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madebygpsmadebygps
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added spanish folder
1 parent 1b864fd commit bbaf285

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Lines changed: 1006 additions & 0 deletions

‎spanish/chained_calls.py‎

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import os
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import azure.identity
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import openai
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from dotenv import load_dotenv
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# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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client = openai.AzureOpenAI(
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api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_ad_token_provider=token_provider,
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)
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MODEL_NAME = os.environ["AZURE_OPENAI_DEPLOYMENT"]
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elif API_HOST == "ollama":
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client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")
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MODEL_NAME = os.environ["OLLAMA_MODEL"]
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elif API_HOST == "github":
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client = openai.OpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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MODEL_NAME = os.environ["GITHUB_MODEL"]
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else:
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client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])
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MODEL_NAME = os.environ["OPENAI_MODEL"]
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response = client.chat.completions.create(
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model=MODEL_NAME,
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temperature=0.7,
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messages=[{"role": "user", "content": "Explica cómo funcionan los LLM en un solo párrafo."}],
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)
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explanation = response.choices[0].message.content
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print("Explicación: ", explanation)
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response = client.chat.completions.create(
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model=MODEL_NAME,
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temperature=0.7,
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messages=[
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{
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"role": "user",
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"content": (
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"Eres un editor. Revisa la explicación y proporciona comentarios detallados sobre claridad, coherencia "
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"y cautivación (pero no la edites tú mismo):\n\n"
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)
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+ explanation,
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}
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],
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)
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feedback = response.choices[0].message.content
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print("\n\nRetroalimentación: ", feedback)
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response = client.chat.completions.create(
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model=MODEL_NAME,
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temperature=0.7,
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messages=[
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{
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"role": "user",
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"content": (
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"Revisa el artículo utilizando los siguientes comentarios, pero mantenlo a un solo párrafo."
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f"\nExplicación:\n{explanation}\n\nComentarios:\n{feedback}"
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),
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}
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],
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)
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final_article = response.choices[0].message.content
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print("\n\nFinal Article: ", final_article)

‎spanish/chat.py‎

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import os
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import azure.identity
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import openai
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from dotenv import load_dotenv
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# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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client = openai.AzureOpenAI(
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api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_ad_token_provider=token_provider,
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)
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MODEL_NAME = os.environ["AZURE_OPENAI_DEPLOYMENT"]
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elif API_HOST == "ollama":
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client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")
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MODEL_NAME = os.environ["OLLAMA_MODEL"]
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elif API_HOST == "github":
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client = openai.OpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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MODEL_NAME = os.environ["GITHUB_MODEL"]
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else:
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client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])
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MODEL_NAME = os.environ["OPENAI_MODEL"]
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response = client.chat.completions.create(
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model=MODEL_NAME,
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temperature=0.7,
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n=1,
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messages=[
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{"role": "system", "content": "Eres un asistente útil que hace muchas referencias a gatos y usa emojis."},
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{"role": "user", "content": "Escribe un haiku sobre un gato hambriento que quiere atún"},
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],
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)
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print(f"Response from {API_HOST}: \n")
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print(response.choices[0].message.content)

‎spanish/chat_async.py‎

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import asyncio
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import os
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import azure.identity
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import openai
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from dotenv import load_dotenv
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# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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client = openai.AsyncAzureOpenAI(
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api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_ad_token_provider=token_provider,
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)
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MODEL_NAME = os.environ["AZURE_OPENAI_DEPLOYMENT"]
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elif API_HOST == "ollama":
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client = openai.AsyncOpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")
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MODEL_NAME = os.environ["OLLAMA_MODEL"]
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elif API_HOST == "github":
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client = openai.AsyncOpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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MODEL_NAME = os.environ["GITHUB_MODEL"]
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else:
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client = openai.AsyncOpenAI(api_key=os.environ["OPENAI_KEY"])
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MODEL_NAME = os.environ["OPENAI_MODEL"]
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async def generate_response(location):
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print("Generating response for", location)
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response = await client.chat.completions.create(
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model=MODEL_NAME,
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messages=[
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{"role": "system", "content": "You are a helpful assistant."},
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{
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"role": "user",
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"content": f"Name a single place I should visit on my trip to {location} and describe in one sentence",
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},
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],
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temperature=1,
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max_tokens=400,
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top_p=0.95,
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frequency_penalty=0,
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presence_penalty=0,
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stop=None,
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)
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print("Got response for ", location)
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return response.choices[0].message.content
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async def single():
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print(await generate_response("Tokyo"))
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async def multiple():
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answers = await asyncio.gather(
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generate_response("Tokyo"),
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generate_response("Berkeley"),
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generate_response("Singapore"),
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)
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for answer in answers:
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print(answer, "\n")
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asyncio.run(single())

‎spanish/chat_history.py‎

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import os
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import azure.identity
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import openai
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from dotenv import load_dotenv
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7+
# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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client = openai.AzureOpenAI(
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api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_ad_token_provider=token_provider,
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)
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MODEL_NAME = os.environ["AZURE_OPENAI_DEPLOYMENT"]
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elif API_HOST == "ollama":
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client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")
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MODEL_NAME = os.environ["OLLAMA_MODEL"]
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elif API_HOST == "github":
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client = openai.OpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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MODEL_NAME = os.environ["GITHUB_MODEL"]
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else:
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client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])
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MODEL_NAME = os.environ["OPENAI_MODEL"]
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messages = [
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{"role": "system", "content": "I am a teaching assistant helping with Python questions for Berkeley CS 61A."},
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]
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while True:
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question = input("\nYour question: ")
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print("Sending question...")
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messages.append({"role": "user", "content": question})
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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temperature=1,
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max_tokens=400,
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top_p=0.95,
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frequency_penalty=0,
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presence_penalty=0,
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stop=None,
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)
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bot_response = response.choices[0].message.content
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messages.append({"role": "assistant", "content": bot_response})
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print("Answer: ")
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print(bot_response)

‎spanish/chat_history_stream.py‎

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import os
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import azure.identity
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import openai
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from dotenv import load_dotenv
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7+
# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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client = openai.AzureOpenAI(
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api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_ad_token_provider=token_provider,
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)
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MODEL_NAME = os.environ["AZURE_OPENAI_DEPLOYMENT"]
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elif API_HOST == "ollama":
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client = openai.OpenAI(base_url=os.environ["OLLAMA_ENDPOINT"], api_key="nokeyneeded")
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MODEL_NAME = os.environ["OLLAMA_MODEL"]
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elif API_HOST == "github":
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client = openai.OpenAI(base_url="https://models.inference.ai.azure.com", api_key=os.environ["GITHUB_TOKEN"])
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MODEL_NAME = os.environ["GITHUB_MODEL"]
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else:
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client = openai.OpenAI(api_key=os.environ["OPENAI_KEY"])
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MODEL_NAME = os.environ["OPENAI_MODEL"]
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messages = [
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{"role": "system", "content": "I am a large language model."},
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]
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while True:
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question = input("\nYour question: ")
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print("Sending question...")
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messages.append({"role": "user", "content": question})
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response = client.chat.completions.create(
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model=MODEL_NAME,
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messages=messages,
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temperature=1,
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max_tokens=400,
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top_p=0.95,
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frequency_penalty=0,
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presence_penalty=0,
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stop=None,
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stream=True,
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)
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print("\nAnswer: ")
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bot_response = ""
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for event in response:
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if event.choices and event.choices[0].delta.content:
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content = event.choices[0].delta.content
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print(content, end="", flush=True)
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bot_response += content
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print("\n")
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messages.append({"role": "assistant", "content": bot_response})

‎spanish/chat_langchain.py‎

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import os
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import azure.identity
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from dotenv import load_dotenv
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from langchain_core.prompts import ChatPromptTemplate
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from langchain_openai import AzureChatOpenAI, ChatOpenAI
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# Setup the OpenAI client to use either Azure, OpenAI.com, or Ollama API
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load_dotenv(override=True)
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API_HOST = os.getenv("API_HOST")
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if API_HOST == "azure":
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token_provider = azure.identity.get_bearer_token_provider(
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azure.identity.DefaultAzureCredential(), "https://cognitiveservices.azure.com/.default"
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)
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llm = AzureChatOpenAI(
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azure_endpoint=os.environ["AZURE_OPENAI_ENDPOINT"],
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azure_deployment=os.environ["AZURE_OPENAI_DEPLOYMENT"],
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openai_api_version=os.environ["AZURE_OPENAI_VERSION"],
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azure_ad_token_provider=token_provider,
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)
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elif API_HOST == "ollama":
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llm = ChatOpenAI(
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model_name=os.environ["OLLAMA_MODEL"],
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openai_api_base=os.environ["OLLAMA_ENDPOINT"],
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openai_api_key=os.environ["OPENAI_KEY"],
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)
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elif API_HOST == "github":
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llm = ChatOpenAI(
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model_name=os.environ["GITHUB_MODEL"],
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openai_api_base="https://models.inference.ai.azure.com",
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openai_api_key=os.environ["GITHUB_TOKEN"],
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)
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else:
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llm = ChatOpenAI(model_name=os.environ["OPENAI_MODEL"], openai_api_key=os.environ["OPENAI_KEY"])
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prompt = ChatPromptTemplate.from_messages(
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[("system", "Eres un asistente útil que hace muchas referencias a gatos y usa emojis."), ("user", "{input}")]
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)
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chain = prompt | llm
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response = chain.invoke({"input": "escribe un haiku sobre un gato hambriento que quiere atún"})
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print(f"Respuesta de {API_HOST}: \n")
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print(response.content)

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