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weird python update
Browse files- app/main.py +5 -5
app/main.py
CHANGED
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@@ -18,7 +18,7 @@ from pydantic import BaseModel
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# Import LangChain components
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from langchain_openai import ChatOpenAI
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from langchain.prompts import PromptTemplate
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from langchain
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# Import the base LLM class to build our custom wrapper
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from langchain.llms.base import LLM
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@@ -305,9 +305,9 @@ async def get_suggestions(
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input_variables=["prompt"],
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template=prompt
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)
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suggestion_chain
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# Run the chain without additional inputs, since prompt is fully baked
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raw_text = await asyncio.to_thread(suggestion_chain.
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# Strip out any leading numbers, bullets or whitespace
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suggestion_array = [
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re.sub(r'^\s*[\-\d\.\)\s]+', '', line).strip()
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@@ -396,13 +396,13 @@ async def generate_business_plan(data: GenerateRequest):
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llm_selected.max_tokens = 6000 # Increase max tokens to ensure full completion
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logging.info(f"Increased max_tokens from {original_max_tokens} to {llm_selected.max_tokens}")
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plan_chain
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logging.info(f"Generated prompt for business plan with model {data.model}")
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try:
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logging.info(f"Generating business plan with model: {data.model}")
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full_plan = await asyncio.to_thread(plan_chain.
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logging.info(f"Successfully generated business plan with model: {data.model}")
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# Check if plan seems truncated
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# Import LangChain components
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from langchain_openai import ChatOpenAI
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from langchain.prompts import PromptTemplate
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from langchain import RunnableSequence
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# Import the base LLM class to build our custom wrapper
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from langchain.llms.base import LLM
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input_variables=["prompt"],
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template=prompt
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)
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suggestion_chain: RunnableSequence = suggestion_prompt | llm
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# Run the chain without additional inputs, since prompt is fully baked
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raw_text = await asyncio.to_thread(suggestion_chain.invoke, {})
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# Strip out any leading numbers, bullets or whitespace
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suggestion_array = [
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re.sub(r'^\s*[\-\d\.\)\s]+', '', line).strip()
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llm_selected.max_tokens = 6000 # Increase max tokens to ensure full completion
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logging.info(f"Increased max_tokens from {original_max_tokens} to {llm_selected.max_tokens}")
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plan_chain: RunnableSequence = plan_prompt | llm_selected
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logging.info(f"Generated prompt for business plan with model {data.model}")
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try:
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logging.info(f"Generating business plan with model: {data.model}")
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full_plan = await asyncio.to_thread(plan_chain.invoke, {"q_and_a": q_and_a, "business_context": business_context})
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logging.info(f"Successfully generated business plan with model: {data.model}")
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# Check if plan seems truncated
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