讀破萬卷,神交古人,突破ChatGPT4096的Token限制,建立自己的垂直領域資料人工智慧助理

ChatGPT的泛用性極高，上知天文，下通地理，參考古今，博稽中外，幾乎無所不知，無所不曉。但如果涉及垂直領域的專業知識點，ChatGPT難免也會有語焉不詳，閃爍其詞的毛病，本次我們將特定領域的學習材料“喂”給ChatGPT，讓它“學習”後再來回答專業問題。

專業領域語料問題

所謂專業領域語料問題，可以理解為特定範圍內的知識圖譜，也就是給GPT提供前置的檢索維度，舉個例子，大家都讀過魯迅的名篇《從百草園到三味書屋》，文章中涉及一個“美女蛇”的典故，假設我們沒有給GPT設定一個特定範圍，直接問“美女蛇”的相關問題：

一望而知，ChatGPT對於“美女蛇”典故的理解出現了資訊偏差問題，它以為“美女蛇”指的是《白蛇傳》中的白素貞和許仙以及法海的故事。

但其實我們都知道，《從百草園到三味書屋》中“美女蛇”指的是人首蛇身的怪物，能喚人名，倘一答應，夜間便要來吃這人的肉的故事。

所以，如果我們想談論“美女蛇”相關的問題，必須讓ChatGPT有一個特定的“語境”，它才能理解真正要談論的話題，所以需要把《從百草園到三味書屋》作為語料“喂”給ChatGPT才可以，當然了《從百草園到三味書屋》作為人盡皆知的雜文，它肯定預設儲存於ChatGPT的語料庫中，但假設如果某一個領域的論文或者其他資料，並未出現在ChatGPT的語料庫中，而該文章的長度又超過ChatGPT輸入的4096個token的限制，那麼就非常麻煩了，所以讓ChatGPT具備學習“新材料”的能力就顯得十分必要了。

llama_index配置語料索引

LlamaIndex（GPT Index）是一個針對特定語料檢索的GPT專案，可以透過索引檔案把外部語料資料和GPT連線起來，首先安裝專案：

pip3 install llama-index

注意該專案依賴langchain模組，為了確保不出問題，最好升級一下langchain模組：

pip3 install --upgrade langchain

LlamaIndex所做的是將我們的原始語料資料轉換成一個基於向量的索引，這對檢索來說是非常高效的。它將使用這個索引，根據查詢和資料的相似性，找到最相關的部分。然後，它將把檢索到的內容插入到它將傳送給GPT的引導詞(prompt)中，這樣GPT就有了回答問題的“語境”:

具體工作流：

將本地答案資料集，轉為向量儲存到向量資料(index.json)

當使用者輸入查詢的問題時，把問題轉為向量然後從向量資料庫中查詢相近的答案topK 這個時候其實就是我們最普遍的問答查詢方案，在沒有GPT的時候就直接返回相關的答案整個流程就結束了。

基於GPT可以最佳化回答內容的整體結構，在單純的搜尋場景下其實這個最佳化沒什麼意義。但如果在垂直領域特定的聊天場景下，引用相關領域內容回覆時，資料檢索會更加精準。

首先把《從百草園到三味書屋》這篇文章寫入到專案的data目錄中，隨後編寫程式碼：

import os  
from llama_index import SimpleDirectoryReader, GPTSimpleVectorIndex,LLMPredictor,ServiceContext  
  
from langchain import OpenAI  
  
os.environ["OPENAI_API_KEY"] = 'apikey'  
  
  
class LLma:  
  
    # 建立本地索引  
    def create_index(self,dir_path="./data"):  
  
  
        # 讀取data資料夾下的文件  
        documents = SimpleDirectoryReader(dir_path).load_data()  
  
        index = GPTSimpleVectorIndex.from_documents(documents)  
  
        print(documents)  
  
        # 儲存索引  
        index.save_to_disk('./index.json')

這裡透過GPTSimpleVectorIndex.from_documents方法讀取data目錄中的語料文章，隨後轉換為向量索引儲存在本地磁碟的index.json檔案中。

執行建立索引方法：

if __name__ == '__main__':  
      
    llma = LLma()  
  
    # 建立索引  
    llma.create_index()

索引的內容：

{"index_struct": {"__type__": "simple_dict", "__data__": {"index_id": "86c83b5a-a975-43ab-8505-cbc8f0ae68e2", "summary": null, "nodes_dict": {"da552579-e0f4-4ee0-be68-a3c392e39dc2": "a2521cfa-13c5-49b2-9cfd-7206fe493666", "c1f7df04-5e6c-4327-a0cc-4a3489d50d19": "68b609e3-2ec5-4de2-ac43-eb28105364ca"}, "doc_id_dict": {"87411099-60d8-4272-a7d1-6e8676fc42a0": ["da552579-e0f4-4ee0-be68-a3c392e39dc2", "c1f7df04-5e6c-4327-a0cc-4a3489d50d19"]}, "embeddings_dict": {"da552579-e0f4-4ee0-be68-a3c392e39dc2": [0.004821529611945152, -0.005787167698144913, 0.00886388961225748, -0.0005273548304103315, -0.0007779211737215519, 0.022242968901991844, -0.0035828494001179934, -0.023534925654530525, -0.03012790158390999, -0.014744291082024574, 0.004718306474387646, -0.0010788505896925926, -0.006236688699573278, 0.0033247910905629396, 0.01692862994968891, 0.02300216071307659, 0.01628931239247322, 0.008157975040376186, 0.028822625055909157, -0.0011337919859215617, 0.006499741692095995, 0.02746407315135002, 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llama_index根據向量索引進行語義化查詢

llama_index可以在本地匹配向量索引後再構建提示詞：

class LLma:  
  
    def query_index(self,prompt,index_path="./index.json"):  
  
        # 載入索引  
        local_index = GPTSimpleVectorIndex.load_from_disk(index_path)  
        # 查詢索引  
        res = local_index.query(prompt)  
  
        print(res)

透過GPTSimpleVectorIndex.load_from_disk方法將向量索引匯入，執行方法：

if __name__ == '__main__':  
      
    llma = LLma()  
  
    # 建立索引  
    #llma.create_index()  
  
    # 查詢索引  
    llma.query_index("講一下美女蛇的故事")

程式返回：

美女蛇的故事可以追溯到古廟裡的一個讀書人。晚間，他在院子裡納涼的時候，突然聽到有人在叫他的名字。他四面看時，卻見一個美女的臉露在牆頭上，向他一笑，隱去了。他很高興，但竟給那走來夜談的老和尚識破了機關，說他臉上有些妖氣，一定遇見“美女蛇”了。

可以看到，“美女蛇”的故事終於是我們想要的“美女蛇”的故事了。

llama_index模型定製化

llama_index預設的答案生成模型方案為text-davinci-002，我們也可以定製化適合自己的模型配置：

class LLma:  
  
    def __init__(self) -> None:  
          
        self.llm_predictor = LLMPredictor(llm=OpenAI(temperature=0, model_name="text-davinci-003",max_tokens=1800))  
        self.service_context = ServiceContext.from_defaults(llm_predictor=self.llm_predictor)  
  
    # 查詢本地索引  
    def query_index(self,prompt,index_path="./index.json"):  
  
        # 載入索引  
        local_index = GPTSimpleVectorIndex.load_from_disk(index_path)  
        # 查詢索引  
        res = local_index.query(prompt)  
  
        print(res)  
  
  
    # 建立本地索引  
    def create_index(self,dir_path="./data"):  
  
  
        # 讀取data資料夾下的文件  
        documents = SimpleDirectoryReader(dir_path).load_data()  
  
        index = GPTSimpleVectorIndex.from_documents(documents,service_context=self.service_context)  
  
        print(documents)  
  
        # 儲存索引  
        index.save_to_disk('./index.json')  
  
  
if __name__ == '__main__':  
      
    llma = LLma()

這裡透過初始化函式定製self.llm_predictor屬性，生成本地向量索引時，透過service_context引數進行動態呼叫即可：index = GPTSimpleVectorIndex.from_documents(documents,service_context=self.service_context) 。

結語

藉此，我們就可以透過垂直領域語料來“定製化”ChatGPT的回答了，最後奉上專案地址：github.com/zcxey2911/llama_index_examples_python3.10，與君共觴。