (e.g. Azure) - default is 60s, override with `force_timeout`
|
||||
def embedding(model, input=[], azure=False, force_timeout=60, litellm_call_id=None, logger_fn=None):
|
||||
def embedding(
|
||||
model, input=[], azure=False, force_timeout=60, litellm_call_id=None, logger_fn=None
|
||||
):
|
||||
try:
|
||||
response = None
|
||||
logging = Logging(model=model, messages=input, optional_params={}, litellm_params={"azure": azure, "force_timeout": force_timeout, "logger_fn": logger_fn, "litellm_call_id": litellm_call_id})
|
||||
logging = Logging(
|
||||
model=model,
|
||||
messages=input,
|
||||
optional_params={},
|
||||
litellm_params={
|
||||
"azure": azure,
|
||||
"force_timeout": force_timeout,
|
||||
"logger_fn": logger_fn,
|
||||
"litellm_call_id": litellm_call_id,
|
||||
},
|
||||
)
|
||||
if azure == True:
|
||||
# azure configs
|
||||
openai.api_type = "azure"
|
||||
|
|
@ -687,7 +802,15 @@ def embedding(model, input=[], azure=False, force_timeout=60, litellm_call_id=No
|
|||
openai.api_version = get_secret("AZURE_API_VERSION")
|
||||
openai.api_key = get_secret("AZURE_API_KEY")
|
||||
## LOGGING
|
||||
logging.pre_call(input=input, api_key=openai.api_key, additional_args={"api_type": openai.api_type, "api_base": openai.api_base, "api_version": openai.api_version})
|
||||
logging.pre_call(
|
||||
input=input,
|
||||
api_key=openai.api_key,
|
||||
additional_args={
|
||||
"api_type": openai.api_type,
|
||||
"api_base": openai.api_base,
|
||||
"api_version": openai.api_version,
|
||||
},
|
||||
)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, engine=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
|
|
@ -697,7 +820,15 @@ def embedding(model, input=[], azure=False, force_timeout=60, litellm_call_id=No
|
|||
openai.api_version = None
|
||||
openai.api_key = get_secret("OPENAI_API_KEY")
|
||||
## LOGGING
|
||||
logging.pre_call(input=input, api_key=openai.api_key, additional_args={"api_type": openai.api_type, "api_base": openai.api_base, "api_version": openai.api_version})
|
||||
logging.pre_call(
|
||||
input=input,
|
||||
api_key=openai.api_key,
|
||||
additional_args={
|
||||
"api_type": openai.api_type,
|
||||
"api_base": openai.api_base,
|
||||
"api_version": openai.api_version,
|
||||
},
|
||||
)
|
||||
## EMBEDDING CALL
|
||||
response = openai.Embedding.create(input=input, model=model)
|
||||
print_verbose(f"response_value: {str(response)[:50]}")
|
||||
|
|
@ -710,7 +841,11 @@ def embedding(model, input=[], azure=False, force_timeout=60, litellm_call_id=No
|
|||
## LOGGING
|
||||
logging.post_call(input=input, api_key=openai.api_key, original_response=e)
|
||||
## Map to OpenAI Exception
|
||||
raise exception_type(model=model, original_exception=e, custom_llm_provider="azure" if azure==True else None)
|
||||
raise exception_type(
|
||||
model=model,
|
||||
original_exception=e,
|
||||
custom_llm_provider="azure" if azure == True else None,
|
||||
)
|
||||
|
||||
|
||||
####### HELPER FUNCTIONS ################
|
||||
|
|
|
|||
|
|
@ -9,7 +9,7 @@ import asyncio
|
|||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system path
|
||||
from litellm import acompletion
|
||||
from litellm import acompletion, acreate
|
||||
|
||||
|
||||
async def test_get_response():
|
||||
|
|
@ -24,3 +24,16 @@ async def test_get_response():
|
|||
|
||||
response = asyncio.run(test_get_response())
|
||||
print(response)
|
||||
|
||||
# async def test_get_response():
|
||||
# user_message = "Hello, how are you?"
|
||||
# messages = [{"content": user_message, "role": "user"}]
|
||||
# try:
|
||||
# response = await acreate(model="gpt-3.5-turbo", messages=messages)
|
||||
# except Exception as e:
|
||||
# pytest.fail(f"error occurred: {e}")
|
||||
# return response
|
||||
|
||||
|
||||
# response = asyncio.run(test_get_response())
|
||||
# print(response)
|
||||
|
|
|
|||
|
|
@ -34,7 +34,6 @@ def test_caching():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
|
||||
def test_caching_with_models():
|
||||
litellm.caching_with_models = True
|
||||
response2 = completion(model="gpt-3.5-turbo", messages=messages)
|
||||
|
|
@ -47,6 +46,3 @@ def test_caching_with_models():
|
|||
print(f"response2: {response2}")
|
||||
print(f"response3: {response3}")
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
|
||||
|
|
|
|||
|
|
@ -12,6 +12,8 @@ import pytest
|
|||
import litellm
|
||||
from litellm import embedding, completion
|
||||
|
||||
litellm.debugger = True
|
||||
|
||||
# from infisical import InfisicalClient
|
||||
|
||||
# litellm.set_verbose = True
|
||||
|
|
@ -28,14 +30,18 @@ def logger_fn(user_model_dict):
|
|||
def test_completion_custom_provider_model_name():
|
||||
try:
|
||||
response = completion(
|
||||
model="together_ai/togethercomputer/llama-2-70b-chat", messages=messages, logger_fn=logger_fn
|
||||
model="together_ai/togethercomputer/llama-2-70b-chat",
|
||||
messages=messages,
|
||||
logger_fn=logger_fn,
|
||||
)
|
||||
# Add any assertions here to check the response
|
||||
print(response)
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
test_completion_custom_provider_model_name()
|
||||
|
||||
# test_completion_custom_provider_model_name()
|
||||
|
||||
|
||||
def test_completion_claude():
|
||||
try:
|
||||
|
|
@ -89,7 +95,10 @@ def test_completion_claude_stream():
|
|||
def test_completion_cohere():
|
||||
try:
|
||||
response = completion(
|
||||
model="command-nightly", messages=messages, max_tokens=100, logit_bias={40: 10}
|
||||
model="command-nightly",
|
||||
messages=messages,
|
||||
max_tokens=100,
|
||||
logit_bias={40: 10},
|
||||
)
|
||||
# Add any assertions here to check the response
|
||||
print(response)
|
||||
|
|
@ -103,6 +112,7 @@ def test_completion_cohere():
|
|||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_cohere_stream():
|
||||
try:
|
||||
messages = [
|
||||
|
|
@ -254,8 +264,7 @@ def test_completion_openai_with_functions():
|
|||
def test_completion_azure():
|
||||
try:
|
||||
response = completion(
|
||||
model="gpt-3.5-turbo",
|
||||
deployment_id="chatgpt-test",
|
||||
model="chatgpt-test",
|
||||
messages=messages,
|
||||
custom_llm_provider="azure",
|
||||
)
|
||||
|
|
@ -340,6 +349,18 @@ def test_petals():
|
|||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
def test_completion_with_fallbacks():
|
||||
fallbacks = ["gpt-3.5-turb", "gpt-3.5-turbo", "command-nightly"]
|
||||
try:
|
||||
response = completion(
|
||||
model="bad-model", messages=messages, force_timeout=120, fallbacks=fallbacks
|
||||
)
|
||||
# Add any assertions here to check the response
|
||||
print(response)
|
||||
except Exception as e:
|
||||
pytest.fail(f"Error occurred: {e}")
|
||||
|
||||
|
||||
# def test_baseten_falcon_7bcompletion():
|
||||
# model_name = "qvv0xeq"
|
||||
# try:
|
||||
|
|
|
|||
11
litellm/tests/test_get_model_list.py
Normal file
11
litellm/tests/test_get_model_list.py
Normal file
|
|
@ -0,0 +1,11 @@
|
|||
import os, sys, traceback
|
||||
|
||||
sys.path.insert(
|
||||
0, os.path.abspath("../..")
|
||||
) # Adds the parent directory to the system path
|
||||
import litellm
|
||||
from litellm import get_model_list
|
||||
|
||||
print(get_model_list())
|
||||
print(get_model_list())
|
||||
# print(litellm.model_list)
|
||||
|
|
@ -9,18 +9,16 @@
|
|||
# import litellm
|
||||
# from litellm import embedding, completion
|
||||
|
||||
# litellm.input_callback = ["lite_debugger"]
|
||||
# litellm.success_callback = ["lite_debugger"]
|
||||
# litellm.failure_callback = ["lite_debugger"]
|
||||
|
||||
# litellm.set_verbose = True
|
||||
|
||||
# litellm.email = "krrish@berri.ai"
|
||||
|
||||
# user_message = "Hello, how are you?"
|
||||
# messages = [{ "content": user_message,"role": "user"}]
|
||||
|
||||
|
||||
# #openai call
|
||||
# response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
|
||||
|
||||
# print(f"response: {response}")
|
||||
# #bad request call
|
||||
# response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
|
||||
# # response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
|
||||
|
|
|
|||
|
|
@ -22,6 +22,32 @@ def logger_fn(model_call_object: dict):
|
|||
user_message = "Hello, how are you?"
|
||||
messages = [{"content": user_message, "role": "user"}]
|
||||
|
||||
# test on openai completion call
|
||||
try:
|
||||
response = completion(
|
||||
model="gpt-3.5-turbo", messages=messages, stream=True, logger_fn=logger_fn
|
||||
)
|
||||
for chunk in response:
|
||||
print(chunk["choices"][0]["delta"])
|
||||
score += 1
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
|
||||
# test on azure completion call
|
||||
try:
|
||||
response = completion(
|
||||
model="azure/chatgpt-test", messages=messages, stream=True, logger_fn=logger_fn
|
||||
)
|
||||
for chunk in response:
|
||||
print(chunk["choices"][0]["delta"])
|
||||
score += 1
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
|
||||
# test on anthropic completion call
|
||||
try:
|
||||
response = completion(
|
||||
|
|
@ -35,19 +61,19 @@ except:
|
|||
pass
|
||||
|
||||
|
||||
# test on anthropic completion call
|
||||
try:
|
||||
response = completion(
|
||||
model="meta-llama/Llama-2-7b-chat-hf",
|
||||
messages=messages,
|
||||
custom_llm_provider="huggingface",
|
||||
custom_api_base="https://s7c7gytn18vnu4tw.us-east-1.aws.endpoints.huggingface.cloud",
|
||||
stream=True,
|
||||
logger_fn=logger_fn,
|
||||
)
|
||||
for chunk in response:
|
||||
print(chunk["choices"][0]["delta"])
|
||||
score += 1
|
||||
except:
|
||||
print(f"error occurred: {traceback.format_exc()}")
|
||||
pass
|
||||
# # test on huggingface completion call
|
||||
# try:
|
||||
# response = completion(
|
||||
# model="meta-llama/Llama-2-7b-chat-hf",
|
||||
# messages=messages,
|
||||
# custom_llm_provider="huggingface",
|
||||
# custom_api_base="https://s7c7gytn18vnu4tw.us-east-1.aws.endpoints.huggingface.cloud",
|
||||
# stream=True,
|
||||
# logger_fn=logger_fn,
|
||||
# )
|
||||
# for chunk in response:
|
||||
# print(chunk["choices"][0]["delta"])
|
||||
# score += 1
|
||||
# except:
|
||||
# print(f"error occurred: {traceback.format_exc()}")
|
||||
# pass
|
||||
|
|
|
|||
|
|
@ -24,5 +24,6 @@
|
|||
# #openai call
|
||||
# response = completion(model="gpt-3.5-turbo", messages=[{"role": "user", "content": "Hi 👋 - i'm openai"}])
|
||||
|
||||
# fix
|
||||
# #bad request call
|
||||
# response = completion(model="chatgpt-test", messages=[{"role": "user", "content": "Hi 👋 - i'm a bad request"}])
|
||||
|
|
|
|||
228
litellm/utils.py
228
litellm/utils.py
|
|
@ -1,7 +1,20 @@
|
|||
import aiohttp
|
||||
import subprocess
|
||||
import importlib
|
||||
from typing import List, Dict, Union, Optional
|
||||
import sys
|
||||
import dotenv, json, traceback, threading
|
||||
import subprocess, os
|
||||
import litellm, openai
|
||||
import random, uuid, requests
|
||||
import datetime, time
|
||||
import tiktoken
|
||||
|
||||
encoding = tiktoken.get_encoding("cl100k_base")
|
||||
import importlib.metadata
|
||||
from .integrations.helicone import HeliconeLogger
|
||||
from .integrations.aispend import AISpendLogger
|
||||
from .integrations.berrispend import BerriSpendLogger
|
||||
from .integrations.supabase import Supabase
|
||||
from .integrations.litedebugger import LiteDebugger
|
||||
from openai.error import OpenAIError as OriginalError
|
||||
from openai.openai_object import OpenAIObject
|
||||
from .exceptions import (
|
||||
AuthenticationError,
|
||||
InvalidRequestError,
|
||||
|
|
@ -9,34 +22,10 @@ from .exceptions import (
|
|||
ServiceUnavailableError,
|
||||
OpenAIError,
|
||||
)
|
||||
from openai.openai_object import OpenAIObject
|
||||
from openai.error import OpenAIError as OriginalError
|
||||
from .integrations.llmonitor import LLMonitorLogger
|
||||
from .integrations.litedebugger import LiteDebugger
|
||||
from .integrations.supabase import Supabase
|
||||
from .integrations.berrispend import BerriSpendLogger
|
||||
from .integrations.aispend import AISpendLogger
|
||||
from .integrations.helicone import HeliconeLogger
|
||||
import pkg_resources
|
||||
import sys
|
||||
import dotenv
|
||||
import json
|
||||
import traceback
|
||||
import threading
|
||||
import subprocess
|
||||
import os
|
||||
import litellm
|
||||
import openai
|
||||
import random
|
||||
import uuid
|
||||
import requests
|
||||
import datetime
|
||||
import time
|
||||
import tiktoken
|
||||
from typing import List, Dict, Union, Optional
|
||||
|
||||
encoding = tiktoken.get_encoding("cl100k_base")
|
||||
|
||||
####### ENVIRONMENT VARIABLES ###################
|
||||
####### ENVIRONMENT VARIABLES ####################
|
||||
dotenv.load_dotenv() # Loading env variables using dotenv
|
||||
sentry_sdk_instance = None
|
||||
capture_exception = None
|
||||
|
|
@ -49,12 +38,11 @@ aispendLogger = None
|
|||
berrispendLogger = None
|
||||
supabaseClient = None
|
||||
liteDebuggerClient = None
|
||||
llmonitorLogger = None
|
||||
callback_list: Optional[List[str]] = []
|
||||
user_logger_fn = None
|
||||
additional_details: Optional[Dict[str, str]] = {}
|
||||
local_cache: Optional[Dict[str, str]] = {}
|
||||
|
||||
last_fetched_at = None
|
||||
######## Model Response #########################
|
||||
# All liteLLM Model responses will be in this format, Follows the OpenAI Format
|
||||
# https://docs.litellm.ai/docs/completion/output
|
||||
|
|
@ -174,7 +162,7 @@ class Logging:
|
|||
|
||||
def pre_call(self, input, api_key, additional_args={}):
|
||||
try:
|
||||
print(f"logging pre call for model: {self.model}")
|
||||
print_verbose(f"logging pre call for model: {self.model}")
|
||||
self.model_call_details["input"] = input
|
||||
self.model_call_details["api_key"] = api_key
|
||||
self.model_call_details["additional_args"] = additional_args
|
||||
|
|
@ -214,7 +202,7 @@ class Logging:
|
|||
print_verbose("reaches litedebugger for logging!")
|
||||
model = self.model
|
||||
messages = self.messages
|
||||
print(f"liteDebuggerClient: {liteDebuggerClient}")
|
||||
print_verbose(f"liteDebuggerClient: {liteDebuggerClient}")
|
||||
liteDebuggerClient.input_log_event(
|
||||
model=model,
|
||||
messages=messages,
|
||||
|
|
@ -271,7 +259,6 @@ class Logging:
|
|||
|
||||
# Add more methods as needed
|
||||
|
||||
|
||||
def exception_logging(
|
||||
additional_args={},
|
||||
logger_fn=None,
|
||||
|
|
@ -305,20 +292,34 @@ def exception_logging(
|
|||
####### CLIENT ###################
|
||||
# make it easy to log if completion/embedding runs succeeded or failed + see what happened | Non-Blocking
|
||||
def client(original_function):
|
||||
global liteDebuggerClient, get_all_keys
|
||||
|
||||
def function_setup(
|
||||
def function_setup(
|
||||
*args, **kwargs
|
||||
): # just run once to check if user wants to send their data anywhere - PostHog/Sentry/Slack/etc.
|
||||
try:
|
||||
global callback_list, add_breadcrumb, user_logger_fn
|
||||
if (len(litellm.input_callback) > 0
|
||||
or len(litellm.success_callback) > 0
|
||||
or len(litellm.failure_callback)
|
||||
> 0) and len(callback_list) == 0:
|
||||
if litellm.email is not None or os.getenv("LITELLM_EMAIL", None) is not None: # add to input, success and failure callbacks if user is using hosted product
|
||||
get_all_keys()
|
||||
if "lite_debugger" not in callback_list:
|
||||
litellm.input_callback.append("lite_debugger")
|
||||
litellm.success_callback.append("lite_debugger")
|
||||
litellm.failure_callback.append("lite_debugger")
|
||||
if (
|
||||
len(litellm.input_callback) > 0
|
||||
or len(litellm.success_callback) > 0
|
||||
or len(litellm.failure_callback) > 0
|
||||
) and len(callback_list) == 0:
|
||||
callback_list = list(
|
||||
set(litellm.input_callback + litellm.success_callback +
|
||||
litellm.failure_callback))
|
||||
set_callbacks(callback_list=callback_list, )
|
||||
set(
|
||||
litellm.input_callback
|
||||
+ litellm.success_callback
|
||||
+ litellm.failure_callback
|
||||
)
|
||||
)
|
||||
set_callbacks(
|
||||
callback_list=callback_list,
|
||||
)
|
||||
if add_breadcrumb:
|
||||
add_breadcrumb(
|
||||
category="litellm.llm_call",
|
||||
|
|
@ -432,6 +433,11 @@ def client(original_function):
|
|||
kwargs),
|
||||
) # don't interrupt execution of main thread
|
||||
my_thread.start()
|
||||
if hasattr(e, "message"):
|
||||
if (
|
||||
liteDebuggerClient and liteDebuggerClient.dashboard_url != None
|
||||
): # make it easy to get to the debugger logs if you've initialized it
|
||||
e.message += f"\n Check the log in your dashboard - {liteDebuggerClient.dashboard_url}"
|
||||
raise e
|
||||
|
||||
return wrapper
|
||||
|
|
@ -515,7 +521,7 @@ def get_litellm_params(
|
|||
"verbose": verbose,
|
||||
"custom_llm_provider": custom_llm_provider,
|
||||
"custom_api_base": custom_api_base,
|
||||
"litellm_call_id": litellm_call_id
|
||||
"litellm_call_id": litellm_call_id,
|
||||
}
|
||||
|
||||
return litellm_params
|
||||
|
|
@ -738,7 +744,7 @@ def set_callbacks(callback_list):
|
|||
supabaseClient = Supabase()
|
||||
elif callback == "lite_debugger":
|
||||
print(f"instantiating lite_debugger")
|
||||
liteDebuggerClient = LiteDebugger()
|
||||
liteDebuggerClient = LiteDebugger(email=litellm.email)
|
||||
except Exception as e:
|
||||
raise e
|
||||
|
||||
|
|
@ -1036,7 +1042,7 @@ def handle_success(args, kwargs, result, start_time, end_time):
|
|||
print_verbose("reaches lite_debugger for logging!")
|
||||
model = args[0] if len(args) > 0 else kwargs["model"]
|
||||
messages = args[1] if len(args) > 1 else kwargs["messages"]
|
||||
print(f"liteDebuggerClient: {liteDebuggerClient}")
|
||||
print_verbose(f"liteDebuggerClient: {liteDebuggerClient}")
|
||||
liteDebuggerClient.log_event(
|
||||
model=model,
|
||||
messages=messages,
|
||||
|
|
@ -1066,6 +1072,9 @@ def handle_success(args, kwargs, result, start_time, end_time):
|
|||
)
|
||||
pass
|
||||
|
||||
def acreate(*args, **kwargs): ## Thin client to handle the acreate langchain call
|
||||
return litellm.acompletion(*args, **kwargs)
|
||||
|
||||
|
||||
def prompt_token_calculator(model, messages):
|
||||
# use tiktoken or anthropic's tokenizer depending on the model
|
||||
|
|
@ -1082,6 +1091,21 @@ def prompt_token_calculator(model, messages):
|
|||
return num_tokens
|
||||
|
||||
|
||||
def valid_model(model):
|
||||
try:
|
||||
# for a given model name, check if the user has the right permissions to access the model
|
||||
if (
|
||||
model in litellm.open_ai_chat_completion_models
|
||||
or model in litellm.open_ai_text_completion_models
|
||||
):
|
||||
openai.Model.retrieve(model)
|
||||
else:
|
||||
messages = [{"role": "user", "content": "Hello World"}]
|
||||
litellm.completion(model=model, messages=messages)
|
||||
except:
|
||||
raise InvalidRequestError(message="", model=model, llm_provider="")
|
||||
|
||||
|
||||
# integration helper function
|
||||
def modify_integration(integration_name, integration_params):
|
||||
global supabaseClient
|
||||
|
|
@ -1089,9 +1113,65 @@ def modify_integration(integration_name, integration_params):
|
|||
if "table_name" in integration_params:
|
||||
Supabase.supabase_table_name = integration_params["table_name"]
|
||||
|
||||
####### [BETA] HOSTED PRODUCT ################ - https://docs.litellm.ai/docs/debugging/hosted_debugging
|
||||
|
||||
def get_all_keys(llm_provider=None):
|
||||
try:
|
||||
global last_fetched_at
|
||||
# if user is using hosted product -> instantiate their env with their hosted api keys - refresh every 5 minutes
|
||||
user_email = os.getenv("LITELLM_EMAIL") or litellm.email
|
||||
if user_email:
|
||||
time_delta = 0
|
||||
if last_fetched_at != None:
|
||||
current_time = time.time()
|
||||
time_delta = current_time - last_fetched_at
|
||||
if time_delta > 300 or last_fetched_at == None or llm_provider: # if the llm provider is passed in , assume this happening due to an AuthError for that provider
|
||||
# make the api call
|
||||
last_fetched_at = time.time()
|
||||
print(f"last_fetched_at: {last_fetched_at}")
|
||||
response = requests.post(url="http://api.litellm.ai/get_all_keys", headers={"content-type": "application/json"}, data=json.dumps({"user_email": user_email}))
|
||||
print_verbose(f"get model key response: {response.text}")
|
||||
data = response.json()
|
||||
# update model list
|
||||
for key, value in data["model_keys"].items(): # follows the LITELLM API KEY format - <UPPERCASE_PROVIDER_NAME>_API_KEY - e.g. HUGGINGFACE_API_KEY
|
||||
os.environ[key] = value
|
||||
return "it worked!"
|
||||
return None
|
||||
# return None by default
|
||||
return None
|
||||
except:
|
||||
print_verbose(f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}")
|
||||
pass
|
||||
|
||||
def get_model_list():
|
||||
global last_fetched_at
|
||||
try:
|
||||
# if user is using hosted product -> get their updated model list - refresh every 5 minutes
|
||||
user_email = os.getenv("LITELLM_EMAIL") or litellm.email
|
||||
if user_email:
|
||||
time_delta = 0
|
||||
if last_fetched_at != None:
|
||||
current_time = time.time()
|
||||
time_delta = current_time - last_fetched_at
|
||||
if time_delta > 300 or last_fetched_at == None:
|
||||
# make the api call
|
||||
last_fetched_at = time.time()
|
||||
print(f"last_fetched_at: {last_fetched_at}")
|
||||
response = requests.post(url="http://api.litellm.ai/get_model_list", headers={"content-type": "application/json"}, data=json.dumps({"user_email": user_email}))
|
||||
print_verbose(f"get_model_list response: {response.text}")
|
||||
data = response.json()
|
||||
# update model list
|
||||
model_list = data["model_list"]
|
||||
return model_list
|
||||
return None
|
||||
return None # return None by default
|
||||
except:
|
||||
print_verbose(f"[Non-Blocking Error] get_all_keys error - {traceback.format_exc()}")
|
||||
|
||||
|
||||
####### EXCEPTION MAPPING ################
|
||||
def exception_type(model, original_exception, custom_llm_provider):
|
||||
global user_logger_fn
|
||||
global user_logger_fn, liteDebuggerClient
|
||||
exception_mapping_worked = False
|
||||
try:
|
||||
if isinstance(original_exception, OriginalError):
|
||||
|
|
@ -1232,12 +1312,16 @@ def exception_type(model, original_exception, custom_llm_provider):
|
|||
},
|
||||
exception=e,
|
||||
)
|
||||
## AUTH ERROR
|
||||
if isinstance(e, AuthenticationError) and (litellm.email or "LITELLM_EMAIL" in os.environ):
|
||||
threading.Thread(target=get_all_keys, args=(e.llm_provider,)).start()
|
||||
if exception_mapping_worked:
|
||||
raise e
|
||||
else: # don't let an error with mapping interrupt the user from receiving an error from the llm api calls
|
||||
raise original_exception
|
||||
|
||||
|
||||
####### CRASH REPORTING ################
|
||||
def safe_crash_reporting(model=None, exception=None, custom_llm_provider=None):
|
||||
data = {
|
||||
"model": model,
|
||||
|
|
@ -1273,7 +1357,7 @@ def litellm_telemetry(data):
|
|||
payload = {
|
||||
"uuid": uuid_value,
|
||||
"data": data,
|
||||
"version": pkg_resources.get_distribution("litellm").version,
|
||||
"version:": importlib.metadata.version("litellm"),
|
||||
}
|
||||
# Make the POST request to litellm logging api
|
||||
response = requests.post(
|
||||
|
|
@ -1443,7 +1527,7 @@ async def stream_to_string(generator):
|
|||
return response
|
||||
|
||||
|
||||
########## Together AI streaming #############################
|
||||
########## Together AI streaming ############################# [TODO] move together ai to it's own llm class
|
||||
async def together_ai_completion_streaming(json_data, headers):
|
||||
session = aiohttp.ClientSession()
|
||||
url = "https://api.together.xyz/inference"
|
||||
|
|
@ -1480,3 +1564,49 @@ async def together_ai_completion_streaming(json_data, headers):
|
|||
pass
|
||||
finally:
|
||||
await session.close()
|
||||
|
||||
|
||||
def completion_with_fallbacks(**kwargs):
|
||||
response = None
|
||||
rate_limited_models = set()
|
||||
model_expiration_times = {}
|
||||
start_time = time.time()
|
||||
fallbacks = [kwargs["model"]] + kwargs["fallbacks"]
|
||||
del kwargs["fallbacks"] # remove fallbacks so it's not recursive
|
||||
|
||||
while response == None and time.time() - start_time < 45:
|
||||
for model in fallbacks:
|
||||
# loop thru all models
|
||||
try:
|
||||
if (
|
||||
model in rate_limited_models
|
||||
): # check if model is currently cooling down
|
||||
if (
|
||||
model_expiration_times.get(model)
|
||||
and time.time() >= model_expiration_times[model]
|
||||
):
|
||||
rate_limited_models.remove(
|
||||
model
|
||||
) # check if it's been 60s of cool down and remove model
|
||||
else:
|
||||
continue # skip model
|
||||
|
||||
# delete model from kwargs if it exists
|
||||
if kwargs.get("model"):
|
||||
del kwargs["model"]
|
||||
|
||||
print("making completion call", model)
|
||||
response = litellm.completion(**kwargs, model=model)
|
||||
|
||||
if response != None:
|
||||
return response
|
||||
|
||||
except Exception as e:
|
||||
print(f"got exception {e} for model {model}")
|
||||
rate_limited_models.add(model)
|
||||
model_expiration_times[model] = (
|
||||
time.time() + 60
|
||||
) # cool down this selected model
|
||||
# print(f"rate_limited_models {rate_limited_models}")
|
||||
pass
|
||||
return response
|
||||
|
|
|
|||
6
poetry.lock
generated
6
poetry.lock
generated
|
|
@ -423,13 +423,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "openai"
|
||||
version = "0.27.8"
|
||||
version = "0.27.9"
|
||||
description = "Python client library for the OpenAI API"
|
||||
optional = false
|
||||
python-versions = ">=3.7.1"
|
||||
files = [
|
||||
{file = "openai-0.27.8-py3-none-any.whl", hash = "sha256:e0a7c2f7da26bdbe5354b03c6d4b82a2f34bd4458c7a17ae1a7092c3e397e03c"},
|
||||
{file = "openai-0.27.8.tar.gz", hash = "sha256:2483095c7db1eee274cebac79e315a986c4e55207bb4fa7b82d185b3a2ed9536"},
|
||||
{file = "openai-0.27.9-py3-none-any.whl", hash = "sha256:6a3cf8e276d1a6262b50562fbc0cba7967cfebb78ed827d375986b48fdad6475"},
|
||||
{file = "openai-0.27.9.tar.gz", hash = "sha256:b687761c82f5ebb6f61efc791b2083d2d068277b94802d4d1369efe39851813d"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
|
|||
|
|
@ -1,6 +1,6 @@
|
|||
[tool.poetry]
|
||||
name = "litellm"
|
||||
version = "0.1.437"
|
||||
version = "0.1.457"
|
||||
description = "Library to easily interface with LLM API providers"
|
||||
authors = ["BerriAI"]
|
||||
license = "MIT License"
|
||||
|
|
@ -11,6 +11,7 @@ python = "^3.8"
|
|||
openai = "^0.27.8"
|
||||
python-dotenv = "^1.0.0"
|
||||
tiktoken = "^0.4.0"
|
||||
importlib-metadata = "^6.8.0"
|
||||
|
||||
[build-system]
|
||||
requires = ["poetry-core"]
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue