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from gradio_client import Client
client = Client("https://svjack-entity-property-extractor-zh.hf.space/--replicas/boyb8/")
import pandas as pd
import numpy as np
import os
import re
from langchain.vectorstores import FAISS
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain import chains
from rapidfuzz import fuzz
from huggingface_hub import snapshot_download
if not os.path.exists("genshin_book_chunks_with_qa_sp"):
path = snapshot_download(
repo_id="svjack/genshin_book_chunks_with_qa_sp",
repo_type="dataset",
local_dir="genshin_book_chunks_with_qa_sp",
local_dir_use_symlinks = False
)
if not os.path.exists("bge_small_book_chunks_prebuld"):
path = snapshot_download(
repo_id="svjack/bge_small_book_chunks_prebuld",
repo_type="dataset",
local_dir="bge_small_book_chunks_prebuld",
local_dir_use_symlinks = False
)
if not os.path.exists("mistral-7b"):
path = snapshot_download(
repo_id="svjack/mistral-7b",
repo_type="model",
local_dir="mistral-7b",
local_dir_use_symlinks = False
)
'''
query = "警察是如何破获邪恶计划的?" ## 警 执律 盗
k = 10
uniform_recall_docs_to_pairwise_cos(
query,
docsearch_bge_loaded.similarity_search_with_score(query, k = k, ),
bge_book_embeddings
)
'''
def uniform_recall_docs_to_pairwise_cos(query ,doc_list, embeddings):
assert type(doc_list) == type([])
from langchain.evaluation import load_evaluator
from langchain.evaluation import EmbeddingDistance
hf_evaluator = load_evaluator("pairwise_embedding_distance", embeddings=embeddings,
distance_metric = EmbeddingDistance.COSINE)
return sorted(pd.Series(doc_list).map(lambda x: x[0].page_content).map(lambda x:
(x ,hf_evaluator.evaluate_string_pairs(prediction=query, prediction_b=x)["score"])
).values.tolist(), key = lambda t2: t2[1])
'''
sort_by_kw("深渊使徒", book_df)["content_chunks_formatted"].head(5).values.tolist() ### 深渊
'''
def sort_by_kw(kw, book_df):
req = book_df.copy()
req["sim_score"] = req.apply(
lambda x:
max(map(lambda y: fuzz.ratio(y, kw) ,eval(x["person"]) + eval(x["locate"]) + eval(x["locate"]))) if \
eval(x["person"]) + eval(x["locate"]) + eval(x["locate"]) else 0
, axis = 1
)
req = req.sort_values(by = "sim_score", ascending = False)
return req
def recall_chuncks(query, docsearch, embedding, book_df,
sparse_threshold = 30,
dense_top_k = 10,
rerank_by = "emb",
):
sparse_output = sort_by_kw(query, book_df)[["content_chunks_formatted", "sim_score"]]
sparse_output_list = sparse_output[
sparse_output["sim_score"] >= sparse_threshold
]["content_chunks_formatted"].values.tolist()
dense_output = uniform_recall_docs_to_pairwise_cos(
query,
docsearch.similarity_search_with_score(query, k = dense_top_k,),
embedding
)
for chunck, score in dense_output:
if chunck not in sparse_output_list:
sparse_output_list.append(chunck)
if rerank_by == "emb":
from langchain.evaluation import load_evaluator
from langchain.evaluation import EmbeddingDistance
hf_evaluator = load_evaluator("pairwise_embedding_distance", embeddings=embedding,
distance_metric = EmbeddingDistance.COSINE)
return pd.Series(sorted(pd.Series(sparse_output_list).map(lambda x:
(x ,hf_evaluator.evaluate_string_pairs(prediction=query, prediction_b=x)["score"])
).values.tolist(), key = lambda t2: t2[1])).map(lambda x: x[0]).values.tolist()
else:
sparse_output_list = sorted(sparse_output_list, key = lambda x: fuzz.ratio(x, query), reverse = True)
return sparse_output_list
def reduce_list_by_order(text_list, as_text = False):
if not text_list:
return
df = pd.DataFrame(
list(map(lambda x: (x.split("\n")[0], x.split("\n")[1], "\n".join(x.split("\n")[2:])), text_list))
).groupby([0, 1])[2].apply(list).map(lambda x: sorted(x, key = len, reverse=True)).map(
"\n\n".join
).reset_index()
d = dict(df.apply(lambda x: ((x.iloc[0], x.iloc[1]), x.iloc[2]), axis = 1).values.tolist())
#return df
order_list = []
for x in text_list:
a, b = x.split("\n")[0], x.split("\n")[1]
if not order_list:
order_list = [[a, [b]]]
elif a in list(map(lambda t2: t2[0], order_list)):
order_list[list(map(lambda t2: t2[0], order_list)).index(a)][1].append(b)
elif a not in list(map(lambda t2: t2[0], order_list)):
order_list.append([a, [b]])
df = pd.DataFrame(pd.DataFrame(order_list).explode(1).dropna().apply(
lambda x: (x.iloc[0], x.iloc[1], d[(x.iloc[0], x.iloc[1])]), axis = 1
).values.tolist()).drop_duplicates()
if as_text:
return "\n\n".join(
df.apply(lambda x: "{}\n{}\n{}".format(x.iloc[0], x.iloc[1], x.iloc[2]), axis = 1).values.tolist()
)
return df
def build_gpt_prompt(query, docsearch, embedding, book_df, max_context_length = 4090):
l = recall_chuncks(query, docsearch, embedding, book_df)
context = reduce_list_by_order(l, as_text = True)
context_l = []
for ele in context.split("\n"):
if sum(map(len, context_l)) >= max_context_length:
break
context_l.append(ele)
context = "\n".join(context_l).strip()
template = """使用以下上下文来回答最后的问题。如果你不知道答案,就说你不知道,不要试图编造答案。尽量使答案简明扼要。总是在回答的最后说“谢谢你的提问!”。
{context}
问题: {question}
有用的回答:"""
return template.format(
**{
"context": context,
"question": query
}
)
def collect_prompt_to_hist_list(prompt, add_assistant = False):
l = pd.Series(prompt.split("\n\n")).map(lambda x: x.strip()).values.tolist()
ll = []
for ele in l:
if not ll:
ll.append(ele)
else:
if ele.startswith("文章标题:") or ele.startswith("问题:"):
ll.append(ele)
else:
ll[-1] += ("\n\n" + ele)
if add_assistant:
ll_ = []
for i in range(len(ll)):
if i == 0:
ll_.append((ll[i], "好的。"))
elif i < len(ll) - 1:
ll_.append((ll[i], "我读懂了。"))
else:
ll_.append((ll[i], ""))
return ll_
else:
return ll
def row_to_content_ask(r):
question = r["question"]
content_list = r["content_list"]
assert type(content_list) == type([])
content_prompt_list = pd.Series(content_list).map(
lambda x: '''
{}\n从上面的相关的叙述中抽取包含"{}"中词汇的相关语段。
'''.format(x, question).strip()
).values.tolist()
return content_prompt_list
def entity_extractor_by_llm(query, llm, show_process = False, max_length = 512):
import re
hist = [['请从下面的句子中提取实体和属性。不需要进行进一步解释。', '好的。'],
['宁波在哪个省份?', '实体:宁波 属性:省份'],
['中国的货币是什么?', '实体:中国 属性:货币'],
['百慕大三角在什么地方?', '实体:百慕大三角 属性:地方'],
['谁是最可爱的人?', "实体:人 属性:可爱"],
['黄河的拐点在哪里?', "实体:黄河 属性:拐点"],
#["玉米的引进时间是什么时候?", ""]
]
re_hist = pd.DataFrame(
pd.Series(hist).map(
lambda t2: (
{
"role": "user",
"content": t2[0]
},
{
"role": "assistant",
"content": t2[1]
},
)
).values.tolist()).values.reshape([-1]).tolist()
out = llm.create_chat_completion(
messages = re_hist + [
{
"role": "user",
#"content": prompt + "如果没有提到相关内容,请回答不知道。使用中文进行回答,不要包含任何英文。"
"content": query
}
],
stream=True
)
out_text = ""
for chunk in out:
delta = chunk['choices'][0]['delta']
if "content" in delta:
out_text += delta['content']
#from IPython.display import clear_output
#clear_output(wait=True)
if show_process:
print(out_text)
if len(out_text) >= max_length:
break
e_list = re.findall(r"实体(.*?)属性", out_text.replace("\n", " "))
if e_list:
return re.findall(u"[\u4e00-\u9fa5]+" ,e_list[0])
return None
def unzip_string(x, size = 2):
if len(x) <= size:
return [x]
req = []
for i in range(len(x) - size + 1):
req.append(x[i: i + size])
return req
def entity_extractor_by_adapter(x):
import json
result = client.predict(
x, # str in 'question' Textbox component
api_name="/predict"
)
with open(result, "r") as f:
req = json.load(f)
req_list = req.get("E-TAG", [])
req_ = []
for ele in req_list:
for x in unzip_string(ele, 2):
if x not in req_:
req_.append(x)
return req_
def query_content_ask_func(question, content_list,
llm, setfit_model, show_process = False, max_length = 1024):
ask_list = row_to_content_ask(
{
"question": question,
"content_list": content_list
}
)
#return ask_list
req = []
for prompt in ask_list:
out = llm.create_chat_completion(
messages = [
{
"role": "user",
"content": prompt + "如果没有提到相关内容,请回答不知道。使用中文进行回答,不要包含任何英文。"
}
],
stream=True
)
out_text = ""
for chunk in out:
delta = chunk['choices'][0]['delta']
if "content" in delta:
out_text += delta['content']
#from IPython.display import clear_output
#clear_output(wait=True)
if show_process:
print(out_text)
if len(out_text) >= max_length:
break
req.append(out_text)
d = {
"question": question,
"content_list": content_list
}
assert len(req) == len(ask_list)
d["question_content_relate_list"] = req
d["relate_prob_list"] = setfit_model.predict_proba(
req
).numpy()[:, 1].tolist()
return d
def build_relate_ask_list(query, docsearch_bge_loaded, bge_book_embeddings, book_df,
llm, setfit_model, as_content_score_df = True,
show_process = False, add_relate_entities = False,
max_length = 1024):
prompt = build_gpt_prompt(query, docsearch_bge_loaded, bge_book_embeddings, book_df)
prompt_list = collect_prompt_to_hist_list(prompt)
question = prompt_list[-1].split("\n")[0]
content_list = prompt_list[1:-1]
d = query_content_ask_func(question, content_list,
llm, setfit_model, show_process = show_process)
#entity_list = entity_extractor_by_llm(query, llm, show_process = show_process, max_length = max_length)
entity_list = entity_extractor_by_adapter(query)
if type(entity_list) != type([]):
entity_list = []
d["in_content_entity_list"] = list(map(lambda x:
list(filter(lambda e: e in x, entity_list))
, d["content_list"]))
if add_relate_entities:
relate_content_entity_list = [[]] * len(content_list)
for entity in entity_list:
entity_content_score_d = query_content_ask_func(entity, d["content_list"],
llm, setfit_model, show_process = show_process)
lookup_df = pd.DataFrame(
list(zip(*[entity_content_score_d["content_list"],
entity_content_score_d["relate_prob_list"]]))
)
for ii, (i, r) in enumerate(lookup_df.iterrows()):
if r.iloc[1] >= 0.5 and entity not in relate_content_entity_list[ii]:
#relate_content_entity_list[ii].append(entity)
relate_content_entity_list[ii] = relate_content_entity_list[ii] + [entity]
d["relate_content_entity_list"] = relate_content_entity_list
if as_content_score_df:
if add_relate_entities:
df = pd.concat(
[
pd.Series(d["content_list"]).map(lambda x: x.strip()),
pd.Series(d["in_content_entity_list"]),
pd.Series(d["relate_content_entity_list"]),
pd.Series(d["question_content_relate_list"]).map(lambda x: x.strip()),
pd.Series(d["relate_prob_list"])
], axis = 1
)
df.columns = ["content", "entities", "relate_entities", "relate_eval_str", "score"]
else:
df = pd.concat(
[
pd.Series(d["content_list"]).map(lambda x: x.strip()),
pd.Series(d["in_content_entity_list"]),
#pd.Series(d["relate_content_entity_list"]),
pd.Series(d["question_content_relate_list"]).map(lambda x: x.strip()),
pd.Series(d["relate_prob_list"])
], axis = 1
)
df.columns = ["content", "entities", "relate_eval_str", "score"]
req = []
entities_num_list = df["entities"].map(len).drop_duplicates().dropna().sort_values(ascending = False).\
values.tolist()
for e_num in entities_num_list:
req.append(
df[
df["entities"].map(lambda x: len(x) == e_num)
].sort_values(by = "score", ascending = False)
)
return pd.concat(req, axis = 0)
#df = df.sort_values(by = "score", ascending = False)
#return df
return d
def mistral_predict(prompt, llm, show_process = True, max_length = 512):
out = llm.create_chat_completion(
messages = [] + [
{
"role": "user",
#"content": prompt + "如果没有提到相关内容,请回答不知道。使用中文进行回答,不要包含任何英文。"
"content": prompt
}
],
stream=True
)
#from IPython.display import clear_output
out_text = ""
for chunk in out:
delta = chunk['choices'][0]['delta']
if "content" in delta:
out_text += delta['content']
if show_process:
print(out_text)
if len(out_text) >= max_length:
break
#clear_output(wait=True)
#clear_output(wait=True)
return out_text
def run_all(query, docsearch_bge_loaded, bge_book_embeddings, book_df,
llm, setfit_model, only_return_prompt = False):
df = build_relate_ask_list(query, docsearch_bge_loaded, bge_book_embeddings, book_df,
llm, setfit_model, show_process=False)
info_list = df[
df.apply(
lambda x: x["score"] >= 0.5 and bool(x["entities"]), axis = 1
)
].values.tolist()
if not info_list:
return df, info_list, "没有相关内容,谢谢你的提问。"
prompt = '''
问题: {}
根据下面的内容回答上面的问题,如果无法根据内容确定答案,请回答不知道。
{}
'''.format(query, "\n\n".join(pd.Series(info_list).map(lambda x: x[0]).values.tolist()))
if only_return_prompt:
return df, info_list, prompt
out = mistral_predict(prompt + "\n使用中文进行回答,不要包含任何英文。", llm)
return df, info_list, out
#book_df = pd.read_csv("genshin_book_chunks_with_qa_sp.csv")
book_df = pd.read_csv("genshin_book_chunks_with_qa_sp/genshin_book_chunks_with_qa_sp.csv")
book_df["content_chunks"].dropna().drop_duplicates().shape
book_df["content_chunks_formatted"] = book_df.apply(
lambda x: "文章标题:{}\n子标题:{}\n内容:{}".format(x["title"], x["sub_title"], x["content_chunks"]),
axis = 1
)
texts = book_df["content_chunks_formatted"].dropna().drop_duplicates().values.tolist()
#embedding_path = "bge-small-book-qa/"
embedding_path = "svjack/bge-small-book-qa"
bge_book_embeddings = HuggingFaceEmbeddings(model_name=embedding_path)
docsearch_bge_loaded = FAISS.load_local("bge_small_book_chunks_prebuld/", bge_book_embeddings,
allow_dangerous_deserialization = True
)
from llama_cpp import Llama
#true_path = "mistral-7b-instruct-v0.2.Q4_0.gguf"
true_path = "mistral-7b/mistral-7b-instruct-v0.2.Q4_0.gguf"
#### 16g +
# Set gpu_layers to the number of layers to offload to GPU. Set to 0 if no GPU acceleration is available on your system.
llm = Llama(
model_path=true_path, # Download the model file first
n_ctx=8000, # The max sequence length to use - note that longer sequence lengths require much more resources
n_threads=8, # The number of CPU threads to use, tailor to your system and the resulting performance
#n_gpu_layers=-1, # The number of layers to offload to GPU, if you have GPU acceleration available
chat_format="llama-2"
)
from setfit import SetFitModel
#setfit_model = SetFitModel.from_pretrained("setfit_info_cls")
setfit_model = SetFitModel.from_pretrained("svjack/setfit_info_cls")
import gradio as gr
with gr.Blocks() as demo:
title = gr.HTML(
"""<h1 align="center"> <font size="+3"> Genshin Impact Book QA Mistral-7B Demo ⚡ </font> </h1>""",
elem_id="title",
)
with gr.Column():
with gr.Row():
query = gr.Text(label = "输入问题:", lines = 1, interactive = True, scale = 5.0)
run_button = gr.Button("得到答案")
output = gr.Text(label = "回答:", lines = 5, interactive = True)
recall_items = gr.JSON(label = "召回相关内容", interactive = False)
with gr.Row():
gr.Examples(
[
'丘丘人有哪些生活习惯?',
#'岩王帝君和归终是什么关系?',
'盐之魔神的下场是什么样的?',
#'归终是谁?',
'岩王帝君是一个什么样的人?',
'铳枪手的故事内容是什么样的?',
#'白夜国的子民遭遇了什么?',
'大蛇居住在哪里?',
'珊瑚宫有哪些传说?',
'灵光颂的内容是什么样的?',
#'连心珠讲了一件什么事情?',
#'梓心是谁?',
'枫丹有哪些故事?',
'璃月有哪些故事?',
'轻策庄有哪些故事?',
'瑶光滩有哪些故事?',
'稻妻有哪些故事?',
'海祇岛有哪些故事?',
#'须弥有哪些故事?',
'蒙德有哪些故事?',
'璃月有哪些奇珍异宝?',
'狸猫和天狗是什么关系?',
'岩王帝君和归终是什么关系?',
],
inputs = query,
label = "被书目内容包含的问题"
)
with gr.Row():
gr.Examples(
[
'爱丽丝女士是可莉的妈妈吗?',
'摘星崖是什么样的?',
'丘丘人使用的是什么文字?',
'深渊使徒哪里来的?',
'发条机关可以用来做什么?',
'那先朱那做了什么?',
],
inputs = query,
label = "没有被书目明确提到的问题"
)
run_button.click(lambda x:
run_all(x, docsearch_bge_loaded, bge_book_embeddings, book_df, llm,
setfit_model = setfit_model)[1:],
query, [recall_items, output]
)
demo.queue(max_size=4, concurrency_count=1).launch(debug=True, show_api=False, share = True)
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