AI4Bharat (IndicCorp)
AI4Bharat is a research group at IIT Madras that has spent years building open resources for India's languages, which most mainstream datasets barely touch. Its headline corpus, IndicCorp, gathers billions of words of monolingual text (largely from news sites and the open web) across the major Indic languages, from Hindi, Bengali, and Tamil to lower-resource scripts like Assamese and Odia. Around it sit parallel corpora such as Samanantar (sentence pairs aligned between English and Indian languages), evaluation suites like IndicGLUE and IndicXTREME, and ready-made models including IndicBERT and the IndicTrans translation family.
Most of this is published through the project website and mirrored on Hugging Face, so you can pull a single language split straight into a datasets pipeline or download the compressed text files directly. The corpora arrive as cleaned plain text rather than raw crawl, which saves you a filtering pass, though you will still want to normalise scripts and handle code-mixing (sentences that switch between English and an Indian language mid-flow) before you chunk and embed.
For a RAG system serving Indian audiences, this is the strongest single starting point: it gives you retrieval text, a translation layer, and evaluation sets from one group with consistent methodology, which matters when you need to compare quality across languages that share little vocabulary. It suits multilingual assistants, government and public-service tools, and any project where English-only embeddings fall short.
The main caveat is licensing. Terms vary from one dataset to the next, and while most are permissive, commercial use is not guaranteed to be consistent across the collection, so check the specific corpus, benchmark, or model you plan to ship. Alongside similar efforts, treat AI4Bharat as the Indic counterpart to Masakhane for African languages: regional, community-grounded, and far deeper in its focus area than any general-purpose web corpus.
Related sources
Aya Collection
Cohere Labs' massively multilingual instruction dataset: around 513 million templated, translated and human-written instances across 114 languages, released under Apache 2.0 on the Hugging Face Hub.
CC-100
A high-quality multilingual corpus derived from Common Crawl. It splits every document into separate paragraphs, so it is effectively a paragraph-level rather than document-level corpus, which matters for your chunking strategy.
CulturaX
CulturaX (uonlp/CulturaX on Hugging Face) is a multilingual text dataset of 6.3 T tokens across 167 languages, built for training large language models. It combines cleaned versions of mC4 and OSCAR, two large web corpora, after a multi-stage filtering pipeline.
FineWeb2-HQ
A high-quality multilingual pretraining corpus of roughly the top 10% of FineWeb2 documents in each of 20 languages, selected by a model-based quality classifier. Built by EPFL and released on Hugging Face.