Update qabot.py

qabot.py

@@ -1,66 +1,68 @@
-from langchain_community.llms import CTransformers
-from langchain.prompts import PromptTemplate
-from langchain_core.runnables import RunnableSequence
-from langchain.chains import RetrievalQA
-from langchain_community.embeddings import GPT4AllEmbeddings
-from langchain_community.vectorstores import FAISS
-
-from huggingface_hub import hf_hub_download
-
-model_file = hf_hub_download(
-    repo_id="Pudding48/TinyLlamaTest",   # Replace with your model repo
-    filename="tinyllama-1.1b-chat-v1.0.Q8_0.gguf",
-    cache_dir="model"  # Will be created in the Space's environment
-)
-
-# Cấu hình
-#model_file = "model/tinyllama-1.1b-chat-v1.0.Q8_0.gguf"
-vector_dp_path = "vectorstores/db_faiss"
-
-# Load LLM
-def load_llm(model_file):
-    llm = CTransformers(
-        model=model_file,
-        model_type="llama",
-        temperature=0.01,
-        config={'gpu_layers': 0},
-        max_new_tokens=128,  
-        context_length=512
-    )
-    return llm
-
-# Tạo prompt template
-def creat_prompt(template):
-    prompt = PromptTemplate(template=template, input_variables=["context","question"])
-    return prompt
-
-# Tạo pipeline chain (thay cho LLMChain)
-def create_qa_chain(prompt, llm, db):
-    llm_chain = RetrievalQA.from_chain_type(
-        llm = llm,
-        chain_type = "stuff",
-        retriever =db.as_retriever(search_kwargs = {"k":1}),
-        return_source_documents = False,
-        chain_type_kwargs={'prompt':prompt}
-    )
-    return llm_chain
-
-def read_vector_db():
-    embedding_model = GPT4AllEmbeddings(model_file = "model/all-minilm-l6-v2-q4_0.gguf") 
-    db = FAISS.load_local(vector_dp_path, embedding_model,allow_dangerous_deserialization=True)
-    return db
-
-db = read_vector_db()
-llm = load_llm(model_file)
-# Mẫu prompt
-template = """<|im_start|>system\nSử dụng thông tin sau đây để trả lời câu hỏi. Nếu bạn không biết câu trả lời, hãy nói không biết, đừng cố tạo ra câu trả lời\n
-    {context}<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant"""
-
-# Khởi tạo các thành phần
-prompt = creat_prompt(template)
-llm_chain  =create_qa_chain(prompt, llm, db)
-
-# Chạy thử chain
-question = "Khoa công nghệ thông tin thành lập năm nào ?"
-response = llm_chain.invoke({"query": question})
-print(response)
+from langchain_community.llms import CTransformers
+from langchain.prompts import PromptTemplate
+from langchain_core.runnables import RunnableSequence
+from langchain.chains import RetrievalQA
+from langchain_community.embeddings import GPT4AllEmbeddings
+from langchain_community.vectorstores import FAISS
+
+from huggingface_hub import hf_hub_download
+# !pip install llama-cpp-python
+
+# from llama_cpp import Llama
+
+# model_file = Llama.from_pretrained(
+# 	repo_id="Pudding48/TinyLLamaTest",
+# 	filename="tinyllama-1.1b-chat-v1.0.Q8_0.gguf",
+# )
+
+model_file = hf_hub_download(
+    repo_id="Pudding48/TinyLlamaTest",  # 🟢 This must be a model repo, not a Space
+    filename="tinyllama-1.1b-chat-v1.0.Q8_0.gguf",
+    cache_dir="model"
+)
+
+# Vector store location
+vector_dp_path = "vectorstores/db_faiss"
+
+from prepare_vector_dp import create_db_from_text
+create_db_from_text()
+
+# Load LLM with CTransformers
+def load_llm(model_file):
+    return CTransformers(
+        model=model_file,
+        model_type="llama",
+        temperature=0.01,
+        config={'gpu_layers': 0},
+        max_new_tokens=128,
+        context_length=512
+    )
+
+# Create the prompt
+def creat_prompt(template):
+    return PromptTemplate(template=template, input_variables=["context", "question"])
+
+# Create QA pipeline
+def create_qa_chain(prompt, llm, db):
+    return RetrievalQA.from_chain_type(
+        llm=llm,
+        chain_type="stuff",
+        retriever=db.as_retriever(search_kwargs={"k": 1}),
+        return_source_documents=False,
+        chain_type_kwargs={'prompt': prompt}
+    )
+
+# Load vector DB
+def read_vector_db():
+    embedding_model = GPT4AllEmbeddings(model_file=model_file)
+    return FAISS.load_local(vector_dp_path, embedding_model, allow_dangerous_deserialization=True)
+
+# Build everything
+db = read_vector_db()
+llm = load_llm(model_file)
+
+template = """<|im_start|>system\nSử dụng thông tin sau đây để trả lời câu hỏi. Nếu bạn không biết câu trả lời, hãy nói không biết, đừng cố tạo ra câu trả lời\n
+{context}<|im_end|>\n<|im_start|>user\n{question}<|im_end|>\n<|im_start|>assistant"""
+
+prompt = creat_prompt(template)
+llm_chain = create_qa_chain(prompt, llm, db)