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。
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。
LlamaIndex(GPT Index)是一個針對特定語料檢索的GPT專案,可以通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)
當用戶輸入查詢的問題時,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可以優化回答內容的整體結構,在單純的搜尋場景下其實這個優化沒什麼意義。但如果在垂直領域特定的聊天場景下,參照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資料夾下的檔案
documents = SimpleDirectoryReader(dir_path).load_data()
index = GPTSimpleVectorIndex.from_documents(documents)
print(documents)
# 儲存索引
index.save_to_disk('./index.json')
這裡通過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)
通過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,他在院子里納涼的時候,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預設的答案生成模型方案為text-davinci-002,我們也可以客製化化適合自己的模型設定:
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資料夾下的檔案
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()
這裡通過初始化函數客製化self.llm_predictor屬性,生成本地向量索引時,通過service_context引數進行動態呼叫即可:index = GPTSimpleVectorIndex.from_documents(documents,service_context=self.service_context) 。
藉此,我們就可以通過垂直領域語料來「客製化化」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。