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docs litellm responses api
This commit is contained in:
Ishaan Jaff
parent
7c3df984da
commit
eb29cfee1d
1 changed files with 408 additions and 25 deletions
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@ -14,14 +14,14 @@ LiteLLM provides a BETA endpoint in the spec of [OpenAI's `/responses` API](http
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| Fallbacks | ✅ | Works between supported models |
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| Loadbalancing | ✅ | Works between supported models |
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| Supported LiteLLM Versions | 1.63.8+ | |
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| Supported LLM providers | `openai` | |
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| Supported LLM providers | **All LiteLLM supported providers** | |
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## Usage
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## Create a model response
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### LiteLLM Python SDK
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<Tabs>
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<TabItem value="litellm-sdk" label="LiteLLM SDK">
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<TabItem value="openai" label="OpenAI">
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#### Non-streaming
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```python showLineNumbers
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@ -29,7 +29,7 @@ import litellm
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# Non-streaming response
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response = litellm.responses(
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model="o1-pro",
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model="openai/o1-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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max_output_tokens=100
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)
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@ -43,7 +43,7 @@ import litellm
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# Streaming response
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response = litellm.responses(
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model="o1-pro",
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model="openai/o1-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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@ -53,25 +53,201 @@ for event in response:
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```
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</TabItem>
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<TabItem value="proxy" label="OpenAI SDK with LiteLLM Proxy">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: o1-pro
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litellm_params:
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model: openai/o1-pro
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api_key: os.environ/OPENAI_API_KEY
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<TabItem value="anthropic" label="Anthropic">
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#### Non-streaming
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```python showLineNumbers
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import litellm
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import os
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# Set API key
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os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
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# Non-streaming response
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response = litellm.responses(
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model="anthropic/claude-3-5-sonnet-20240620",
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input="Tell me a three sentence bedtime story about a unicorn.",
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max_output_tokens=100
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)
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print(response)
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```
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Start your LiteLLM proxy:
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#### Streaming
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```python showLineNumbers
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import litellm
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import os
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# Set API key
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os.environ["ANTHROPIC_API_KEY"] = "your-anthropic-api-key"
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# Streaming response
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response = litellm.responses(
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model="anthropic/claude-3-5-sonnet-20240620",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="vertex" label="Vertex AI">
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#### Non-streaming
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```python showLineNumbers
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import litellm
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import os
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# Set credentials - Vertex AI uses application default credentials
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# Run 'gcloud auth application-default login' to authenticate
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os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
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os.environ["VERTEXAI_LOCATION"] = "us-central1"
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# Non-streaming response
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response = litellm.responses(
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model="vertex_ai/gemini-1.5-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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max_output_tokens=100
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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
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import litellm
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import os
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# Set credentials - Vertex AI uses application default credentials
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# Run 'gcloud auth application-default login' to authenticate
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os.environ["VERTEXAI_PROJECT"] = "your-gcp-project-id"
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os.environ["VERTEXAI_LOCATION"] = "us-central1"
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# Streaming response
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response = litellm.responses(
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model="vertex_ai/gemini-1.5-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="bedrock" label="AWS Bedrock">
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#### Non-streaming
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```python showLineNumbers
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import litellm
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import os
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# Set AWS credentials
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os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
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os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
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os.environ["AWS_REGION_NAME"] = "us-west-2" # or your AWS region
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# Non-streaming response
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response = litellm.responses(
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model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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input="Tell me a three sentence bedtime story about a unicorn.",
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max_output_tokens=100
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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
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import litellm
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import os
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# Set AWS credentials
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os.environ["AWS_ACCESS_KEY_ID"] = "your-access-key-id"
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os.environ["AWS_SECRET_ACCESS_KEY"] = "your-secret-access-key"
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os.environ["AWS_REGION_NAME"] = "us-west-2" # or your AWS region
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# Streaming response
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response = litellm.responses(
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model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="gemini" label="Google AI Studio">
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#### Non-streaming
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```python showLineNumbers
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import litellm
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import os
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# Set API key for Google AI Studio
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os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
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# Non-streaming response
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response = litellm.responses(
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model="gemini/gemini-1.5-flash",
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input="Tell me a three sentence bedtime story about a unicorn.",
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max_output_tokens=100
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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
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import litellm
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import os
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# Set API key for Google AI Studio
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os.environ["GEMINI_API_KEY"] = "your-gemini-api-key"
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# Streaming response
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response = litellm.responses(
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model="gemini/gemini-1.5-flash",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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</Tabs>
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### LiteLLM Proxy with OpenAI SDK
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First, set up and start your LiteLLM proxy server.
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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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Then use the OpenAI SDK pointed to your proxy:
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<Tabs>
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<TabItem value="openai" label="OpenAI">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: openai/o1-pro
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litellm_params:
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model: openai/o1-pro
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api_key: os.environ/OPENAI_API_KEY
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```
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#### Non-streaming
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```python showLineNumbers
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@ -85,7 +261,7 @@ client = OpenAI(
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# Non-streaming response
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response = client.responses.create(
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model="o1-pro",
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model="openai/o1-pro",
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input="Tell me a three sentence bedtime story about a unicorn."
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)
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@ -104,7 +280,222 @@ client = OpenAI(
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# Streaming response
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response = client.responses.create(
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model="o1-pro",
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model="openai/o1-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="anthropic" label="Anthropic">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: anthropic/claude-3-5-sonnet-20240620
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litellm_params:
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model: anthropic/claude-3-5-sonnet-20240620
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api_key: os.environ/ANTHROPIC_API_KEY
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```
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#### Non-streaming
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```python showLineNumbers
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Non-streaming response
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response = client.responses.create(
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model="anthropic/claude-3-5-sonnet-20240620",
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input="Tell me a three sentence bedtime story about a unicorn."
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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
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Streaming response
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response = client.responses.create(
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model="anthropic/claude-3-5-sonnet-20240620",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="vertex" label="Vertex AI">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: vertex_ai/gemini-1.5-pro
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litellm_params:
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model: vertex_ai/gemini-1.5-pro
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vertex_project: your-gcp-project-id
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vertex_location: us-central1
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```
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#### Non-streaming
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```python showLineNumbers
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Non-streaming response
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response = client.responses.create(
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model="vertex_ai/gemini-1.5-pro",
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input="Tell me a three sentence bedtime story about a unicorn."
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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
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Streaming response
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response = client.responses.create(
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model="vertex_ai/gemini-1.5-pro",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="bedrock" label="AWS Bedrock">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
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litellm_params:
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model: bedrock/anthropic.claude-3-sonnet-20240229-v1:0
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aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID
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aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY
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aws_region_name: us-west-2
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```
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#### Non-streaming
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```python showLineNumbers
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Non-streaming response
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response = client.responses.create(
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model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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input="Tell me a three sentence bedtime story about a unicorn."
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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
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Streaming response
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response = client.responses.create(
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model="bedrock/anthropic.claude-3-sonnet-20240229-v1:0",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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for event in response:
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print(event)
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```
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</TabItem>
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<TabItem value="gemini" label="Google AI Studio">
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First, add this to your litellm proxy config.yaml:
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```yaml showLineNumbers
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model_list:
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- model_name: gemini/gemini-1.5-flash
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litellm_params:
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model: gemini/gemini-1.5-flash
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api_key: os.environ/GEMINI_API_KEY
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```
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#### Non-streaming
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```python showLineNumbers
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Non-streaming response
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response = client.responses.create(
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model="gemini/gemini-1.5-flash",
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input="Tell me a three sentence bedtime story about a unicorn."
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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
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from openai import OpenAI
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# Initialize client with your proxy URL
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client = OpenAI(
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base_url="http://localhost:4000", # Your proxy URL
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api_key="your-api-key" # Your proxy API key
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)
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# Streaming response
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response = client.responses.create(
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model="gemini/gemini-1.5-flash",
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input="Tell me a three sentence bedtime story about a unicorn.",
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stream=True
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)
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@ -115,11 +506,3 @@ for event in response:
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</TabItem>
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</Tabs>
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## **Supported Providers**
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| Provider | Link to Usage |
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|-------------|--------------------|
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| OpenAI| [Usage](#usage) |
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| Azure OpenAI| [Usage](../docs/providers/azure#responses-api) |
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