Merge branch 'main' into main

Dockerfile

@@ -25,7 +25,7 @@ COPY backend .
 
 # Install backend dependencies
 COPY backend/requirements.txt .
-RUN pip install --no-cache-dir -r requirements.txt --vvv
+RUN pip install --no-cache-dir -r requirements.txt
 
 # Stage 3: Serve frontend and backend using nginx and gunicorn
 FROM nginx:latest AS production

app.py

@@ -1,43 +1,151 @@
-import io
 import os
-import streamlit as st
-import tempfile
+import io
+import argparse
+import json
+import openai
+import sys
+from dotenv import load_dotenv
+from langchain_community.document_loaders import TextLoader
+from langchain_community.document_loaders import UnstructuredPDFLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Vectara
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.prompts import ChatPromptTemplate
+from langchain_core.runnables import RunnablePassthrough
+from langchain.prompts import PromptTemplate
+from langchain_text_splitters import RecursiveCharacterTextSplitter
 
-from scripts import analyze_metadata, generate_metadata, ingest, MODEL_NAME
 
-st.title('# DocVerifyRAG')
-st.write('## Anomaly detection for BIM document metadata')
+load_dotenv()
 
-with st.form('analyze_form'):
-    st.write('Enter your file metadata in the following schema:')
-    text = st.text_input(label='Filename, Description, Discipline',                                                
-                        value="", placeholder=str)
-    submitted = st.form_submit_button('Submit')
+MODEL_NAME = "mistralai/Mixtral-8x7B-Instruct-v0.1"
 
-    if submitted:
-        filename, description, discipline = text.split(',')
+vectara_customer_id = os.environ['VECTARA_CUSTOMER_ID']
+vectara_corpus_id = os.environ['VECTARA_CORPUS_ID']
+vectara_api_key = os.environ['VECTARA_API_KEY']
 
-        st.write('## Analyzing with Vectara + together.ai')
-        analysis = analyze_metadata(filename, description, discipline)
+embeddings = HuggingFaceEmbeddings(model_name="intfloat/multilingual-e5-large")
 
-        st.write(analysis)
+vectara = Vectara(vectara_customer_id=vectara_customer_id,
+                      vectara_corpus_id=vectara_corpus_id,
+                      vectara_api_key=vectara_api_key)
 
-st.write('## Generate metadata?')
-uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf","txt"])
 
-if uploaded_file is not None:
-    with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
-        tmp.write(uploaded_file.read())
-        file_path = tmp.name
-        st.write(f'Created temporary file {file_path}')
+summary_config = {"is_enabled": True, "max_results": 3, "response_lang": "eng"}
+retriever = vectara.as_retriever(
+    search_kwargs={"k": 3, "summary_config": summary_config}
+)
 
-    docs = ingest(file_path)
-    st.write('## Querying Together.ai API')
-    metadata = generate_metadata(docs)
+template = """
+passage: You are a helpful assistant that understands BIM building documents.
+passage: You will analyze BIM document metadata composed of filename, description, and engineering discipline.
+passage: The metadata is written in German.
+passage: Filename: {filename}, Description: {description}, Engineering discipline: {discipline}.
+query: Does the filename match other filenames within the same discipline?
+query: Does the description match the engineering discipline?
+query: How different is the metadata to your curated information?
+query: Highligh any discrepancies and comment on wether or not the metadata is anomalous.
+"""
+
+prompt = PromptTemplate(template=template, input_variables=['filename', 'description', 'discipline'])
+
+
+def get_sources(documents):
+    return documents[:-1]
+
+def get_summary(documents):
+    return documents[-1].page_content
+
+def ingest(file_path):
+    extension = os.path.splitext(file_path)[1].lower()
+
+    if extension == '.pdf':
+        loader = UnstructuredPDFLoader(file_path)
+    elif extension == '.txt':
+        loader = TextLoader(file_path)
+    else:
+        raise NotImplementedError('Only .txt or .pdf files are supported')
+
+    # transform locally
+    documents = loader.load()
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=0,
+    separators=[
+        "\n\n",
+        "\n",
+        " ",
+        ",",
+        "\uff0c",  # Fullwidth comma
+        "\u3001",  # Ideographic comma
+        "\uff0e",  # Fullwidth full stop
+        # "\u200B",  # Zero-width space (Asian languages)
+        # "\u3002",  # Ideographic full stop (Asian languages)
+        "",
+    ])
+    docs = text_splitter.split_documents(documents)
+
+    return docs
+
+
+
+def generate_metadata(docs):
+    prompt_template = """
+    BimDiscipline = ['plumbing', 'network', 'heating', 'electrical', 'ventilation', 'architecture']
 
-    form = st.form(key='generate_form')
-    st.write(f'## Suggested Metadata Generated by {MODEL_NAME}')    
-    st.write(f'### {metadata}')
-    delete_file_button = form.form_submit_button(label='Delete file')        
-    if delete_file_button:
-        os.remove(file_path)
+    You are a helpful assistant that understands BIM documents and engineering disciplines. Your answer should be in JSON format and only include the filename, a short description, and the engineering discipline the document belongs to, distinguishing between {[d.value for d in BimDiscipline]} based on the given document."
+
+    Analyze the provided document, which could be in either German or English. Extract the filename, its description, and infer the engineering discipline it belongs to. Document:
+    context="
+    """     
+    # plain text     
+    filepath = [doc.metadata for doc in docs][0]['source']
+    context = "".join(
+        [doc.page_content.replace('\n\n','').replace('..','') for doc in docs])
+
+    prompt = f'{prompt_template}{context}"\nFilepath:{filepath}'
+
+    #print(prompt)
+    
+    # Create client
+    client = openai.OpenAI(
+        base_url="https://api.together.xyz/v1",
+        api_key=os.environ["TOGETHER_API_KEY"],
+        #api_key=userdata.get('TOGETHER_API_KEY'),    
+    )
+
+    # Call the LLM with the JSON schema
+    chat_completion = client.chat.completions.create(
+        model=MODEL_NAME,        
+        messages=[
+            {
+                "role": "system",
+                "content": f"You are a helpful assistant that responsds in JSON format"                
+            },
+            {
+                "role": "user",
+                "content": prompt                                
+            }
+        ]
+    )
+
+    return json.loads(chat_completion.choices[0].message.content)    
+
+
+def analyze_metadata(filename, description, discipline):
+    formatted_prompt = prompt.format(filename=filename, description=description, discipline=discipline)
+    return (retriever | get_summary).invoke(formatted_prompt)
+
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Generate metadata for a BIM document")
+    parser.add_argument("document", metavar="FILEPATH", type=str,
+                        help="Path to the BIM document")
+
+    args = parser.parse_args()
+
+    if not os.path.exists(args.document) or not os.path.isfile(args.document):
+        print("File '{}' not found or not accessible.".format(args.document))
+        sys.exit(-1)
+
+    docs = ingest(args.document)
+    metadata = generate_metadata(docs)
+    print(metadata)

app_V2.py

@@ -0,0 +1,247 @@
+import tempfile
+import streamlit as st
+from PyPDF2 import PdfReader
+from langchain.text_splitter import CharacterTextSplitter
+from langchain.embeddings import OpenAIEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.chat_models import ChatOpenAI
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+import os
+import pickle
+from datetime import datetime
+from backend.generate_metadata import generate_metadata, ingest
+
+MODEL_NAME = "mixtral"
+css = '''
+<style>
+.chat-message {
+    padding: 1.5rem; border-radius: 0.5rem; margin-bottom: 1rem; display: flex
+}
+.chat-message.user {
+    background-color: #2b313e
+}
+.chat-message.bot {
+    background-color: #475063
+}
+.chat-message .avatar {
+  width: 20%;
+}
+.chat-message .avatar img {
+  max-width: 78px;
+  max-height: 78px;
+  border-radius: 50%;
+  object-fit: cover;
+}
+.chat-message .message {
+  width: 80%;
+  padding: 0 1.5rem;
+  color: #fff;
+}
+'''
+bot_template = '''
+<div class="chat-message bot">
+    <div class="avatar">
+        <img src="https://i.ibb.co/cN0nmSj/Screenshot-2023-05-28-at-02-37-21.png" 
+        style="max-height: 78px; max-width: 78px; border-radius: 50%; object-fit: cover;">
+    </div>
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+user_template = '''
+<div class="chat-message user">
+    <div class="avatar">
+        <img src="https://i.ibb.co/rdZC7LZ/Photo-logo-1.png">
+    </div>    
+    <div class="message">{{MSG}}</div>
+</div>
+'''
+
+
+def get_pdf_text(pdf_docs):
+    text = ""
+    for pdf in pdf_docs:
+        pdf_reader = PdfReader(pdf)
+        for page in pdf_reader.pages:
+            text += page.extract_text()
+    return text
+
+
+def get_text_chunks(text):
+    text_splitter = CharacterTextSplitter(
+        separator="\n",
+        chunk_size=1000,
+        chunk_overlap=200,
+        length_function=len
+    )
+    chunks = text_splitter.split_text(text)
+    return chunks
+
+
+def get_vectorstore(text_chunks):
+    embeddings = OpenAIEmbeddings()
+    # embeddings = HuggingFaceInstructEmbeddings(model_name="hkunlp/instructor-xl")
+    vectorstore = FAISS.from_texts(texts=text_chunks, embedding=embeddings)
+    return vectorstore
+
+
+def get_conversation_chain(vectorstore):
+    llm = ChatOpenAI()
+    # llm = HuggingFaceHub(repo_id="google/flan-t5-xxl", model_kwargs={"temperature":0.5, "max_length":512})
+
+    memory = ConversationBufferMemory(
+        memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=llm,
+        retriever=vectorstore.as_retriever(),
+        memory=memory
+    )
+    return conversation_chain
+
+
+def handle_userinput(user_question):
+    response = st.session_state.conversation({'question': user_question})
+    st.session_state.chat_history = response['chat_history']
+
+    for i, message in enumerate(st.session_state.chat_history):
+        # Display user message
+        if i % 2 == 0:
+            st.write(user_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+        else:
+            print(message)
+            # Display AI response
+            st.write(bot_template.replace("{{MSG}}", message.content), unsafe_allow_html=True)
+
+
+def safe_vec_store():
+    # USE VECTARA INSTEAD
+    os.makedirs('vectorstore', exist_ok=True)
+    filename = 'vectors' + datetime.now().strftime('%Y%m%d%H%M') + '.pkl'
+    file_path = os.path.join('vectorstore', filename)
+    vector_store = st.session_state.vectorstore
+
+    # Serialize and save the entire FAISS object using pickle
+    with open(file_path, 'wb') as f:
+        pickle.dump(vector_store, f)
+
+
+"""
+def main():
+
+
+
+    st.subheader("Your documents")
+
+    if st.session_state.classify:
+        pdf_doc = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=False)
+    else:
+        pdf_docs = st.file_uploader("Upload your PDFs here and click on 'Process'", accept_multiple_files=True)
+        filenames = [file.name for file in pdf_docs if file is not None]
+    if st.button("Process"):
+        with st.spinner("Processing"):
+            if st.session_state.classify:
+                # THE CLASSIFICATION APP
+                st.write("Classifying")
+                plain_text_doc = ingest(pdf_doc.name)
+                classification_result = generate_metadata(plain_text_doc)
+                st.write(classification_result)
+            else:
+                # NORMAL RAG
+                loaded_vec_store = None
+                for filename in filenames:
+                    if ".pkl" in filename:
+                        file_path = os.path.join('vectorstore', filename)
+                        with open(file_path, 'rb') as f:
+                            loaded_vec_store = pickle.load(f)
+                raw_text = get_pdf_text(pdf_docs)
+                text_chunks = get_text_chunks(raw_text)
+                vec = get_vectorstore(text_chunks)
+                if loaded_vec_store:
+                    vec.merge_from(loaded_vec_store)
+                    st.warning("loaded vectorstore")
+                if "vectorstore" in st.session_state:
+                    vec.merge_from(st.session_state.vectorstore)
+                    st.warning("merged to existing")
+                st.session_state.vectorstore = vec
+                st.session_state.conversation = get_conversation_chain(vec)
+        st.success("data loaded")
+
+    if "conversation" not in st.session_state:
+        st.session_state.conversation = None
+    if "chat_history" not in st.session_state:
+        st.session_state.chat_history = None
+
+    user_question = st.text_input("Ask a question about your documents:")
+    if user_question:
+        handle_userinput(user_question)
+    with st.sidebar:
+        st.subheader("Classification instructions")
+        classifier_docs = st.file_uploader("Upload your instructions here and click on 'Process'",
+                                           accept_multiple_files=True)
+        filenames = [file.name for file in classifier_docs if file is not None]
+
+        if st.button("Process Classification"):
+            st.session_state.classify = True
+            with st.spinner("Processing"):
+                st.warning("set classify")
+                time.sleep(3)
+
+        if st.button("Save Embeddings"):
+            if "vectorstore" in st.session_state:
+                safe_vec_store()
+                # st.session_state.vectorstore.save_local("faiss_index")
+                st.sidebar.success("saved")
+            else:
+                st.sidebar.warning("No embeddings to save. Please process documents first.")
+
+        if st.button("Load Embeddings"):
+            st.warning("this function is not in use, just upload the vectorstore")
+"""
+
+
+def main():
+
+    st.set_page_config(page_title="Doc Verify RAG", page_icon=":mag:")
+    st.write('Anomaly detection for document metadata', unsafe_allow_html=True)
+    st.header("Doc Verify RAG :mag:")
+
+    def set_pw():
+        st.session_state.openai_api_key = True
+
+    if "openai_api_key" not in st.session_state:
+        st.session_state.openai_api_key = False
+    if "openai_org" not in st.session_state:
+        st.session_state.openai_org = False
+    if "classify" not in st.session_state:
+        st.session_state.classify = False
+
+    col1, col2 = st.columns(2)
+    with col1:
+        uploaded_file = st.file_uploader("Choose a PDF file", type=["pdf", "txt"])
+
+        if uploaded_file is not None:
+            try:
+                with tempfile.NamedTemporaryFile(delete=False, suffix=os.path.splitext(uploaded_file.name)[1]) as tmp:
+                    tmp.write(uploaded_file.read())
+                    file_path = tmp.name
+                    st.write(f'Created temporary file {file_path}')
+
+                docs = ingest(file_path)
+                st.write('## Querying Together.ai API')
+                metadata = generate_metadata(docs)
+                st.write(f'## Metadata Generated by {MODEL_NAME}')
+                st.write(metadata)
+
+                # Clean up the temporary file
+                os.remove(file_path)
+
+            except Exception as e:
+                st.error(f'Error: {e}')
+    with col2:
+        OPENAI_API_KEY = st.text_input("OPENAI API KEY:", type="password",
+                                       disabled=st.session_state.openai_api_key, on_change=set_pw)
+        classification = st.file_uploader("upload the metadata", type=["csv", "txt"])
+
+
+if __name__ == '__main__':
+    main()