<div align="center">
<a href="https://www.weavel.ai">
<img src="https://i.imgur.com/uQ7ulX3.png" title="Logo" style="" />
</a>
<h1>Weavel Python SDK</h1>
<h3>Prompt Optimization & Evaluation for LLM Applications</h3>
<div>
<a href="https://pypi.org/project/weavel" target="_blank">
<img src="https://img.shields.io/pypi/v/weavel.svg" alt="PyPI Version"/>
</a>
</div>
</div>
## Installation
```bash
pip install weavel
```
## Documentation
You can find our full documentation [here](https://weavel.ai/docs/python-sdk).
## How to use
### Option 1: Using OpenAI wrapper
```python
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
```python
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
```python
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"
)
```
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