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docs: introduce docs site (#141)
Signed-off-by: Panos Vagenas <35837085+vagenas@users.noreply.github.com>
This commit is contained in:
Panos Vagenas
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369
docs/examples/rag_langchain.ipynb
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369
docs/examples/rag_langchain.ipynb
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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# RAG with LangChain 🦜🔗"
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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": 1,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"Note: you may need to restart the kernel to use updated packages.\n"
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]
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}
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],
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"source": [
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"# requirements for this example:\n",
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"%pip install -qq docling docling-core python-dotenv langchain-text-splitters langchain-huggingface langchain-milvus"
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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": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"True"
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]
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},
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"execution_count": 2,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"import os\n",
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"\n",
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"from dotenv import load_dotenv\n",
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"\n",
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"load_dotenv()"
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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": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"import warnings\n",
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"\n",
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"warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic|torch\")\n",
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"warnings.filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## Setup"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Loader and splitter"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Below we set up:\n",
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"- a `Loader` which will be used to create LangChain documents, and\n",
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"- a splitter, which will be used to split these documents"
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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": 4,
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"metadata": {},
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"outputs": [],
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"source": [
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"from enum import Enum\n",
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"from typing import Iterator\n",
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"\n",
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"from langchain_core.document_loaders import BaseLoader\n",
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"from langchain_core.documents import Document as LCDocument\n",
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"from pydantic import BaseModel\n",
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"\n",
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"from docling.document_converter import DocumentConverter\n",
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"\n",
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"\n",
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"class DocumentMetadata(BaseModel):\n",
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" dl_doc_hash: str\n",
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" # source: str\n",
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"\n",
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"\n",
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"class DoclingPDFLoader(BaseLoader):\n",
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" class ParseType(str, Enum):\n",
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" MARKDOWN = \"markdown\"\n",
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" # JSON = \"json\"\n",
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"\n",
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" def __init__(self, file_path: str | list[str], parse_type: ParseType) -> None:\n",
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" self._file_paths = file_path if isinstance(file_path, list) else [file_path]\n",
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" self._parse_type = parse_type\n",
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" self._converter = DocumentConverter()\n",
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"\n",
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" def lazy_load(self) -> Iterator[LCDocument]:\n",
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" for source in self._file_paths:\n",
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" dl_doc = self._converter.convert_single(source).output\n",
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" match self._parse_type:\n",
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" case self.ParseType.MARKDOWN:\n",
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" text = dl_doc.export_to_markdown()\n",
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" # case self.ParseType.JSON:\n",
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" # text = dl_doc.model_dump_json()\n",
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" case _:\n",
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" raise RuntimeError(\n",
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" f\"Unexpected parse type encountered: {self._parse_type}\"\n",
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" )\n",
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" lc_doc = LCDocument(\n",
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" page_content=text,\n",
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" metadata=DocumentMetadata(\n",
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" dl_doc_hash=dl_doc.file_info.document_hash,\n",
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" ).model_dump(),\n",
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" )\n",
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" yield lc_doc"
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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": 5,
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"metadata": {},
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"outputs": [],
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"source": [
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"FILE_PATH = \"https://arxiv.org/pdf/2206.01062\" # DocLayNet paper"
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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": 6,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"application/vnd.jupyter.widget-view+json": {
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"model_id": "1b38d07d5fed4618a44ecf261e1e5c44",
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"version_major": 2,
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"version_minor": 0
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},
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"text/plain": [
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"Fetching 7 files: 0%| | 0/7 [00:00<?, ?it/s]"
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]
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},
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"metadata": {},
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"output_type": "display_data"
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}
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],
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"source": [
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"from langchain_text_splitters import RecursiveCharacterTextSplitter\n",
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"\n",
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"loader = DoclingPDFLoader(\n",
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" file_path=FILE_PATH,\n",
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" parse_type=DoclingPDFLoader.ParseType.MARKDOWN,\n",
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")\n",
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"text_splitter = RecursiveCharacterTextSplitter(\n",
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" chunk_size=1000,\n",
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" chunk_overlap=200,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"We now used the above-defined objects to get the document splits:"
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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": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"docs = loader.load()\n",
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"splits = text_splitter.split_documents(docs)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### Embeddings"
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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": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"from langchain_huggingface.embeddings import HuggingFaceEmbeddings\n",
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"\n",
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"HF_EMBED_MODEL_ID = \"BAAI/bge-small-en-v1.5\"\n",
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"embeddings = HuggingFaceEmbeddings(model_name=HF_EMBED_MODEL_ID)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 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": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"from tempfile import TemporaryDirectory\n",
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"\n",
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"from langchain_milvus import Milvus\n",
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"\n",
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"MILVUS_URI = os.environ.get(\n",
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" \"MILVUS_URL\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n",
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")\n",
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"\n",
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"vectorstore = Milvus.from_documents(\n",
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" splits,\n",
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" embeddings,\n",
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" connection_args={\"uri\": MILVUS_URI},\n",
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" drop_old=True,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### LLM"
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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": 10,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"The token has not been saved to the git credentials helper. Pass `add_to_git_credential=True` in this function directly or `--add-to-git-credential` if using via `huggingface-cli` if you want to set the git credential as well.\n",
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"Token is valid (permission: write).\n",
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"Your token has been saved to /Users/pva/.cache/huggingface/token\n",
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"Login successful\n"
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]
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}
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],
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"source": [
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"from langchain_huggingface import HuggingFaceEndpoint\n",
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"\n",
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"HF_API_KEY = os.environ.get(\"HF_API_KEY\")\n",
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"HF_LLM_MODEL_ID = \"mistralai/Mistral-7B-Instruct-v0.3\"\n",
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"\n",
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"llm = HuggingFaceEndpoint(\n",
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" repo_id=HF_LLM_MODEL_ID,\n",
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" huggingfacehub_api_token=HF_API_KEY,\n",
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")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## RAG"
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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": 11,
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"metadata": {},
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"outputs": [],
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"source": [
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"from typing import Iterable\n",
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"\n",
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"from langchain_core.documents import Document as LCDocument\n",
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"from langchain_core.output_parsers import StrOutputParser\n",
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"from langchain_core.prompts import PromptTemplate\n",
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"from langchain_core.runnables import RunnablePassthrough\n",
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"\n",
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"\n",
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"def format_docs(docs: Iterable[LCDocument]):\n",
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" return \"\\n\\n\".join(doc.page_content for doc in docs)\n",
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"\n",
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"\n",
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"retriever = vectorstore.as_retriever()\n",
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"\n",
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"prompt = PromptTemplate.from_template(\n",
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" \"Context information is below.\\n---------------------\\n{context}\\n---------------------\\nGiven the context information and not prior knowledge, answer the query.\\nQuery: {question}\\nAnswer:\\n\"\n",
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")\n",
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"\n",
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"rag_chain = (\n",
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" {\"context\": retriever | format_docs, \"question\": RunnablePassthrough()}\n",
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" | prompt\n",
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" | llm\n",
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" | StrOutputParser()\n",
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")"
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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": 12,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"'The human annotation of DocLayNet was performed on 80863 pages.\\n\\nExplanation:\\nThe information is found in the paragraph \"DocLayNet contains 80863 PDF pages\" in the context.'"
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]
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},
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"execution_count": 12,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"rag_chain.invoke(\"How many pages were human annotated for DocLayNet?\")"
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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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"metadata": {},
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"outputs": [],
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"source": []
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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": "3.12.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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