1 changed files with 224 additions and 0 deletions
@ -0,0 +1,224 @@
|
||||
{ |
||||
"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": "venv", |
||||
"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": "3.12.5" |
||||
} |
||||
}, |
||||
"nbformat": 4, |
||||
"nbformat_minor": 2 |
||||
} |
Loading…
Reference in new issue