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Context for your AI agents
Crawl websites, sync to vector databases, and power RAG applications. Pre-built integrations for LLM pipelines and AI assistants.
Build data pipelines that feed your AI models and agents without managing infrastructure. Crawl any website, transform content, and push directly to your preferred vector store. Use 10,000+ tools for RAG applications, AI assistants, and real-time knowledge bases. Monitor site changes, trigger workflows on new data, and keep your AIs fed with fresh, structured information. Cloud-native, API-first, and free to start until you need to scale.
GDAL wheels for python and C/C++ projects (Linux only)
To use precompiled wheels:
1) go to releases (Files) and download tarball needed;
2) install it with command:
python3 -m pip install /path/to/wheel.whl
Or simply use URL in pip:
python3 -m pip install https://sourceforge.net/projects/gdal-wheels-for-linux/files/GDAL-3.1.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.whl/download
URL may be found under "View details" button (i)
To use GDAL in C/C++ project you need to link gdal lib AND all libs located at dir GDAL.libs (usually this folder resides inside python site-packages)
To compile your own wheels see information given at forefather project: https://github.com/youngpm/gdalmanylinux
Usually this is done via command `make wheels`
GDAL wheels for Windows are provided by Christoph Gohlke at https://www.lfd.uci.edu/~gohlke/pythonlibs/#gdal
Built with PROJ (proj.db is included), GEOS, EXPAT.
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This is an Object Pascal Wrapper for the Shapefile C Library. It offers an elegant way to work with the Enviromental Systems Research Institute (ESRI)'s ShapeFiles using the Frank Warmerdam's well-known library released under the MIT license.
The R-tree Library aims to implement a generic, portable, easy-to-use collection of R-tree variants. Tested to work under Linux and Windows, using GNU, Intel and VC++ compilers, and also implementing many variants already, we are well underway.
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