Nouvelle app

app.py

@@ -1,48 +1,85 @@
-import os
-from langchain.document_loaders import PyPDFLoader
-from langchain_community.llms import Ollama
-from langchain.chains import RetrievalQA
-from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.text_splitter import RecursiveCharacterTextSplitter
 from langchain.vectorstores import Chroma
+from langchain.document_loaders import PyPDFLoader
+from langchain import PromptTemplate
+from langchain.chains import ConversationalRetrievalChain
+from langchain.memory import ConversationBufferWindowMemory
 import gradio as gr
-from transformers import AutoModelForCausalLM, AutoTokenizer, pipeline
+from langchain_community.llms import Ollama
+
+db = Chroma(persist_directory='content/Sugar cane/db', embedding_function=HuggingFaceEmbeddings())
 
-loader = PyPDFLoader("content/China.pdf")
-text_splitter = RecursiveCharacterTextSplitter(
-    chunk_size = 500,
-    chunk_overlap  = 20,
-    length_function = len,
-)
-pages = loader.load_and_split(text_splitter)
-db = Chroma.from_documents(pages, HuggingFaceEmbeddings(), persist_directory = 'content/db')
 retriever = db.as_retriever()
 
-def build_qa_chain() -> RetrievalQA:
-    llm = Ollama(model="tinyllama")
-    '''
-    index = VectorstoreIndexCreator(
-        embedding=embedding,
-        text_splitter=splitter).from_loaders(loaders)
-    print(len(index.vectorstore.get()))
-    '''
-
-    # Prepare the pipeline
-    return RetrievalQA.from_chain_type(llm=llm,
-                                       chain_type="stuff",
-                                       retriever=retriever,
-                                       return_source_documents=True,
-                                       input_key="question")
-
-qa_chain = build_qa_chain()
-
-# creating the function
-def func(question):
-  result = qa_chain({'question': question, 'include_run_info': True})
-  return result['result']
-
-# creating the interface
-app = gr.Interface(fn=func, inputs = [gr.File(), 'text'], outputs = 'textbox', title = 'Question Answering bot', description = 'Input context and question, then get answers!')
-
-# launching the app
-app.launch()
+B_INST, E_INST = "[INST]", "[/INST]"
+B_SYS, E_SYS = "<<SYS>>\n", "\n<</SYS>>\n\n"
+
+instruction = "Given the context that has been provided. \n {context}, Answer the following question - \n{question}"
+
+system_prompt = """You are an expert in sugar cane cultivation.
+Be precise in your answers and give answers as concise as possible."""
+
+
+def get_prompt(instruction, system_prompt):
+    SYSTEM_PROMPT = B_SYS + system_prompt + E_SYS
+    prompt_template =  B_INST + SYSTEM_PROMPT + instruction + E_INST
+    return prompt_template
+
+template = get_prompt(instruction, system_prompt)
+
+prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+memory = ConversationBufferWindowMemory(
+    memory_key="chat_history", k=5,
+    return_messages=True
+)
+
+class ChatBot:
+  def __init__(self, memory, prompt, retriever = retriever):
+    self.memory = memory
+    self.prompt = prompt
+    self.retriever = retriever
+
+  def create_chat_bot(self):
+    llm = Ollama(model="llama2")
+    qa = ConversationalRetrievalChain.from_llm(
+      llm=llm,
+      retriever=self.retriever,
+      memory=self.memory,
+      combine_docs_chain_kwargs={"prompt": self.prompt}
+    )
+    return qa
+  
+chatbot = ChatBot(memory = memory, prompt = prompt)
+
+bot = chatbot.create_chat_bot()
+
+def clear_llm_memory():
+  bot.memory.clear()
+
+def update_prompt(sys_prompt):
+  if sys_prompt == "":
+    sys_prompt = system_prompt
+  template = get_prompt(instruction, sys_prompt)
+
+  prompt = PromptTemplate(template=template, input_variables=["context", "question"])
+
+  bot.combine_docs_chain.llm_chain.prompt = prompt
+
+with gr.Blocks() as demo:
+    chatbot = gr.Chatbot(label="Chat Bot", height = 300)
+    msg = gr.Textbox(label = "Question")
+    clear = gr.ClearButton([msg, chatbot])
+    clear_memory = gr.Button(value = "Clear LLM Memory")
+
+
+    def respond(message, chat_history):
+        bot_message = bot({"question": message})['answer']
+        chat_history.append((message, bot_message))
+        return "", chat_history
+
+    msg.submit(respond, inputs=[msg, chatbot], outputs=[msg, chatbot])
+    clear_memory.click(clear_llm_memory)
+
+demo.launch(share=False, debug=True)

requirements.txt

@@ -1,2 +1,3 @@
 langchain
-transformers
+transformers
+pypdf