L336 of agent script set max_iterations
Browse files- utils/helper.py +4 -4
utils/helper.py
CHANGED
@@ -294,7 +294,7 @@ OPENAI_API_KEY = os.environ["OPENAI_API_KEY"]
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def run_langchain_agent_(
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question: str = "What is your question?"
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) -> str:
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"""
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Executes a language chain agent to answer questions by using a series of tools.
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@@ -317,12 +317,11 @@ def run_langchain_agent_(
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# Creating a prompt template that structures the input question and a step-by-step thinking format
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template = """Question: {question};
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Information given about interested stock tickers in the financial market: {interested_tickers}
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You are a financial advisor and user has a question above regarding related tickers provided.
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Let's think step by step.
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Answer: """
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prompt = PromptTemplate(
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template=template, input_variables=["question"
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)
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# Building a chain of language model actions based on the prompt template
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@@ -333,7 +332,8 @@ def run_langchain_agent_(
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# Initializing the agent with the loaded tools, the language model, default name, and verbosity
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agent = initialize_agent(
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tools, llm, agent="zero-shot-react-description", verbose=True
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)
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# Running the agent to process the input question and generate an output
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def run_langchain_agent_(
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question: str = "What is your question?"
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) -> str:
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"""
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Executes a language chain agent to answer questions by using a series of tools.
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# Creating a prompt template that structures the input question and a step-by-step thinking format
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template = """Question: {question};
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You are a financial advisor and user has a question above regarding related tickers provided.
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Let's think step by step.
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Answer: """
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prompt = PromptTemplate(
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template=template, input_variables=["question"]
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)
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# Building a chain of language model actions based on the prompt template
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# Initializing the agent with the loaded tools, the language model, default name, and verbosity
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agent = initialize_agent(
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tools, llm, agent="zero-shot-react-description", verbose=True,
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max_iterations=5,
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)
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# Running the agent to process the input question and generate an output
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