244 lines
8.5 KiB
Plaintext
244 lines
8.5 KiB
Plaintext
{
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"cells": [
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{
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"cell_type": "markdown",
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"id": "dfe37963-1af6-44fc-a841-8e462443f5e6",
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"metadata": {},
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"source": [
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"## Expert Knowledge Worker\n",
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"\n",
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"### A question answering agent that is an expert knowledge worker\n",
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"### To be used by Anyone on their LinkedIn data\n",
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"The easiest and fastest way to obtain a copy of your LinkedIn data is to initiate a data download from your Settings & Privacy page:\n",
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"\n",
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"1. Click the Me icon at the top of your LinkedIn homepage.\n",
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"2. Select Settings & Privacy from the dropdown.\n",
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"3. Click the Data Privacy on the left rail.\n",
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"4 .Under the How LinkedIn uses your data section, click Get a copy of your data.\n",
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"5. Select the data that you’re looking for and Request archive.\n",
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"\n",
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"This project will use RAG (Retrieval Augmented Generation) to ensure our question/answering assistant has high accuracy."
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]
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},
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{
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"cell_type": "code",
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"execution_count": 17,
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"id": "ba2779af-84ef-4227-9e9e-6eaf0df87e77",
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"metadata": {},
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"outputs": [],
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"source": [
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"# imports\n",
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"\n",
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"import os\n",
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"import glob\n",
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"from dotenv import load_dotenv\n",
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"import gradio as gr\n",
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"\n",
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"from langchain.document_loaders import DirectoryLoader, TextLoader\n",
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"from langchain.text_splitter import CharacterTextSplitter\n",
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"from langchain.schema import Document\n",
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"from langchain_openai import OpenAIEmbeddings, ChatOpenAI\n",
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"from langchain_chroma import Chroma\n",
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"import plotly.graph_objects as go\n",
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"from langchain.memory import ConversationBufferMemory\n",
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"from langchain.chains import ConversationalRetrievalChain\n",
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"from langchain.embeddings import HuggingFaceEmbeddings\n",
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"\n",
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"import matplotlib.pyplot as plt\n",
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"from sklearn.manifold import TSNE\n",
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"import numpy as np\n",
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"\n",
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"MODEL = \"gpt-4o-mini\"\n",
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"db_name = \"linkedin_db\"\n",
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"\n",
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"load_dotenv(override=True)\n",
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"os.environ['OPENAI_API_KEY'] = os.getenv('OPENAI_API_KEY', 'your-key-if-not-using-env')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "730711a9-6ffe-4eee-8f48-d6cfb7314905",
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"metadata": {},
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"outputs": [],
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"source": [
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"# Read in documents using LangChain's loaders\n",
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"# Put the chunks of data into a Vector Store (Chroma) that associates a Vector Embedding with each chunk\n",
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"\n",
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"folders = glob.glob(\"linkedin-base/*\")\n",
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"\n",
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"def add_metadata(doc, doc_type):\n",
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" doc.metadata[\"doc_type\"] = doc_type\n",
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" return doc\n",
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"\n",
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"text_loader_kwargs = {'encoding': 'utf-8'}\n",
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"\n",
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"documents = []\n",
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"for folder in folders:\n",
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" doc_type = os.path.basename(folder)\n",
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" loader = DirectoryLoader(folder, glob=\"**/*.md\", loader_cls=TextLoader, loader_kwargs=text_loader_kwargs)\n",
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" folder_docs = loader.load()\n",
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" documents.extend([add_metadata(doc, doc_type) for doc in folder_docs])\n",
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"\n",
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"text_splitter = CharacterTextSplitter(chunk_size=500, chunk_overlap=100)\n",
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"chunks = text_splitter.split_documents(documents)\n",
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"\n",
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"embeddings = OpenAIEmbeddings()\n",
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"\n",
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"if os.path.exists(db_name):\n",
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" Chroma(persist_directory=db_name, embedding_function=embeddings).delete_collection()\n",
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"\n",
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"vectorstore = Chroma.from_documents(documents=chunks, embedding=embeddings, persist_directory=db_name)\n",
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"\n",
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"collection = vectorstore._collection\n",
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"count = collection.count()\n",
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"\n",
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"sample_embedding = collection.get(limit=1, include=[\"embeddings\"])[\"embeddings\"][0]\n",
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"dimensions = len(sample_embedding)\n",
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"\n",
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"\n",
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"print(f\"Total number of chunks: {len(chunks)}\")\n",
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"print(f\"Document types found: {set(doc.metadata['doc_type'] for doc in documents)}\")\n",
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"print(f\"Vectorstore created with {vectorstore._collection.count()} documents\")\n",
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"print(f\"There are {count:,} vectors with {dimensions:,} dimensions in the vector store\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "b98adf5e-d464-4bd2-9bdf-bc5b6770263b",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 2D scatter plot\n",
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"\n",
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"result = collection.get(include=['embeddings', 'documents', 'metadatas'])\n",
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"vectors = np.array(result['embeddings'])\n",
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"documents = result['documents']\n",
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"metadatas = result['metadatas']\n",
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"doc_types = [metadata['doc_type'] for metadata in metadatas]\n",
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"colors = [['blue', 'green', 'red'][['connections', 'recommendations', 'profiles'].index(t)] for t in doc_types]\n",
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"\n",
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"n = vectors.shape[0]\n",
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"if n < 3:\n",
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" raise ValueError(f\"t-SNE needs at least 3 samples, got {n}\")\n",
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"\n",
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"perp = max(5.0, min(30.0, (n - 1) / 3.0)) # always < n, within [5, 30]\n",
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"\n",
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"tsne = TSNE(n_components=2, random_state=42, perplexity=perp)\n",
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"reduced_vectors = tsne.fit_transform(vectors)\n",
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"\n",
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"fig = go.Figure(data=[go.Scatter(\n",
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" x=reduced_vectors[:, 0],\n",
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" y=reduced_vectors[:, 1],\n",
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" mode='markers',\n",
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" marker=dict(size=5, color=colors, opacity=0.8),\n",
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
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" hoverinfo='text'\n",
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")])\n",
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"\n",
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"fig.update_layout(\n",
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" title='2D Chroma Vector Store Visualization',\n",
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" scene=dict(xaxis_title='x',yaxis_title='y'),\n",
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" width=800,\n",
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" height=600,\n",
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" margin=dict(r=20, b=10, l=10, t=40)\n",
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")\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "e1418e88-acd5-460a-bf2b-4e6efc88e3dd",
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"metadata": {},
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"outputs": [],
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"source": [
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"# 3D scatter plot!\n",
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"\n",
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"n = vectors.shape[0]\n",
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"if n < 3:\n",
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" raise ValueError(f\"t-SNE needs at least 3 samples, got {n}\")\n",
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"\n",
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"perp = max(5.0, min(30.0, (n - 1) / 3.0))\n",
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"\n",
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"tsne = TSNE(n_components=3, random_state=42, perplexity=perp)\n",
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"reduced_vectors = tsne.fit_transform(vectors)\n",
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"\n",
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"fig = go.Figure(data=[go.Scatter3d(\n",
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" x=reduced_vectors[:, 0],\n",
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" y=reduced_vectors[:, 1],\n",
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" z=reduced_vectors[:, 2],\n",
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" mode='markers',\n",
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" marker=dict(size=5, color=colors, opacity=0.8),\n",
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" text=[f\"Type: {t}<br>Text: {d[:100]}...\" for t, d in zip(doc_types, documents)],\n",
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" hoverinfo='text'\n",
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")])\n",
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"\n",
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"fig.update_layout(\n",
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" title='3D Chroma Vector Store Visualization',\n",
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" scene=dict(xaxis_title='x', yaxis_title='y', zaxis_title='z'),\n",
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" width=900,\n",
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" height=700,\n",
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" margin=dict(r=20, b=10, l=10, t=40)\n",
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")\n",
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"\n",
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"fig.show()"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "2136153b-d2f6-4c58-a0e3-78c3a932cf55",
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"metadata": {},
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"outputs": [],
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"source": [
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"# The main Langchain Abstraction are: Memory, LLM, and Retriever\n",
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"llm = ChatOpenAI(temperature=0.7, model_name=MODEL)\n",
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"\n",
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"memory = ConversationBufferMemory(memory_key='chat_history', return_messages=True)\n",
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"retriever = vectorstore.as_retriever(search_kwargs={\"k\": 25})\n",
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"conversation_chain = ConversationalRetrievalChain.from_llm(llm=llm, retriever=retriever, memory=memory)\n",
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"\n",
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"def chat(question, history):\n",
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" result = conversation_chain.invoke({\"question\": question})\n",
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" return result[\"answer\"]\n",
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"\n",
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"with gr.Blocks(theme=\"gradio/monochrome\") as ui:\n",
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" gr.Markdown(\n",
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" \"\"\"\n",
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" <h2 style=\"color: #f5f5f5;\">Linkedin Knowledge Worker</h2>\n",
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" <p style=\"color: #f5f5f5;\">Chat with your auto-generated Linkedin knowledge base </p>\n",
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" \"\"\",\n",
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" elem_id=\"title\"\n",
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" )\n",
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" gr.ChatInterface(chat, type=\"messages\")\n",
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"\n",
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"ui.launch(inbrowser=True)"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.12.12"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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