forked from phoenix/litellm-mirror
docs(assistants.md): add assistants api to docs
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docs/my-website/docs/assistants.md
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docs/my-website/docs/assistants.md
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import Tabs from '@theme/Tabs';
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import TabItem from '@theme/TabItem';
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# Assistants API
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Covers Threads, Messages, Assistants.
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LiteLLM currently covers:
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- Get Assistants
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- Create Thread
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- Get Thread
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- Add Messages
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- Get Messages
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- Run Thread
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## Quick Start
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Call an existing Assistant.
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- Get the Assistant
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- Create a Thread when a user starts a conversation.
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- Add Messages to the Thread as the user asks questions.
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- Run the Assistant on the Thread to generate a response by calling the model and the tools.
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<Tabs>
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<TabItem value="sdk" label="SDK">
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**Get the Assistant**
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```python
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from litellm import get_assistants, aget_assistants
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import os
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os.environ["OPENAI_API_KEY"] = "sk-.."
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assistants = get_assistants(custom_llm_provider="openai")
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### ASYNC USAGE ###
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# assistants = await aget_assistants(custom_llm_provider="openai")
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```
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**Create a Thread**
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```python
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from litellm import create_thread, acreate_thread
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import os
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os.environ["OPENAI_API_KEY"] = "sk-.."
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new_thread = create_thread(
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custom_llm_provider="openai",
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messages=[{"role": "user", "content": "Hey, how's it going?"}], # type: ignore
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)
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### ASYNC USAGE ###
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# new_thread = await acreate_thread(custom_llm_provider="openai",messages=[{"role": "user", "content": "Hey, how's it going?"}])
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```
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**Add Messages to the Thread**
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```python
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from litellm import create_thread, get_thread, aget_thread, add_message, a_add_message
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import os
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os.environ["OPENAI_API_KEY"] = "sk-.."
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## CREATE A THREAD
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_new_thread = create_thread(
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custom_llm_provider="openai",
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messages=[{"role": "user", "content": "Hey, how's it going?"}], # type: ignore
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)
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## OR retrieve existing thread
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received_thread = get_thread(
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custom_llm_provider="openai",
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thread_id=_new_thread.id,
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)
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### ASYNC USAGE ###
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# received_thread = await aget_thread(custom_llm_provider="openai", thread_id=_new_thread.id,)
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## ADD MESSAGE TO THREAD
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message = {"role": "user", "content": "Hey, how's it going?"}
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added_message = add_message(
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thread_id=_new_thread.id, custom_llm_provider="openai", **message
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)
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### ASYNC USAGE ###
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# added_message = await a_add_message(thread_id=_new_thread.id, custom_llm_provider="openai", **message)
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```
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**Run the Assistant on the Thread**
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```python
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from litellm import get_assistants, create_thread, add_message, run_thread, arun_thread
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import os
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os.environ["OPENAI_API_KEY"] = "sk-.."
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assistants = get_assistants(custom_llm_provider="openai")
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## get the first assistant ###
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assistant_id = assistants.data[0].id
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## GET A THREAD
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_new_thread = create_thread(
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custom_llm_provider="openai",
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messages=[{"role": "user", "content": "Hey, how's it going?"}], # type: ignore
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)
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## ADD MESSAGE
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message = {"role": "user", "content": "Hey, how's it going?"}
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added_message = add_message(
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thread_id=_new_thread.id, custom_llm_provider="openai", **message
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)
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## 🚨 RUN THREAD
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response = run_thread(
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custom_llm_provider="openai", thread_id=thread_id, assistant_id=assistant_id
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)
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### ASYNC USAGE ###
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# response = await arun_thread(custom_llm_provider="openai", thread_id=thread_id, assistant_id=assistant_id)
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print(f"run_thread: {run_thread}")
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```
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</TabItem>
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<TabItem value="proxy" label="PROXY">
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```bash
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$ export OPENAI_API_KEY="sk-..."
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$ litellm
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# RUNNING on http://0.0.0.0:4000
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```
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**Get the Assistant**
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```bash
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curl "http://0.0.0.0:4000/v1/assistants?order=desc&limit=20" \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer sk-1234" \
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```
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**Create a Thread**
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```bash
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curl http://0.0.0.0:4000/v1/threads \
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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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```
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**Add Messages to the Thread**
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```bash
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curl http://0.0.0.0:4000/v1/threads/{thread_id}/messages \
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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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"role": "user",
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"content": "How does AI work? Explain it in simple terms."
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}'
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```
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**Run the Assistant on the Thread**
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```bash
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curl http://0.0.0.0:4000/v1/threads/thread_abc123/runs \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"assistant_id": "asst_abc123"
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}'
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```
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</TabItem>
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</Tabs>
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## [👉 Proxy API Reference](https://litellm-api.up.railway.app/#/assistants)
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docs/my-website/docs/text_to_speech.md
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docs/my-website/docs/text_to_speech.md
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# Text to Speech
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## Quick Start
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```python
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from pathlib import Path
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from litellm import speech
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import os
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os.environ["OPENAI_API_KEY"] = "sk-.."
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speech_file_path = Path(__file__).parent / "speech.mp3"
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response = speech(
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model="openai/tts-1",
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voice="alloy",
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input="the quick brown fox jumped over the lazy dogs",
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api_base=None,
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api_key=None,
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organization=None,
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project=None,
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max_retries=1,
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timeout=600,
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client=None,
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optional_params={},
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)
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response.stream_to_file(speech_file_path)
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```
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## Async Usage
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```python
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from litellm import aspeech
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from pathlib import Path
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import os, asyncio
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os.environ["OPENAI_API_KEY"] = "sk-.."
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async def test_async_speech():
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speech_file_path = Path(__file__).parent / "speech.mp3"
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response = await litellm.aspeech(
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model="openai/tts-1",
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voice="alloy",
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input="the quick brown fox jumped over the lazy dogs",
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api_base=None,
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api_key=None,
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organization=None,
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project=None,
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max_retries=1,
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timeout=600,
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client=None,
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optional_params={},
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)
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response.stream_to_file(speech_file_path)
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asyncio.run(test_async_speech())
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```
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## Proxy Usage
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LiteLLM provides an openai-compatible `/audio/speech` endpoint for Text-to-speech calls.
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```bash
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curl http://0.0.0.0:4000/v1/audio/speech \
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-H "Authorization: Bearer sk-1234" \
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-H "Content-Type: application/json" \
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-d '{
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"model": "tts-1",
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"input": "The quick brown fox jumped over the lazy dog.",
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"voice": "alloy"
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}' \
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--output speech.mp3
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```
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**Setup**
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```bash
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- model_name: tts
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litellm_params:
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model: openai/tts-1
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api_key: os.environ/OPENAI_API_KEY
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```
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```bash
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litellm --config /path/to/config.yaml
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# RUNNING on http://0.0.0.0:4000
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```
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@ -100,13 +100,15 @@ const sidebars = {
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},
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{
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type: "category",
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label: "Embedding(), Moderation(), Image Generation(), Audio Transcriptions()",
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label: "Embedding(), Image Generation(), Assistants(), Moderation(), Audio Transcriptions(), TTS()",
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items: [
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"embedding/supported_embedding",
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"embedding/async_embedding",
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"embedding/moderation",
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"image_generation",
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"audio_transcription"
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"audio_transcription",
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"text_to_speech",
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"assistants"
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],
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},
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{
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or litellm.api_key
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or litellm.azure_key
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or get_secret("AZURE_API_KEY")
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)
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) # type: ignore
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response = azure_chat_completions.audio_transcriptions(
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model=model,
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Returns a list of assistants.
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API Reference docs - https://platform.openai.com/docs/api-reference/assistants/listAssistants
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a thread.
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API Reference - https://platform.openai.com/docs/api-reference/threads/createThread
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Retrieves a thread.
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API Reference - https://platform.openai.com/docs/api-reference/threads/getThread
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a message.
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API Reference - https://platform.openai.com/docs/api-reference/messages/createMessage
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Returns a list of messages for a given thread.
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API Reference - https://platform.openai.com/docs/api-reference/messages/listMessages
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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fastapi_response: Response,
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user_api_key_dict: UserAPIKeyAuth = Depends(user_api_key_auth),
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):
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"""
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Create a run.
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API Reference: https://platform.openai.com/docs/api-reference/runs/createRun
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"""
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global proxy_logging_obj
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data: Dict = {}
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try:
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