autogpt 还原，开新分支编写
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w_xiaolizu
@ -1,234 +1,174 @@
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"""Text processing functions"""
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from math import ceil
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from typing import Optional
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from typing import Dict, Generator, Optional
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import spacy
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import tiktoken
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from selenium.webdriver.remote.webdriver import WebDriver
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from autogpt import token_counter
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from autogpt.config import Config
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from autogpt.llm.base import ChatSequence
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from autogpt.llm.providers.openai import OPEN_AI_MODELS
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from autogpt.llm.utils import count_string_tokens, create_chat_completion
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from autogpt.logs import logger
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from autogpt.utils import batch
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from autogpt.llm_utils import create_chat_completion
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from autogpt.memory import get_memory
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CFG = Config()
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def _max_chunk_length(model: str, max: Optional[int] = None) -> int:
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model_max_input_tokens = OPEN_AI_MODELS[model].max_tokens - 1
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if max is not None and max > 0:
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return min(max, model_max_input_tokens)
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return model_max_input_tokens
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def must_chunk_content(
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text: str, for_model: str, max_chunk_length: Optional[int] = None
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) -> bool:
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return count_string_tokens(text, for_model) > _max_chunk_length(
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for_model, max_chunk_length
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)
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def chunk_content(
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content: str,
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for_model: str,
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max_chunk_length: Optional[int] = None,
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with_overlap=True,
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):
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"""Split content into chunks of approximately equal token length."""
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MAX_OVERLAP = 200 # limit overlap to save tokens
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if not must_chunk_content(content, for_model, max_chunk_length):
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yield content, count_string_tokens(content, for_model)
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return
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max_chunk_length = max_chunk_length or _max_chunk_length(for_model)
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tokenizer = tiktoken.encoding_for_model(for_model)
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tokenized_text = tokenizer.encode(content)
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total_length = len(tokenized_text)
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n_chunks = ceil(total_length / max_chunk_length)
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chunk_length = ceil(total_length / n_chunks)
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overlap = min(max_chunk_length - chunk_length, MAX_OVERLAP) if with_overlap else 0
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for token_batch in batch(tokenized_text, chunk_length + overlap, overlap):
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yield tokenizer.decode(token_batch), len(token_batch)
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def summarize_text(
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text: str, instruction: Optional[str] = None, question: Optional[str] = None
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) -> tuple[str, None | list[tuple[str, str]]]:
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"""Summarize text using the OpenAI API
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Args:
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text (str): The text to summarize
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instruction (str): Additional instruction for summarization, e.g. "focus on information related to polar bears", "omit personal information contained in the text"
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Returns:
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str: The summary of the text
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list[(summary, chunk)]: Text chunks and their summary, if the text was chunked.
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None otherwise.
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"""
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if not text:
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raise ValueError("No text to summarize")
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if instruction and question:
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raise ValueError("Parameters 'question' and 'instructions' cannot both be set")
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model = CFG.fast_llm_model
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if question:
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instruction = (
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f'include any information that can be used to answer the question "{question}". '
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"Do not directly answer the question itself"
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)
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summarization_prompt = ChatSequence.for_model(model)
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token_length = count_string_tokens(text, model)
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logger.info(f"Text length: {token_length} tokens")
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# reserve 50 tokens for summary prompt, 500 for the response
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max_chunk_length = _max_chunk_length(model) - 550
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logger.info(f"Max chunk length: {max_chunk_length} tokens")
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if not must_chunk_content(text, model, max_chunk_length):
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# summarization_prompt.add("user", text)
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summarization_prompt.add(
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"user",
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"Write a concise summary of the following text"
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f"{f'; {instruction}' if instruction is not None else ''}:"
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"\n\n\n"
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f'LITERAL TEXT: """{text}"""'
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"\n\n\n"
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"CONCISE SUMMARY: The text is best summarized as"
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# "Only respond with a concise summary or description of the user message."
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)
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logger.debug(f"Summarizing with {model}:\n{summarization_prompt.dump()}\n")
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summary = create_chat_completion(
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summarization_prompt, temperature=0, max_tokens=500
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)
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logger.debug(f"\n{'-'*16} SUMMARY {'-'*17}\n{summary}\n{'-'*42}\n")
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return summary.strip(), None
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summaries: list[str] = []
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chunks = list(split_text(text, for_model=model, max_chunk_length=max_chunk_length))
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for i, (chunk, chunk_length) in enumerate(chunks):
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logger.info(
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f"Summarizing chunk {i + 1} / {len(chunks)} of length {chunk_length} tokens"
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)
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summary, _ = summarize_text(chunk, instruction)
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summaries.append(summary)
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logger.info(f"Summarized {len(chunks)} chunks")
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summary, _ = summarize_text("\n\n".join(summaries))
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return summary.strip(), [
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(summaries[i], chunks[i][0]) for i in range(0, len(chunks))
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]
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def split_text(
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text: str,
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for_model: str = CFG.fast_llm_model,
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with_overlap=True,
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max_chunk_length: Optional[int] = None,
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):
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"""Split text into chunks of sentences, with each chunk not exceeding the maximum length
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max_length: int = CFG.browse_chunk_max_length,
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model: str = CFG.fast_llm_model,
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question: str = "",
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) -> Generator[str, None, None]:
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"""Split text into chunks of a maximum length
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Args:
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text (str): The text to split
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for_model (str): The model to chunk for; determines tokenizer and constraints
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max_length (int, optional): The maximum length of each chunk
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max_length (int, optional): The maximum length of each chunk. Defaults to 8192.
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Yields:
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str: The next chunk of text
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Raises:
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ValueError: when a sentence is longer than the maximum length
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ValueError: If the text is longer than the maximum length
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"""
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max_length = _max_chunk_length(for_model, max_chunk_length)
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# flatten paragraphs to improve performance
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text = text.replace("\n", " ")
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text_length = count_string_tokens(text, for_model)
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if text_length < max_length:
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yield text, text_length
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return
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n_chunks = ceil(text_length / max_length)
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target_chunk_length = ceil(text_length / n_chunks)
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nlp: spacy.language.Language = spacy.load(CFG.browse_spacy_language_model)
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flatened_paragraphs = " ".join(text.split("\n"))
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nlp = spacy.load(CFG.browse_spacy_language_model)
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nlp.add_pipe("sentencizer")
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doc = nlp(text)
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sentences = [sentence.text.strip() for sentence in doc.sents]
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doc = nlp(flatened_paragraphs)
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sentences = [sent.text.strip() for sent in doc.sents]
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current_chunk: list[str] = []
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current_chunk_length = 0
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last_sentence = None
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last_sentence_length = 0
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current_chunk = []
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i = 0
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while i < len(sentences):
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sentence = sentences[i]
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sentence_length = count_string_tokens(sentence, for_model)
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expected_chunk_length = current_chunk_length + 1 + sentence_length
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for sentence in sentences:
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message_with_additional_sentence = [
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create_message(" ".join(current_chunk) + " " + sentence, question)
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]
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if (
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expected_chunk_length < max_length
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# try to create chunks of approximately equal size
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and expected_chunk_length - (sentence_length / 2) < target_chunk_length
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):
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expected_token_usage = (
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token_usage_of_chunk(messages=message_with_additional_sentence, model=model)
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+ 1
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)
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if expected_token_usage <= max_length:
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current_chunk.append(sentence)
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current_chunk_length = expected_chunk_length
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elif sentence_length < max_length:
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if last_sentence:
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yield " ".join(current_chunk), current_chunk_length
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current_chunk = []
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current_chunk_length = 0
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if with_overlap:
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overlap_max_length = max_length - sentence_length - 1
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if last_sentence_length < overlap_max_length:
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current_chunk += [last_sentence]
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current_chunk_length += last_sentence_length + 1
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elif overlap_max_length > 5:
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# add as much from the end of the last sentence as fits
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current_chunk += [
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list(
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chunk_content(
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last_sentence,
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for_model,
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overlap_max_length,
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)
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).pop()[0],
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]
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current_chunk_length += overlap_max_length + 1
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current_chunk += [sentence]
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current_chunk_length += sentence_length
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else: # sentence longer than maximum length -> chop up and try again
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sentences[i : i + 1] = [
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chunk
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for chunk, _ in chunk_content(sentence, for_model, target_chunk_length)
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else:
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yield " ".join(current_chunk)
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current_chunk = [sentence]
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message_this_sentence_only = [
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create_message(" ".join(current_chunk), question)
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]
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continue
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i += 1
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last_sentence = sentence
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last_sentence_length = sentence_length
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expected_token_usage = (
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token_usage_of_chunk(messages=message_this_sentence_only, model=model)
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+ 1
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)
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if expected_token_usage > max_length:
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raise ValueError(
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f"Sentence is too long in webpage: {expected_token_usage} tokens."
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)
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if current_chunk:
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yield " ".join(current_chunk), current_chunk_length
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yield " ".join(current_chunk)
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def token_usage_of_chunk(messages, model):
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return token_counter.count_message_tokens(messages, model)
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def summarize_text(
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url: str, text: str, question: str, driver: Optional[WebDriver] = None
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) -> str:
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"""Summarize text using the OpenAI API
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Args:
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url (str): The url of the text
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text (str): The text to summarize
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question (str): The question to ask the model
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driver (WebDriver): The webdriver to use to scroll the page
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Returns:
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str: The summary of the text
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"""
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if not text:
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return "Error: No text to summarize"
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model = CFG.fast_llm_model
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text_length = len(text)
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print(f"Text length: {text_length} characters")
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summaries = []
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chunks = list(
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split_text(
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text, max_length=CFG.browse_chunk_max_length, model=model, question=question
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),
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)
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scroll_ratio = 1 / len(chunks)
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for i, chunk in enumerate(chunks):
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if driver:
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scroll_to_percentage(driver, scroll_ratio * i)
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print(f"Adding chunk {i + 1} / {len(chunks)} to memory")
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memory_to_add = f"Source: {url}\n" f"Raw content part#{i + 1}: {chunk}"
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memory = get_memory(CFG)
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memory.add(memory_to_add)
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messages = [create_message(chunk, question)]
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tokens_for_chunk = token_counter.count_message_tokens(messages, model)
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print(
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f"Summarizing chunk {i + 1} / {len(chunks)} of length {len(chunk)} characters, or {tokens_for_chunk} tokens"
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)
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summary = create_chat_completion(
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model=model,
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messages=messages,
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)
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summaries.append(summary)
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print(
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f"Added chunk {i + 1} summary to memory, of length {len(summary)} characters"
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)
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memory_to_add = f"Source: {url}\n" f"Content summary part#{i + 1}: {summary}"
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memory.add(memory_to_add)
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print(f"Summarized {len(chunks)} chunks.")
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combined_summary = "\n".join(summaries)
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messages = [create_message(combined_summary, question)]
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return create_chat_completion(
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model=model,
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messages=messages,
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)
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def scroll_to_percentage(driver: WebDriver, ratio: float) -> None:
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"""Scroll to a percentage of the page
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Args:
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driver (WebDriver): The webdriver to use
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ratio (float): The percentage to scroll to
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Raises:
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ValueError: If the ratio is not between 0 and 1
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"""
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if ratio < 0 or ratio > 1:
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raise ValueError("Percentage should be between 0 and 1")
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driver.execute_script(f"window.scrollTo(0, document.body.scrollHeight * {ratio});")
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def create_message(chunk: str, question: str) -> Dict[str, str]:
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"""Create a message for the chat completion
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Args:
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chunk (str): The chunk of text to summarize
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question (str): The question to answer
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Returns:
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Dict[str, str]: The message to send to the chat completion
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"""
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return {
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"role": "user",
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"content": f'"""{chunk}""" Using the above text, answer the following'
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f' question: "{question}" -- if the question cannot be answered using the text,'
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" summarize the text.",
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}
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