{
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"cell_type": "markdown",
"id": "c25c6e94-f3de-4367-b2bf-269ba7160977",
"metadata": {},
"source": [
"## An Expert Knowledge Worker Question-Answering Agent using RAG"
]
},
{
"cell_type": "markdown",
"id": "15169580-cf11-4dee-8ec7-3a4ef59b19ee",
"metadata": {},
"source": [
"Aims\n",
"- Reads README.md files and loads data using TextLoader\n",
"- Splits into chunks using CharacterTextSplitter\n",
"- Converts chunks into vector embeddings and creates a datastore\n",
"- 2D and 3D visualisations\n",
"- Langchain to set up a conversation retrieval chain"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "051cf881-357d-406b-8eae-1610651e40f1",
"metadata": {},
"outputs": [],
"source": [
"# imports\n",
"\n",
"import os\n",
"import glob\n",
"from dotenv import load_dotenv\n",
"import gradio as gr"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ccfd403a-5bdb-4a8c-b3fd-d47ae79e43f7",
"metadata": {},
"outputs": [],
"source": [
"# imports for langchain, plotly and Chroma\n",
"\n",
"from langchain.document_loaders import DirectoryLoader, TextLoader\n",
"from langchain.text_splitter import CharacterTextSplitter\n",
"from langchain.schema import Document\n",
"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
"from langchain.embeddings import HuggingFaceEmbeddings\n",
"from langchain_chroma import Chroma\n",
"from langchain.memory import ConversationBufferMemory\n",
"from langchain.chains import ConversationalRetrievalChain\n",
"import numpy as np\n",
"from sklearn.manifold import TSNE\n",
"import plotly.graph_objects as go\n",
"import plotly.express as px\n",
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2d853868-d2f6-43e1-b27c-b8e91d06b724",
"metadata": {},
"outputs": [],
"source": [
"MODEL = \"gpt-4o-mini\"\n",
"db_name = \"vector_db\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f152fc3b-0bf4-4d51-948f-95da1ebc030a",
"metadata": {},
"outputs": [],
"source": [
"# Load environment variables in a file called .env\n",
"\n",
"load_dotenv(override=True)\n",
"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY')"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "24e621ac-df06-4af6-a60d-a9ed7adb884a",
"metadata": {},
"outputs": [],
"source": [
"# Read in documents using LangChain's loaders\n",
"\n",
"folder = \"my-knowledge-base/\"\n",
"text_loader_kwargs={'autodetect_encoding': True}\n",
"\n",
"loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
"folder_docs = loader.load()\n",
"\n",
"for doc in folder_docs:\n",
" filename_md = os.path.basename(doc.metadata[\"source\"]) \n",
" filename, _ = os.path.splitext(filename_md) \n",
" doc.metadata[\"filename\"] = filename\n",
"\n",
"documents = folder_docs \n",
"\n",
"text_splitter = CharacterTextSplitter(chunk_size=400, chunk_overlap=200)\n",
"chunks = text_splitter.split_documents(documents)\n",
"\n",
"print(f\"Total number of chunks: {len(chunks)}\")\n",
"print(f\"Files found: {set(doc.metadata['filename'] for doc in documents)}\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "f02f08ee-5ade-4f79-a500-045a8f1a532f",
"metadata": {},
"outputs": [],
"source": [
"# Put the chunks of data into a Vector Store that associates a Vector Embedding with each chunk\n",
"\n",
"embeddings = HuggingFaceEmbeddings(model_name=\"sentence-transformers/all-MiniLM-L6-v2\")\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,
"id": "7f665f4d-ccb1-43fb-b901-040117925732",
"metadata": {},
"outputs": [],
"source": [
"# Let's investigate the vectors\n",
"\n",
"collection = vectorstore._collection\n",
"count = collection.count()\n",
"\n",
"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
"dimensions = len(sample_embedding)\n",
"print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "6208a971-e8b7-48bc-be7a-6dcb82967fd2",
"metadata": {},
"outputs": [],
"source": [
"# pre work\n",
"\n",
"result = collection.get(include=['embeddings','documents','metadatas'])\n",
"vectors = np.array(result['embeddings']) \n",
"documents = result['documents']\n",
"metadatas = result['metadatas']\n",
"filenames = [metadata['filename'] for metadata in metadatas]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "eb27bc8a-453b-4b19-84b4-dc495bb0e544",
"metadata": {},
"outputs": [],
"source": [
"import random\n",
"def random_color():\n",
" return f\"rgb({random.randint(0,255)},{random.randint(0,255)},{random.randint(0,255)})\""
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "78db67e5-ef10-4581-b8ac-3e0281ceba45",
"metadata": {},
"outputs": [],
"source": [
"def show_embeddings_2d(result):\n",
" vectors = np.array(result['embeddings']) \n",
" documents = result['documents']\n",
" metadatas = result['metadatas']\n",
" filenames = [metadata['filename'] for metadata in metadatas]\n",
" filenames_unique = sorted(set(filenames))\n",
"\n",
" # color assignment\n",
" color_map = {name: random_color() for name in filenames_unique}\n",
" colors = [color_map[name] for name in filenames]\n",
"\n",
" tsne = TSNE(n_components=2, random_state=42,perplexity=4)\n",
" reduced_vectors = tsne.fit_transform(vectors)\n",
"\n",
" # Create the 2D scatter plot\n",
" fig = go.Figure(data=[go.Scatter(\n",
" x=reduced_vectors[:, 0],\n",
" y=reduced_vectors[:, 1],\n",
" mode='markers',\n",
" marker=dict(size=5,color=colors, opacity=0.8),\n",
" text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
" hoverinfo='text'\n",
" )])\n",
"\n",
" fig.update_layout(\n",
" title='2D Chroma Vector Store Visualization',\n",
" scene=dict(xaxis_title='x',yaxis_title='y'),\n",
" width=800,\n",
" height=600,\n",
" margin=dict(r=20, b=10, l=10, t=40)\n",
" )\n",
"\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "2c250166-cb5b-4a75-8981-fae2d6dfe509",
"metadata": {},
"outputs": [],
"source": [
"show_embeddings_2d(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "3b290e38-0800-4453-b664-7a7622ff5ed2",
"metadata": {},
"outputs": [],
"source": [
"def show_embeddings_3d(result):\n",
" vectors = np.array(result['embeddings']) \n",
" documents = result['documents']\n",
" metadatas = result['metadatas']\n",
" filenames = [metadata['filename'] for metadata in metadatas]\n",
" filenames_unique = sorted(set(filenames))\n",
"\n",
" # color assignment\n",
" color_map = {name: random_color() for name in filenames_unique}\n",
" colors = [color_map[name] for name in filenames]\n",
"\n",
" tsne = TSNE(n_components=3, random_state=42)\n",
" reduced_vectors = tsne.fit_transform(vectors)\n",
"\n",
" fig = go.Figure(data=[go.Scatter3d(\n",
" x=reduced_vectors[:, 0],\n",
" y=reduced_vectors[:, 1],\n",
" z=reduced_vectors[:, 2],\n",
" mode='markers',\n",
" marker=dict(size=5, color=colors, opacity=0.8),\n",
" text=[f\"Type: {t}
Text: {d[:100]}...\" for t, d in zip(filenames, documents)],\n",
" hoverinfo='text'\n",
" )])\n",
"\n",
" fig.update_layout(\n",
" title='3D Chroma Vector Store Visualization',\n",
" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
" width=900,\n",
" height=700,\n",
" margin=dict(r=20, b=10, l=10, t=40)\n",
" )\n",
"\n",
" fig.show()"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "45d1d034-2503-4176-b1e4-f248e31c4770",
"metadata": {},
"outputs": [],
"source": [
"show_embeddings_3d(result)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "e79946a1-f93a-4b3a-8d19-deef40dec223",
"metadata": {},
"outputs": [],
"source": [
"# create a new Chat with OpenAI\n",
"llm = ChatOpenAI(temperature=0.7, model_name=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(search_kwargs={\"k\": 50})\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,
"id": "59f90c85-c113-4482-8574-8a728ef25459",
"metadata": {},
"outputs": [],
"source": [
"def chat(question, history):\n",
" result = conversation_chain.invoke({\"question\": question})\n",
" return result[\"answer\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "0520a8ff-01a4-4fa6-9dc8-57da87272edc",
"metadata": {},
"outputs": [],
"source": [
"view = gr.ChatInterface(chat, type=\"messages\").launch(inbrowser=True)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "b4949b17-cd9c-4bff-bd5b-0f80df72e7dc",
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"outputs": [],
"source": []
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