update code

app.py

@@ -2,42 +2,43 @@ import gradio as gr
 import os
 import time
 
-from langchain.document_loaders import UnstructuredMarkdownLoader
+from langchain.chains import LLMChain
+from langchain.memory import ConversationBufferMemory
+from langchain_community.llms import LlamaCpp
+from langchain_experimental.chat_models import Llama2Chat
 
-from langchain.text_splitter import CharacterTextSplitter
+from langchain.prompts.chat import (
+    ChatPromptTemplate,
+    HumanMessagePromptTemplate,
+    MessagesPlaceholder,
+)
 
-from langchain.llms import OpenAI
+from langchain.schema import SystemMessage
 
-from langchain.embeddings import OpenAIEmbeddings
+import urllib
 
+urllib.request.urlretrieve(
+    "https://huggingface.co/hfl/chinese-alpaca-2-7b-rlhf-gguf/resolve/main/ggml-model-q6_k.gguf?download=true", 
+    "ggml-model-q6_k.gguf"
+    )
 
-from langchain.vectorstores import Chroma
-
-from langchain.chains import ConversationalRetrievalChain
-from langchain.prompts.prompt import PromptTemplate
+template_messages = [
+    SystemMessage(content="你是一名软件工程师，你的名字叫做贺英旭。请你以这个身份回答以下问题！"),
+    MessagesPlaceholder(variable_name="chat_history"),
+    HumanMessagePromptTemplate.from_template("{text}"),
+]
 
-os.environ['OPENAI_API_KEY'] = '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'
+prompt_template = ChatPromptTemplate.from_messages(template_messages)
 
-_template = """Assume you are He Yingxu, please complete the following conversations:
-Chat History:
-{chat_history}
-Follow Up Input: {question}
-"""
+llm = LlamaCpp(
+    model_path="ggml-model-q6_k.gguf",
+    temperature=0.75,
+    max_tokens=64
+)
+model = Llama2Chat(llm=llm)
 
-CUSTOM_QUESTION_PROMPT = PromptTemplate.from_template(_template)
-
-loader = UnstructuredMarkdownLoader('docs/resume.md')
-documents = loader.load()
-text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
-texts = text_splitter.split_documents(documents)
-embeddings = OpenAIEmbeddings()
-db = Chroma.from_documents(texts, embeddings)
-retriever = db.as_retriever()
-qa = ConversationalRetrievalChain.from_llm(
-    llm=OpenAI(temperature=0.3),
-    retriever=retriever,
-    condense_question_prompt=CUSTOM_QUESTION_PROMPT,
-    return_source_documents=False)
+memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
+chain = LLMChain(llm=model, prompt=prompt_template, memory=memory)
 
 
 def add_text(history, text):
@@ -47,7 +48,7 @@ def add_text(history, text):
 
 def bot(history):
     print(history)
-    response = infer(history[-1][0], history)
+    response = infer(history[-1][0])
     history[-1][1] = ""
     
     for character in response:     
@@ -56,19 +57,11 @@ def bot(history):
         yield history
     
 
-def infer(question, history):
-    
-    res = []
-    for human, ai in history[:-1]:
-        pair = (human, ai)
-        res.append(pair)
+def infer(question):
     
-    chat_history = res
-    #print(chat_history)
-    query = question
-    result = qa({"question": query, "chat_history": chat_history})
+    result = chain.run(text=question).strip()
     #print(result)
-    return result["answer"]
+    return result
 
 
 css = """

requirements.txt

@@ -1,7 +1,10 @@
 openai
+urllib
 tiktoken
 chromadb
 pypdf
 langchain
+langchain_community
+langchain_experimental
 unstructured
 unstructured[local-inference]