Kye
1 year ago
parent
121795203a
commit
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# !pip install --upgrade swarms==2.0.6
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from swarms.models import OpenAIChat
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from swarms.models.nougat import Nougat
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from swarms.structs import Flow
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from swarms.structs.sequential_workflow import SequentialWorkflow
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# # URL of the image of the financial document
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IMAGE_OF_FINANCIAL_DOC_URL = "bank_statement_2.jpg"
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# Example usage
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api_key = "" # Your actual API key here
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# Initialize the OCR model
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def ocr_model(img: str):
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ocr = Nougat()
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analyze_finance_docs = ocr(img)
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return str(analyze_finance_docs)
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# Initialize the language flow
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llm = OpenAIChat(
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model_name="gpt-4-turbo",
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openai_api_key=api_key,
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temperature=0.5,
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max_tokens=3000,
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)
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# Create a prompt for the language model
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def summary_agent_prompt(analyzed_doc: str):
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analyzed_doc = ocr_model(img=analyzed_doc)
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return f"""
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Generate an actionable summary of this financial document, provide bulletpoints:
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Here is the Analyzed Document:
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---
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{analyzed_doc}
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"""
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# Initialize the Flow with the language flow
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flow1 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create another Flow for a different task
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flow2 = Flow(llm=llm, max_loops=1, dashboard=False)
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# Create the workflow
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workflow = SequentialWorkflow(max_loops=1)
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# Add tasks to the workflow
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workflow.add(summary_agent_prompt(IMAGE_OF_FINANCIAL_DOC_URL), flow1)
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# Suppose the next task takes the output of the first task as input
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workflow.add("Provide an actionable step by step plan on how to cut costs from the analyzed financial document.", flow2)
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# Run the workflow
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workflow.run()
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# Output the results
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for task in workflow.tasks:
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print(f"Task: {task.description}, Result: {task.result}")
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import re
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from concurrent.futures import ThreadPoolExecutor, as_completed
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from swarms.models import OpenAIChat
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class AutoTempAgent:
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"""
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AutoTemp is a tool for automatically selecting the best temperature setting for a given task.
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Flow:
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1. Generate outputs at a range of temperature settings.
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2. Evaluate each output using the default temperature setting.
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3. Select the best output based on the evaluation score.
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4. Return the best output.
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Args:
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temperature (float, optional): The default temperature setting to use. Defaults to 0.5.
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api_key (str, optional): Your OpenAI API key. Defaults to None.
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alt_temps ([type], optional): A list of alternative temperature settings to try. Defaults to None.
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auto_select (bool, optional): If True, the best temperature setting will be automatically selected. Defaults to True.
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max_workers (int, optional): The maximum number of workers to use when generating outputs. Defaults to 6.
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Returns:
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[type]: [description]
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Examples:
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>>> from swarms.demos.autotemp import AutoTemp
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>>> autotemp = AutoTemp()
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>>> autotemp.run("Generate a 10,000 word blog on mental clarity and the benefits of meditation.", "0.4,0.6,0.8,1.0,1.2,1.4")
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Best AutoTemp Output (Temp 0.4 | Score: 100.0):
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Generate a 10,000 word blog on mental clarity and the benefits of meditation.
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"""
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def __init__(
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self,
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temperature: float = 0.5,
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api_key: str = None,
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alt_temps=None,
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auto_select=True,
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max_workers=6,
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):
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self.alt_temps = alt_temps if alt_temps else [0.4, 0.6, 0.8, 1.0, 1.2, 1.4]
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self.auto_select = auto_select
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self.max_workers = max_workers
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self.temperature = temperature
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self.alt_temps = alt_temps
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self.llm = OpenAIChat(
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openai_api_key=api_key,
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temperature=temperature,
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)
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def evaluate_output(self, output: str):
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"""Evaluate the output using the default temperature setting."""
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eval_prompt = f"""
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Evaluate the following output which was generated at a temperature setting of {self.temperature}.
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Provide a precise score from 0.0 to 100.0, considering the criteria of relevance, clarity, utility, pride, and delight.
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Output to evaluate:
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---
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{output}
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---
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"""
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score_text = self.llm(prompt=eval_prompt)
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score_match = re.search(r"\b\d+(\.\d)?\b", score_text)
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return round(float(score_match.group()), 1) if score_match else 0.0
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def run(self, task: str, temperature_string):
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"""Run the AutoTemp agent."""
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temperature_list = [
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float(temp.strip()) for temp in temperature_string.split(",")
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]
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outputs = {}
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scores = {}
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with ThreadPoolExecutor(max_workers=self.max_workers) as executor:
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future_to_temp = {
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executor.submit(self.llm.generate, task, temp): temp
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for temp in temperature_list
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}
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for future in as_completed(future_to_temp):
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temp = future_to_temp[future]
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output_text = future.result()
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outputs[temp] = output_text
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scores[temp] = self.evaluate_output(output_text, temp)
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if not scores:
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return "No valid outputs generated.", None
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sorted_scores = sorted(scores.items(), key=lambda item: item[1], reverse=True)
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best_temp, best_score = sorted_scores[0]
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best_output = outputs[best_temp]
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return (
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f"Best AutoTemp Output (Temp {best_temp} | Score: {best_score}):\n{best_output}"
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if self.auto_select
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else "\n".join(
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f"Temp {temp} | Score: {score}:\n{outputs[temp]}"
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for temp, score in sorted_scores
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)
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)
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