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feat: add Avian as a new OpenAI-compatible provider
Add Avian (avian.io) as a JSON-configured OpenAI-compatible provider using the JSONProviderRegistry pattern (no custom handler code). Changes: - Add avian entry in providers.json with base URL and API key env - Add 4 model entries in model_prices_and_context_window.json (deepseek-v3.2, kimi-k2.5, glm-5, minimax-m2.5) - Add endpoint support declaration (chat_completions only) - Add docs page with SDK and proxy usage examples - Add sidebar navigation entry - Add unit tests for provider registration, env var resolution, and URL construction Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
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Kyle D
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docs/my-website/docs/providers/avian.md
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docs/my-website/docs/providers/avian.md
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# Avian
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## Overview
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| Property | Details |
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|-------|-------|
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| Description | Avian is an AI inference platform providing fast access to top open-source models through an OpenAI-compatible API. |
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| Provider Route on LiteLLM | `avian/` |
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| Link to Provider Doc | [Avian ↗](https://avian.io) |
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| Base URL | `https://api.avian.io/v1` |
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| Supported Operations | [`/chat/completions`](#usage---litellm-python-sdk), [`/models`](#supported-models) |
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<br />
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## Required Variables
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```python showLineNumbers title="Environment Variables"
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os.environ["AVIAN_API_KEY"] = "" # your Avian API key
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```
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Get your Avian API key from [avian.io](https://avian.io).
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## Usage - LiteLLM Python SDK
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### Non-streaming
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```python showLineNumbers title="Avian Non-streaming Completion"
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import os
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import litellm
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from litellm import completion
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os.environ["AVIAN_API_KEY"] = "" # your Avian API key
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messages = [{"content": "What is the capital of France?", "role": "user"}]
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# Avian call
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response = completion(
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model="avian/deepseek/deepseek-v3.2",
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messages=messages
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)
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print(response)
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```
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### Streaming
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```python showLineNumbers title="Avian Streaming Completion"
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import os
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import litellm
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from litellm import completion
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os.environ["AVIAN_API_KEY"] = "" # your Avian API key
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messages = [{"content": "What is the capital of France?", "role": "user"}]
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# Avian streaming call
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response = completion(
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model="avian/deepseek/deepseek-v3.2",
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messages=messages,
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stream=True
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)
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for chunk in response:
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print(chunk)
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```
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## Usage - LiteLLM Proxy Server
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Add to your `config.yaml`:
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```yaml showLineNumbers title="config.yaml"
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model_list:
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- model_name: deepseek-v3
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litellm_params:
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model: avian/deepseek/deepseek-v3.2
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api_key: os.environ/AVIAN_API_KEY
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- model_name: kimi-k2.5
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litellm_params:
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model: avian/moonshotai/kimi-k2.5
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api_key: os.environ/AVIAN_API_KEY
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- model_name: glm-5
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litellm_params:
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model: avian/z-ai/glm-5
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api_key: os.environ/AVIAN_API_KEY
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- model_name: minimax-m2.5
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litellm_params:
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model: avian/minimax/minimax-m2.5
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api_key: os.environ/AVIAN_API_KEY
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```
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Start the proxy:
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```bash
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litellm --config config.yaml
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```
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Send a request:
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```bash
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curl http://localhost:4000/v1/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer sk-1234" \
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-d '{
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"model": "deepseek-v3",
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"messages": [{"role": "user", "content": "Hello!"}]
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}'
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```
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## Supported Models
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| Model | Model ID |
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|-------|----------|
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| DeepSeek V3.2 | `avian/deepseek/deepseek-v3.2` |
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| Kimi K2.5 | `avian/moonshotai/kimi-k2.5` |
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| GLM-5 | `avian/z-ai/glm-5` |
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| MiniMax M2.5 | `avian/minimax/minimax-m2.5` |
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## Supported OpenAI Parameters
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Avian supports standard OpenAI chat completion parameters including:
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| Parameter | Supported |
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|-----------|-----------|
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| `temperature` | Yes |
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| `top_p` | Yes |
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| `max_tokens` / `max_completion_tokens` | Yes |
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| `stream` | Yes |
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| `stop` | Yes |
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| `tools` / `tool_choice` | Yes |
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| `response_format` | Yes |
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@ -804,6 +804,7 @@ const sidebars = {
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"providers/amazon_nova",
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"providers/anyscale",
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"providers/apertis",
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"providers/avian",
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"providers/baseten",
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"providers/bytez",
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"providers/cerebras",
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@ -69,6 +69,10 @@
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"base_url": "https://api.abliteration.ai/v1",
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"api_key_env": "ABLITERATION_API_KEY"
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},
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"avian": {
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"base_url": "https://api.avian.io/v1",
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"api_key_env": "AVIAN_API_KEY"
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},
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"llamagate": {
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"base_url": "https://api.llamagate.dev/v1",
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"api_key_env": "LLAMAGATE_API_KEY",
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@ -279,6 +279,55 @@
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"mode": "image_generation",
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"output_cost_per_image": 0.06
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},
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"avian/deepseek/deepseek-v3.2": {
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"max_tokens": 65536,
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"max_input_tokens": 163840,
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"max_output_tokens": 65536,
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"input_cost_per_token": 2.6e-07,
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"output_cost_per_token": 3.8e-07,
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"litellm_provider": "avian",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"supports_response_schema": true,
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"source": "https://avian.io"
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},
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"avian/moonshotai/kimi-k2.5": {
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"max_tokens": 262144,
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"max_input_tokens": 131072,
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"max_output_tokens": 262144,
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"input_cost_per_token": 4.5e-07,
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"output_cost_per_token": 2.2e-06,
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"litellm_provider": "avian",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"source": "https://avian.io"
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},
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"avian/z-ai/glm-5": {
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"max_tokens": 131072,
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"max_input_tokens": 131072,
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"max_output_tokens": 131072,
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"input_cost_per_token": 3e-07,
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"output_cost_per_token": 2.55e-06,
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"litellm_provider": "avian",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"source": "https://avian.io"
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},
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"avian/minimax/minimax-m2.5": {
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"max_tokens": 131072,
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"max_input_tokens": 1048576,
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"max_output_tokens": 131072,
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"input_cost_per_token": 3e-07,
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"output_cost_per_token": 1.1e-06,
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"litellm_provider": "avian",
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"mode": "chat",
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"supports_function_calling": true,
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"supports_tool_choice": true,
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"source": "https://avian.io"
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},
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"us.writer.palmyra-x4-v1:0": {
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"input_cost_per_token": 2.5e-06,
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"litellm_provider": "bedrock_converse",
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@ -66,6 +66,23 @@
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"a2a": false
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}
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},
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"avian": {
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"display_name": "Avian (`avian`)",
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"url": "https://docs.litellm.ai/docs/providers/avian",
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"endpoints": {
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"chat_completions": true,
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"messages": false,
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"responses": false,
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"embeddings": false,
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"image_generations": false,
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"audio_transcriptions": false,
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"audio_speech": false,
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"moderations": false,
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"batches": false,
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"rerank": false,
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"a2a": false
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}
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},
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"aiml": {
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"display_name": "AI/ML API (`aiml`)",
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"url": "https://docs.litellm.ai/docs/providers/aiml",
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50
tests/litellm/llms/openai_like/test_avian_provider.py
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tests/litellm/llms/openai_like/test_avian_provider.py
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"""
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Unit tests for the Avian OpenAI-like provider.
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"""
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import os
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import sys
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sys.path.insert(
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0, os.path.abspath(os.path.join(os.path.dirname(__file__), "../../../.."))
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)
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from litellm.llms.openai_like.dynamic_config import create_config_class
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from litellm.llms.openai_like.json_loader import JSONProviderRegistry
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AVIAN_BASE_URL = "https://api.avian.io/v1"
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def _get_config():
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provider = JSONProviderRegistry.get("avian")
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assert provider is not None
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config_class = create_config_class(provider)
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return config_class()
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def test_avian_provider_registered():
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provider = JSONProviderRegistry.get("avian")
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assert provider is not None
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assert provider.base_url == AVIAN_BASE_URL
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assert provider.api_key_env == "AVIAN_API_KEY"
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def test_avian_resolves_env_api_key(monkeypatch):
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config = _get_config()
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monkeypatch.setenv("AVIAN_API_KEY", "test-key")
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api_base, api_key = config._get_openai_compatible_provider_info(None, None)
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assert api_base == AVIAN_BASE_URL
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assert api_key == "test-key"
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def test_avian_complete_url_appends_endpoint():
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config = _get_config()
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url = config.get_complete_url(
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api_base=AVIAN_BASE_URL,
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api_key="test-key",
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model="avian/deepseek/deepseek-v3.2",
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optional_params={},
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litellm_params={},
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stream=False,
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)
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assert url == f"{AVIAN_BASE_URL}/chat/completions"
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