Podsumowywanie i edytowanie tekstu

Praca z DeepSeek w Pythonie

James Chapman

AI Curriculum Lead, DataCamp

Powtórzenie...

Pytania i odpowiedzi

response = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-V4-Pro",
  messages=[{"role": "user", "content": "How many days are in October?"}]
)

print(response.choices[0].message.content)

October has **31 days**.  

It's one of the seven months in the Gregorian calendar with 31 days...

Edytowanie tekstu

Przykład: zmiana imienia, zaimków i stanowiska

prompt = """
Update name to Maarten, pronouns to he/him, and job title to Senior Content Developer
in the following text:

Joanne is a Content Developer at DataCamp. Her favorite programming language is R,
which she uses for her statistical analyses.
"""

Edytowanie tekstu

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",

    messages=[{"role": "user", "content": prompt}]

)

print(response.choices[0].message.content)

Here's the updated text with the requested changes:

Maarten is a Senior Content Developer at DataCamp. His favorite programming language
is R, which he uses for his statistical analyses.

Let me know if you'd like any further adjustments!

Podsumowywanie tekstu

Przykład: podsumowanie transkryptów czatu obsługi klienta

Zespół obsługi klienta

text = """
Customer: Hi, I'm trying to log into 
my account, but it keeps saying 
my password is incorrect. I'm sure 
I'm entering the right one.  

Support: I'm sorry to hear that! 
Have you tried resetting your password?  
...
"""

Podsumowywanie tekstu

prompt = f"""Summarize the customer support chat 
             in three concise key points: {text}"""


response = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-V4-Pro",
  messages=[{"role": "user", "content": prompt}]
)
print(response.choices[0].message.content)

1. **Login Issue**: Customer couldn't log in due to a password issue and missing reset link.  
2. **Password Reset Suggestion**: Support resent the reset email after confirming it was sent.  
3. **Prompt Assistance**: Customer resolved the issue by using Google sign-in.

Kontrolowanie długości odpowiedzi

max_tokens = 5

response = client.chat.completions.create(
  model="deepseek-ai/DeepSeek-V4-Pro",
  messages=[{"role":"user",
      "content":"Write a haiku about AI."}],
  max_tokens=5
)

**Silent circuits hum

max_tokens = 30

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",
    messages=[{"role":"user",
      "content":"Write a haiku about AI."}],
    max_tokens=30
)

**Silent circuits hum,**  
**thoughts of light and logic bloom—**  
**minds beyond our own.**

Czym są tokeny?

Tokeny: jednostki tekstu, które pomagają AI rozumieć i interpretować treść

Zdanie „How can the OpenAI API deliver business value?" z każdym tokenem wyróżnionym innym kolorem.

¹ https://lunary.ai/deepseek-tokenizer

Obliczanie kosztu

Koszt korzystania z API zależy od platformy, modelu i liczby tokenów 💰
- Modele są wyceniane według kosztu/tokenów
- Tokeny wejściowe i wyjściowe mogą mieć różne ceny
Zwiększenie max_tokens podnosi koszt 📈

Screenshot 2025-03-05 at 11.46.54.png

Obliczanie kosztu

prompt = f"""Summarize the customer support chat 
             in three concise key points: {text}"""

max_tokens = 500

response = client.chat.completions.create(
    model="deepseek-ai/DeepSeek-V4-Pro",
    messages=[{"role": "user", "content": prompt}], 
    max_tokens=max_tokens
)

Obliczanie kosztu

# Define price per token
input_token_price = 2.1 / 1_000_000
output_token_price = 4.4 / 1_000_000

# Extract token usage
input_tokens = response.usage.prompt_tokens

output_tokens = max_tokens

# Calculate cost
cost = (input_tokens * input_token_price + output_tokens * output_token_price)
print(f"Estimated cost: ${cost}")

Estimated cost: $0.0153964

Czas na praktykę!

Praca z DeepSeek w Pythonie