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CPU Upgrade
Commit
•
44846b2
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Parent(s):
8ad9e26
Upload 7 files
Browse files- modules/chat_func.py +2 -2
- modules/llama_func.py +42 -38
modules/chat_func.py
CHANGED
@@ -155,7 +155,7 @@ def stream_predict(
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yield get_return_value()
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error_json_str = ""
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for chunk in response.iter_lines():
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if counter == 0:
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counter += 1
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continue
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@@ -272,7 +272,7 @@ def predict(
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if reply_language == "跟随问题语言(不稳定)":
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reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
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if files:
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msg = "
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logging.info(msg)
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yield chatbot+[(inputs, "")], history, msg, all_token_counts
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index = construct_index(openai_api_key, file_src=files)
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yield get_return_value()
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error_json_str = ""
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+
for chunk in tqdm(response.iter_lines()):
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if counter == 0:
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counter += 1
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continue
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if reply_language == "跟随问题语言(不稳定)":
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reply_language = "the same language as the question, such as English, 中文, 日本語, Español, Français, or Deutsch."
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if files:
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msg = "加载索引中……(这可能需要几分钟)"
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logging.info(msg)
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yield chatbot+[(inputs, "")], history, msg, all_token_counts
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index = construct_index(openai_api_key, file_src=files)
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modules/llama_func.py
CHANGED
@@ -13,54 +13,57 @@ from llama_index import (
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from langchain.llms import OpenAI
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import colorama
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-
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from modules.presets import *
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from modules.utils import *
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def get_documents(file_src):
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documents = []
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-
index_name = ""
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logging.debug("Loading documents...")
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logging.debug(f"file_src: {file_src}")
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for file in file_src:
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logging.
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index_name += file.name
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if os.path.splitext(file.name)[1] == ".pdf":
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logging.debug("Loading PDF...")
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CJKPDFReader = download_loader("CJKPDFReader")
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loader = CJKPDFReader()
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-
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elif os.path.splitext(file.name)[1] == ".docx":
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logging.debug("Loading DOCX...")
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DocxReader = download_loader("DocxReader")
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loader = DocxReader()
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-
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elif os.path.splitext(file.name)[1] == ".epub":
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logging.debug("Loading EPUB...")
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EpubReader = download_loader("EpubReader")
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loader = EpubReader()
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-
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else:
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logging.debug("Loading text file...")
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with open(file.name, "r", encoding="utf-8") as f:
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return documents
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def construct_index(
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):
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os.environ["OPENAI_API_KEY"] = api_key
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chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
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@@ -78,12 +81,13 @@ def construct_index(
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chunk_size_limit,
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separator=separator,
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)
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if os.path.exists(f"./index/{index_name}.json"):
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logging.info("找到了缓存的索引文件,加载中……")
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return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
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else:
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try:
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logging.debug("构建索引中……")
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index = GPTSimpleVectorIndex(
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documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
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@@ -97,12 +101,12 @@ def construct_index(
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def chat_ai(
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):
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os.environ["OPENAI_API_KEY"] = api_key
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@@ -133,15 +137,15 @@ def chat_ai(
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def ask_ai(
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):
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os.environ["OPENAI_API_KEY"] = api_key
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@@ -174,7 +178,7 @@ def ask_ai(
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for index, node in enumerate(response.source_nodes):
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brief = node.source_text[:25].replace("\n", "")
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nodes.append(
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f"<details><summary>[{index+1}]\t{brief}...</summary><p>{node.source_text}</p></details>"
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)
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new_response = ret_text + "\n----------\n" + "\n\n".join(nodes)
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logging.info(
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from langchain.llms import OpenAI
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import colorama
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from modules.presets import *
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from modules.utils import *
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def get_index_name(file_src):
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index_name = ""
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for file in file_src:
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index_name += os.path.basename(file.name)
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index_name = sha1sum(index_name)
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return index_name
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def get_documents(file_src):
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documents = []
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logging.debug("Loading documents...")
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logging.debug(f"file_src: {file_src}")
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for file in file_src:
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logging.info(f"loading file: {file.name}")
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if os.path.splitext(file.name)[1] == ".pdf":
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logging.debug("Loading PDF...")
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CJKPDFReader = download_loader("CJKPDFReader")
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loader = CJKPDFReader()
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text_raw = loader.load_data(file=file.name)[0].text
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elif os.path.splitext(file.name)[1] == ".docx":
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logging.debug("Loading DOCX...")
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DocxReader = download_loader("DocxReader")
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loader = DocxReader()
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text_raw = loader.load_data(file=file.name)[0].text
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elif os.path.splitext(file.name)[1] == ".epub":
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logging.debug("Loading EPUB...")
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EpubReader = download_loader("EpubReader")
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loader = EpubReader()
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text_raw = loader.load_data(file=file.name)[0].text
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else:
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logging.debug("Loading text file...")
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with open(file.name, "r", encoding="utf-8") as f:
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text_raw = f.read()
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text = add_space(text_raw)
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documents += [Document(text)]
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return documents
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def construct_index(
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api_key,
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file_src,
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max_input_size=4096,
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num_outputs=1,
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max_chunk_overlap=20,
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chunk_size_limit=600,
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embedding_limit=None,
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separator=" ",
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num_children=10,
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max_keywords_per_chunk=10,
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):
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os.environ["OPENAI_API_KEY"] = api_key
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chunk_size_limit = None if chunk_size_limit == 0 else chunk_size_limit
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chunk_size_limit,
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separator=separator,
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)
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index_name = get_index_name(file_src)
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if os.path.exists(f"./index/{index_name}.json"):
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logging.info("找到了缓存的索引文件,加载中……")
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return GPTSimpleVectorIndex.load_from_disk(f"./index/{index_name}.json")
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else:
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try:
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documents = get_documents(file_src)
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logging.debug("构建索引中……")
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index = GPTSimpleVectorIndex(
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documents, llm_predictor=llm_predictor, prompt_helper=prompt_helper
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def chat_ai(
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api_key,
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index,
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question,
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context,
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chatbot,
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reply_language,
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):
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os.environ["OPENAI_API_KEY"] = api_key
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def ask_ai(
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api_key,
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index,
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question,
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prompt_tmpl,
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refine_tmpl,
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sim_k=1,
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temprature=0,
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prefix_messages=[],
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reply_language="中文",
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):
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os.environ["OPENAI_API_KEY"] = api_key
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for index, node in enumerate(response.source_nodes):
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brief = node.source_text[:25].replace("\n", "")
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nodes.append(
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f"<details><summary>[{index + 1}]\t{brief}...</summary><p>{node.source_text}</p></details>"
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)
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new_response = ret_text + "\n----------\n" + "\n\n".join(nodes)
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logging.info(
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