SFR-RAG

SFR-RAG is an advanced open-source language model developed by Salesforce AI research to address one of the most peRSIstent challenges in Generative AI: balancing Fluency with fACTual reliability. Unlike Standard large language models that rely solely on parametric knowledge, SFR-RAG integrates real-time retrieval from external sources—such as documents, Databases, or knowledge bases—to ground its responses in verifiable Information. This architecture makes it especially valuable for APPlications where correctness, traceability, and contextual fIDElity are non-negotiable, including Enterprise support, technical documentation, legal analysis, and research assistance.

What it does

SFR-RAG enhances traditional LLM capabilities through retrieval-augmented generation (RAG). When a user subMITs a query, the system first retrieves relevant passages from a configured knowledge source, then synthesizes those passages into a coherent, cited response. It supports structured reasoning via explicit Thought and Observation roles in its chat template, enabling transparent step-by-step inference. The model is trAIned using instruction tuning focused on minimizing hallucination and maximizing alignment with retrieved evidence.

Key capabilities

• Context-faithful generation: Prioritizes consistency with provided context over generic fluency.
• Multi-hop reasoning: Chains together infORMation from multiple retrieved documents to answer complex questions.
• Reliable citation: Attributes claims to specific sources when generating responses.
• Function calling integration: Interfaces with external tools and APIs to fetch up-to-date or domain-specific data.
• Preference-aligned fine-tuning: Uses human preference signals to rank and select high-quality, grounded ouTPUts.

Who it is for

SFR-RAG targets developers, researchers, and organizations building Production-Grade AI assistants—especially those requiring auditability, compliance, or domain specificity. Use cases include customer-facing chatbots with live Knowledge Bases, internal enterprise Q&A systems, academic research aids, and regulated-domain assistants (e.g., legal, healthcare, finance) where unverified outputs cARRy significant risk.

How to get started

The model weights and training code are publicly available on Hugging Face and GitHub. Users can deploy SFR-RAG locally or on Cloud Infrastructure by integrating it with a compatible retriever (e.g., FAISS, Elasticsearch, or modern dense retrieval models). A minimal setup requires loading the model, connecting it to a retrieval pipeline, and applying its custom chat template during Inference. Documentation and example Notebooks are maintained by salesforce AI Research, though implementation details may vary depending on the chosen retrieval backend and deployment environment.

Pricing and access caveat

SFR-RAG is released under an open-source license and is free to use, modify, and deploy. There are no usage fees or subscription tiers. However, Operational costs—including GPU resources for inference, storage for knowledge corpora, and maintenance of retrieval infrastructure—are the responsibility of the deploying organization. While the core model is accessible without restrictions, some advanced integrations or enterprise support options may vary by deployment context and are not officially offered by Salesforce AI Research.