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| import os | |
| from duckpy import Client | |
| from langchain import PromptTemplate, OpenAI, LLMChain | |
| from langchain.agents import Tool | |
| from langchain.base_language import BaseLanguageModel | |
| MAX_SEARCH_RESULTS = 20 # Number of search results to observe at a time | |
| search_description = """ Useful for when you need to ask with search. Use direct language and be | |
| EXPLICIT in what you want to search. | |
| ## Examples of incorrect use | |
| 1. Action: Search | |
| Action Input: "[name of bagel shop] menu" | |
| The Action Input cannot be None or empty. | |
| """ | |
| notepad_description = """ Useful for when you need to note-down specific | |
| information for later reference. Please provide full information you want to | |
| note-down in the Action Input and all future prompts will remember it. | |
| This is the mandatory tool after using the search tool. | |
| Using Notepad does not always lead to a final answer. | |
| ## Exampels of using notepad tool | |
| Action: Notepad | |
| Action Input: the information you want to note-down | |
| """ | |
| async def ddg(query: str): | |
| if query is None or query.lower().strip().strip('"') == "none" or query.lower().strip().strip('"') == "null": | |
| x = "The action input field is empty. Please provide a search query." | |
| return [x] | |
| else: | |
| client = Client() | |
| return client.search(query)[:MAX_SEARCH_RESULTS] | |
| async def notepad(x: str) -> str: | |
| return f"{[x]}" | |
| search_tool = Tool(name="Search", | |
| func=lambda x: x, | |
| coroutine=ddg, | |
| description=search_description) | |
| note_tool = Tool(name="Notepad", | |
| func=lambda x: x, | |
| coroutine=notepad, | |
| description=notepad_description) | |
| def rewrite_search_query(q: str, search_history, llm: BaseLanguageModel) -> str: | |
| history_string = '\n'.join(search_history) | |
| template ="""We are using the Search tool. | |
| # Previous queries: | |
| {history_string}. \n\n Rewrite query {action_input} to be | |
| different from the previous ones.""" | |
| prompt = PromptTemplate(template=template, | |
| input_variables=["action_input", "history_string"]) | |
| llm_chain = LLMChain(prompt=prompt, llm=llm) | |
| return llm_chain.predict(action_input=q, history_string=history_string) | |