{ "cells": [ { "cell_type": "markdown", "id": "b1c1ebaa-50de-4851-a720-acbb977551ea", "metadata": {}, "source": [ "# Time-Weighted Rerank\n", "\n", "Showcase capabilities of time-weighted node postprocessor" ] }, { "cell_type": "code", "execution_count": null, "id": "92d06b38-2103-4a40-93c3-60e0708a1124", "metadata": {}, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "/home/loganm/miniconda3/envs/llama-index/lib/python3.11/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n", " from .autonotebook import tqdm as notebook_tqdm\n" ] } ], "source": [ "from llama_index.core import VectorStoreIndex, SimpleDirectoryReader\n", "from llama_index.core.postprocessor import TimeWeightedPostprocessor\n", "from llama_index.core.node_parser import SentenceSplitter\n", "from llama_index.core.storage.docstore import SimpleDocumentStore\n", "from llama_index.core.response.notebook_utils import display_response\n", "from datetime import datetime, timedelta" ] }, { "cell_type": "markdown", "id": "67020156-2975-4bbb-8e98-afc55abb3d72", "metadata": {}, "source": [ "### Parse Documents into Nodes, add to Docstore\n", "\n", "In this example, there are 3 different versions of PG's essay. They are largely identical **except** \n", "for one specific section, which details the amount of funding they raised for Viaweb. \n", "\n", "V1: 50k, V2: 30k, V3: 10K\n", "\n", "V1: -1 day, V2: -2 days, V3: -3 days\n", "\n", "The idea is to encourage index to fetch the most recent info (which is V3)" ] }, { "cell_type": "code", "execution_count": null, "id": "caddd84e-9827-40a4-9520-dba6405fd1fd", "metadata": {}, "outputs": [], "source": [ "# load documents\n", "from llama_index.core import StorageContext\n", "\n", "\n", "now = datetime.now()\n", "key = \"__last_accessed__\"\n", "\n", "\n", "doc1 = SimpleDirectoryReader(\n", " input_files=[\"./test_versioned_data/paul_graham_essay_v1.txt\"]\n", ").load_data()[0]\n", "\n", "\n", "doc2 = SimpleDirectoryReader(\n", " input_files=[\"./test_versioned_data/paul_graham_essay_v2.txt\"]\n", ").load_data()[0]\n", "\n", "doc3 = SimpleDirectoryReader(\n", " input_files=[\"./test_versioned_data/paul_graham_essay_v3.txt\"]\n", ").load_data()[0]\n", "\n", "\n", "# define settings\n", "from llama_index.core import Settings\n", "\n", "Settings.text_splitter = SentenceSplitter(chunk_size=512)\n", "\n", "# use node parser from settings to parse docs into nodes\n", "nodes1 = Settings.text_splitter.get_nodes_from_documents([doc1])\n", "nodes2 = Settings.text_splitter.get_nodes_from_documents([doc2])\n", "nodes3 = Settings.text_splitter.get_nodes_from_documents([doc3])\n", "\n", "\n", "# fetch the modified chunk from each document, set metadata\n", "# also exclude the date from being read by the LLM\n", "nodes1[14].metadata[key] = (now - timedelta(hours=3)).timestamp()\n", "nodes1[14].excluded_llm_metadata_keys = [key]\n", "\n", "nodes2[14].metadata[key] = (now - timedelta(hours=2)).timestamp()\n", "nodes2[14].excluded_llm_metadata_keys = [key]\n", "\n", "nodes3[14].metadata[key] = (now - timedelta(hours=1)).timestamp()\n", "nodes2[14].excluded_llm_metadata_keys = [key]\n", "\n", "\n", "# add to docstore\n", "docstore = SimpleDocumentStore()\n", "nodes = [nodes1[14], nodes2[14], nodes3[14]]\n", "docstore.add_documents(nodes)\n", "\n", "storage_context = StorageContext.from_defaults(docstore=docstore)" ] }, { "cell_type": "markdown", "id": "e5a25b95-de5e-4e56-a846-51e9c6eba181", "metadata": {}, "source": [ "### Build Index" ] }, { "cell_type": "code", "execution_count": null, "id": "5f7f68d6-2389-4f6c-bc4e-8612a1a53fb8", "metadata": {}, "outputs": [], "source": [ "# build index\n", "index = VectorStoreIndex(nodes, storage_context=storage_context)" ] }, { "cell_type": "markdown", "id": "86c5e8aa-18d8-4229-b7b2-a1c97c11a09a", "metadata": {}, "source": [ "### Define Recency Postprocessors" ] }, { "cell_type": "code", "execution_count": null, "id": "ba5e10c9-5a7e-4ea8-a74d-0e0f74b5cd1b", "metadata": {}, "outputs": [], "source": [ "node_postprocessor = TimeWeightedPostprocessor(\n", " time_decay=0.5, time_access_refresh=False, top_k=1\n", ")" ] }, { "cell_type": "markdown", "id": "efcfffe4-a8aa-486d-b46d-f73f985dffca", "metadata": {}, "source": [ "### Query Index" ] }, { "cell_type": "code", "execution_count": null, "id": "78d6c3db-61e6-4d9a-a84d-d7be846b4112", "metadata": {}, "outputs": [], "source": [ "# naive query\n", "query_engine = index.as_query_engine(\n", " similarity_top_k=3,\n", ")\n", "response = query_engine.query(\n", " \"How much did the author raise in seed funding from Idelle's husband\"\n", " \" (Julian) for Viaweb?\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "84a608cd-fd70-40ba-a2f5-1414148db7de", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**`Final Response:`** $50,000" ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_response(response)" ] }, { "cell_type": "code", "execution_count": null, "id": "1d672c52-c0ac-4e5f-9175-855e66eb97ba", "metadata": {}, "outputs": [], "source": [ "# query using time weighted node postprocessor\n", "\n", "query_engine = index.as_query_engine(\n", " similarity_top_k=3, node_postprocessors=[node_postprocessor]\n", ")\n", "response = query_engine.query(\n", " \"How much did the author raise in seed funding from Idelle's husband\"\n", " \" (Julian) for Viaweb?\",\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "cd4b971e", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**`Final Response:`** The author raised $10,000 in seed funding from Idelle's husband (Julian) for Viaweb." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_response(response)" ] }, { "cell_type": "markdown", "id": "dd00cc97-4de7-4c61-9c0c-3f9ee3598528", "metadata": {}, "source": [ "### Query Index (Lower-Level Usage)\n", "\n", "In this example we first get the full set of nodes from a query call, and then send to node postprocessor, and then\n", "finally synthesize response through a summary index." ] }, { "cell_type": "code", "execution_count": null, "id": "350b039e-d45d-4b6b-957a-4b14d8816cbd", "metadata": {}, "outputs": [], "source": [ "from llama_index.core import SummaryIndex" ] }, { "cell_type": "code", "execution_count": null, "id": "234f909f-6faa-43e6-96f8-0966699c9552", "metadata": {}, "outputs": [], "source": [ "query_str = (\n", " \"How much did the author raise in seed funding from Idelle's husband\"\n", " \" (Julian) for Viaweb?\"\n", ")" ] }, { "cell_type": "code", "execution_count": null, "id": "20afbf6b-9473-446e-b522-b90fef2e3bf0", "metadata": {}, "outputs": [], "source": [ "query_engine = index.as_query_engine(\n", " similarity_top_k=3, response_mode=\"no_text\"\n", ")\n", "init_response = query_engine.query(\n", " query_str,\n", ")\n", "resp_nodes = [n for n in init_response.source_nodes]" ] }, { "cell_type": "code", "execution_count": null, "id": "cdc03574-a806-4255-953c-6f82fc3f202f", "metadata": {}, "outputs": [], "source": [ "# get the post-processed nodes -- which should be the top-1 sorted by date\n", "new_resp_nodes = node_postprocessor.postprocess_nodes(resp_nodes)\n", "\n", "summary_index = SummaryIndex([n.node for n in new_resp_nodes])\n", "query_engine = summary_index.as_query_engine()\n", "response = query_engine.query(query_str)" ] }, { "cell_type": "code", "execution_count": null, "id": "9d4de372-653f-4d57-9e0f-17a90f39b874", "metadata": {}, "outputs": [ { "data": { "text/markdown": [ "**`Final Response:`** The author raised $10,000 in seed funding from Idelle's husband (Julian) for Viaweb." ], "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "display_response(response)" ] } ], "metadata": { "kernelspec": { "display_name": "llama-index", "language": "python", "name": "llama-index" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3" } }, "nbformat": 4, "nbformat_minor": 5 }