Tracing

Tracing is a powerful feature in Weave that allows you to track the inputs and outputs of functions seamlessly. Follow these steps to get started:

To track specific functions, decorate them with @weave.op(). This decorator tells Weave to monitor the inputs, outputs, and any code changes for the function. Now, every call to this function will be tracked and logged. Additionally, weave tracks how functions relate by keeping track of parent and children calls and building a trace tree.

import weave
from openai import OpenAI
import requests, random
PROMPT="""Emulate the Pokedex from early Pokémon episodes. State the name of the Pokemon and then describe it.
        Your tone is informative yet sassy, blending factual details with a touch of dry humor. Be concise, no more than 3 sentences. """
POKEMON = ['pikachu', 'charmander', 'squirtle', 'bulbasaur', 'jigglypuff', 'meowth', 'eevee']

@weave.op()
def get_pokemon_data(pokemon_name):
    url = f"https://pokeapi.co/api/v2/pokemon/{pokemon_name}"
    response = requests.get(url)
    if response.status_code == 200:
        data = response.json()
        name = data["name"]
        types = [t["type"]["name"] for t in data["types"]]
        species_url = data["species"]["url"]
        species_response = requests.get(species_url)
        evolved_from = "Unknown"
        if species_response.status_code == 200:
            species_data = species_response.json()
            if species_data["evolves_from_species"]:
                evolved_from = species_data["evolves_from_species"]["name"]
        return {"name": name, "types": types, "evolved_from": evolved_from}
    else:
        return None

@weave.op()
def pokedex(name: str, prompt: str) -> str:
    client = OpenAI()
    data = get_pokemon_data(name)
    if not data: return "Error: Unable to fetch data"
    response = client.chat.completions.create(
        model="gpt-3.5-turbo",
        messages=[
            {"role": "system","content": prompt},
            {"role": "user", "content": str(data)}
        ],
        temperature=0.7,
        max_tokens=100,
        top_p=1
    )
    return response.choices[0].message.content

weave.init('intro-example')
# Get data for a specific Pokémon
pokemon_data = pokedex(random.choice(POKEMON), PROMPT)

Add additional attributes

When calling tracked functions, you can add additional metadata to the call by using the weave.attributes context manager.

For example, you can add a user_id to each call and then filter calls by user. In the example below, any function called within the context manager will have the user_id attribute set to lukas and env attribute set to production.

import weave
import json
from openai import OpenAI

@weave.op()
def extract_fruit(sentence: str) -> dict:
    client = OpenAI()

    response = client.chat.completions.create(
    model="gpt-3.5-turbo-1106",
    messages=[
        {
            "role": "system",
            "content": "You will be provided with unstructured data, and your task is to parse it one JSON dictionary with fruit, color and flavor as keys."
        },
        {
            "role": "user",
            "content": sentence
        }
        ],
        temperature=0.7,
        response_format={ "type": "json_object" }
    )
    extracted = response.choices[0].message.content
    return json.loads(extracted)

weave.init('intro-example')
sentence = "There are many fruits that were found on the recently discovered planet Goocrux. There are neoskizzles that grow there, which are purple and taste like candy."

with weave.attributes({'user_id': 'lukas', 'env': 'production'}):
    extract_fruit(sentence)