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from transformers import pipeline
import requests
# Replace with your actual LangSmith API key
LANGSMITH_API_KEY = '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'
LANGSMITH_ENDPOINT = 'https://smith.langchain.com/o/b7d2cb3f-e589-52bb-9b8a-2e8483e4ee8d/tailor' # Make sure this is the correct endpoint
# Initialize the Hugging Face text generation pipeline with BlenderBot
conversational_pipeline = pipeline('text-generation', model='facebook/blenderbot-3B')
def tailor_with_langsmith(model_data):
headers = {
'Authorization': f'Bearer {LANGSMITH_API_KEY}',
'Content-Type': 'application/json'
}
data = {
'model_data': model_data
}
response = requests.post(LANGSMITH_ENDPOINT, json=data, headers=headers)
response.raise_for_status()
return response.json()
def create_custom_conversation(prompt):
# Step 1: Get response from Hugging Face model
hf_response = conversational_pipeline(prompt, max_length=50) # Adjust max_length as needed
hf_reply = hf_response[0]['generated_text']
# Step 2: Tailor the response using LangSmith
tailored_response = tailor_with_langsmith({'model_data': hf_reply})
tailored_reply = tailored_response.get('tailored_reply', '')
return tailored_reply
if __name__ == '__main__':
user_prompt = "Tell me about the latest advancements in AI."
response = create_custom_conversation(user_prompt)
print("Tailored Response:", response)
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