parea-ai 0.2.9

Parea python sdk

parea-sdk

Parea python sdk

Installation

pip install -U parea-ai

or install with Poetry

poetry add parea-ai

Debugging Chains & Agents

You can iterate on your chains & agents much faster by using a local cache. This will allow you to make changes to your code & prompts without waiting for all previous, valid LLM responses. Simply add these two lines to the beginning your code and start a local redis cache:

from parea import init, RedisCache

init(cache=RedisCache())

Above will use the default redis cache at localhost:6379 with no password. You can also specify your redis database by:

from parea import init, RedisCache

cache = RedisCache(
  host=os.getenv("REDIS_HOST", "localhost"),  # default value
  port=int(os.getenv("REDIS_PORT", 6379)),    # default value
  password=os.getenv("REDIS_PASSWORT", None)  # default value
)
init(cache=cache)

If you set cache = None for init, no cache will be used.

Automatically log all your LLM call traces

You can automatically log all your LLM traces to the Parea dashboard by setting the PAREA_API_KEY environment variable or specifying it in the init function. This will help you debug issues your customers are facing by stepping through the LLM call traces and recreating the issue in your local setup & code.

from parea import init

init(
  api_key=os.getenv("PAREA_API_KEY"),  # default value
  cache=...
)

Use a deployed prompt

import os

from dotenv import load_dotenv

from parea import Parea
from parea.schemas.models import Completion, UseDeployedPrompt, CompletionResponse, UseDeployedPromptResponse

load_dotenv()

p = Parea(api_key=os.getenv("PAREA_API_KEY"))

# You will find this deployment_id in the Parea dashboard
deployment_id = '<DEPLOYMENT_ID>'

# Assuming your deployed prompt's message is:
# {"role": "user", "content": "Write a hello world program using {{x}} and the {{y}} framework."}
inputs = {"x": "Golang", "y": "Fiber"}

# You can easily unpack a dictionary into an attrs class
test_completion = Completion(
  **{
    "deployment_id": deployment_id,
    "llm_inputs": inputs,
    "metadata": {"purpose": "testing"}
  }
)

# By passing in my inputs, in addition to the raw message with unfilled variables {{x}} and {{y}}, 
# you we will also get the filled-in prompt:
# {"role": "user", "content": "Write a hello world program using Golang and the Fiber framework."}
test_get_prompt = UseDeployedPrompt(deployment_id=deployment_id, llm_inputs=inputs)


def main():
  completion_response: CompletionResponse = p.completion(data=test_completion)
  print(completion_response)
  deployed_prompt: UseDeployedPromptResponse = p.get_prompt(data=test_get_prompt)
  print("\n\n")
  print(deployed_prompt)


async def main_async():
  completion_response: CompletionResponse = await p.acompletion(data=test_completion)
  print(completion_response)
  deployed_prompt: UseDeployedPromptResponse = await p.aget_prompt(data=test_get_prompt)
  print("\n\n")
  print(deployed_prompt)

Logging results from LLM providers [Example]

import os

import openai
from dotenv import load_dotenv

from parea import Parea

load_dotenv()

openai.api_key = os.getenv("OPENAI_API_KEY")

p = Parea(api_key=os.getenv("PAREA_API_KEY"))

x = "Golang"
y = "Fiber"
messages = [{
  "role": "user",
  "content": f"Write a hello world program using {x} and the {y} framework."
}]
model = "gpt-3.5-turbo"
temperature = 0.0


# define your OpenAI call as you would normally and we'll automatically log the results
def main():
  openai.ChatCompletion.create(model=model, temperature=temperature, messages=messages).choices[0].message["content"]

Open source community features

Ready-to-use Pull Requests templates and several Issue templates.

• Files such as: LICENSE, CONTRIBUTING.md, CODE_OF_CONDUCT.md, and SECURITY.md are generated automatically.
• Semantic Versions specification with Release Drafter.

🛡 License

This project is licensed under the terms of the Apache Software License 2.0 license. See LICENSE for more details.

📃 Citation

@misc{parea-sdk,
  author = {joel-parea-ai},
  title = {Parea python sdk},
  year = {2023},
  publisher = {GitHub},
  journal = {GitHub repository},
  howpublished = {\url{https://github.com/parea-ai/parea-sdk}}
}