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151 lines
5.1 KiB
151 lines
5.1 KiB
import asyncio
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import os
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# Tools
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from contextlib import contextmanager
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from typing import Optional
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import pandas as pd
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from langchain.agents import tool
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from langchain.agents.agent_toolkits.pandas.base import create_pandas_dataframe_agent
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from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
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from langchain.docstore.document import Document
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from langchain.memory.chat_message_histories import FileChatMessageHistory
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from langchain.tools.human.tool import HumanInputRun
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ROOT_DIR = "./data/"
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from langchain.chains.qa_with_sources.loading import BaseCombineDocumentsChain
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from langchain.chat_models import ChatOpenAI
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.tools import BaseTool, DuckDuckGoSearchRun
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from langchain.tools.file_management.read import ReadFileTool
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from langchain.tools.file_management.write import WriteFileTool
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from pydantic import Field
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llm = ChatOpenAI(model_name="gpt-4", temperature=1.0)
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@contextmanager
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def pushd(new_dir):
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"""Context manager for changing the current working directory."""
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prev_dir = os.getcwd()
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os.chdir(new_dir)
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try:
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yield
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finally:
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os.chdir(prev_dir)
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@tool
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def process_csv(
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llm, csv_file_path: str, instructions: str, output_path: Optional[str] = None
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) -> str:
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"""Process a CSV by with pandas in a limited REPL.\
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Only use this after writing data to disk as a csv file.\
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Any figures must be saved to disk to be viewed by the human.\
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Instructions should be written in natural language, not code. Assume the dataframe is already loaded."""
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with pushd(ROOT_DIR):
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try:
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df = pd.read_csv(csv_file_path)
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except Exception as e:
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return f"Error: {e}"
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agent = create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=False)
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if output_path is not None:
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instructions += f" Save output to disk at {output_path}"
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try:
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result = agent.run(instructions)
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return result
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except Exception as e:
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return f"Error: {e}"
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async def async_load_playwright(url: str) -> str:
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"""Load the specified URLs using Playwright and parse using BeautifulSoup."""
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from bs4 import BeautifulSoup
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from playwright.async_api import async_playwright
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results = ""
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async with async_playwright() as p:
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browser = await p.chromium.launch(headless=True)
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try:
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page = await browser.new_page()
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await page.goto(url)
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page_source = await page.content()
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soup = BeautifulSoup(page_source, "html.parser")
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for script in soup(["script", "style"]):
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script.extract()
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text = soup.get_text()
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lines = (line.strip() for line in text.splitlines())
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chunks = (phrase.strip() for line in lines for phrase in line.split(" "))
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results = "\n".join(chunk for chunk in chunks if chunk)
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except Exception as e:
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results = f"Error: {e}"
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await browser.close()
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return results
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def run_async(coro):
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event_loop = asyncio.get_event_loop()
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return event_loop.run_until_complete(coro)
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@tool
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def browse_web_page(url: str) -> str:
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"""Verbose way to scrape a whole webpage. Likely to cause issues parsing."""
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return run_async(async_load_playwright(url))
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def _get_text_splitter():
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return RecursiveCharacterTextSplitter(
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# Set a really small chunk size, just to show.
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chunk_size = 500,
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chunk_overlap = 20,
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length_function = len,
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)
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class WebpageQATool(BaseTool):
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name = "query_webpage"
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description = "Browse a webpage and retrieve the information relevant to the question."
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text_splitter: RecursiveCharacterTextSplitter = Field(default_factory=_get_text_splitter)
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qa_chain: BaseCombineDocumentsChain
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def _run(self, url: str, question: str) -> str:
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"""Useful for browsing websites and scraping the text information."""
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result = browse_web_page.run(url)
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docs = [Document(page_content=result, metadata={"source": url})]
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web_docs = self.text_splitter.split_documents(docs)
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results = []
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# TODO: Handle this with a MapReduceChain
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for i in range(0, len(web_docs), 4):
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input_docs = web_docs[i:i+4]
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window_result = self.qa_chain({"input_documents": input_docs, "question": question}, return_only_outputs=True)
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results.append(f"Response from window {i} - {window_result}")
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results_docs = [Document(page_content="\n".join(results), metadata={"source": url})]
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return self.qa_chain({"input_documents": results_docs, "question": question}, return_only_outputs=True)
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async def _arun(self, url: str, question: str) -> str:
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raise NotImplementedError
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query_website_tool = WebpageQATool(qa_chain=load_qa_with_sources_chain(llm))
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# !pip install duckduckgo_search
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# web_search = DuckDuckGoSearchRun()
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# from swarms.tools.code_intepretor import CodeInterpreter
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# # @tool
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# code_intepret = CodeInterpreter()
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import interpreter
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@tool
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def compile(task: str):
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task = interpreter.chat(task)
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interpreter.chat()
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