Getting Started - E2E Tutorial
End-to-End tutorial for LiteLLM Proxy to:
- Add an Azure OpenAI model
- Make a successful /chat/completion call
- Generate a virtual key
- Set RPM limit on virtual key
Pre-Requisites
- Install LiteLLM Docker Image OR LiteLLM CLI (pip package)
- Docker
- LiteLLM CLI (pip package)
docker pull ghcr.io/berriai/litellm:main-latest
$ pip install 'litellm[proxy]'
1. Add a model
Control LiteLLM Proxy with a config.yaml file.
Setup your config.yaml with your azure model.
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/my_azure_deployment
      api_base: os.environ/AZURE_API_BASE
      api_key: "os.environ/AZURE_API_KEY"
      api_version: "2024-07-01-preview" # [OPTIONAL] litellm uses the latest azure api_version by default
Model List Specification
- model_name(- str) - This field should contain the name of the model as received.
- litellm_params(- dict) See All LiteLLM Params- model(- str) - Specifies the model name to be sent to- litellm.acompletion/- litellm.aembedding, etc. This is the identifier used by LiteLLM to route to the correct model + provider logic on the backend.
- api_key(- str) - The API key required for authentication. It can be retrieved from an environment variable using- os.environ/.
- api_base(- str) - The API base for your azure deployment.
- api_version(- str) - The API Version to use when calling Azure's OpenAI API. Get the latest Inference API version here.
 
Useful Links
- All Supported LLM API Providers (OpenAI/Bedrock/Vertex/etc.)
- Full Config.Yaml Spec
- Pass provider-specific params
2. Make a successful /chat/completion call
LiteLLM Proxy is 100% OpenAI-compatible. Test your azure model via the /chat/completions route.
2.1 Start Proxy
Save your config.yaml from step 1. as litellm_config.yaml.
- Docker
- LiteLLM CLI (pip package)
docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e AZURE_API_KEY=d6*********** \
    -e AZURE_API_BASE=https://openai-***********/ \
    -p 4000:4000 \
    ghcr.io/berriai/litellm:main-latest \
    --config /app/config.yaml --detailed_debug
# RUNNING on http://0.0.0.0:4000
$ litellm --config /app/config.yaml --detailed_debug
Confirm your config.yaml got mounted correctly
Loaded config YAML (api_key and environment_variables are not shown):
{
"model_list": [
{
"model_name ...
2.2 Make Call
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ]
}'
Expected Response
{
    "id": "chatcmpl-2076f062-3095-4052-a520-7c321c115c68",
    "choices": [
        {
            "finish_reason": "stop",
            "index": 0,
            "message": {
                "content": "I am gpt-3.5-turbo",
                "role": "assistant",
                "tool_calls": null,
                "function_call": null
            }
        }
    ],
    "created": 1724962831,
    "model": "gpt-3.5-turbo",
    "object": "chat.completion",
    "system_fingerprint": null,
    "usage": {
        "completion_tokens": 20,
        "prompt_tokens": 10,
        "total_tokens": 30
    }
}
Useful Links
- All Supported LLM API Providers (OpenAI/Bedrock/Vertex/etc.)
- Call LiteLLM Proxy via OpenAI SDK, Langchain, etc.
- All API Endpoints Swagger
- Other/Non-Chat Completion Endpoints
- Pass-through for VertexAI, Bedrock, etc.
3. Generate a virtual key
Track Spend, and control model access via virtual keys for the proxy
3.1 Set up a Database
Requirements
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/my_azure_deployment
      api_base: os.environ/AZURE_API_BASE
      api_key: "os.environ/AZURE_API_KEY"
      api_version: "2024-07-01-preview" # [OPTIONAL] litellm uses the latest azure api_version by default
general_settings: 
  master_key: sk-1234 
  database_url: "postgresql://<user>:<password>@<host>:<port>/<dbname>" # 👈 KEY CHANGE
Save config.yaml as litellm_config.yaml (used in 3.2).
What is general_settings?
These are settings for the LiteLLM Proxy Server.
See All General Settings here.
- master_key(- str)- Description: - Set a master key, this is your Proxy Admin key - you can use this to create other keys (🚨 must start withsk-).
 
- Set a 
- Usage: -  Set on config.yaml set your master key under general_settings:master_key, example -master_key: sk-1234
-  Set env variable set LITELLM_MASTER_KEY
 
-  Set on config.yaml set your master key under 
 
- Description: 
- database_url(str)- Description: - Set a database_url, this is the connection to your Postgres DB, which is used by litellm for generating keys, users, teams.
 
- Set a 
- Usage: -  Set on config.yaml set your master key under general_settings:database_url, example -database_url: "postgresql://..."
- Set DATABASE_URL=postgresql://<user>:<password>@<host>:<port>/<dbname>in your env
 
-  Set on config.yaml set your master key under 
 
- Description: 
3.2 Start Proxy
docker run \
    -v $(pwd)/litellm_config.yaml:/app/config.yaml \
    -e AZURE_API_KEY=d6*********** \
    -e AZURE_API_BASE=https://openai-***********/ \
    -p 4000:4000 \
    ghcr.io/berriai/litellm:main-latest \
    --config /app/config.yaml --detailed_debug
3.3 Create Key w/ RPM Limit
Create a key with rpm_limit: 1. This will only allow 1 request per minute for calls to proxy with this key.
curl -L -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{
    "rpm_limit": 1
}'
Expected Response
{
    "key": "sk-12..."
}
3.4 Test it!
Use your virtual key from step 3.3
1st call - Expect to work!
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-12...' \
-d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ]
}'
Expected Response
{
    "id": "chatcmpl-2076f062-3095-4052-a520-7c321c115c68",
    "choices": [
        ...
}
2nd call - Expect to fail!
Why did this call fail?
We set the virtual key's requests per minute (RPM) limit to 1. This has now been crossed.
curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-12...' \
-d '{
    "model": "gpt-3.5-turbo",
    "messages": [
      {
        "role": "system",
        "content": "You are a helpful math tutor. Guide the user through the solution step by step."
      },
      {
        "role": "user",
        "content": "how can I solve 8x + 7 = -23"
      }
    ]
}'
Expected Response
{
  "error": {
    "message": "Max parallel request limit reached. Hit limit for api_key: daa1b272072a4c6841470a488c5dad0f298ff506e1cc935f4a181eed90c182ad. tpm_limit: 100, current_tpm: 29, rpm_limit: 1, current_rpm: 2.",
    "type": "None",
    "param": "None",
    "code": "429"
  }
}
Useful Links
- Creating Virtual Keys
- Key Management API Endpoints Swagger
- Set Budgets / Rate Limits per key/user/teams
- Dynamic TPM/RPM Limits for keys
Troubleshooting
Non-root docker image?
If you need to run the docker image as a non-root user, use this.
SSL Verification Issue / Connection Error.
If you see
ssl.SSLCertVerificationError: [SSL: CERTIFICATE_VERIFY_FAILED] certificate verify failed: self-signed certificate in certificate chain (_ssl.c:1006)
OR
Connection Error.
You can disable ssl verification with:
model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: azure/my_azure_deployment
      api_base: os.environ/AZURE_API_BASE
      api_key: "os.environ/AZURE_API_KEY"
      api_version: "2024-07-01-preview"
litellm_settings:
    ssl_verify: false # 👈 KEY CHANGE
(DB) All connection attempts failed
If you see:
httpx.ConnectError: All connection attempts failed                                                                        
                                                                                                                         
ERROR:    Application startup failed. Exiting.                                                                            
3:21:43 - LiteLLM Proxy:ERROR: utils.py:2207 - Error getting LiteLLM_SpendLogs row count: All connection attempts failed 
This might be a DB permission issue.
- Validate db user permission issue
Try creating a new database.
STATEMENT: CREATE DATABASE "litellm"
If you get:
ERROR: permission denied to create 
This indicates you have a permission issue.
- Grant permissions to your DB user
It should look something like this:
psql -U postgres
CREATE DATABASE litellm;
On CloudSQL, this is:
GRANT ALL PRIVILEGES ON DATABASE litellm TO your_username;
What is litellm_settings?
LiteLLM Proxy uses the LiteLLM Python SDK for handling LLM API calls.
litellm_settings are module-level params for the LiteLLM Python SDK (equivalent to doing litellm.<some_param> on the SDK). You can see all params here
Support & Talk with founders
- Our emails ✉️ ishaan@berri.ai / krrish@berri.ai