chinese, langchain_web_Conversation_Retrieval2

langchain_web_Conversation_Retrieval2.py

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+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain.chains import create_retrieval_chain
+from langchain.chains import create_history_aware_retriever
+from langchain_core.messages import HumanMessage, AIMessage
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.prompts import MessagesPlaceholder
+from langchain_community.llms import Ollama
+from langchain_community.embeddings import OllamaEmbeddings
+from langchain_community.vectorstores import FAISS
+
+from langchain_community.document_loaders import WebBaseLoader
+from langchain.callbacks.manager import CallbackManager
+from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
+
+# 使用 WebBaseLoader 從指定網址加載文檔
+loader = WebBaseLoader("https://www.ollama.com/")
+docs = loader.load()
+
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+# 使用 RecursiveCharacterTextSplitter 將文檔分割成較小的塊
+text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+documents = text_splitter.split_documents(docs)
+
+# 初始化 Ollama 語言模型
+llm = Ollama(model='kenneth85/llama-3-taiwan:8b-instruct', callback_manager=CallbackManager([StreamingStdOutCallbackHandler()]))
+# 初始化 Ollama 嵌入模型
+embeddings = OllamaEmbeddings()
+
+# 使用 FAISS 創建向量數據庫並加載分割後的文檔
+vector = FAISS.from_documents(documents, embeddings)
+retriever = vector.as_retriever()
+
+# 創建用於生成搜索查詢的提示模板
+prompt_search_query = ChatPromptTemplate.from_messages([
+    MessagesPlaceholder(variable_name="chat_history"),
+    ("user", "{input}"),
+    ("user", "根據上述對話，生成一個搜尋查詢以檢索與對話相關的資訊")
+])
+# 創建帶有歷史上下文的檢索器
+retriever_chain = create_history_aware_retriever(llm, retriever, prompt_search_query)
+
+# 創建用於獲取答案的提示模板
+prompt_get_answer = ChatPromptTemplate.from_messages([
+    MessagesPlaceholder(variable_name="chat_history"),
+    ('system', '請根據以下上下文回答使用者的問題:\n\n{context}'),
+    ('user', '{input}'),
+])
+# 創建文檔鏈以生成答案
+document_chain = create_stuff_documents_chain(llm, prompt_get_answer)
+
+# 結合檢索器和文檔鏈，創建檢索鏈
+retrieval_chain_combine = create_retrieval_chain(retriever_chain, document_chain)
+
+# 初始化聊天歷史記錄
+chat_history = []
+input_text = input('>>> ')
+while input_text.lower() != 'bye':
+    if input_text:
+        # 調用檢索鏈並獲取回答
+        response = retrieval_chain_combine.invoke({
+            'input': input_text,
+            'chat_history': chat_history,
+        })
+        # 打印回答
+        # print(response['answer'])
+        # print(response)
+        print("-----------------")
+        # 將用戶輸入和 AI 回答添加到聊天歷史中
+        chat_history.append(HumanMessage(content=input_text))
+        chat_history.append(AIMessage(content=response['answer']))
+
+    input_text = input('>>> ')
+
+# https://www.linkedin.com/pulse/beginners-guide-conversational-retrieval-chain-using-langchain-pxhjc