# 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](https://www.python.org/) >= 3.10
- [Langfuse](https://github.com/langfuse/langfuse-python) >= 2
## Installation
You can install it using pip:
```bash
pip install dta-utils
```
## Basic usage
1. Install this package into your app
2. Add to your environment variables the following keys:
```bash
DTA_PROXY_KEY="sk..."
DTA_LOGS_PUBLIC_KEY="pk..."
DTA_LOGS_SECRET_KEY="sk-..."
```
3. Make calls to LLM models using *DTA Proxy*:
```python
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:
```python
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
```python
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:
```python
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:
```python
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)
```
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"description": "# Dta Utils\n=======\n\n**Connect your app to DTA Services**\n\n\n### What are DTA Services?\n\nA collection of services to ease and speed up the development and monitoring of Applications, focused on gen AI apps.\n\n\n## Introduction\n\nYou can use this package to easily integrate your application with the following services:\n- DTA Proxy (manage and control access to LLM models)\n- DTA Logs (monitor app usage and user feedback)\n\nAny integration rely on having a service key generated on DTA to use its services.\n\n\n## Dependencies\n\n- [Python](https://www.python.org/) >= 3.10\n- [Langfuse](https://github.com/langfuse/langfuse-python) >= 2\n\n\n## Installation\n\nYou can install it using pip:\n\n```bash\npip install dta-utils\n```\n\n\n## Basic usage\n\n1. Install this package into your app\n\n2. Add to your environment variables the following keys:\n\n```bash\nDTA_PROXY_KEY=\"sk...\"\nDTA_LOGS_PUBLIC_KEY=\"pk...\"\nDTA_LOGS_SECRET_KEY=\"sk-...\"\n```\n\n3. Make calls to LLM models using *DTA Proxy*:\n\n```python\nfrom dta_utils import DtaProxy\n\nclient = DtaProxy.openai_client()\n\nresponse = client.chat.completions.create(model=\"gpt-3.5-turbo\", messages = [\n {\n \"role\": \"user\",\n \"content\": \"tell me a random city of the world and its country\"\n }\n])\n\nprint(response)\n```\n\n\n4. Send logs to *Dta Logs*:\n\n\n 4.1. Sending logs with a `DtaTracer` instance:\n\n\n```python\nfrom dta_utils import DtaTracer\nimport uuid\n\ntrace_id = f\"trace_{uuid.uuid4()}\"\n\ntracer = DtaTracer(name=\"app-test\")\n\n# trace is a thread that will aggreagate events and scores\ntracer.trace(\n id=trace_id,\n input={\"data\": \"input\"},\n)\n\n# event can be emitted anytime with anything relevant to track\ntracer.event(\n name=\"event registered\",\n input={\"event-data\": \"event-input\"},\n output={\"event-data\": \"event-output\"},\n)\n\n# traces can be updated at any time\ntracer.trace(\n output={\"data\": \"output\"},\n tags=[\"example\", \"event\", \"tracer\"],\n)\n\n# score is how to register any evaluation over a trace\ntracer.score(\n name=\"user-feedback\",\n # value can be any number\n value=5,\n comment=\"optional comments\",\n)\n```\n\n\n 4.2. Sending logs without an instance:\n\n\n Note: the `trace_id` have to be informed on each call\n\n\n```python\nfrom dta_utils import DtaLog\nimport uuid\n\ntrace_id = f\"trace_{uuid.uuid4()}\"\n\n# trace is a thread that will aggreagate events\nDtaLog.trace(\n id=trace_id,\n name=\"app-test\",\n input={\"data\": \"input\"},\n)\n\n# event can be emitted anytime with anything relevant to track\nDtaLog.event(\n trace_id=trace_id,\n name=\"event registered\",\n input={\"event-data\": \"event-input\"},\n output={\"event-data\": \"event-output\"},\n)\n\n# traces can be updated at any time\nDtaLog.trace(\n id=trace_id,\n output={\"data\": \"output\"},\n tags=[\"example\", \"event\", \"logs\"],\n)\n\n# score is how to register any evaluation over a trace\nDtaLog.score(\n trace_id=trace_id,\n name=\"user-feedback\",\n # value can be any number\n value=-5,\n comment=\"optional comments\",\n)\n```\n\n\n 4.3. Log Model generation:\n\nTo log model generation, the most commom uses are either through openai lib (using Dta wrapper), or with Langchain, adding a set of callbacks provided.\n\n\n 4.3.1. Using Openai lib wrapper:\n\n```python\nfrom dta_utils import DtaLog, DtaProxy\nimport uuid\n\ntrace_id = f\"trace_{uuid.uuid4()}\"\n\n# get openai lib ready to log into DtaLog\n# create the openai client using DtaProxy\nclient = DtaProxy.openai_client(openai=DtaLog.openai())\n\n# example of using OpenAI functions\ntools = [\n {\n \"type\": \"function\",\n \"function\": {\n \"name\": \"get_current_weather\",\n \"description\": \"Get the current weather in a given location\",\n \"parameters\": {\n \"type\": \"object\",\n \"properties\": {\n \"location\": {\n \"type\": \"string\",\n \"description\": \"The city and state, e.g. San Francisco, CA\",\n },\n \"unit\": {\"type\": \"string\", \"enum\": [\"celsius\", \"fahrenheit\"]},\n },\n \"required\": [\"location\"],\n },\n },\n }\n]\n\n# calling the model\nresponse = client.chat.completions.create(\n model=\"gpt-3.5-turbo\",\n messages=[\n {\n \"role\": \"user\",\n \"content\": \"how is the wheather in Reykjavik Iceland?\",\n },\n ],\n tools=tools,\n # parameters for DTA log\n name=\"log-gen-name\",\n trace_id=trace_id,\n)\n\nprint(response)\n\n# for performance improvement, the logs can be not immediately emitted\n# so the following can be important if running the app in a mode that can be abruptly closed (like from command line)\n# usually not important if running on a webserver where the app is not immediatelly closed\nDtaLog.openai_flush(dta_openai)\n```\n\n\n 4.3.2. Using Langchain callbacks:\n\n```python\nfrom dta_utils import DtaLog, DtaProxy\nfrom langchain_openai import ChatOpenAI\n\ndta_handler = DtaLog.langchain_handler()\n\nmodel = ChatOpenAI(\n temperature=0,\n model=\"gpt-3.5-turbo\",\n openai_api_base=DtaProxy.url(),\n openai_api_key=DtaProxy.key(),\n callbacks=[dta_handler],\n)\n\nresponse = model.invoke(\n [\n {\n \"role\": \"user\",\n \"content\": \"tell me a random city of the world and its country\",\n }\n ]\n)\n\nprint(response)\n\n```\n\n",
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