Update app.py
Browse files
app.py
CHANGED
@@ -5,18 +5,20 @@ from langchain_openai import ChatOpenAI
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from transformers import pipeline
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# Choose model
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model_name = "
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# Load the chosen LLM model
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llm = pipeline("text-generation", model=
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#Vectara config:
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# customer_id =
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# corpus_id =
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# api_key =
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# DSPy-based prompt generation
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from dspy.agents import Agent
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from dspy.utils import SentenceSplitter, SentimentAnalyzer, NamedEntityRecognizer
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def dspy_generate_agent_prompts(prompt):
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@@ -177,10 +179,10 @@ def query_vectara(text):
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# Define the main function to be used with Gradio
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def generate_outputs(user_prompt):
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# 1. Process prompt with langchain (replace with your actual implementation)
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processed_prompt =
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# 2. Generate synthetic data using DSPy's distributed computing capabilities
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synthetic_data = generate_synthetic_data_distributed(
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# 3. Combine user prompt and synthetic data
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combined_data = f"{user_prompt}\n{synthetic_data}"
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from transformers import pipeline
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# Choose model
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model_name = "dolphin-phi"
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# Load the chosen LLM model
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llm = pipeline("text-generation", model=dolphin-phi)
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#Vectara config:
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# customer_id =
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# corpus_id =
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# api_key =
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import requests
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# DSPy-based prompt generation
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from dspy.agents import Agent
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from dspy import spawn_processes
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from dspy.utils import SentenceSplitter, SentimentAnalyzer, NamedEntityRecognizer
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def dspy_generate_agent_prompts(prompt):
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# Define the main function to be used with Gradio
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def generate_outputs(user_prompt):
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# 1. Process prompt with langchain (replace with your actual implementation)
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# processed_prompt = dspy_generate_agent_prompts(user_prompt) # Replaced langchain logic with DSPy function below
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# 2. Generate synthetic data using DSPy's distributed computing capabilities
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synthetic_data = generate_synthetic_data_distributed(user_prompt)
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# 3. Combine user prompt and synthetic data
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combined_data = f"{user_prompt}\n{synthetic_data}"
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