diff --git "a/evaluate_rag.ipynb" "b/evaluate_rag.ipynb" deleted file mode 100644--- "a/evaluate_rag.ipynb" +++ /dev/null @@ -1,4855 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": { - "id": "view-in-github", - "colab_type": "text" - }, - "source": [ - "\"Open" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": { - "id": "WiSIIehBpwJu" - }, - "outputs": [], - "source": [ - "import os\n", - "from getpass import getpass\n", - "\n", - "from dotenv import load_dotenv\n", - "\n" - ] - }, - { - "cell_type": "code", - "source": [ - "!git clone https://github.com/mbudisic/pstuts-rag.git\n", - "%cd pstuts-rag\n", - "!source .venv/bin/activate\n", - "!uv sync\n", - "!pip install pstuts_rag" - ], - "metadata": { - "id": "5n4Kq-BXp16Y", - "outputId": "f816ec96-b8df-4c27-ae24-100e9bcd2682", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "execution_count": 2, - "outputs": [ - { - "output_type": "stream", - "name": "stdout", - "text": [ - "fatal: destination path 'pstuts-rag' already exists and is not an empty directory.\n", - "/content/pstuts-rag\n", - "\u001b[2mResolved \u001b[1m252 packages\u001b[0m \u001b[2min 1ms\u001b[0m\u001b[0m\n", - "\u001b[2mAudited \u001b[1m238 packages\u001b[0m \u001b[2min 0.51ms\u001b[0m\u001b[0m\n", - "Requirement already satisfied: pstuts_rag in /usr/local/lib/python3.11/dist-packages (2025.5.12)\n", - "Requirement already satisfied: aiohttp>=3.8.0 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (3.11.15)\n", - "Requirement already satisfied: chainlit==2.0.4 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (2.0.4)\n", - "Requirement already satisfied: dotenv>=0.9.9 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (0.9.9)\n", - "Requirement already satisfied: fastapi<0.116,>=0.115.3 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (0.115.12)\n", - "Requirement already satisfied: grandalf>=0.8 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (0.8)\n", - "Requirement already satisfied: httpx==0.27.0 in /usr/local/lib/python3.11/dist-packages (from pstuts_rag) (0.27.0)\n", - 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"%autoreload 2\n" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "id": "qjRVy191pwJv" - }, - "outputs": [], - "source": [ - "from dataclasses import dataclass\n", - "@dataclass\n", - "class ApplicationParameters:\n", - " filename = \"data/test.json\"\n", - " embedding_model = \"text-embedding-3-small\"\n", - " n_context_docs = 2\n", - "\n", - "params = ApplicationParameters()" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": { - "id": "Lc07SJ--pwJv" - }, - "outputs": [], - "source": [ - "import os\n", - "load_dotenv()\n", - "from google.colab import userdata\n", - "\n", - "\n", - "def set_api_key_if_not_present(key_name, prompt_message=\"\"):\n", - " if len(prompt_message) == 0:\n", - " prompt_message=key_name\n", - " if key_name not in os.environ or not os.environ[key_name]:\n", - " os.environ[key_name] = userdata.get(key_name)\n", - "\n", - "set_api_key_if_not_present(\"OPENAI_API_KEY\")\n", - "set_api_key_if_not_present(\"HF_USER\")\n", - "set_api_key_if_not_present(\"HF_TOKEN\")" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "J5R6dUtupwJv" - }, - "source": [ - "# Data Preparation\n", - "\n", - "First, we will read in the transcripts of the videos and convert them to Documents\n", - "with appropriate metadata." - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": { - "id": "VPCN935SpwJw" - }, - "outputs": [], - "source": [ - "from ast import Dict\n", - "import json\n", - "\n", - "from pstuts_rag.loader import load_json_files\n", - "filename = [\"../data/test.json\",\"../data/dev.json\"]\n", - "from typing import List, Dict, Any\n", - "data:List[Dict[str,Any]] = await load_json_files(filename)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": { - "id": "2AGuaeq1pwJw", - "outputId": "d097fae8-6ac9-4b7e-8c9e-0794b6494b73", - "colab": { - "base_uri": "https://localhost:8080/" - } - }, - "outputs": [ - { - "output_type": "execute_result", - "data": { - "text/plain": [ - "[]" - ] - }, - "metadata": {}, - "execution_count": 8 - } - ], - "source": [ - "[ d[\"title\"] for d in data ]" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "XqJ6Q0vvpwJw" - }, - "source": [ - "## R - retrieval" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "4Li5C0lQpwJw" - }, - "source": [ - "Let's hit it with a semantic chunker." - ] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": { - "id": "7Utnbvz4pwJw", - "outputId": "958b8199-a59a-4127-a98b-90a4fe48794f", - "colab": { - "base_uri": "https://localhost:8080/", - "height": 411, - "referenced_widgets": [ - "9253040dfa2b4191a2090ef9b21dc623", - "91359dd886be43b592ebd7e6810b3caf", - "e233370527e14c12bc3b10aa2b6b56a4", - "96667aafda634003ac6399cf5c47aaf7", - "b39aaeffb29d4f288832a446f54591c3", - "1ee6e65b1df246038e06ad6b915a3c90", - "f440514a1a5b45ce9e0ffb1fbf529457", - "f9e5c8e3bdb846cab31d675af39a5443", - "f7cecdbc09f447169ced6b313d2a759e", - "72f68d129af24a68bffbfbe5a89aff34", - 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"\u001b[0;32m/usr/local/lib/python3.11/dist-packages/pstuts_rag/datastore.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, embeddings, qdrant_client, name)\u001b[0m\n\u001b[1;32m 148\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 149\u001b[0m \u001b[0;31m# determine embedding dimension\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdimensions\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0membeddings\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0membed_query\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"test\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 151\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 152\u001b[0m self.qdrant_client.recreate_collection(\n", - "\u001b[0;32m/usr/local/lib/python3.11/dist-packages/torch/nn/modules/module.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m 1926\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mname\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1927\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0mmodules\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1928\u001b[0;31m raise AttributeError(\n\u001b[0m\u001b[1;32m 1929\u001b[0m \u001b[0;34mf\"'{type(self).__name__}' object has no attribute '{name}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 1930\u001b[0m )\n", - "\u001b[0;31mAttributeError\u001b[0m: 'SentenceTransformer' object has no attribute 'embed_query'" - ] - } - ], - "source": [ - "from pstuts_rag.datastore import DatastoreManager\n", - "from qdrant_client import QdrantClient\n", - "\n", - "client = QdrantClient(\":memory:\")\n", - "from sentence_transformers import SentenceTransformer\n", - "embeddings = SentenceTransformer(\"Snowflake/snowflake-arctic-embed-l\")\n", - "retriever_factory = DatastoreManager(qdrant_client=client,name=\"local_test\", embeddings=embeddings)\n", - "if retriever_factory.count_docs() == 0:\n", - " await retriever_factory.populate_database(raw_docs=data)" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "_iL5KHqUpwJw" - }, - "source": [ - "## A - Augmentation\n", - "\n", - "We need to populate a prompt for LLM.\n" - ] - }, - { - "cell_type": "markdown", - "metadata": { - "id": "f_a7kOl7pwJw" - }, - "source": [ - "## Generation\n", - "\n", - "We will use a 4.1-nano to generate answers." - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "yfMukkubpwJw" - }, - "outputs": [], - "source": [ - "from pstuts_rag.rag import RAGChainFactory\n", - "\n", - "rag_factory = RAGChainFactory(retriever=retriever_factory.get_retriever())" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "XBoiK4_bpwJx" - }, - "outputs": [], - "source": [ - "from langchain_openai import ChatOpenAI\n", - "\n", - "llm = ChatOpenAI(model=\"gpt-4.1-mini\",temperature=0)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "1T6gpTHkpwJx" - }, - "outputs": [], - "source": [ - "get_videos = rag_factory.get_rag_chain(llm)\n", - "\n" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "id": "gZHvfrq9pwJx" - }, - "outputs": [], - "source": [ - "val = await get_videos.ainvoke({\"question\":\"What are layers\"})" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - 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