In this repository we showcase some common usage of Deep Search for Document conversion as well as Data and Knowledge exploration.
Each example starts by defining its input parameters. This is supported by Pydantic
Settings, allowing automated loading from a .env file or env vars. Furthermore, access
is based on Profiles. Unless otherwise configured, the profile used is the active one.
| Name | Description | |
|---|---|---|
| 1. | Convert documents quick start | Full example on programmatic document conversion  |
| 2. | Convert documents with custom settings | Full example on programmatic document conversion with custom conversion settings |
| 3. | Visualize bounding boxes | Visualize the bbox of the text elements  |
| 4. | Extract figures from documents | Given a PDF file, extract the figures  |
| 5. | Extract tables | Given a PDF file, extract the tables  |
| Name | Description | |
|---|---|---|
| 1. | NLP on documents* | A few quick examples on how to apply NLP models on documents (eg extracting key-terms) |
| 2. | Reference Parsing* | Examples on how to parse references from Documents |
| 3. | Material Extraction* | Examples on how to extract materials from Documents |
This section will showcase examples which query data processed via Deep Search.
| Name | Description | |
|---|---|---|
| 1. | Data query quick start | Example listing data collections, making search in one and more document collections, using source for projection  |
| 2. | Chemistry search queries | Search the chemistry databases for known molecules  |
| 3. | Chemistry and patent searches via PatCID | Explore the chemistry databases using substructure and similarity searches and navigate to the world-wide patents which reference molecules  |
| 4. | Snippets and aggregations in data queries | Extract snippets in search queries and leverage aggregations for exploratory analysis  |
This section will showcase examples of semantic capabilitilies in the area of Q&A using RAG.
| Name | Description | |
|---|---|---|
| 1. | QA on a specific document | Interact with a specific document using Conversational AI  |
| 2. | QA on a document collection | Interact with a document collection using Conversational AI  |
This section will showcase examples for bringing your own documents, csv data, nlp models and more.
| Name | Description | |
|---|---|---|
| 1. | Bring your own PDF | Upload your own PDF documents, search on them and export the result as JSON files.  |
| 2. | Bring your own converted documents | Upload your documents already formatted as JSON.  |
| 3. | Bring your own DataFrame | Bring your own DataFrame from CSV, XLSX, etc and explore the content in a knowledge graph |
This section will showcase examples for managing index item attachments and metadata.
| Name | Description | |
|---|---|---|
| 1. | Manage attachments | Manage index item attachments  |
This section will showcase examples related to the use of knowledge graphs (KGs) in Deep Search.
| Name | Description | |
|---|---|---|
| 1. | Using Deep Search KGs with PyTorch Geometric | Download knowledge graphs from Deep Search and import them in PyTorch Geometric.  |
This section will showcase examples related to the integration of Deep Search with other tools and utilities.
| Name | Description | |
|---|---|---|
| 1. | Annotations on argilla.io | Use argilla.io for annotating the content of documents.  |
The examples contained in this catalog depend on the deepsearch-toolkit as well as
other modules needed for the showcase demonstrated (e.g. pandas, matplotlib, rdkit, etc).
Please refer to the poetry pyproject.toml or requirements.txt for a complete list.
Python dependencies are installed with
pip install -r requirements.txtAdditionally, some examples rely on system packages. When this is the case, the README of the individual example will contain more details on which package is required. The auxiliary file apt.txt list all such packages for a Debian-bases OS. They can be installed with
xargs sudo apt-get install < apt.txtNote that some examples require dependencies that are not available on Windows platform. We flagged those examples with an asterisk * in the index above.
The Deep Search Toolkit codebase is under MIT license.
For individual model usage, please refer to the model licenses found in the original packages.