Create app.py

app.py

@@ -0,0 +1,511 @@
+from gradio_client import Client
+client = Client("https://svjack-entity-property-extractor-zh.hf.space")
+
+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
+
+import pandas as pd
+
+import chatglm_cpp
+from pathlib import Path
+import pandas as pd
+
+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("chatglm3-6b-bin"):
+    path = snapshot_download(
+        repo_id="svjack/chatglm3-6b-bin",
+        repo_type="model",
+        local_dir="chatglm3-6b-bin",
+        local_dir_use_symlinks = False
+    )
+
+
+#model_file_path = "chatglm3-6b-int4.bin"
+model_file_path = "chatglm3-6b-bin/chatglm3-6b-int4.bin"
+print("load {}".format(model_file_path))
+chatglm_llm = chatglm_cpp.Pipeline(Path(model_file_path))
+
+def chat_messages(message, history, chatglm_llm = chatglm_llm,
+    max_length = 128, show_process = False
+):
+    flatten_history = []
+    for a, b in history:
+        flatten_history.append(
+            chatglm_cpp.ChatMessage(role="user", content=a)
+        )
+        flatten_history.append(
+            chatglm_cpp.ChatMessage(role="assistant", content=b)
+        )
+
+    streamer = chatglm_llm.chat(
+        flatten_history + [
+        chatglm_cpp.ChatMessage(role="user", content=message)
+        ], do_sample=False,
+        stream = True,
+        max_length = 5120
+        )
+
+    response = ""
+    for new_text in streamer:
+        response += new_text.content
+        if show_process:
+            print(response)
+            from IPython.display import clear_output
+            clear_output(wait=True)
+        yield response
+        if len(response) >= max_length:
+            break
+    #return response
+
+'''
+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_glm(query,
+    show_process = False, max_length = 512,
+    return_out_text = False,
+    ):
+    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。'],
+     ['问题：宁波在哪个省份？', '实体：宁波 属性：省份'],
+     ['问题：中国的货币是什么？', '实体：中国 属性：货币'],
+     ['问题：百慕大三角在什么地方？', '实体：百慕大三角 属性：地方'],
+     ['问题：谁是最可爱的人？', "实体：人 属性：可爱"],
+     ['问题：黄河的拐点在哪里？', "实体：黄河 属性：拐点"],
+     #['问题：魔神归终在哪里？', '实体：归终 属性：哪里'],
+     #["玉米的引进时间是什么时候？", ""]
+     ]
+
+    out_text = chat_messages("问题：{}".format(query),
+        hist,
+    )
+    req = ""
+    for ele in out_text:
+        req = ele
+    out_text = req
+
+    if return_out_text:
+        return out_text
+    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_
+
+##### maybe 0.1
+def query_content_ask_func(question, content_list,
+    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_text = chat_messages(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。",
+                [], show_process = show_process, max_length = max_length
+            )
+        req_ = ""
+        for ele in out_text:
+            req_ = ele
+        out_text = req_
+
+        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,
+                          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)
+    #print(prompt_list)
+    question = prompt_list[-1].split("\n")[0]
+    content_list = prompt_list[1:-1]
+
+    d = query_content_ask_func(question, content_list,
+            setfit_model, show_process = show_process)
+
+    #entity_list = entity_extractor_by_glm(query,
+    #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"],
+                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 run_all(query, docsearch_bge_loaded, bge_book_embeddings, book_df,
+                          setfit_model, only_return_prompt = False):
+    df = build_relate_ask_list(query, docsearch_bge_loaded, bge_book_embeddings, book_df,
+                              setfit_model, show_process=False)
+    info_list = df[
+        df.apply(
+            lambda x: x["score"] >= 0.1 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
+
+    q_head = "\n".join(prompt.split("\n")[:2])
+    c_tail = "\n".join(prompt.split("\n")[2:])[:4000]
+    out_text = chat_messages(
+            c_tail + "\n" + q_head.replace("下面的内容回答上面的问题", "上面的内容回答问题") + "用中文回答问题。",
+            [], show_process = False, max_length = 512
+        )
+    req_ = ""
+    for ele in out_text:
+        req_ = ele
+    out_text = req_.strip()
+
+    return df, info_list, out_text
+
+import gradio as gr
+
+#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)
+
+from setfit import SetFitModel
+setfit_model = SetFitModel.from_pretrained("svjack/setfit_info_cls")
+
+with gr.Blocks() as demo:
+    title = gr.HTML(
+            """<h1 align="center"> <font size="+3"> Genshin Impact Book QA ChatGLM3 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？',
+            ],
+            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,
+                                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)