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Integracija sa pozivanjem funkcija

Naučili ste dosta do sada u prethodnim lekcijama. Međutim, možemo još više poboljšati. Neke stvari koje možemo obraditi su kako možemo dobiti konzistentniji format odgovora kako bi bilo lakše raditi sa odgovorom nizvodno. Takođe, možda želimo dodati podatke iz drugih izvora kako bismo dodatno obogatili našu aplikaciju.

Gore navedeni problemi su ono što ovo poglavlje želi da reši.

Uvod

Ova lekcija će pokriti:

  • Objašnjenje šta je pozivanje funkcija i njegovi slučajevi korišćenja.
  • Kreiranje poziva funkcije koristeći Azure OpenAI.
  • Kako integrisati poziv funkcije u aplikaciju.

Ciljevi učenja

Do kraja ove lekcije, moći ćete da:

  • Objasnite svrhu korišćenja pozivanja funkcija.
  • Postavite Poziv Funkcije koristeći Azure OpenAI Service.
  • Dizajnirate efikasne pozive funkcija za slučaj korišćenja vaše aplikacije.

Scenario: Poboljšanje našeg chatbota sa funkcijama

Za ovu lekciju, želimo da izgradimo funkciju za našu startup edukaciju koja omogućava korisnicima da koriste chatbot za pronalaženje tehničkih kurseva. Preporučićemo kurseve koji odgovaraju njihovom nivou veština, trenutnoj ulozi i tehnologiji koja ih interesuje.

Da bismo završili ovaj scenario, koristićemo kombinaciju:

  • Azure OpenAI da kreiramo chat iskustvo za korisnika.
  • Microsoft Learn Catalog API da pomognemo korisnicima da pronađu kurseve na osnovu zahteva korisnika.
  • Function Calling da uzmemo upit korisnika i pošaljemo ga funkciji da napravi API zahtev.

Da bismo počeli, pogledajmo zašto bismo uopšte želeli da koristimo pozivanje funkcija:

Zašto Pozivanje Funkcija

Pre pozivanja funkcija, odgovori iz LLM-a bili su nestrukturirani i nekonzistentni. Programeri su morali pisati složen kod za validaciju kako bi bili sigurni da mogu obraditi svaku varijaciju odgovora. Korisnici nisu mogli dobiti odgovore kao "Kakvo je trenutno vreme u Stokholmu?". To je zato što su modeli bili ograničeni na vreme kada su podaci bili obučeni.

Pozivanje funkcija je funkcija Azure OpenAI Service koja prevazilazi sledeće ograničenja:

  • Konzistentan format odgovora. Ako možemo bolje kontrolisati format odgovora, možemo lakše integrisati odgovor nizvodno u druge sisteme.
  • Spoljni podaci. Mogućnost korišćenja podataka iz drugih izvora aplikacije u kontekstu chata.

Ilustracija problema kroz scenario

Preporučujemo da koristite priloženi notebook ako želite da pokrenete scenario ispod. Takođe možete samo čitati dalje dok pokušavamo da ilustrujemo problem gde funkcije mogu pomoći u rešavanju problema.

Pogledajmo primer koji ilustruje problem formata odgovora:

Recimo da želimo da kreiramo bazu podataka studentskih podataka kako bismo im mogli predložiti pravi kurs. Ispod imamo dva opisa studenata koji su veoma slični u podacima koje sadrže.

  1. Kreirajte vezu sa našim Azure OpenAI resursom:

    import os
import json
from openai import AzureOpenAI
from dotenv import load_dotenv
load_dotenv()

client = AzureOpenAI(
api_key=os.environ['AZURE_OPENAI_API_KEY'],  # this is also the default, it can be omitted
api_version = "2023-07-01-preview"
)

deployment=os.environ['AZURE_OPENAI_DEPLOYMENT']

    Ispod je neki Python kod za konfigurisanje naše veze sa Azure OpenAI gde postavljamo api_type, api_base, api_version and api_key.

  2. Creating two student descriptions using variables student_1_description and student_2_description.

    student_1_description="Emily Johnson is a sophomore majoring in computer science at Duke University. She has a 3.7 GPA. Emily is an active member of the university's Chess Club and Debate Team. She hopes to pursue a career in software engineering after graduating."

student_2_description = "Michael Lee is a sophomore majoring in computer science at Stanford University. He has a 3.8 GPA. Michael is known for his programming skills and is an active member of the university's Robotics Club. He hopes to pursue a career in artificial intelligence after finishing his studies."

    Želimo da pošaljemo gore navedene studentske opise LLM-u da parsira podatke. Ovi podaci kasnije mogu biti korišćeni u našoj aplikaciji i poslati API-ju ili sačuvani u bazi podataka.

  3. Kreirajmo dva identična podsticaja u kojima instruiramo LLM o informacijama koje nas interesuju:

    prompt1 = f'''
Please extract the following information from the given text and return it as a JSON object:

name
major
school
grades
club

This is the body of text to extract the information from:
{student_1_description}
'''

prompt2 = f'''
Please extract the following information from the given text and return it as a JSON object:

name
major
school
grades
club

This is the body of text to extract the information from:
{student_2_description}
'''

    Gore navedeni podsticaji instruiraju LLM da izvuče informacije i vrati odgovor u JSON formatu.

  4. Nakon postavljanja podsticaja i veze sa Azure OpenAI, sada ćemo poslati podsticaje LLM-u koristeći openai.ChatCompletion. We store the prompt in the messages variable and assign the role to user. Ovo je da imitira poruku od korisnika koja se piše chatbotu.

    # response from prompt one
openai_response1 = client.chat.completions.create(
model=deployment,
messages = [{'role': 'user', 'content': prompt1}]
)
openai_response1.choices[0].message.content

# response from prompt two
openai_response2 = client.chat.completions.create(
model=deployment,
messages = [{'role': 'user', 'content': prompt2}]
)
openai_response2.choices[0].message.content

Sada možemo poslati oba zahteva LLM-u i ispitati odgovor koji dobijemo pronalazeći ga ovako openai_response1['choices'][0]['message']['content'].

  1. Lastly, we can convert the response to JSON format by calling json.loads:

    # Loading the response as a JSON object
json_response1 = json.loads(openai_response1.choices[0].message.content)
json_response1

    Odgovor 1:

    {
  "name": "Emily Johnson",
  "major": "computer science",
  "school": "Duke University",
  "grades": "3.7",
  "club": "Chess Club"
}

    Odgovor 2:

    {
  "name": "Michael Lee",
  "major": "computer science",
  "school": "Stanford University",
  "grades": "3.8 GPA",
  "club": "Robotics Club"
}

    Iako su podsticaji isti, a opisi slični, vidimo vrednosti Grades property formatted differently, as we can sometimes get the format 3.7 or 3.7 GPA for example.

    This result is because the LLM takes unstructured data in the form of the written prompt and returns also unstructured data. We need to have a structured format so that we know what to expect when storing or using this data

So how do we solve the formatting problem then? By using functional calling, we can make sure that we receive structured data back. When using function calling, the LLM does not actually call or run any functions. Instead, we create a structure for the LLM to follow for its responses. We then use those structured responses to know what function to run in our applications.

function flow

We can then take what is returned from the function and send this back to the LLM. The LLM will then respond using natural language to answer the user's query.

Use Cases for using function calls

There are many different use cases where function calls can improve your app like:

  • Calling External Tools. Chatbots are great at providing answers to questions from users. By using function calling, the chatbots can use messages from users to complete certain tasks. For example, a student can ask the chatbot to "Send an email to my instructor saying I need more assistance with this subject". This can make a function call to send_email(to: string, body: string)

  • Create API or Database Queries. Users can find information using natural language that gets converted into a formatted query or API request. An example of this could be a teacher who requests "Who are the students that completed the last assignment" which could call a function named get_completed(student_name: string, assignment: int, current_status: string)

  • Creating Structured Data. Users can take a block of text or CSV and use the LLM to extract important information from it. For example, a student can convert a Wikipedia article about peace agreements to create AI flashcards. This can be done by using a function called get_important_facts(agreement_name: string, date_signed: string, parties_involved: list)

Creating Your First Function Call

The process of creating a function call includes 3 main steps:

  1. Calling the Chat Completions API with a list of your functions and a user message.
  2. Reading the model's response to perform an action i.e. execute a function or API Call.
  3. Making another call to Chat Completions API with the response from your function to use that information to create a response to the user.

LLM Flow

Step 1 - creating messages

The first step is to create a user message. This can be dynamically assigned by taking the value of a text input or you can assign a value here. If this is your first time working with the Chat Completions API, we need to define the role and the content of the message.

The role can be either system (creating rules), assistant (the model) or user (the end-user). For function calling, we will assign this as user i primer pitanja.

messages= [ {"role": "user", "content": "Find me a good course for a beginner student to learn Azure."} ]

Dodeljivanjem različitih uloga, jasno je LLM-u da li sistem nešto govori ili korisnik, što pomaže u izgradnji istorije razgovora na kojoj LLM može graditi.

Korak 2 - kreiranje funkcija

Sledeće, definišemo funkciju i parametre te funkcije. Koristićemo samo jednu funkciju ovde nazvanu search_courses but you can create multiple functions.

Important : Functions are included in the system message to the LLM and will be included in the amount of available tokens you have available.

Below, we create the functions as an array of items. Each item is a function and has properties name, description and parameters:

functions = [
   {
      "name":"search_courses",
      "description":"Retrieves courses from the search index based on the parameters provided",
      "parameters":{
         "type":"object",
         "properties":{
            "role":{
               "type":"string",
               "description":"The role of the learner (i.e. developer, data scientist, student, etc.)"
            },
            "product":{
               "type":"string",
               "description":"The product that the lesson is covering (i.e. Azure, Power BI, etc.)"
            },
            "level":{
               "type":"string",
               "description":"The level of experience the learner has prior to taking the course (i.e. beginner, intermediate, advanced)"
            }
         },
         "required":[
            "role"
         ]
      }
   }
]

Hajde da opišemo svaku instancu funkcije detaljnije ispod:

  • name - The name of the function that we want to have called.
  • description - This is the description of how the function works. Here it's important to be specific and clear.
  • parameters - A list of values and format that you want the model to produce in its response. The parameters array consists of items where the items have the following properties:
    1. type - The data type of the properties will be stored in.
    2. properties - List of the specific values that the model will use for its response
      1. name - The key is the name of the property that the model will use in its formatted response, for example, product.
      2. type - The data type of this property, for example, string.
      3. description - Description of the specific property.

There's also an optional property required - required property for the function call to be completed.

Step 3 - Making the function call

After defining a function, we now need to include it in the call to the Chat Completion API. We do this by adding functions to the request. In this case functions=functions.

There is also an option to set function_call to auto. This means we will let the LLM decide which function should be called based on the user message rather than assigning it ourselves.

Here's some code below where we call ChatCompletion.create, note how we set functions=functions and function_call="auto" i time dajemo LLM-u izbor kada da pozove funkcije koje mu pružamo:

response = client.chat.completions.create(model=deployment,
                                        messages=messages,
                                        functions=functions,
                                        function_call="auto")

print(response.choices[0].message)

Odgovor koji dolazi nazad sada izgleda ovako:

{
  "role": "assistant",
  "function_call": {
    "name": "search_courses",
    "arguments": "{\n  \"role\": \"student\",\n  \"product\": \"Azure\",\n  \"level\": \"beginner\"\n}"
  }
}

Ovde možemo videti kako funkcija search_courses was called and with what arguments, as listed in the arguments property in the JSON response.

The conclusion the LLM was able to find the data to fit the arguments of the function as it was extracting it from the value provided to the messages parameter in the chat completion call. Below is a reminder of the messages vrednost:

messages= [ {"role": "user", "content": "Find me a good course for a beginner student to learn Azure."} ]

Kao što vidite, student, Azure and beginner was extracted from messages and set as input to the function. Using functions this way is a great way to extract information from a prompt but also to provide structure to the LLM and have reusable functionality.

Next, we need to see how we can use this in our app.

Integrating Function Calls into an Application

After we have tested the formatted response from the LLM, we can now integrate this into an application.

Managing the flow

To integrate this into our application, let's take the following steps:

  1. First, let's make the call to the OpenAI services and store the message in a variable called response_message.

    response_message = response.choices[0].message

  2. Sada ćemo definisati funkciju koja će pozvati Microsoft Learn API da dobije listu kurseva:

    import requests

def search_courses(role, product, level):
  url = "https://learn.microsoft.com/api/catalog/"
  params = {
     "role": role,
     "product": product,
     "level": level
  }
  response = requests.get(url, params=params)
  modules = response.json()["modules"]
  results = []
  for module in modules[:5]:
     title = module["title"]
     url = module["url"]
     results.append({"title": title, "url": url})
  return str(results)

    Obratite pažnju kako sada kreiramo stvarnu Python funkciju koja se mapira na nazive funkcija uvedene u functions variable. We're also making real external API calls to fetch the data we need. In this case, we go against the Microsoft Learn API to search for training modules.

Ok, so we created functions variables and a corresponding Python function, how do we tell the LLM how to map these two together so our Python function is called?

  1. To see if we need to call a Python function, we need to look into the LLM response and see if function_call je deo toga i poziva istaknutu funkciju. Evo kako možete napraviti pomenutu proveru ispod:

    # Check if the model wants to call a function
if response_message.function_call.name:
 print("Recommended Function call:")
 print(response_message.function_call.name)
 print()

 # Call the function.
 function_name = response_message.function_call.name

 available_functions = {
         "search_courses": search_courses,
 }
 function_to_call = available_functions[function_name]

 function_args = json.loads(response_message.function_call.arguments)
 function_response = function_to_call(**function_args)

 print("Output of function call:")
 print(function_response)
 print(type(function_response))


 # Add the assistant response and function response to the messages
 messages.append( # adding assistant response to messages
     {
         "role": response_message.role,
         "function_call": {
             "name": function_name,
             "arguments": response_message.function_call.arguments,
         },
         "content": None
     }
 )
 messages.append( # adding function response to messages
     {
         "role": "function",
         "name": function_name,
         "content":function_response,
     }
 )

    Ove tri linije osiguravaju da izdvojimo naziv funkcije, argumente i napravimo poziv:

    function_to_call = available_functions[function_name]

function_args = json.loads(response_message.function_call.arguments)
function_response = function_to_call(**function_args)

    Ispod je izlaz iz pokretanja našeg koda:

    Izlaz

    {
  "name": "search_courses",
  "arguments": "{\n  \"role\": \"student\",\n  \"product\": \"Azure\",\n  \"level\": \"beginner\"\n}"
}

Output of function call:
[{'title': 'Describe concepts of cryptography', 'url': 'https://learn.microsoft.com/training/modules/describe-concepts-of-cryptography/?
WT.mc_id=api_CatalogApi'}, {'title': 'Introduction to audio classification with TensorFlow', 'url': 'https://learn.microsoft.com/en-
us/training/modules/intro-audio-classification-tensorflow/?WT.mc_id=api_CatalogApi'}, {'title': 'Design a Performant Data Model in Azure SQL
Database with Azure Data Studio', 'url': 'https://learn.microsoft.com/training/modules/design-a-data-model-with-ads/?
WT.mc_id=api_CatalogApi'}, {'title': 'Getting started with the Microsoft Cloud Adoption Framework for Azure', 'url':
'https://learn.microsoft.com/training/modules/cloud-adoption-framework-getting-started/?WT.mc_id=api_CatalogApi'}, {'title': 'Set up the
Rust development environment', 'url': 'https://learn.microsoft.com/training/modules/rust-set-up-environment/?WT.mc_id=api_CatalogApi'}]
<class 'str'>

  2. Sada ćemo poslati ažuriranu poruku, messages LLM-u kako bismo mogli primiti odgovor u prirodnom jeziku umesto API JSON formatiranog odgovora.

    print("Messages in next request:")
print(messages)
print()

second_response = client.chat.completions.create(
   messages=messages,
   model=deployment,
   function_call="auto",
   functions=functions,
   temperature=0
      )  # get a new response from GPT where it can see the function response


print(second_response.choices[0].message)

    Izlaz

    {
  "role": "assistant",
  "content": "I found some good courses for beginner students to learn Azure:\n\n1. [Describe concepts of cryptography] (https://learn.microsoft.com/training/modules/describe-concepts-of-cryptography/?WT.mc_id=api_CatalogApi)\n2. [Introduction to audio classification with TensorFlow](https://learn.microsoft.com/training/modules/intro-audio-classification-tensorflow/?WT.mc_id=api_CatalogApi)\n3. [Design a Performant Data Model in Azure SQL Database with Azure Data Studio](https://learn.microsoft.com/training/modules/design-a-data-model-with-ads/?WT.mc_id=api_CatalogApi)\n4. [Getting started with the Microsoft Cloud Adoption Framework for Azure](https://learn.microsoft.com/training/modules/cloud-adoption-framework-getting-started/?WT.mc_id=api_CatalogApi)\n5. [Set up the Rust development environment](https://learn.microsoft.com/training/modules/rust-set-up-environment/?WT.mc_id=api_CatalogApi)\n\nYou can click on the links to access the courses."
}

Zadatak

Da biste nastavili svoje učenje o Azure OpenAI Pozivanju Funkcija možete izgraditi:

  • Više parametara funkcije koji mogu pomoći učenicima da pronađu više kurseva.
  • Kreirajte drugi poziv funkcije koji uzima više informacija od učenika kao što je njihov maternji jezik.
  • Kreirajte rukovanje greškama kada poziv funkcije i/ili API poziv ne vrate odgovarajuće kurseve.

Saveti: Pratite stranicu Learn API reference documentation da vidite kako i gde su ti podaci dostupni.

Odličan rad! Nastavite putovanje

Nakon završetka ove lekcije, pogledajte našu Generative AI Learning collection da nastavite da unapređujete svoje znanje o Generativnoj AI!

Pređite na Lekciju 12, gde ćemo pogledati kako dizajnirati UX za AI aplikacije!

Одрицање од одговорности:
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