weavel 1.11.0

Weavel, automated prompt engineering and observability for LLM applications

Weavel Python SDK

Prompt Optimization & Evaluation for LLM Applications

Installation

pip install weavel

Documentation

You can find our full documentation here.

How to use

Option 1: Using OpenAI wrapper

from weavel import WeavelOpenAI as OpenAI

openai = OpenAI()

response = openai.chat.completions.create(
    model="gpt-4o",
    messages=[
        {"role": "user", "content": "Hello, world!"}
    ],
    headers={
      "generation_name": "hello",
    }
)

Option 2: Logging inputs/outputs of LLM calls

from weavel import Weavel
from openai import OpenAI
from pydantic import BaseModel

openai = OpenAI()
# initialize Weavel
weavel = Weavel()

class Answer(BaseModel):
    reasoning: str
    answer: str

question = "What is x if x + 2 = 4?"
response = openai.beta.chat.completions.parse(
    model="gpt-4o-2024-08-06",
    messages=[
        {"role": "system", "content": "You are a math teacher."},
        {"role": "user", "content": question}
    ],
    response_format=Answer
).choices[0].message.parsed

# log the generation
weavel.generation(
    name="solve-math", # optional
    inputs={"question": question},
    outputs=response.model_dump()
)

Option 3 (Advanced Usage): OTEL-compatible trace logging

from weavel import Weavel

weavel = Weavel()

session = weavel.session(user_id = "UNIQUE_USER_ID")

session.message(
    role="user",
    content="Nice to meet you!"
)

session.track(
    name="Main Page Viewed"
)

trace = session.trace(
    name="retrieval_module"
)

trace.log(
    name="google_search"
)