diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 0f95e067..7c5be835 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -2,39 +2,43 @@ fail_fast: true repos: - repo: local hooks: - - id: system + - id: black name: Black entry: poetry run black docling examples tests pass_filenames: false language: system files: '\.py$' - - repo: local - hooks: - - id: system + - id: isort name: isort entry: poetry run isort docling examples tests pass_filenames: false language: system files: '\.py$' -# - repo: local -# hooks: -# - id: system +# - id: flake8 # name: flake8 # entry: poetry run flake8 docling # pass_filenames: false # language: system # files: '\.py$' -# - repo: local -# hooks: -# - id: system +# - id: mypy # name: MyPy # entry: poetry run mypy docling # pass_filenames: false # language: system # files: '\.py$' - - repo: local - hooks: - - id: system + - id: nbqa_black + name: nbQA Black + entry: poetry run nbqa black examples + pass_filenames: false + language: system + files: '\.ipynb$' + - id: nbqa_isort + name: nbQA isort + entry: poetry run nbqa isort examples + pass_filenames: false + language: system + files: '\.ipynb$' + - id: poetry name: Poetry check entry: poetry check --lock pass_filenames: false diff --git a/README.md b/README.md index 9652692f..0cb0c95c 100644 --- a/README.md +++ b/README.md @@ -23,8 +23,7 @@ Docling bundles PDF document conversion to JSON and Markdown in an easy, self-co * 📑 Understands detailed page layout, reading order and recovers table structures * 📝 Extracts metadata from the document, such as title, authors, references and language * 🔍 Optionally applies OCR (use with scanned PDFs) - -For RAG, check out [Quackling](https://github.com/DS4SD/quackling) to get the most out of your docs, be it using LlamaIndex, LangChain or your pipeline. +* 🤖 Integrates easily with LLM app / RAG frameworks like 🦙 LlamaIndex and 🦜🔗 LangChain ## Installation @@ -143,6 +142,10 @@ results = doc_converter.convert(conv_input) You can limit the CPU threads used by Docling by setting the environment variable `OMP_NUM_THREADS` accordingly. The default setting is using 4 CPU threads. +### RAG +Check out the following examples showcasing RAG using Docling with standard LLM application frameworks: +- [Basic RAG pipeline with 🦙 LlamaIndex](https://github.com/DS4SD/docling/tree/main/examples/rag_llamaindex.ipynb) +- [Basic RAG pipeline with 🦜🔗 LangChain](https://github.com/DS4SD/docling/tree/main/examples/rag_langchain.ipynb) ## Technical report diff --git a/examples/rag_langchain.ipynb b/examples/rag_langchain.ipynb new file mode 100644 index 00000000..fd26908a --- /dev/null +++ b/examples/rag_langchain.ipynb @@ -0,0 +1,362 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "# requirements for this example:\n", + "%pip install -qq docling docling-core python-dotenv langchain-text-splitters langchain-huggingface langchain-milvus" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "\n", + "from dotenv import load_dotenv\n", + "\n", + "load_dotenv()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [], + "source": [ + "import warnings\n", + "\n", + "warnings.filterwarnings(action=\"ignore\", category=UserWarning, module=\"pydantic|torch\")\n", + "warnings.filterwarnings(action=\"ignore\", category=FutureWarning, module=\"easyocr\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Setup" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Loader and splitter" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Below we set up:\n", + "- a `Loader` which will be used to create LangChain documents, and\n", + "- a splitter, which will be used to split these documents" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [], + "source": [ + "from enum import Enum\n", + "from typing import Iterator\n", + "\n", + "from langchain_core.document_loaders import BaseLoader\n", + "from langchain_core.documents import Document as LCDocument\n", + "from pydantic import BaseModel\n", + "\n", + "from docling.document_converter import DocumentConverter\n", + "\n", + "\n", + "class DocumentMetadata(BaseModel):\n", + " dl_doc_hash: str\n", + " # source: str\n", + "\n", + "\n", + "class DoclingPDFLoader(BaseLoader):\n", + " class ParseType(str, Enum):\n", + " MARKDOWN = \"markdown\"\n", + " # JSON = \"json\"\n", + "\n", + " def __init__(self, file_path: str | list[str], parse_type: ParseType) -> None:\n", + " self._file_paths = file_path if isinstance(file_path, list) else [file_path]\n", + " self._parse_type = parse_type\n", + " self._converter = DocumentConverter()\n", + "\n", + " def lazy_load(self) -> Iterator[LCDocument]:\n", + " for source in self._file_paths:\n", + " dl_doc = self._converter.convert_single(source).output\n", + " match self._parse_type:\n", + " case self.ParseType.MARKDOWN:\n", + " text = dl_doc.export_to_markdown()\n", + " # case self.ParseType.JSON:\n", + " # text = dl_doc.model_dump_json()\n", + " case _:\n", + " raise RuntimeError(\n", + " f\"Unexpected parse type encountered: {self._parse_type}\"\n", + " )\n", + " lc_doc = LCDocument(\n", + " page_content=text,\n", + " metadata=DocumentMetadata(\n", + " dl_doc_hash=dl_doc.file_info.document_hash,\n", + " ).model_dump(),\n", + " )\n", + " yield lc_doc" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "FILE_PATH = \"https://arxiv.org/pdf/2206.01062\" # DocLayNet paper" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "1b38d07d5fed4618a44ecf261e1e5c44", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 7 files: 0%| | 0/7 [00:00 Iterable[LIDocument]:\n", + " file_paths = file_path if isinstance(file_path, list) else [file_path]\n", + " converter = DocumentConverter()\n", + " for source in file_paths:\n", + " dl_doc = converter.convert_single(source).output\n", + " match self.parse_type:\n", + " case self.ParseType.MARKDOWN:\n", + " text = dl_doc.export_to_markdown()\n", + " # case self.ParseType.JSON:\n", + " # text = dl_doc.model_dump_json()\n", + " case _:\n", + " raise RuntimeError(\n", + " f\"Unexpected parse type encountered: {self.parse_type}\"\n", + " )\n", + " excl_metadata_keys = [\"dl_doc_hash\"]\n", + " li_doc = LIDocument(\n", + " doc_id=dl_doc.file_info.document_hash,\n", + " text=text,\n", + " excluded_embed_metadata_keys=excl_metadata_keys,\n", + " excluded_llm_metadata_keys=excl_metadata_keys,\n", + " )\n", + " li_doc.metadata = DocumentMetadata(\n", + " dl_doc_hash=dl_doc.file_info.document_hash,\n", + " ).model_dump()\n", + " yield li_doc" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.core.node_parser import MarkdownNodeParser\n", + "\n", + "reader = DoclingPDFReader(parse_type=DoclingPDFReader.ParseType.MARKDOWN)\n", + "node_parser = MarkdownNodeParser()\n", + "transformations = [node_parser]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "One can include add more transformations, e.g. further chunking based on text size / overlap, as shown below:" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "# from llama_index.core.node_parser import TokenTextSplitter\n", + "\n", + "# splitter = TokenTextSplitter(\n", + "# chunk_size=1024,\n", + "# chunk_overlap=20,\n", + "# )\n", + "# transformations.append(splitter)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Embed model" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.embeddings.huggingface import HuggingFaceEmbedding\n", + "\n", + "embed_model = HuggingFaceEmbedding(model_name=\"BAAI/bge-small-en-v1.5\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Vector store" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "INGEST = True # whether to ingest from scratch or reuse an existing vector store" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "huggingface/tokenizers: The current process just got forked, after parallelism has already been used. Disabling parallelism to avoid deadlocks...\n", + "To disable this warning, you can either:\n", + "\t- Avoid using `tokenizers` before the fork if possible\n", + "\t- Explicitly set the environment variable TOKENIZERS_PARALLELISM=(true | false)\n" + ] + } + ], + "source": [ + "from llama_index.vector_stores.milvus import MilvusVectorStore\n", + "\n", + "MILVUS_URL = os.environ.get(\n", + " \"MILVUS_URL\", f\"{(tmp_dir := TemporaryDirectory()).name}/milvus_demo.db\"\n", + ")\n", + "MILVUS_COLL_NAME = os.environ.get(\"MILVUS_COLL_NAME\", \"basic_llamaindex_pipeline\")\n", + "MILVUS_KWARGS = TypeAdapter(dict).validate_json(os.environ.get(\"MILVUS_KWARGS\", \"{}\"))\n", + "vector_store = MilvusVectorStore(\n", + " uri=MILVUS_URL,\n", + " collection_name=MILVUS_COLL_NAME,\n", + " dim=len(embed_model.get_text_embedding(\"hi\")),\n", + " overwrite=INGEST,\n", + " **MILVUS_KWARGS,\n", + ")" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "536daee038de4d52a793445c6d853c72", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Fetching 7 files: 0%| | 0/7 [00:00[\n", + "│ Document(\n", + "│ │ id_='5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c84663'+14,\n", + "│ │ embedding=None,\n", + "│ │ metadata={'dl_doc_hash': '5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c84663'+14},\n", + "│ │ excluded_embed_metadata_keys=['dl_doc_hash'],\n", + "│ │ excluded_llm_metadata_keys=['dl_doc_hash'],\n", + "│ │ relationships={},\n", + "│ │ text='## DocLayNet: A Large Human-Annotated Dataset for '+50593,\n", + "│ │ mimetype='text/plain',\n", + "│ │ start_char_idx=None,\n", + "│ │ end_char_idx=None,\n", + "│ │ text_template='{metadata_str}\\n\\n{content}',\n", + "│ │ metadata_template='{key}: {value}',\n", + "│ │ metadata_seperator='\\n'\n", + "│ )\n", + "]\n", + "\n" + ], + "text/plain": [ + "\u001b[1m[\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1;35mDocument\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mid_\u001b[0m=\u001b[32m'5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c84663'\u001b[0m+\u001b[1;36m14\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33membedding\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[32m'dl_doc_hash'\u001b[0m: \u001b[32m'5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c84663'\u001b[0m+\u001b[1;36m14\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mexcluded_embed_metadata_keys\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'dl_doc_hash'\u001b[0m\u001b[1m]\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mexcluded_llm_metadata_keys\u001b[0m=\u001b[1m[\u001b[0m\u001b[32m'dl_doc_hash'\u001b[0m\u001b[1m]\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mrelationships\u001b[0m=\u001b[1m{\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mtext\u001b[0m=\u001b[32m'## DocLayNet: A Large Human-Annotated Dataset for '\u001b[0m+\u001b[1;36m50593\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmimetype\u001b[0m=\u001b[32m'text/plain'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mstart_char_idx\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mend_char_idx\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mtext_template\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32mmetadata_str\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\\n\\n\u001b[0m\u001b[32m{\u001b[0m\u001b[32mcontent\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata_template\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32mkey\u001b[0m\u001b[32m}\u001b[0m\u001b[32m: \u001b[0m\u001b[32m{\u001b[0m\u001b[32mvalue\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33mmetadata_seperator\u001b[0m=\u001b[32m'\\n'\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m)\u001b[0m\n", + "\u001b[1m]\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from llama_index.core import StorageContext, VectorStoreIndex\n", + "\n", + "if INGEST:\n", + " # in this case we ingest the data into the vector store\n", + " docs = reader.load_data(\n", + " file_path=\"https://arxiv.org/pdf/2206.01062\", # DocLayNet paper\n", + " )\n", + " pprint(docs, max_length=1, max_string=50, max_depth=4)\n", + " storage_context = StorageContext.from_defaults(vector_store=vector_store)\n", + " index = VectorStoreIndex.from_documents(\n", + " documents=docs,\n", + " embed_model=embed_model,\n", + " storage_context=storage_context,\n", + " transformations=transformations,\n", + " )\n", + "else:\n", + " # in this case we just load the vector store index\n", + " index = VectorStoreIndex.from_vector_store(\n", + " vector_store=vector_store,\n", + " embed_model=embed_model,\n", + " )" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### LLM" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "from llama_index.llms.huggingface_api import HuggingFaceInferenceAPI\n", + "\n", + "HF_API_KEY = os.environ.get(\"HF_API_KEY\")\n", + "\n", + "llm = HuggingFaceInferenceAPI(\n", + " token=HF_API_KEY,\n", + " model_name=\"mistralai/Mistral-7B-Instruct-v0.3\",\n", + ")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## RAG" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
Response(\n",
+       "│   response='80863 pages were human annotated.',\n",
+       "│   source_nodes=[\n",
+       "│   │   NodeWithScore(\n",
+       "│   │   │   node=TextNode(\n",
+       "│   │   │   │   id_='8874a117-d181-4f4f-a30b-0b5604370d77',\n",
+       "│   │   │   │   embedding=None,\n",
+       "│   │   │   │   metadata={...},\n",
+       "│   │   │   │   excluded_embed_metadata_keys=[...],\n",
+       "│   │   │   │   excluded_llm_metadata_keys=[...],\n",
+       "│   │   │   │   relationships={...},\n",
+       "│   │   │   │   text='3 THE DOCLAYNET DATASET\\n\\nDocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape o'+5775,\n",
+       "│   │   │   │   mimetype='text/plain',\n",
+       "│   │   │   │   start_char_idx=9089,\n",
+       "│   │   │   │   end_char_idx=15114,\n",
+       "│   │   │   │   text_template='{metadata_str}\\n\\n{content}',\n",
+       "│   │   │   │   metadata_template='{key}: {value}',\n",
+       "│   │   │   │   metadata_seperator='\\n'\n",
+       "│   │   │   ),\n",
+       "│   │   │   score=0.7367570400238037\n",
+       "│   │   ),\n",
+       "│   │   ... +1\n",
+       "│   ],\n",
+       "│   metadata={\n",
+       "│   │   '8874a117-d181-4f4f-a30b-0b5604370d77': {\n",
+       "│   │   │   'dl_doc_hash': '5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c846634ff924e635e0dc',\n",
+       "│   │   │   ... +1\n",
+       "│   │   },\n",
+       "│   │   ... +1\n",
+       "│   }\n",
+       ")\n",
+       "
\n" + ], + "text/plain": [ + "\u001b[1;35mResponse\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mresponse\u001b[0m=\u001b[32m'80863 pages were human annotated.'\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33msource_nodes\u001b[0m=\u001b[1m[\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[1;35mNodeWithScore\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mnode\u001b[0m=\u001b[1;35mTextNode\u001b[0m\u001b[1m(\u001b[0m\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mid_\u001b[0m=\u001b[32m'8874a117-d181-4f4f-a30b-0b5604370d77'\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33membedding\u001b[0m=\u001b[3;35mNone\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\u001b[33m...\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mexcluded_embed_metadata_keys\u001b[0m=\u001b[1m[\u001b[0m\u001b[33m...\u001b[0m\u001b[1m]\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mexcluded_llm_metadata_keys\u001b[0m=\u001b[1m[\u001b[0m\u001b[33m...\u001b[0m\u001b[1m]\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mrelationships\u001b[0m=\u001b[1m{\u001b[0m\u001b[33m...\u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mtext\u001b[0m=\u001b[32m'3 THE DOCLAYNET DATASET\\n\\nDocLayNet contains 80863 PDF pages. Among these, 7059 carry two instances of human annotations, and 1591 carry three. This amounts to 91104 total annotation instances. The annotations provide layout information in the shape o'\u001b[0m+\u001b[1;36m5775\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mmimetype\u001b[0m=\u001b[32m'text/plain'\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mstart_char_idx\u001b[0m=\u001b[1;36m9089\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mend_char_idx\u001b[0m=\u001b[1;36m15114\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mtext_template\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32mmetadata_str\u001b[0m\u001b[32m}\u001b[0m\u001b[32m\\n\\n\u001b[0m\u001b[32m{\u001b[0m\u001b[32mcontent\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mmetadata_template\u001b[0m=\u001b[32m'\u001b[0m\u001b[32m{\u001b[0m\u001b[32mkey\u001b[0m\u001b[32m}\u001b[0m\u001b[32m: \u001b[0m\u001b[32m{\u001b[0m\u001b[32mvalue\u001b[0m\u001b[32m}\u001b[0m\u001b[32m'\u001b[0m,\n", + "\u001b[2;32m│ │ │ │ \u001b[0m\u001b[33mmetadata_seperator\u001b[0m=\u001b[32m'\\n'\u001b[0m\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33mscore\u001b[0m=\u001b[1;36m0\u001b[0m\u001b[1;36m.7367570400238037\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[1m)\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33m...\u001b[0m +\u001b[1;36m1\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m]\u001b[0m,\n", + "\u001b[2;32m│ \u001b[0m\u001b[33mmetadata\u001b[0m=\u001b[1m{\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[32m'8874a117-d181-4f4f-a30b-0b5604370d77'\u001b[0m: \u001b[1m{\u001b[0m\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[32m'dl_doc_hash'\u001b[0m: \u001b[32m'5dfbd8c115a15fd3396b68409124cfee29fc8efac7b5c846634ff924e635e0dc'\u001b[0m,\n", + "\u001b[2;32m│ │ │ \u001b[0m\u001b[33m...\u001b[0m +\u001b[1;36m1\u001b[0m\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[1m}\u001b[0m,\n", + "\u001b[2;32m│ │ \u001b[0m\u001b[33m...\u001b[0m +\u001b[1;36m1\u001b[0m\n", + "\u001b[2;32m│ \u001b[0m\u001b[1m}\u001b[0m\n", + "\u001b[1m)\u001b[0m\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "query_engine = index.as_query_engine(llm=llm)\n", + "query_res = query_engine.query(\"How many pages were human annotated?\")\n", + "pprint(query_res, max_length=1, max_string=250, max_depth=4)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": ".venv", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.4" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/poetry.lock b/poetry.lock index b8419b69..e711babc 100644 --- a/poetry.lock +++ b/poetry.lock @@ 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[tool.poetry.dependencies] python = "^3.10" pydantic = "^2.0.0" -docling-core = "^1.2.0" +docling-core = "^1.3.0" docling-ibm-models = "^1.1.7" deepsearch-glm = "^0.21.0" filetype = "^1.2.0" @@ -38,6 +38,29 @@ rtree = "^1.3.0" scipy = "^1.14.1" pyarrow = "^16.1.0" +######### +# extras: +######### +python-dotenv = { version = "^1.0.1", optional = true } +llama-index-embeddings-huggingface = { version = "^0.3.1", optional = true } +llama-index-llms-huggingface-api = { version = "^0.2.0", optional = true } +llama-index-vector-stores-milvus = { version = "^0.2.1", optional = true } +langchain-huggingface = { version = "^0.0.3", optional = true} +langchain-milvus = { version = "^0.1.4", optional = true } +langchain-text-splitters = { version = "^0.2.4", optional = true } + +############## +# constraints: +############## +torch = [ + {version = "^2.2.2", optional = true, markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'"}, + {version = "~2.2.2", optional = true, markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'"} +] +torchvision = [ + {version = "^0", optional = true, markers = "sys_platform != 'darwin' or platform_machine != 'x86_64'"}, + {version = "~0.17.2", optional = true, markers = "sys_platform == 'darwin' and platform_machine == 'x86_64'"} +] + [tool.poetry.group.dev.dependencies] black = {extras = ["jupyter"], version = "^24.4.2"} pytest = "^7.2.2" @@ -51,11 +74,26 @@ pytest-xdist = "^3.3.1" types-requests = "^2.31.0.2" flake8-pyproject = "^1.2.3" pylint = "^2.17.5" - +ipykernel = "^6.29.5" +ipywidgets = "^8.1.5" +nbqa = "^1.9.0" [tool.poetry.group.examples.dependencies] datasets = "^2.21.0" +[tool.poetry.extras] +examples = [ + "python-dotenv", + # LlamaIndex examples: + "llama-index-embeddings-huggingface", + "llama-index-llms-huggingface-api", + "llama-index-vector-stores-milvus", + # LangChain examples: + "langchain-huggingface", + "langchain-milvus", + "langchain-text-splitters", +] + [build-system] requires = ["poetry-core"] build-backend = "poetry.core.masonry.api"