Create app.py

Create Llama-3.2 RAG PDF

app.py

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+import streamlit as st
+import ollama
+import os
+import logging
+
+from langchain_ollama import ChatOllama
+
+
+from langchain_community.document_loaders import PyMuPDFLoader
+from langchain_text_splitters import RecursiveCharacterTextSplitter
+
+from langchain_ollama import OllamaEmbeddings
+
+import faiss
+from langchain_community.vectorstores import FAISS
+from langchain_community.docstore.in_memory import InMemoryDocstore
+
+from langchain import hub
+from langchain_core.output_parsers import StrOutputParser
+from langchain_core.runnables import RunnablePassthrough
+from langchain_core.prompts import ChatPromptTemplate
+
+from typing import List, Tuple, Dict, Any, Optional
+
+
+# pip install -qU langchain-ollama
+# pip install langchain
+
+        ##### Logging
+
+
+
+def format_docs(docs):
+    return "\n\n".join([doc.page_content for doc in docs])
+
+@st.cache_resource(show_spinner=True)
+def extract_model_names(
+    models_info: Dict[str, List[Dict[str, Any]]],
+) -> Tuple[str, ...]:
+    """
+    Extract model names from the provided models information.
+
+    Args:
+        models_info (Dict[str, List[Dict[str, Any]]]): Dictionary containing information about available models.
+
+    Returns:
+        Tuple[str, ...]: A tuple of model names.
+    """
+
+    # Logging configuration
+    logging.basicConfig(
+        level=logging.INFO,
+        format="%(asctime)s - %(levelname)s - %(message)s",
+        datefmt="%Y-%m-%d %H:%M:%S",
+    )
+
+    logger = logging.getLogger(__name__)
+
+    logger.info("Extracting model names from models_info")
+    model_names = tuple(model["name"] for model in models_info["models"])
+    logger.info(f"Extracted model names: {model_names}")
+    return model_names
+
+
+def generate_response(rag_chain, input_text):
+
+    response = rag_chain.invoke(input_text)
+
+    return response
+
+
+
+
+def main() -> None:
+
+    st.title("🧠 This is a RAG Chatbot with Ollama and Langchain !!!")
+
+    st.write("The LLM model unsloth/Llama-3.2-3B-Instruct is used")
+    st.write("You can upload a PDF to chat with !!!")
+
+    with st.sidebar:
+        st.title("PDF FILE UPLOAD:")
+        docs = st.file_uploader("Upload your PDF File and Click on the Submit & Process Button", accept_multiple_files=False, key="pdf_uploader")
+
+    text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
+
+    chunks = text_splitter.split_documents(docs)
+
+    embeddings = OllamaEmbeddings(model='nomic-embed-text', base_url="http://localhost:11434")
+
+    single_vector = embeddings.embed_query("this is some text data")
+
+    index = faiss.IndexFlatL2(len(single_vector))
+
+    vector_store = FAISS(
+            embedding_function=embeddings,
+            index=index,
+            docstore=InMemoryDocstore(),
+            index_to_docstore_id={}
+    )
+
+    ids = vector_store.add_documents(documents=chunks)
+
+    ## Retreival
+
+    retriever = vector_store.as_retriever(search_type="mmr", search_kwargs = {'k': 3, 
+                                                                            'fetch_k': 100,
+                                                                            'lambda_mult': 1})
+
+
+    prompt = """
+        You are an assistant for question-answering tasks. Use the following pieces of retrieved context to answer the question.
+        If you don't know the answer, just say that you don't know.
+        Answer in bullet points. Make sure your answer is relevant to the question and it is answered from the context only.
+        Question: {question} 
+        Context: {context} 
+        Answer:
+    """
+
+    prompt = ChatPromptTemplate.from_template(prompt)
+
+    model = ChatOllama(model="unsloth/Llama-3.2-3B-Instruct")
+
+    rag_chain = (
+        {"context": retriever|format_docs, "question": RunnablePassthrough()}
+        | prompt
+        | model
+        | StrOutputParser()
+    )
+
+
+    with st.form("llm-form"):
+        text = st.text_area("Enter your question or statement:")
+        submit = st.form_submit_button("Submit")
+
+    if "chat_history" not in st.session_state:
+        st.session_state['chat_history'] = []
+
+    if submit and text:
+        with st.spinner("Generating response..."):
+            response = generate_response(rag_chain, text)
+            st.session_state['chat_history'].append({"user": text, "ollama": response})
+            st.write(response)
+
+    st.write("## Chat History")
+    for chat in reversed(st.session_state['chat_history']):
+        st.write(f"**🧑 User**: {chat['user']}")
+        st.write(f"**🧠 Assistant**: {chat['ollama']}")
+        st.write("---")
+
+if __name__ == "__main__":
+    main()