initial commit

.gitignore

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+__pycache__/
+*$py.class
+
+*.so
+.env
+.~env
+venv/

app.py

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+# UI comes here
+import streamlit as st
+
+from langchain_text_splitters import Language
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.embeddings import HuggingFaceEmbeddings
+from langchain.vectorstores import FAISS
+from langchain.memory import ConversationBufferMemory
+from langchain.chains import ConversationalRetrievalChain
+from transformers import pipeline
+from langchain import HuggingFacePipeline
+
+
+
+gpt_model = 'gpt-4-1106-preview'
+embedding_model = 'text-embedding-3-small'
+
+def init():
+    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        
+
+def init_llm_pipeline(openai_key):
+    if "llm" not in st.session_state:
+        model_id = "bigcode/starcoder2-15b"
+        quantization_config = BitsAndBytesConfig(
+        load_in_4bit=True,
+        bnb_4bit_compute_dtype=torch.float16
+        )
+
+        tokenizer = AutoTokenizer.from_pretrained(model_id)
+        model = AutoModelForCausalLM.from_pretrained(
+            model_id,
+            quantization_config=quantization_config,
+            device_map="auto",
+        )
+        tokenizer.add_eos_token = True
+        tokenizer.pad_token_id = 0
+        tokenizer.padding_side = "left"
+
+        text_generation_pipeline = pipeline(
+        model=model,
+        tokenizer=tokenizer,
+        task="text-generation",
+        temperature=0.7,
+        repetition_penalty=1.1,
+        return_full_text=True,
+        max_new_tokens=300,
+        )
+        st.session_state.llm = HuggingFacePipeline(pipeline=text_generation_pipeline)          
+
+def get_text(docs):
+    return docs.getvalue().decode("utf-8")
+
+def get_vectorstore(documents):
+    python_splitter = RecursiveCharacterTextSplitter.from_language(
+        language=Language.PYTHON, chunk_size=2000, chunk_overlap=200
+    )
+    texts = python_splitter.split_documents(documents)
+
+    embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+
+    db = FAISS.from_documents(texts, embeddings)
+    retriever = db.as_retriever(
+        search_type="mmr",  # Also test "similarity"
+        search_kwargs={"k": 8},
+    )
+    return retriever
+    
+def get_conversation(retriever):
+    memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)
+    conversation_chain = ConversationalRetrievalChain.from_llm(
+        llm=st.session_state.llm,
+        retriever=retriever,
+        memory = memory   
+    )
+    return conversation_chain
+
+def handle_user_input(question):
+    response = st.session_state.conversation({'question':question})
+    st.session_state.chat_history = response['chat_history']
+    for i, message in enumerate(st.session_state.chat_history):
+        if i % 2 == 0:
+            with st.chat_message("user"):
+                st.write(message.content)
+        else:
+            with st.chat_message("assistant"):
+                st.write(message.content)
+
+def main():
+    #load_dotenv()
+    init()
+
+    st.set_page_config(page_title="Coding-Assistent", page_icon=":books:")
+
+    st.header(":books: Coding-Assistent ")
+    user_input = st.chat_input("Stellen Sie Ihre Frage hier")
+    if user_input:
+        with st.spinner("Führe Anfrage aus ..."):        
+            handle_user_input(user_input)
+
+
+    with st.sidebar:
+        st.subheader("Code Upload")
+        upload_docs=st.file_uploader("Dokumente hier hochladen", accept_multiple_files=True)
+        if st.button("Hochladen"):
+            with st.spinner("Analysiere Dokumente ..."):
+                init_llm_pipeline()
+                raw_text = get_text(upload_docs)
+                vectorstore = get_vectorstore(raw_text)
+                st.session_state.conversation = get_conversation(vectorstore) 
+
+
+if __name__ == "__main__":
+    main()

requirements.txt

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+streamlit
+langchain
+langchain-community
+python-dotenv
+faiss-cpu
+huggingface-hub
+accelerate 
+bitsandbytes
+torch
+langchain-text-splitters
+sentence_transformers 
+git+https://github.com/huggingface/transformers.git