Upload app.py

app.py

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+import gradio as gr
+import os
+from langchain_community.document_loaders import PyPDFLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.chat_models import ChatOpenAI
+
+from langchain.retrievers.document_compressors import LLMChainExtractor
+from langchain.retrievers.multi_query import MultiQueryRetriever
+from langchain.retrievers import ContextualCompressionRetriever
+from langchain.prompts.chat import ChatPromptTemplate, HumanMessagePromptTemplate
+
+
+
+from langchain.vectorstores import Chroma
+
+with open('../../openai_api_key.txt') as f:
+    api_key = f.read()
+os.environ['OPENAI_API_KEY'] = api_key
+
+chat = ChatOpenAI()
+
+embedding_function = HuggingFaceEmbeddings(model_name = "BAAI/bge-large-en-v1.5",model_kwargs={'device': 'cpu'},encode_kwargs={"normalize_embeddings": True})
+
+def add_docs(path):
+
+    loader = PyPDFLoader(file_path=path)
+    docs = loader.load_and_split(text_splitter=RecursiveCharacterTextSplitter(chunk_size = 500,
+                                                                                chunk_overlap = 100,
+                                                                                length_function = len,
+                                                                                is_separator_regex=False))
+    model_vectorstore = Chroma
+    db = model_vectorstore.from_documents(documents=docs,embedding= embedding_function, persist_directory="output/general_knowledge")
+    return db
+
+
+def answer_query(message, chat_history):
+    base_compressor = LLMChainExtractor.from_llm(chat)
+    db = Chroma(persist_directory = "output/general_knowledge", embedding_function=embedding_function)
+    base_retriever = db.as_retriever()
+    mq_retriever = MultiQueryRetriever.from_llm(retriever = base_retriever, llm=chat)
+    compression_retriever = ContextualCompressionRetriever(base_compressor=base_compressor, base_retriever=mq_retriever)
+
+    matched_docs = compression_retriever.get_relevant_documents(query = message)
+
+    context = ""
+
+    for doc in matched_docs:
+        page_content = doc.page_content
+        context+=page_content
+        context += "\n\n"
+    template = """
+    Answer the following question only by using the context given below in the triple backticks, do not use any other information to answer the question.
+    If you can't answer the given question with the given context, you can return an emtpy string ('')
+
+    Context: ```{context}```
+    ----------------------------
+    Question: {query}
+    ----------------------------
+    Answer: """
+    
+    human_message_prompt = HumanMessagePromptTemplate.from_template(template=template)
+    chat_prompt = ChatPromptTemplate.from_messages([human_message_prompt])
+    prompt = chat_prompt.format_prompt(query = message, context = context)
+    response = chat(messages=prompt.to_messages()).content
+
+    chat_history.append((message,response))
+    return "", chat_history
+
+
+
+with gr.Blocks() as demo:
+    gr.HTML("<h1 align = 'center'>Smart Assistant</h1>")
+
+    with gr.Row():
+
+        upload_files = gr.File(label = 'Upload a PDF',file_types=['.pdf'],file_count='single')
+
+    chatbot = gr.Chatbot()
+    msg = gr.Textbox(label = "Enter your question here")
+    upload_files.upload(add_docs,upload_files)
+    msg.submit(answer_query,[msg,chatbot],[msg,chatbot])
+
+
+if __name__ == "__main__":
+    demo.launch(share = True)