forked from phoenix/litellm-mirror
(docs) Simplify /vertex_ai/ pass through docs (#6910)
* simplify vertex pass through docs * allow using known path for setting up pass throughs * add unit testing for vtx pass through auth
This commit is contained in:
Ishaan Jaff
GitHub
parent
d26ad42f86
commit
c285132ad6
4 changed files with 186 additions and 778 deletions
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@ -69,6 +69,44 @@ generateContent();
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</Tabs>
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## Quick Start
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Let's call the Vertex AI [`/generateContent` endpoint](https://cloud.google.com/vertex-ai/generative-ai/docs/model-reference/inference)
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1. Add Vertex AI Credentials to your environment
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```bash
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export DEFAULT_VERTEXAI_PROJECT="" # "adroit-crow-413218"
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export DEFAULT_VERTEXAI_LOCATION="" # "us-central1"
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export DEFAULT_GOOGLE_APPLICATION_CREDENTIALS="" # "/Users/Downloads/adroit-crow-413218-a956eef1a2a8.json"
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```
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2. Start LiteLLM Proxy
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```bash
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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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3. Test it!
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Let's call the Google AI Studio token counting endpoint
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```bash
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curl http://localhost:4000/vertex-ai/publishers/google/models/gemini-1.0-pro:generateContent \
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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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"contents":[{
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"role": "user",
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"parts":[{"text": "How are you doing today?"}]
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}]
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}'
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```
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## Supported API Endpoints
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- Gemini API
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@ -87,206 +125,12 @@ LiteLLM Proxy Server supports two methods of authentication to Vertex AI:
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2. Set Vertex AI credentials on proxy server
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## Quick Start Usage
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<Tabs>
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<TabItem value="without_default_config" label="Pass Vertex Credetials client side to proxy server">
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#### 1. Start litellm proxy
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```shell
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litellm --config /path/to/config.yaml
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```
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#### 2. Test it
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```python
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import vertexai
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from vertexai.preview.generative_models import GenerativeModel
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LITE_LLM_ENDPOINT = "http://localhost:4000"
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vertexai.init(
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project="<your-vertex_ai-project-id>", # enter your project id
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location="<your-vertex_ai-location>", # enter your region
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api_endpoint=f"{LITE_LLM_ENDPOINT}/vertex_ai", # route on litellm
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api_transport="rest",
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)
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model = GenerativeModel(model_name="gemini-1.0-pro")
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model.generate_content("hi")
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```
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</TabItem>
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<TabItem value="with_default_config" label="Set Vertex AI Credentials on Proxy Server">
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#### 1. Set `default_vertex_config` on your `config.yaml`
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Add the following credentials to your litellm config.yaml to use the Vertex AI endpoints.
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```yaml
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default_vertex_config:
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vertex_project: "adroit-crow-413218"
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vertex_location: "us-central1"
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vertex_credentials: "/Users/ishaanjaffer/Downloads/adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
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```
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#### 2. Start litellm proxy
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```shell
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litellm --config /path/to/config.yaml
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```
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#### 3. Test it
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```python
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import vertexai
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from google.auth.credentials import Credentials
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from vertexai.generative_models import GenerativeModel
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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class CredentialsWrapper(Credentials):
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def __init__(self, token=None):
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super().__init__()
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self.token = token
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self.expiry = None # or set to a future date if needed
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def refresh(self, request):
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pass
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def apply(self, headers, token=None):
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headers["Authorization"] = f"Bearer {self.token}"
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@property
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def expired(self):
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return False # Always consider the token as non-expired
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@property
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def valid(self):
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return True # Always consider the credentials as valid
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credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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credentials=credentials,
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api_transport="rest",
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)
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model = GenerativeModel("gemini-1.5-flash-001")
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response = model.generate_content(
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"What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
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)
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print(response.text)
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```
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</TabItem>
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</Tabs>
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## Usage Examples
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### Gemini API (Generate Content)
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<Tabs>
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<TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
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```python
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import vertexai
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from vertexai.generative_models import GenerativeModel
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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api_transport="rest",
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)
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model = GenerativeModel("gemini-1.5-flash-001")
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response = model.generate_content(
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"What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
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)
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print(response.text)
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```
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</TabItem>
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<TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
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```python
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import vertexai
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from google.auth.credentials import Credentials
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from vertexai.generative_models import GenerativeModel
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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class CredentialsWrapper(Credentials):
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def __init__(self, token=None):
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super().__init__()
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self.token = token
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self.expiry = None # or set to a future date if needed
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def refresh(self, request):
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pass
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def apply(self, headers, token=None):
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headers["Authorization"] = f"Bearer {self.token}"
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@property
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def expired(self):
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return False # Always consider the token as non-expired
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@property
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def valid(self):
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return True # Always consider the credentials as valid
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credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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credentials=credentials,
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api_transport="rest",
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)
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model = GenerativeModel("gemini-1.5-flash-001")
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response = model.generate_content(
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"What's a good name for a flower shop that specializes in selling bouquets of dried flowers?"
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)
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print(response.text)
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```
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</TabItem>
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<TabItem value="Curl" label="Curl">
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```shell
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curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-001:generateContent \
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@ -295,114 +139,10 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-0
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-d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
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```
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</TabItem>
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</Tabs>
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### Embeddings API
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<Tabs>
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<TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
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```python
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from typing import List, Optional
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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import vertexai
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from vertexai.generative_models import GenerativeModel
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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api_transport="rest",
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)
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def embed_text(
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texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
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task: str = "RETRIEVAL_DOCUMENT",
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model_name: str = "text-embedding-004",
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dimensionality: Optional[int] = 256,
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) -> List[List[float]]:
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"""Embeds texts with a pre-trained, foundational model."""
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model = TextEmbeddingModel.from_pretrained(model_name)
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inputs = [TextEmbeddingInput(text, task) for text in texts]
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kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
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embeddings = model.get_embeddings(inputs, **kwargs)
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return [embedding.values for embedding in embeddings]
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```
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</TabItem>
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<TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
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```python
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from typing import List, Optional
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from vertexai.language_models import TextEmbeddingInput, TextEmbeddingModel
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import vertexai
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from google.auth.credentials import Credentials
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from vertexai.generative_models import GenerativeModel
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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class CredentialsWrapper(Credentials):
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def __init__(self, token=None):
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super().__init__()
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self.token = token
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self.expiry = None # or set to a future date if needed
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def refresh(self, request):
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pass
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def apply(self, headers, token=None):
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headers["Authorization"] = f"Bearer {self.token}"
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@property
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def expired(self):
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return False # Always consider the token as non-expired
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@property
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def valid(self):
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return True # Always consider the credentials as valid
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credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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credentials=credentials,
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api_transport="rest",
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)
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def embed_text(
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texts: List[str] = ["banana muffins? ", "banana bread? banana muffins?"],
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task: str = "RETRIEVAL_DOCUMENT",
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model_name: str = "text-embedding-004",
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dimensionality: Optional[int] = 256,
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) -> List[List[float]]:
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"""Embeds texts with a pre-trained, foundational model."""
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model = TextEmbeddingModel.from_pretrained(model_name)
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inputs = [TextEmbeddingInput(text, task) for text in texts]
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kwargs = dict(output_dimensionality=dimensionality) if dimensionality else {}
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embeddings = model.get_embeddings(inputs, **kwargs)
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return [embedding.values for embedding in embeddings]
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```
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</TabItem>
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<TabItem value="curl" label="Curl">
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```shell
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curl http://localhost:4000/vertex_ai/publishers/google/models/textembedding-gecko@001:predict \
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@ -411,133 +151,9 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/textembedding-geck
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-d '{"instances":[{"content": "gm"}]}'
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```
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</TabItem>
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</Tabs>
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### Imagen API
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<Tabs>
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<TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
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```python
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from typing import List, Optional
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from vertexai.preview.vision_models import ImageGenerationModel
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import vertexai
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from google.auth.credentials import Credentials
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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api_transport="rest",
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)
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model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")
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images = model.generate_images(
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prompt=prompt,
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# Optional parameters
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number_of_images=1,
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language="en",
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# You can't use a seed value and watermark at the same time.
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# add_watermark=False,
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# seed=100,
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aspect_ratio="1:1",
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safety_filter_level="block_some",
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person_generation="allow_adult",
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)
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images[0].save(location=output_file, include_generation_parameters=False)
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# Optional. View the generated image in a notebook.
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# images[0].show()
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print(f"Created output image using {len(images[0]._image_bytes)} bytes")
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```
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</TabItem>
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<TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
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```python
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from typing import List, Optional
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from vertexai.preview.vision_models import ImageGenerationModel
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import vertexai
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from google.auth.credentials import Credentials
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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class CredentialsWrapper(Credentials):
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def __init__(self, token=None):
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super().__init__()
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self.token = token
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self.expiry = None # or set to a future date if needed
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def refresh(self, request):
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pass
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def apply(self, headers, token=None):
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headers["Authorization"] = f"Bearer {self.token}"
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@property
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def expired(self):
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return False # Always consider the token as non-expired
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@property
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def valid(self):
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return True # Always consider the credentials as valid
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credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
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vertexai.init(
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project="adroit-crow-413218",
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location="us-central1",
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api_endpoint=LITELLM_PROXY_BASE,
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credentials=credentials,
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api_transport="rest",
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)
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model = ImageGenerationModel.from_pretrained("imagen-3.0-generate-001")
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images = model.generate_images(
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prompt=prompt,
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# Optional parameters
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number_of_images=1,
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language="en",
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# You can't use a seed value and watermark at the same time.
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# add_watermark=False,
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# seed=100,
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aspect_ratio="1:1",
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safety_filter_level="block_some",
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person_generation="allow_adult",
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)
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images[0].save(location=output_file, include_generation_parameters=False)
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# Optional. View the generated image in a notebook.
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# images[0].show()
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print(f"Created output image using {len(images[0]._image_bytes)} bytes")
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```
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</TabItem>
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<TabItem value="curl" label="Curl">
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```shell
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curl http://localhost:4000/vertex_ai/publishers/google/models/imagen-3.0-generate-001:predict \
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-H "Content-Type: application/json" \
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@ -545,252 +161,19 @@ curl http://localhost:4000/vertex_ai/publishers/google/models/imagen-3.0-generat
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-d '{"instances":[{"prompt": "make an otter"}], "parameters": {"sampleCount": 1}}'
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```
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</TabItem>
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</Tabs>
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### Count Tokens API
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<Tabs>
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<TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
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```python
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from typing import List, Optional
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from vertexai.generative_models import GenerativeModel
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import vertexai
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LITELLM_PROXY_API_KEY = "sk-1234"
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LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
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import datetime
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vertexai.init(
|
||||
project="adroit-crow-413218",
|
||||
location="us-central1",
|
||||
api_endpoint=LITELLM_PROXY_BASE,
|
||||
api_transport="rest",
|
||||
)
|
||||
|
||||
|
||||
model = GenerativeModel("gemini-1.5-flash-001")
|
||||
|
||||
prompt = "Why is the sky blue?"
|
||||
|
||||
# Prompt tokens count
|
||||
response = model.count_tokens(prompt)
|
||||
print(f"Prompt Token Count: {response.total_tokens}")
|
||||
print(f"Prompt Character Count: {response.total_billable_characters}")
|
||||
|
||||
# Send text to Gemini
|
||||
response = model.generate_content(prompt)
|
||||
|
||||
# Response tokens count
|
||||
usage_metadata = response.usage_metadata
|
||||
print(f"Prompt Token Count: {usage_metadata.prompt_token_count}")
|
||||
print(f"Candidates Token Count: {usage_metadata.candidates_token_count}")
|
||||
print(f"Total Token Count: {usage_metadata.total_token_count}")
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
|
||||
<TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
|
||||
|
||||
```python
|
||||
from typing import List, Optional
|
||||
from vertexai.generative_models import GenerativeModel
|
||||
import vertexai
|
||||
from google.auth.credentials import Credentials
|
||||
|
||||
LITELLM_PROXY_API_KEY = "sk-1234"
|
||||
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
|
||||
|
||||
import datetime
|
||||
|
||||
|
||||
class CredentialsWrapper(Credentials):
|
||||
def __init__(self, token=None):
|
||||
super().__init__()
|
||||
self.token = token
|
||||
self.expiry = None # or set to a future date if needed
|
||||
|
||||
def refresh(self, request):
|
||||
pass
|
||||
|
||||
def apply(self, headers, token=None):
|
||||
headers["Authorization"] = f"Bearer {self.token}"
|
||||
|
||||
@property
|
||||
def expired(self):
|
||||
return False # Always consider the token as non-expired
|
||||
|
||||
@property
|
||||
def valid(self):
|
||||
return True # Always consider the credentials as valid
|
||||
|
||||
|
||||
credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
|
||||
|
||||
vertexai.init(
|
||||
project="adroit-crow-413218",
|
||||
location="us-central1",
|
||||
api_endpoint=LITELLM_PROXY_BASE,
|
||||
credentials=credentials,
|
||||
api_transport="rest",
|
||||
)
|
||||
|
||||
|
||||
model = GenerativeModel("gemini-1.5-flash-001")
|
||||
|
||||
prompt = "Why is the sky blue?"
|
||||
|
||||
# Prompt tokens count
|
||||
response = model.count_tokens(prompt)
|
||||
print(f"Prompt Token Count: {response.total_tokens}")
|
||||
print(f"Prompt Character Count: {response.total_billable_characters}")
|
||||
|
||||
# Send text to Gemini
|
||||
response = model.generate_content(prompt)
|
||||
|
||||
# Response tokens count
|
||||
usage_metadata = response.usage_metadata
|
||||
print(f"Prompt Token Count: {usage_metadata.prompt_token_count}")
|
||||
print(f"Candidates Token Count: {usage_metadata.candidates_token_count}")
|
||||
print(f"Total Token Count: {usage_metadata.total_token_count}")
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="curl" label="Curl">
|
||||
|
||||
|
||||
|
||||
```shell
|
||||
curl http://localhost:4000/vertex_ai/publishers/google/models/gemini-1.5-flash-001:countTokens \
|
||||
-H "Content-Type: application/json" \
|
||||
-H "x-litellm-api-key: Bearer sk-1234" \
|
||||
-d '{"contents":[{"role": "user", "parts":[{"text": "hi"}]}]}'
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
### Tuning API
|
||||
|
||||
Create Fine Tuning Job
|
||||
|
||||
<Tabs>
|
||||
|
||||
<TabItem value="client_side" label="Vertex Python SDK (client side vertex credentials)">
|
||||
|
||||
```python
|
||||
from typing import List, Optional
|
||||
from vertexai.preview.tuning import sft
|
||||
import vertexai
|
||||
|
||||
LITELLM_PROXY_API_KEY = "sk-1234"
|
||||
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
|
||||
|
||||
|
||||
vertexai.init(
|
||||
project="adroit-crow-413218",
|
||||
location="us-central1",
|
||||
api_endpoint=LITELLM_PROXY_BASE,
|
||||
api_transport="rest",
|
||||
)
|
||||
|
||||
|
||||
# TODO(developer): Update project
|
||||
vertexai.init(project=PROJECT_ID, location="us-central1")
|
||||
|
||||
sft_tuning_job = sft.train(
|
||||
source_model="gemini-1.0-pro-002",
|
||||
train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
|
||||
)
|
||||
|
||||
# Polling for job completion
|
||||
while not sft_tuning_job.has_ended:
|
||||
time.sleep(60)
|
||||
sft_tuning_job.refresh()
|
||||
|
||||
print(sft_tuning_job.tuned_model_name)
|
||||
print(sft_tuning_job.tuned_model_endpoint_name)
|
||||
print(sft_tuning_job.experiment)
|
||||
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="py" label="Vertex Python SDK (litellm virtual keys client side)">
|
||||
|
||||
```python
|
||||
from typing import List, Optional
|
||||
from vertexai.preview.tuning import sft
|
||||
import vertexai
|
||||
from google.auth.credentials import Credentials
|
||||
|
||||
LITELLM_PROXY_API_KEY = "sk-1234"
|
||||
LITELLM_PROXY_BASE = "http://0.0.0.0:4000/vertex_ai"
|
||||
|
||||
import datetime
|
||||
|
||||
|
||||
class CredentialsWrapper(Credentials):
|
||||
def __init__(self, token=None):
|
||||
super().__init__()
|
||||
self.token = token
|
||||
self.expiry = None # or set to a future date if needed
|
||||
|
||||
def refresh(self, request):
|
||||
pass
|
||||
|
||||
def apply(self, headers, token=None):
|
||||
headers["Authorization"] = f"Bearer {self.token}"
|
||||
|
||||
@property
|
||||
def expired(self):
|
||||
return False # Always consider the token as non-expired
|
||||
|
||||
@property
|
||||
def valid(self):
|
||||
return True # Always consider the credentials as valid
|
||||
|
||||
|
||||
credentials = CredentialsWrapper(token=LITELLM_PROXY_API_KEY)
|
||||
|
||||
vertexai.init(
|
||||
project="adroit-crow-413218",
|
||||
location="us-central1",
|
||||
api_endpoint=LITELLM_PROXY_BASE,
|
||||
credentials=credentials,
|
||||
api_transport="rest",
|
||||
)
|
||||
|
||||
|
||||
# TODO(developer): Update project
|
||||
vertexai.init(project=PROJECT_ID, location="us-central1")
|
||||
|
||||
sft_tuning_job = sft.train(
|
||||
source_model="gemini-1.0-pro-002",
|
||||
train_dataset="gs://cloud-samples-data/ai-platform/generative_ai/sft_train_data.jsonl",
|
||||
)
|
||||
|
||||
# Polling for job completion
|
||||
while not sft_tuning_job.has_ended:
|
||||
time.sleep(60)
|
||||
sft_tuning_job.refresh()
|
||||
|
||||
print(sft_tuning_job.tuned_model_name)
|
||||
print(sft_tuning_job.tuned_model_endpoint_name)
|
||||
print(sft_tuning_job.experiment)
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
<TabItem value="curl" label="Curl">
|
||||
|
||||
```shell
|
||||
curl http://localhost:4000/vertex_ai/tuningJobs \
|
||||
|
|
@ -804,118 +187,6 @@ curl http://localhost:4000/vertex_ai/tuningJobs \
|
|||
}'
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
|
||||
</Tabs>
|
||||
|
||||
|
||||
### Context Caching
|
||||
|
||||
Use Vertex AI Context Caching
|
||||
|
||||
[**Relevant VertexAI Docs**](https://cloud.google.com/vertex-ai/generative-ai/docs/context-cache/context-cache-overview)
|
||||
|
||||
<Tabs>
|
||||
|
||||
<TabItem value="proxy" label="LiteLLM PROXY">
|
||||
|
||||
1. Add model to config.yaml
|
||||
```yaml
|
||||
model_list:
|
||||
# used for /chat/completions, /completions, /embeddings endpoints
|
||||
- model_name: gemini-1.5-pro-001
|
||||
litellm_params:
|
||||
model: vertex_ai/gemini-1.5-pro-001
|
||||
vertex_project: "project-id"
|
||||
vertex_location: "us-central1"
|
||||
vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
|
||||
|
||||
# used for the /cachedContent and vertexAI native endpoints
|
||||
default_vertex_config:
|
||||
vertex_project: "adroit-crow-413218"
|
||||
vertex_location: "us-central1"
|
||||
vertex_credentials: "adroit-crow-413218-a956eef1a2a8.json" # Add path to service account.json
|
||||
|
||||
```
|
||||
|
||||
2. Start Proxy
|
||||
|
||||
```
|
||||
$ litellm --config /path/to/config.yaml
|
||||
```
|
||||
|
||||
3. Make Request!
|
||||
We make the request in two steps:
|
||||
- Create a cachedContents object
|
||||
- Use the cachedContents object in your /chat/completions
|
||||
|
||||
**Create a cachedContents object**
|
||||
|
||||
First, create a cachedContents object by calling the Vertex `cachedContents` endpoint. The LiteLLM proxy forwards the `/cachedContents` request to the VertexAI API.
|
||||
|
||||
```python
|
||||
import httpx
|
||||
|
||||
# Set Litellm proxy variables
|
||||
LITELLM_BASE_URL = "http://0.0.0.0:4000"
|
||||
LITELLM_PROXY_API_KEY = "sk-1234"
|
||||
|
||||
httpx_client = httpx.Client(timeout=30)
|
||||
|
||||
print("Creating cached content")
|
||||
create_cache = httpx_client.post(
|
||||
url=f"{LITELLM_BASE_URL}/vertex_ai/cachedContents",
|
||||
headers={"x-litellm-api-key": f"Bearer {LITELLM_PROXY_API_KEY}"},
|
||||
json={
|
||||
"model": "gemini-1.5-pro-001",
|
||||
"contents": [
|
||||
{
|
||||
"role": "user",
|
||||
"parts": [{
|
||||
"text": "This is sample text to demonstrate explicit caching." * 4000
|
||||
}]
|
||||
}
|
||||
],
|
||||
}
|
||||
)
|
||||
|
||||
print("Response from create_cache:", create_cache)
|
||||
create_cache_response = create_cache.json()
|
||||
print("JSON from create_cache:", create_cache_response)
|
||||
cached_content_name = create_cache_response["name"]
|
||||
```
|
||||
|
||||
**Use the cachedContents object in your /chat/completions request to VertexAI**
|
||||
|
||||
```python
|
||||
import openai
|
||||
|
||||
# Set Litellm proxy variables
|
||||
LITELLM_BASE_URL = "http://0.0.0.0:4000"
|
||||
LITELLM_PROXY_API_KEY = "sk-1234"
|
||||
|
||||
client = openai.OpenAI(api_key=LITELLM_PROXY_API_KEY, base_url=LITELLM_BASE_URL)
|
||||
|
||||
response = client.chat.completions.create(
|
||||
model="gemini-1.5-pro-001",
|
||||
max_tokens=8192,
|
||||
messages=[
|
||||
{
|
||||
"role": "user",
|
||||
"content": "What is the sample text about?",
|
||||
},
|
||||
],
|
||||
temperature=0.7,
|
||||
extra_body={"cached_content": cached_content_name}, # Use the cached content
|
||||
)
|
||||
|
||||
print("Response from proxy:", response)
|
||||
```
|
||||
|
||||
</TabItem>
|
||||
</Tabs>
|
||||
|
||||
|
||||
## Advanced
|
||||
|
||||
Pre-requisites
|
||||
|
|
@ -930,6 +201,11 @@ Use this, to avoid giving developers the raw Anthropic API key, but still lettin
|
|||
```bash
|
||||
export DATABASE_URL=""
|
||||
export LITELLM_MASTER_KEY=""
|
||||
|
||||
# vertex ai credentials
|
||||
export DEFAULT_VERTEXAI_PROJECT="" # "adroit-crow-413218"
|
||||
export DEFAULT_VERTEXAI_LOCATION="" # "us-central1"
|
||||
export DEFAULT_GOOGLE_APPLICATION_CREDENTIALS="" # "/Users/Downloads/adroit-crow-413218-a956eef1a2a8.json"
|
||||
```
|
||||
|
||||
```bash
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue