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litellm/docs/my-website/docs/proxy/logging.md
Marc Abramowitz 9dadbe52f7 Shorten title 
I think it looks better to have a shorter title in the left sidebar
and then describe the longer details in the body of the document.
2024-07-10 17:07:51 -07:00

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# 🪢 Logging
Log Proxy input, output, and exceptions using:
- Langfuse
- OpenTelemetry
- Custom Callbacks
- DataDog
- DynamoDB
- s3 Bucket
- etc.
import Image from '@theme/IdealImage';
import Tabs from '@theme/Tabs';
import TabItem from '@theme/TabItem';
## Logging Proxy Input/Output - Langfuse
We will use the `--config` to set `litellm.success_callback = ["langfuse"]` this will log all successfull LLM calls to langfuse. Make sure to set `LANGFUSE_PUBLIC_KEY` and `LANGFUSE_SECRET_KEY` in your environment
**Step 1** Install langfuse
```shell
pip install langfuse>=2.0.0
```
**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["langfuse"]
```
**Step 3**: Set required env variables for logging to langfuse
```shell
export LANGFUSE_PUBLIC_KEY="pk_kk"
export LANGFUSE_SECRET_KEY="sk_ss"
# Optional, defaults to https://cloud.langfuse.com
export LANGFUSE_HOST="https://xxx.langfuse.com"
```
**Step 4**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```
litellm --test
```
Expected output on Langfuse
<Image img={require('../../img/langfuse_small.png')} />
### Logging Metadata to Langfuse
<Tabs>
<TabItem value="Curl" label="Curl Request">
Pass `metadata` as part of the request body
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"metadata": {
"generation_name": "ishaan-test-generation",
"generation_id": "gen-id22",
"trace_id": "trace-id22",
"trace_user_id": "user-id2"
}
}'
```
</TabItem>
<TabItem value="openai" label="OpenAI v1.0.0+">
Set `extra_body={"metadata": { }}` to `metadata` you want to pass
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"metadata": {
"generation_name": "ishaan-generation-openai-client",
"generation_id": "openai-client-gen-id22",
"trace_id": "openai-client-trace-id22",
"trace_user_id": "openai-client-user-id2"
}
}
)
print(response)
```
</TabItem>
<TabItem value="langchain" label="Langchain">
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "gpt-3.5-turbo",
temperature=0.1,
extra_body={
"metadata": {
"generation_name": "ishaan-generation-langchain-client",
"generation_id": "langchain-client-gen-id22",
"trace_id": "langchain-client-trace-id22",
"trace_user_id": "langchain-client-user-id2"
}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
```
</TabItem>
</Tabs>
### Team based Logging to Langfuse
**Example:**
This config would send langfuse logs to 2 different langfuse projects, based on the team id
```yaml
litellm_settings:
default_team_settings:
- team_id: my-secret-project
success_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_1 # Project 1
langfuse_secret: os.environ/LANGFUSE_PRIVATE_KEY_1 # Project 1
- team_id: ishaans-secret-project
success_callback: ["langfuse"]
langfuse_public_key: os.environ/LANGFUSE_PUB_KEY_2 # Project 2
langfuse_secret: os.environ/LANGFUSE_SECRET_2 # Project 2
```
Now, when you [generate keys](./virtual_keys.md) for this team-id
```bash
curl -X POST 'http://0.0.0.0:4000/key/generate' \
-H 'Authorization: Bearer sk-1234' \
-H 'Content-Type: application/json' \
-d '{"team_id": "ishaans-secret-project"}'
```
All requests made with these keys will log data to their team-specific logging.
### Redacting Messages, Response Content from Langfuse Logging
Set `litellm.turn_off_message_logging=True` This will prevent the messages and responses from being logged to langfuse, but request metadata will still be logged.
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["langfuse"]
turn_off_message_logging: True
```
If you have this feature turned on, you can override it for specific requests by
setting a request header `LiteLLM-Disable-Message-Redaction: true`.
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--header 'LiteLLM-Disable-Message-Redaction: true' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
### 🔧 Debugging - Viewing RAW CURL sent from LiteLLM to provider
Use this when you want to view the RAW curl request sent from LiteLLM to the LLM API
<Tabs>
<TabItem value="Curl" label="Curl Request">
Pass `metadata` as part of the request body
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"metadata": {
"log_raw_request": true
}
}'
```
</TabItem>
<TabItem value="openai" label="OpenAI v1.0.0+">
Set `extra_body={"metadata": {"log_raw_request": True }}` to `metadata` you want to pass
```python
import openai
client = openai.OpenAI(
api_key="anything",
base_url="http://0.0.0.0:4000"
)
# request sent to model set on litellm proxy, `litellm --model`
response = client.chat.completions.create(
model="gpt-3.5-turbo",
messages = [
{
"role": "user",
"content": "this is a test request, write a short poem"
}
],
extra_body={
"metadata": {
"log_raw_request": True
}
}
)
print(response)
```
</TabItem>
<TabItem value="langchain" label="Langchain">
```python
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain.schema import HumanMessage, SystemMessage
chat = ChatOpenAI(
openai_api_base="http://0.0.0.0:4000",
model = "gpt-3.5-turbo",
temperature=0.1,
extra_body={
"metadata": {
"log_raw_request": True
}
}
)
messages = [
SystemMessage(
content="You are a helpful assistant that im using to make a test request to."
),
HumanMessage(
content="test from litellm. tell me why it's amazing in 1 sentence"
),
]
response = chat(messages)
print(response)
```
</TabItem>
</Tabs>
**Expected Output on Langfuse**
You will see `raw_request` in your Langfuse Metadata. This is the RAW CURL command sent from LiteLLM to your LLM API provider
<Image img={require('../../img/debug_langfuse.png')} />
## Logging Proxy Input/Output in OpenTelemetry format
:::info
[Optional] Customize OTEL Service Name and OTEL TRACER NAME by setting the following variables in your environment
```shell
OTEL_TRACER_NAME=<your-trace-name> # default="litellm"
OTEL_SERVICE_NAME=<your-service-name>` # default="litellm"
```
:::
<Tabs>
<TabItem value="Console Exporter" label="Log to console">
**Step 1:** Set callbacks and env vars
Add the following to your env
```shell
OTEL_EXPORTER="console"
```
Add `otel` as a callback on your `litellm_config.yaml`
```shell
litellm_settings:
callbacks: ["otel"]
```
**Step 2**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --detailed_debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
**Step 3**: **Expect to see the following logged on your server logs / console**
This is the Span from OTEL Logging
```json
{
"name": "litellm-acompletion",
"context": {
"trace_id": "0x8d354e2346060032703637a0843b20a3",
"span_id": "0xd8d3476a2eb12724",
"trace_state": "[]"
},
"kind": "SpanKind.INTERNAL",
"parent_id": null,
"start_time": "2024-06-04T19:46:56.415888Z",
"end_time": "2024-06-04T19:46:56.790278Z",
"status": {
"status_code": "OK"
},
"attributes": {
"model": "llama3-8b-8192"
},
"events": [],
"links": [],
"resource": {
"attributes": {
"service.name": "litellm"
},
"schema_url": ""
}
}
```
</TabItem>
<TabItem value="Honeycomb" label="Log to Honeycomb">
#### Quick Start - Log to Honeycomb
**Step 1:** Set callbacks and env vars
Add the following to your env
```shell
OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="https://api.honeycomb.io/v1/traces"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>"
```
Add `otel` as a callback on your `litellm_config.yaml`
```shell
litellm_settings:
callbacks: ["otel"]
```
**Step 2**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --detailed_debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
<TabItem value="otel-col" label="Log to OTEL HTTP Collector">
#### Quick Start - Log to OTEL Collector
**Step 1:** Set callbacks and env vars
Add the following to your env
```shell
OTEL_EXPORTER="otlp_http"
OTEL_ENDPOINT="http:/0.0.0.0:4317"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional
```
Add `otel` as a callback on your `litellm_config.yaml`
```shell
litellm_settings:
callbacks: ["otel"]
```
**Step 2**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --detailed_debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
<TabItem value="otel-col-grpc" label="Log to OTEL GRPC Collector">
#### Quick Start - Log to OTEL GRPC Collector
**Step 1:** Set callbacks and env vars
Add the following to your env
```shell
OTEL_EXPORTER="otlp_grpc"
OTEL_ENDPOINT="http:/0.0.0.0:4317"
OTEL_HEADERS="x-honeycomb-team=<your-api-key>" # Optional
```
Add `otel` as a callback on your `litellm_config.yaml`
```shell
litellm_settings:
callbacks: ["otel"]
```
**Step 2**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --detailed_debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
<TabItem value="traceloop" label="Log to Traceloop Cloud">
#### Quick Start - Log to Traceloop
**Step 1:** Install the `traceloop-sdk` SDK
```shell
pip install traceloop-sdk==0.21.2
```
**Step 2:** Add `traceloop` as a success_callback
```shell
litellm_settings:
success_callback: ["traceloop"]
environment_variables:
TRACELOOP_API_KEY: "XXXXX"
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --detailed_debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
</TabItem>
</Tabs>
** 🎉 Expect to see this trace logged in your OTEL collector**
### Context propagation across Services `Traceparent HTTP Header`
❓ Use this when you want to **pass information about the incoming request in a distributed tracing system**
✅ Key change: Pass the **`traceparent` header** in your requests. [Read more about traceparent headers here](https://uptrace.dev/opentelemetry/opentelemetry-traceparent.html#what-is-traceparent-header)
```curl
traceparent: 00-80e1afed08e019fc1110464cfa66635c-7a085853722dc6d2-01
```
Example Usage
1. Make Request to LiteLLM Proxy with `traceparent` header
```python
import openai
import uuid
client = openai.OpenAI(api_key="sk-1234", base_url="http://0.0.0.0:4000")
example_traceparent = f"00-80e1afed08e019fc1110464cfa66635c-02e80198930058d4-01"
extra_headers = {
"traceparent": example_traceparent
}
_trace_id = example_traceparent.split("-")[1]
print("EXTRA HEADERS: ", extra_headers)
print("Trace ID: ", _trace_id)
response = client.chat.completions.create(
model="llama3",
messages=[
{"role": "user", "content": "this is a test request, write a short poem"}
],
extra_headers=extra_headers,
)
print(response)
```
```shell
# EXTRA HEADERS: {'traceparent': '00-80e1afed08e019fc1110464cfa66635c-02e80198930058d4-01'}
# Trace ID: 80e1afed08e019fc1110464cfa66635c
```
2. Lookup Trace ID on OTEL Logger
Search for Trace=`80e1afed08e019fc1110464cfa66635c` on your OTEL Collector
<Image img={require('../../img/otel_parent.png')} />
## Custom Callback Class [Async]
Use this when you want to run custom callbacks in `python`
#### Step 1 - Create your custom `litellm` callback class
We use `litellm.integrations.custom_logger` for this, **more details about litellm custom callbacks [here](https://docs.litellm.ai/docs/observability/custom_callback)**
Define your custom callback class in a python file.
Here's an example custom logger for tracking `key, user, model, prompt, response, tokens, cost`. We create a file called `custom_callbacks.py` and initialize `proxy_handler_instance`
```python
from litellm.integrations.custom_logger import CustomLogger
import litellm
# This file includes the custom callbacks for LiteLLM Proxy
# Once defined, these can be passed in proxy_config.yaml
class MyCustomHandler(CustomLogger):
def log_pre_api_call(self, model, messages, kwargs):
print(f"Pre-API Call")
def log_post_api_call(self, kwargs, response_obj, start_time, end_time):
print(f"Post-API Call")
def log_stream_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Stream")
def log_success_event(self, kwargs, response_obj, start_time, end_time):
print("On Success")
def log_failure_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Failure")
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Async Success!")
# log: key, user, model, prompt, response, tokens, cost
# Access kwargs passed to litellm.completion()
model = kwargs.get("model", None)
messages = kwargs.get("messages", None)
user = kwargs.get("user", None)
# Access litellm_params passed to litellm.completion(), example access `metadata`
litellm_params = kwargs.get("litellm_params", {})
metadata = litellm_params.get("metadata", {}) # headers passed to LiteLLM proxy, can be found here
# Calculate cost using litellm.completion_cost()
cost = litellm.completion_cost(completion_response=response_obj)
response = response_obj
# tokens used in response
usage = response_obj["usage"]
print(
f"""
Model: {model},
Messages: {messages},
User: {user},
Usage: {usage},
Cost: {cost},
Response: {response}
Proxy Metadata: {metadata}
"""
)
return
async def async_log_failure_event(self, kwargs, response_obj, start_time, end_time):
try:
print(f"On Async Failure !")
print("\nkwargs", kwargs)
# Access kwargs passed to litellm.completion()
model = kwargs.get("model", None)
messages = kwargs.get("messages", None)
user = kwargs.get("user", None)
# Access litellm_params passed to litellm.completion(), example access `metadata`
litellm_params = kwargs.get("litellm_params", {})
metadata = litellm_params.get("metadata", {}) # headers passed to LiteLLM proxy, can be found here
# Acess Exceptions & Traceback
exception_event = kwargs.get("exception", None)
traceback_event = kwargs.get("traceback_exception", None)
# Calculate cost using litellm.completion_cost()
cost = litellm.completion_cost(completion_response=response_obj)
print("now checking response obj")
print(
f"""
Model: {model},
Messages: {messages},
User: {user},
Cost: {cost},
Response: {response_obj}
Proxy Metadata: {metadata}
Exception: {exception_event}
Traceback: {traceback_event}
"""
)
except Exception as e:
print(f"Exception: {e}")
proxy_handler_instance = MyCustomHandler()
# Set litellm.callbacks = [proxy_handler_instance] on the proxy
# need to set litellm.callbacks = [proxy_handler_instance] # on the proxy
```
#### Step 2 - Pass your custom callback class in `config.yaml`
We pass the custom callback class defined in **Step1** to the config.yaml.
Set `callbacks` to `python_filename.logger_instance_name`
In the config below, we pass
- python_filename: `custom_callbacks.py`
- logger_instance_name: `proxy_handler_instance`. This is defined in Step 1
`callbacks: custom_callbacks.proxy_handler_instance`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
callbacks: custom_callbacks.proxy_handler_instance # sets litellm.callbacks = [proxy_handler_instance]
```
#### Step 3 - Start proxy + test request
```shell
litellm --config proxy_config.yaml
```
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Authorization: Bearer sk-1234' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "good morning good sir"
}
],
"user": "ishaan-app",
"temperature": 0.2
}'
```
#### Resulting Log on Proxy
```shell
On Success
Model: gpt-3.5-turbo,
Messages: [{'role': 'user', 'content': 'good morning good sir'}],
User: ishaan-app,
Usage: {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21},
Cost: 3.65e-05,
Response: {'id': 'chatcmpl-8S8avKJ1aVBg941y5xzGMSKrYCMvN', 'choices': [{'finish_reason': 'stop', 'index': 0, 'message': {'content': 'Good morning! How can I assist you today?', 'role': 'assistant'}}], 'created': 1701716913, 'model': 'gpt-3.5-turbo-0613', 'object': 'chat.completion', 'system_fingerprint': None, 'usage': {'completion_tokens': 10, 'prompt_tokens': 11, 'total_tokens': 21}}
Proxy Metadata: {'user_api_key': None, 'headers': Headers({'host': '0.0.0.0:4000', 'user-agent': 'curl/7.88.1', 'accept': '*/*', 'authorization': 'Bearer sk-1234', 'content-length': '199', 'content-type': 'application/x-www-form-urlencoded'}), 'model_group': 'gpt-3.5-turbo', 'deployment': 'gpt-3.5-turbo-ModelID-gpt-3.5-turbo'}
```
#### Logging Proxy Request Object, Header, Url
Here's how you can access the `url`, `headers`, `request body` sent to the proxy for each request
```python
class MyCustomHandler(CustomLogger):
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Async Success!")
litellm_params = kwargs.get("litellm_params", None)
proxy_server_request = litellm_params.get("proxy_server_request")
print(proxy_server_request)
```
**Expected Output**
```shell
{
"url": "http://testserver/chat/completions",
"method": "POST",
"headers": {
"host": "testserver",
"accept": "*/*",
"accept-encoding": "gzip, deflate",
"connection": "keep-alive",
"user-agent": "testclient",
"authorization": "Bearer None",
"content-length": "105",
"content-type": "application/json"
},
"body": {
"model": "Azure OpenAI GPT-4 Canada",
"messages": [
{
"role": "user",
"content": "hi"
}
],
"max_tokens": 10
}
}
```
#### Logging `model_info` set in config.yaml
Here is how to log the `model_info` set in your proxy `config.yaml`. Information on setting `model_info` on [config.yaml](https://docs.litellm.ai/docs/proxy/configs)
```python
class MyCustomHandler(CustomLogger):
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Async Success!")
litellm_params = kwargs.get("litellm_params", None)
model_info = litellm_params.get("model_info")
print(model_info)
```
**Expected Output**
```json
{'mode': 'embedding', 'input_cost_per_token': 0.002}
```
### Logging responses from proxy
Both `/chat/completions` and `/embeddings` responses are available as `response_obj`
**Note: for `/chat/completions`, both `stream=True` and `non stream` responses are available as `response_obj`**
```python
class MyCustomHandler(CustomLogger):
async def async_log_success_event(self, kwargs, response_obj, start_time, end_time):
print(f"On Async Success!")
print(response_obj)
```
**Expected Output /chat/completion [for both `stream` and `non-stream` responses]**
```json
ModelResponse(
id='chatcmpl-8Tfu8GoMElwOZuj2JlHBhNHG01PPo',
choices=[
Choices(
finish_reason='stop',
index=0,
message=Message(
content='As an AI language model, I do not have a physical body and therefore do not possess any degree or educational qualifications. My knowledge and abilities come from the programming and algorithms that have been developed by my creators.',
role='assistant'
)
)
],
created=1702083284,
model='chatgpt-v-2',
object='chat.completion',
system_fingerprint=None,
usage=Usage(
completion_tokens=42,
prompt_tokens=5,
total_tokens=47
)
)
```
**Expected Output /embeddings**
```json
{
'model': 'ada',
'data': [
{
'embedding': [
-0.035126980394124985, -0.020624293014407158, -0.015343423001468182,
-0.03980357199907303, -0.02750781551003456, 0.02111034281551838,
-0.022069307044148445, -0.019442008808255196, -0.00955679826438427,
-0.013143060728907585, 0.029583381488919258, -0.004725852981209755,
-0.015198921784758568, -0.014069183729588985, 0.00897879246622324,
0.01521205808967352,
# ... (truncated for brevity)
]
}
]
}
```
## Custom Callback APIs [Async]
:::info
This is an Enterprise only feature [Get Started with Enterprise here](https://github.com/BerriAI/litellm/tree/main/enterprise)
:::
Use this if you:
- Want to use custom callbacks written in a non Python programming language
- Want your callbacks to run on a different microservice
#### Step 1. Create your generic logging API endpoint
Set up a generic API endpoint that can receive data in JSON format. The data will be included within a "data" field.
Your server should support the following Request format:
```shell
curl --location https://your-domain.com/log-event \
--request POST \
--header "Content-Type: application/json" \
--data '{
"data": {
"id": "chatcmpl-8sgE89cEQ4q9biRtxMvDfQU1O82PT",
"call_type": "acompletion",
"cache_hit": "None",
"startTime": "2024-02-15 16:18:44.336280",
"endTime": "2024-02-15 16:18:45.045539",
"model": "gpt-3.5-turbo",
"user": "ishaan-2",
"modelParameters": "{'temperature': 0.7, 'max_tokens': 10, 'user': 'ishaan-2', 'extra_body': {}}",
"messages": "[{'role': 'user', 'content': 'This is a test'}]",
"response": "ModelResponse(id='chatcmpl-8sgE89cEQ4q9biRtxMvDfQU1O82PT', choices=[Choices(finish_reason='length', index=0, message=Message(content='Great! How can I assist you with this test', role='assistant'))], created=1708042724, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=10, prompt_tokens=11, total_tokens=21))",
"usage": "Usage(completion_tokens=10, prompt_tokens=11, total_tokens=21)",
"metadata": "{}",
"cost": "3.65e-05"
}
}'
```
Reference FastAPI Python Server
Here's a reference FastAPI Server that is compatible with LiteLLM Proxy:
```python
# this is an example endpoint to receive data from litellm
from fastapi import FastAPI, HTTPException, Request
app = FastAPI()
@app.post("/log-event")
async def log_event(request: Request):
try:
print("Received /log-event request")
# Assuming the incoming request has JSON data
data = await request.json()
print("Received request data:")
print(data)
# Your additional logic can go here
# For now, just printing the received data
return {"message": "Request received successfully"}
except Exception as e:
print(f"Error processing request: {str(e)}")
import traceback
traceback.print_exc()
raise HTTPException(status_code=500, detail="Internal Server Error")
if __name__ == "__main__":
import uvicorn
uvicorn.run(app, host="127.0.0.1", port=4000)
```
#### Step 2. Set your `GENERIC_LOGGER_ENDPOINT` to the endpoint + route we should send callback logs to
```shell
os.environ["GENERIC_LOGGER_ENDPOINT"] = "http://localhost:4000/log-event"
```
#### Step 3. Create a `config.yaml` file and set `litellm_settings`: `success_callback` = ["generic"]
Example litellm proxy config.yaml
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["generic"]
```
Start the LiteLLM Proxy and make a test request to verify the logs reached your callback API
## Logging LLM IO to Galileo
[BETA]
Log LLM I/O on [www.rungalileo.io](https://www.rungalileo.io/)
:::info
Beta Integration
:::
**Required Env Variables**
```bash
export GALILEO_BASE_URL="" # For most users, this is the same as their console URL except with the word 'console' replaced by 'api' (e.g. http://www.console.galileo.myenterprise.com -> http://www.api.galileo.myenterprise.com)
export GALILEO_PROJECT_ID=""
export GALILEO_USERNAME=""
export GALILEO_PASSWORD=""
```
### Quick Start
1. Add to Config.yaml
```yaml
model_list:
- litellm_params:
api_base: https://exampleopenaiendpoint-production.up.railway.app/
api_key: my-fake-key
model: openai/my-fake-model
model_name: fake-openai-endpoint
litellm_settings:
success_callback: ["galileo"] # 👈 KEY CHANGE
```
2. Start Proxy
```
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "fake-openai-endpoint",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
🎉 That's it - Expect to see your Logs on your Galileo Dashboard
## Logging Proxy Cost + Usage - OpenMeter
Bill customers according to their LLM API usage with [OpenMeter](../observability/openmeter.md)
**Required Env Variables**
```bash
# from https://openmeter.cloud
export OPENMETER_API_ENDPOINT="" # defaults to https://openmeter.cloud
export OPENMETER_API_KEY=""
```
### Quick Start
1. Add to Config.yaml
```yaml
model_list:
- litellm_params:
api_base: https://openai-function-calling-workers.tasslexyz.workers.dev/
api_key: my-fake-key
model: openai/my-fake-model
model_name: fake-openai-endpoint
litellm_settings:
success_callback: ["openmeter"] # 👈 KEY CHANGE
```
2. Start Proxy
```
litellm --config /path/to/config.yaml
```
3. Test it!
```bash
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "fake-openai-endpoint",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
}
'
```
<Image img={require('../../img/openmeter_img_2.png')} />
## Logging Proxy Input/Output - DataDog
We will use the `--config` to set `litellm.success_callback = ["datadog"]` this will log all successfull LLM calls to DataDog
**Step 1**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["datadog"]
```
**Step 2**: Set Required env variables for datadog
```shell
DD_API_KEY="5f2d0f310***********" # your datadog API Key
DD_SITE="us5.datadoghq.com" # your datadog base url
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
],
"metadata": {
"your-custom-metadata": "custom-field",
}
}'
```
Expected output on Datadog
<Image img={require('../../img/dd_small1.png')} />
## Logging Proxy Input/Output - s3 Buckets
We will use the `--config` to set
- `litellm.success_callback = ["s3"]`
This will log all successfull LLM calls to s3 Bucket
**Step 1** Set AWS Credentials in .env
```shell
AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
AWS_REGION_NAME = ""
```
**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["s3"]
s3_callback_params:
s3_bucket_name: logs-bucket-litellm # AWS Bucket Name for S3
s3_region_name: us-west-2 # AWS Region Name for S3
s3_aws_access_key_id: os.environ/AWS_ACCESS_KEY_ID # us os.environ/<variable name> to pass environment variables. This is AWS Access Key ID for S3
s3_aws_secret_access_key: os.environ/AWS_SECRET_ACCESS_KEY # AWS Secret Access Key for S3
s3_path: my-test-path # [OPTIONAL] set path in bucket you want to write logs to
s3_endpoint_url: https://s3.amazonaws.com # [OPTIONAL] S3 endpoint URL, if you want to use Backblaze/cloudflare s3 buckets
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "Azure OpenAI GPT-4 East",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
Your logs should be available on the specified s3 Bucket
## Logging Proxy Input/Output - DynamoDB
We will use the `--config` to set
- `litellm.success_callback = ["dynamodb"]`
- `litellm.dynamodb_table_name = "your-table-name"`
This will log all successfull LLM calls to DynamoDB
**Step 1** Set AWS Credentials in .env
```shell
AWS_ACCESS_KEY_ID = ""
AWS_SECRET_ACCESS_KEY = ""
AWS_REGION_NAME = ""
```
**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["dynamodb"]
dynamodb_table_name: your-table-name
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```shell
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "Azure OpenAI GPT-4 East",
"messages": [
{
"role": "user",
"content": "what llm are you"
}
]
}'
```
Your logs should be available on DynamoDB
#### Data Logged to DynamoDB /chat/completions
```json
{
"id": {
"S": "chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen"
},
"call_type": {
"S": "acompletion"
},
"endTime": {
"S": "2023-12-15 17:25:58.424118"
},
"messages": {
"S": "[{'role': 'user', 'content': 'This is a test'}]"
},
"metadata": {
"S": "{}"
},
"model": {
"S": "gpt-3.5-turbo"
},
"modelParameters": {
"S": "{'temperature': 0.7, 'max_tokens': 100, 'user': 'ishaan-2'}"
},
"response": {
"S": "ModelResponse(id='chatcmpl-8W15J4480a3fAQ1yQaMgtsKJAicen', choices=[Choices(finish_reason='stop', index=0, message=Message(content='Great! What can I assist you with?', role='assistant'))], created=1702641357, model='gpt-3.5-turbo-0613', object='chat.completion', system_fingerprint=None, usage=Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20))"
},
"startTime": {
"S": "2023-12-15 17:25:56.047035"
},
"usage": {
"S": "Usage(completion_tokens=9, prompt_tokens=11, total_tokens=20)"
},
"user": {
"S": "ishaan-2"
}
}
```
#### Data logged to DynamoDB /embeddings
```json
{
"id": {
"S": "4dec8d4d-4817-472d-9fc6-c7a6153eb2ca"
},
"call_type": {
"S": "aembedding"
},
"endTime": {
"S": "2023-12-15 17:25:59.890261"
},
"messages": {
"S": "['hi']"
},
"metadata": {
"S": "{}"
},
"model": {
"S": "text-embedding-ada-002"
},
"modelParameters": {
"S": "{'user': 'ishaan-2'}"
},
"response": {
"S": "EmbeddingResponse(model='text-embedding-ada-002-v2', data=[{'embedding': [-0.03503197431564331, -0.020601635798811913, -0.015375726856291294,
}
}
```
## Logging Proxy Input/Output - Sentry
If api calls fail (llm/database) you can log those to Sentry:
**Step 1** Install Sentry
```shell
pip install --upgrade sentry-sdk
```
**Step 2**: Save your Sentry_DSN and add `litellm_settings`: `failure_callback`
```shell
export SENTRY_DSN="your-sentry-dsn"
```
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
# other settings
failure_callback: ["sentry"]
general_settings:
database_url: "my-bad-url" # set a fake url to trigger a sentry exception
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```
litellm --test
```
## Logging Proxy Input/Output Athina
[Athina](https://athina.ai/) allows you to log LLM Input/Output for monitoring, analytics, and observability.
We will use the `--config` to set `litellm.success_callback = ["athina"]` this will log all successfull LLM calls to athina
**Step 1** Set Athina API key
```shell
ATHINA_API_KEY = "your-athina-api-key"
```
**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
success_callback: ["athina"]
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "which llm are you"
}
]
}'
```
## (BETA) Moderation with Azure Content Safety
[Azure Content-Safety](https://azure.microsoft.com/en-us/products/ai-services/ai-content-safety) is a Microsoft Azure service that provides content moderation APIs to detect potential offensive, harmful, or risky content in text.
We will use the `--config` to set `litellm.success_callback = ["azure_content_safety"]` this will moderate all LLM calls using Azure Content Safety.
**Step 0** Deploy Azure Content Safety
Deploy an Azure Content-Safety instance from the Azure Portal and get the `endpoint` and `key`.
**Step 1** Set Athina API key
```shell
AZURE_CONTENT_SAFETY_KEY = "<your-azure-content-safety-key>"
```
**Step 2**: Create a `config.yaml` file and set `litellm_settings`: `success_callback`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
callbacks: ["azure_content_safety"]
azure_content_safety_params:
endpoint: "<your-azure-content-safety-endpoint>"
key: "os.environ/AZURE_CONTENT_SAFETY_KEY"
```
**Step 3**: Start the proxy, make a test request
Start proxy
```shell
litellm --config config.yaml --debug
```
Test Request
```
curl --location 'http://0.0.0.0:4000/chat/completions' \
--header 'Content-Type: application/json' \
--data ' {
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "Hi, how are you?"
}
]
}'
```
An HTTP 400 error will be returned if the content is detected with a value greater than the threshold set in the `config.yaml`.
The details of the response will describe :
- The `source` : input text or llm generated text
- The `category` : the category of the content that triggered the moderation
- The `severity` : the severity from 0 to 10
**Step 4**: Customizing Azure Content Safety Thresholds
You can customize the thresholds for each category by setting the `thresholds` in the `config.yaml`
```yaml
model_list:
- model_name: gpt-3.5-turbo
litellm_params:
model: gpt-3.5-turbo
litellm_settings:
callbacks: ["azure_content_safety"]
azure_content_safety_params:
endpoint: "<your-azure-content-safety-endpoint>"
key: "os.environ/AZURE_CONTENT_SAFETY_KEY"
thresholds:
Hate: 6
SelfHarm: 8
Sexual: 6
Violence: 4
```
:::info
`thresholds` are not required by default, but you can tune the values to your needs.
Default values is `4` for all categories
:::
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