feat: add agents/app

agents/app/langchain_tools/cost.py

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+import tiktoken
+
+
+# Model costs
+LLM_COSTS = {
+    "o1-mini": {
+        "input": 3.00 / 1_000_000,  # $3.00 per 1M input tokens
+        "output": 12.00 / 1_000_000,  # $12.00 per 1M output tokens
+    },
+    "gpt-4o-mini": {
+        "input": 0.150 / 1_000_000,  # $0.150 per 1M input tokens
+        "output": 0.600 / 1_000_000,  # $0.600 per 1M output tokens
+    },
+}
+
+
+def calculate_total_cost(model, input_tokens, output_tokens):
+    """
+    Calculate the total cost of using a language model based on token usage.
+
+    Parameters:
+        model (str): The model's name (e.g., "gpt-4o-mini").
+        input_tokens (int): The number of input tokens used.
+        output_tokens (int): The number of output tokens generated.
+
+    Returns:
+        float: The total cost in USD.
+
+    Raises:
+        ValueError: If the model name is not found in LLM_COSTS.
+    """
+    if model not in LLM_COSTS:
+        raise ValueError(f"Model '{model}' not found in LLM_COSTS.")
+
+    # Get per-token costs for the specified model
+    input_cost_per_token = LLM_COSTS[model]["input"]
+    output_cost_per_token = LLM_COSTS[model]["output"]
+
+    # Calculate total cost for input and output tokens
+    total_input_cost = input_tokens * input_cost_per_token
+    total_output_cost = output_tokens * output_cost_per_token
+
+    # Combine the costs
+    total_cost = total_input_cost + total_output_cost
+
+    return total_cost
+
+
+def calculate_tokens(text):
+    encoding = tiktoken.get_encoding("o200k_base")
+
+    # Calculate the number of tokens
+    num_tokens = len(encoding.encode(text))
+
+    return num_tokens

agents/app/langchain_tools/llm.py

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+from openai import OpenAI
+from dotenv import load_dotenv
+from langchain_openai import ChatOpenAI
+import os
+# from .cost import calculate_tokens
+
+load_dotenv()
+
+
+def load_llm_openai(
+    temperature: float = 0.1, max_tokens: int = 2000, model: str = "gpt-4o-mini"
+) -> ChatOpenAI:
+    llm = ChatOpenAI(temperature=temperature, max_tokens=max_tokens, model=model)
+    return llm
+
+
+def load_llm_o1(
+    prompt: str,
+    model: str = "o1-mini",
+    max_tokens: int = 2000,
+    temperature: float = 0.1,
+) -> dict:
+    client = OpenAI()
+    response = client.chat.completions.create(
+        model=model,
+        max_completion_tokens=max_tokens,
+        messages=[{"role": "user", "content": prompt}],
+    )
+
+    result = {
+        "text": response.choices[0].message.content,
+        "token_input": calculate_tokens(prompt),
+        "token_output": calculate_tokens(response.choices[0].message.content),
+    }
+    return result
+
+
+# Please install OpenAI SDK first: `pip3 install openai`
+def load_llm_deepseek():
+    api_key = os.getenv("DEEPSEEK_API_KEY")
+
+    llm = ChatOpenAI(
+        model="deepseek-chat",
+        # model = "deepseek-reasoner",
+        openai_api_base="https://api.deepseek.com",
+        openai_api_key=api_key,
+    )
+    return llm
+
+# probar el llm de deepseek
+
+# llm = load_llm_deepseek()
+# response = llm.invoke("Hello, how are you?")
+
+# print(response)
+