Braintrust - Evals + Logging

Braintrust manages evaluations, logging, prompt playground, to data management for AI products.

Quick Start

# pip install braintrust
import litellm
import os

# set env
os.environ["BRAINTRUST_API_KEY"] = ""
os.environ['OPENAI_API_KEY']=""

# set braintrust as a callback, litellm will send the data to braintrust
litellm.callbacks = ["braintrust"]

# openai call
response = litellm.completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ]
)

OpenAI Proxy Usage

1. Add keys to env

BRAINTRUST_API_KEY=""

2. Add braintrust to callbacks

model_list:
  - model_name: gpt-3.5-turbo
    litellm_params:
      model: gpt-3.5-turbo
      api_key: os.environ/OPENAI_API_KEY

litellm_settings:
  callbacks: ["braintrust"]

3. Test it!

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
    "model": "groq-llama3",
    "messages": [
        { "role": "system", "content": "Use your tools smartly"},
        { "role": "user", "content": "What time is it now? Use your tool"}
    ]
}'

Advanced - pass Project ID or name

It is recommended that you include the project_id or project_name to ensure your traces are being written out to the correct Braintrust project.

SDK

response = litellm.completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata={
    "project_id": "1234",
    # passing project_name will try to find a project with that name, or create one if it doesn't exist
    # if both project_id and project_name are passed, project_id will be used
    # "project_name": "my-special-project"
  }
)

Note: Other metadata can be included here as well when using the SDK.

response = litellm.completion(
  model="gpt-3.5-turbo",
  messages=[
    {"role": "user", "content": "Hi 👋 - i'm openai"}
  ],
  metadata={
    "project_id": "1234",
    "item1": "an item",
    "item2": "another item"
  }
)

PROXY

Curl

curl -X POST 'http://0.0.0.0:4000/chat/completions' \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer sk-1234' \
-D '{
    "model": "groq-llama3",
    "messages": [
        { "role": "system", "content": "Use your tools smartly"},
        { "role": "user", "content": "What time is it now? Use your tool"}
    ],
    "metadata": {
        "project_id": "my-special-project"
    }
}'

OpenAI SDK

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={ # pass in any provider-specific param, if not supported by openai, https://docs.litellm.ai/docs/completion/input#provider-specific-params
        "metadata": { # 👈 use for logging additional params (e.g. to braintrust)
            "project_id": "my-special-project"
        }
    }
)

print(response)

For more examples, Click Here

Full API Spec

Here's everything you can pass in metadata for a braintrust request

braintrust_* - If you are adding metadata from proxy request headers, any metadata field starting with braintrust_ will be passed as metadata to the logging request. If you are using the SDK, just pass your metadata like normal (e.g., metadata={"project_name": "my-test-project", "item1": "an item", "item2": "another item"})

project_id - Set the project id for a braintrust call. Default is litellm.