LLM Training & RAG in One Package.
pip install kerdosai — the official Python SDK for building RAG pipelines, fine-tuning HuggingFace LLMs, and deploying enterprise chat UIs without writing frontend code.
pip install kerdosaipip install "kerdosai[all]"Everything in One Package
From document ingestion to LLM fine-tuning to production chat UIs — all from a single pip install.
End-to-end Retrieval-Augmented Generation: document loading, FAISS indexing, and LLM answering out of the box.
Index PDF, DOCX, TXT, Markdown, and CSV files via the KnowledgeBase API — all parsed and chunked automatically.
Fine-tune any HuggingFace model on your domain data with a clean, high-level KerdosAgent API.
RAGAgent supports both streaming and blocking chat with full conversation history maintained across turns.
Deploy an enterprise-grade Chat UI with a single line: deployment_type="gradio-rag". Zero frontend code required.
Launch an on-premise RAG UI from the terminal with `kerdosai rag-chat`. No Python code needed.
Start in 3 Lines
Real code — no boilerplate, no configuration files.
from kerdosai.rag import KnowledgeBase, RAGAgent
# 1. Index your documents
kb = KnowledgeBase().index_documents([
"report.pdf",
"policy.docx",
"notes.txt",
])
# 2. Create the agent
agent = RAGAgent(knowledge_base=kb)
# 3. Stream answers grounded in your docs
for chunk in agent.chat("What are the key findings?"):
print(chunk, end="", flush=True)Answers are grounded strictly in your uploaded documents — no internet hallucinations.
The RAG Pipeline Under the Hood
kerdosai wires up the full pipeline automatically — you just provide the documents and ask questions.
Battle-Tested Open-Source Stack
Built for Enterprise
kerdosai is purpose-built for teams that handle large volumes of internal documents and need private, auditable AI answers.
Healthcare
Clinical docs, protocols, patient FAQs
Financial Services
Compliance docs, annual reports, policy Q&A
Legal
Contract analysis, case law search
Enterprise IT
Internal wikis, runbooks, knowledge bases
Start Building With kerdosai Today
Install the package, index your documents, and have a production-grade RAG pipeline running in minutes. Need a private deployment or custom fine-tuning? Talk to our team.