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import os
import logging
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
from langchain_community.llms import HuggingFaceHub
from langchain_community.chat_models.huggingface import ChatHuggingFace
from dotenv import load_dotenv
# Configure logging
logging.basicConfig(level=logging.INFO, format='%(asctime)s - %(levelname)s - %(message)s')
MISTRAL_ID = "mistralai/Mistral-7B-Instruct-v0.1"
ZEPHYR_ID = "HuggingFaceH4/zephyr-7b-beta"
def get_model(repo_id="ChatGPT", **kwargs):
"""
Loads and configures the specified language model.
Args:
repo_id: The model identifier ("ChatGPT", MISTRAL_ID, or ZEPHYR_ID).
**kwargs: Additional keyword arguments for model configuration.
Returns:
A configured ChatOpenAI or ChatHuggingFace model.
"""
try:
if repo_id == "ChatGPT":
model_name = kwargs.get("model_name", "gpt-4o-mini")
logging.info(f"Loading OpenAI model: {model_name}")
chat_model = ChatOpenAI(
openai_api_key = kwargs.get("openai_api_key", None),
base_url = "https://openrouter.ai/api/v1",
model = "nousresearch/hermes-3-llama-3.1-405b",
temperature = 0
)
else:
logging.info(f"Loading Hugging Face model: {repo_id}")
huggingfacehub_api_token = kwargs.get("HUGGINGFACEHUB_API_TOKEN", None)
if not huggingfacehub_api_token:
huggingfacehub_api_token = os.environ.get("HUGGINGFACEHUB_API_TOKEN", None)
if not huggingfacehub_api_token:
raise ValueError("HuggingFace Hub API token not found. "
"Set HUGGINGFACEHUB_API_TOKEN environment variable.")
os.environ["HF_TOKEN"] = huggingfacehub_api_token
llm = HuggingFaceHub(
repo_id=repo_id,
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
"huggingfacehub_api_token": huggingfacehub_api_token,
})
chat_model = ChatHuggingFace(llm=llm)
return chat_model
except Exception as e:
logging.error(f"Error loading model '{repo_id}': {e}")
# Handle the error based on your needs:
# - Return a default model:
# return ChatOpenAI(temperature=0, model="gpt-3.5-turbo")
# - Raise a custom exception:
# raise RuntimeError(f"Failed to load model: {e}")
# - Exit the application:
# sys.exit(1)
def basic_chain(model=None, prompt=None):
"""
Creates a basic LangChain chain with a prompt and a language model.
Args:
model: The language model to use.
prompt: The prompt template.
Returns:
A LangChain chain.
"""
if not model:
model = get_model()
if not prompt:
prompt = ChatPromptTemplate.from_template("Tell me the most noteworthy books by the author {author}")
chain = prompt | model
return chain
def main():
"""
Main function to demonstrate the basic chain.
"""
load_dotenv()
prompt = ChatPromptTemplate.from_template("Tell me the most noteworthy books by the author {author}")
chain = basic_chain(prompt=prompt) | StrOutputParser()
try:
results = chain.invoke({"author": "William Faulkner"})
print(results)
except Exception as e:
logging.error(f"Error during chain execution: {e}")
if __name__ == '__main__':
main() |