totvs-dta-utils 0.1.6

Lib for integration with DTA services

Dta Utils

=======

Connect your app to DTA Services

What are DTA Services?

A collection of services to ease and speed up the development and monitoring of Applications, focused on gen AI apps.

Introduction

You can use this package to easily integrate your application with the following services:

• DTA Proxy (manage and control access to LLM models)
• DTA Logs (monitor app usage and user feedback)

Any integration rely on having a service key generated on DTA to use its services.

Dependencies

• Python >= 3.10
• Langfuse >= 2

Installation

You can install it using pip:

pip install dta-utils

Basic usage

1. Install this package into your app

2. Add to your environment variables the following keys:

DTA_PROXY_KEY="sk..."
DTA_LOGS_PUBLIC_KEY="pk..."
DTA_LOGS_SECRET_KEY="sk-..."

3. Make calls to LLM models using DTA Proxy:

from dta_utils import DtaProxy

client = DtaProxy.openai_client()

response = client.chat.completions.create(model="gpt-3.5-turbo", messages = [
    {
        "role": "user",
        "content": "tell me a random city of the world and its country"
    }
])

print(response)

4. Send logs to Dta Logs:

   4.1. Sending logs with a DtaTracer instance:

from dta_utils import DtaTracer
import uuid

trace_id = f"trace_{uuid.uuid4()}"

tracer = DtaTracer(name="app-test")

# trace is a thread that will aggreagate events and scores
tracer.trace(
    id=trace_id,
    input={"data": "input"},
)

# event can be emitted anytime with anything relevant to track
tracer.event(
    name="event registered",
    input={"event-data": "event-input"},
    output={"event-data": "event-output"},
)

# traces can be updated at any time
tracer.trace(
    output={"data": "output"},
    tags=["example", "event", "tracer"],
)

# score is how to register any evaluation over a trace
tracer.score(
    name="user-feedback",
    # value can be any number
    value=5,
    comment="optional comments",
)

4.2. Sending logs without an instance:

Note: the trace_id have to be informed on each call

from dta_utils import DtaLog
import uuid

trace_id = f"trace_{uuid.uuid4()}"

# trace is a thread that will aggreagate events
DtaLog.trace(
    id=trace_id,
    name="app-test",
    input={"data": "input"},
)

# event can be emitted anytime with anything relevant to track
DtaLog.event(
    trace_id=trace_id,
    name="event registered",
    input={"event-data": "event-input"},
    output={"event-data": "event-output"},
)

# traces can be updated at any time
DtaLog.trace(
    id=trace_id,
    output={"data": "output"},
    tags=["example", "event", "logs"],
)

# score is how to register any evaluation over a trace
DtaLog.score(
    trace_id=trace_id,
    name="user-feedback",
    # value can be any number
    value=-5,
    comment="optional comments",
)

4.3. Log Model generation:

To log model generation, the most commom uses are either through openai lib (using Dta wrapper), or with Langchain, adding a set of callbacks provided.

    4.3.1. Using Openai lib wrapper:

from dta_utils import DtaLog, DtaProxy
import uuid

trace_id = f"trace_{uuid.uuid4()}"

# get openai lib ready to log into DtaLog
# create the openai client using DtaProxy
client = DtaProxy.openai_client(openai=DtaLog.openai())

# example of using OpenAI functions
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_current_weather",
            "description": "Get the current weather in a given location",
            "parameters": {
                "type": "object",
                "properties": {
                    "location": {
                        "type": "string",
                        "description": "The city and state, e.g. San Francisco, CA",
                    },
                    "unit": {"type": "string", "enum": ["celsius", "fahrenheit"]},
                },
                "required": ["location"],
            },
        },
    }
]

# calling the model
response = client.chat.completions.create(
    model="gpt-3.5-turbo",
    messages=[
        {
            "role": "user",
            "content": "how is the wheather in Reykjavik Iceland?",
        },
    ],
    tools=tools,
    # parameters for DTA log
    name="log-gen-name",
    trace_id=trace_id,
)

print(response)

# for performance improvement, the logs can be not immediately emitted
# so the following can be important if running the app in a mode that can be abruptly closed (like from command line)
# usually not important if running on a webserver where the app is not immediatelly closed
DtaLog.openai_flush(dta_openai)

    4.3.2. Using Langchain callbacks:

from dta_utils import DtaLog, DtaProxy
from langchain_openai import ChatOpenAI

dta_handler = DtaLog.langchain_handler()

model = ChatOpenAI(
    temperature=0,
    model="gpt-3.5-turbo",
    openai_api_base=DtaProxy.url(),
    openai_api_key=DtaProxy.key(),
    callbacks=[dta_handler],
)

response = model.invoke(
    [
        {
            "role": "user",
            "content": "tell me a random city of the world and its country",
        }
    ]
)

print(response)