{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# # Document loading, retrieval methods and text splitting\n", "# !pip install -qU langchain langchain_community\n", "\n", "# # Local vector store via Chroma\n", "# !pip install -qU langchain_chroma\n", "\n", "# # Local inference and embeddings via Ollama\n", "# !pip install -qU langchain_ollama\n", "\n", "# # Web Loader\n", "# !pip install -qU beautifulsoup4\n", "\n", "# # Pull the model first\n", "# !ollama pull nomic-embed-text\n", "\n", "# !pip install -qU pypdf" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#Imports\n", "import os\n", "import glob\n", "from dotenv import load_dotenv\n", "import gradio as gr\n", "from langchain_community.document_loaders import PyPDFLoader, DirectoryLoader\n", "from langchain_text_splitters import CharacterTextSplitter, RecursiveCharacterTextSplitter\n", "from langchain_chroma import Chroma\n", "from langchain_ollama import OllamaEmbeddings\n", "from langchain_ollama import ChatOllama\n", "from langchain_core.output_parsers import StrOutputParser\n", "from langchain_core.prompts import ChatPromptTemplate\n", "from langchain_core.runnables import RunnablePassthrough" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Read in documents using LangChain's loaders\n", "# Take everything in all the sub-folders of our knowledgebase\n", "\n", "folders = glob.glob(\"Manuals/*\")\n", "\n", "def add_metadata(doc, doc_type):\n", " doc.metadata[\"doc_type\"] = doc_type\n", " return doc\n", "\n", "documents = []\n", "for folder in folders:\n", " doc_type = os.path.basename(folder)\n", " loader = DirectoryLoader(folder, glob=\"**/*.pdf\", loader_cls=PyPDFLoader)\n", " folder_docs = loader.load()\n", " documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n", "\n", "text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=200)\n", "chunks = text_splitter.split_documents(documents)\n", "\n", "print(f\"Total number of chunks: {len(chunks)}\")\n", "print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n", "# Chroma is a popular open source Vector Database based on SQLLite\n", "DB_NAME = \"vector_db\"\n", "\n", "embeddings = OllamaEmbeddings(model=\"nomic-embed-text\")\n", "\n", "# Delete if already exists\n", "\n", "if os.path.exists(DB_NAME):\n", " Chroma(persist_directory=DB_NAME, embedding_function=embeddings).delete_collection()\n", "\n", "# Create vectorstore\n", "\n", "vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=DB_NAME)\n", "print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "#run a quick test - should return a list of documents = 4\n", "question = \"What kind of grill is the Spirt II?\"\n", "docs = vectorstore.similarity_search(question)\n", "len(docs)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "docs[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# create a new Chat with Ollama\n", "from langchain.memory import ConversationBufferMemory\n", "from langchain.chains import ConversationalRetrievalChain\n", "MODEL = \"llama3.2:latest\"\n", "llm = ChatOllama(temperature=0.7, model=MODEL)\n", "\n", "# set up the conversation memory for the chat\n", "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", "\n", "# the retriever is an abstraction over the VectorStore that will be used during RAG\n", "retriever = vectorstore.as_retriever()\n", "\n", "# putting it together: set up the conversation chain with the GPT 3.5 LLM, the vector store and memory\n", "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# Let's try a simple question\n", "\n", "query = \"How do I change the water bottle ?\"\n", "result = conversation_chain.invoke({\"question\": query})\n", "print(result[\"answer\"])" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [], "source": [ "# set up a new conversation memory for the chat\n", "memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n", "\n", "# putting it together: set up the conversation chain with the LLM, the vector store and memory\n", "conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)" ] }, { "cell_type": "code", "execution_count": 16, "metadata": {}, "outputs": [], "source": [ "# Wrapping that in a function\n", "\n", "def chat(question, history):\n", " result = conversation_chain.invoke({\"question\": question})\n", " return result[\"answer\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Now we will bring this up in Gradio using the Chat interface -\n", "\n", "A quick and easy way to prototype a chat with an LLM" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# And in Gradio:\n", "\n", "view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.11" } }, "nbformat": 4, "nbformat_minor": 4 }