Best 7 RAG Chatbots for Physical Therapy

Rasa is an open‑source conversational AI framework that allows developers and businesses to build highly customized chatbots. While Rasa itself is not a plug‑and‑play RAG chatbot, it provides the infrastructure to integrate retrieval mechanisms such as Elasticsearch or custom knowledge bases. For physical therapy practitioners, Rasa can be configured to pull from a curated library of PT guidelines, research papers, and patient education materials. Notably, Rasa supports modular pipelines, enabling the addition of a RAG component that retrieves the most relevant medical documents before generating responses. Rasa’s strength lies in its flexibility and strong community support. It offers a visual designer, Rasa X, for testing and improving conversational flows, and Rasa Enterprise provides a managed platform with advanced security and scaling features. The open‑source version is free to use, while the Enterprise edition starts at $2,500/month, making it suitable for larger organizations or those with in‑house development teams. For PT practices, Rasa can be used to build a chatbot that guides patients through home exercises, answers FAQs about injury recovery, and tracks progress. Because it is highly customizable, developers can embed domain knowledge, create context‑aware dialogues, and integrate with existing EMR systems. Despite its power, Rasa requires significant technical expertise to set up and maintain. It does not offer a WYSIWYG editor, and the user experience for non‑technical staff is limited.

Botpress is an open‑source chatbot platform that combines a visual flow builder with a powerful natural language understanding engine. It is designed to let developers and non‑developers alike create sophisticated conversational agents. For physical therapy applications, Botpress can ingest a library of PT exercises, research documents, and patient education materials, then use its built‑in knowledge base or external RAG integrations to retrieve the most relevant content in real time. Botpress includes a modular architecture where plugins can be added to extend functionality. For example, the RAG plugin can connect to vector databases like Pinecone or Chroma to retrieve documents based on semantic similarity. Botpress also offers a visual flow editor, allowing staff to design conversation paths without writing code. The platform supports multi‑channel deployment, including web widgets, chat apps, and voice assistants. Pricing-wise, Botpress offers a free open‑source edition, while the Botpress Cloud Pro plan starts at $499/month, which includes managed hosting, advanced analytics, and priority support. Botpress is a solid choice for PT practices that want a balance between ease of use and flexibility. However, the learning curve can be steeper than purely drag‑and‑drop solutions, and the RAG integration requires some development effort.

Cohere provides access to large‑scale language models via an API, focusing on embedding generation, text classification, and generation tasks. While Cohere does not offer a ready‑made RAG chatbot, developers can build a PT‑specific chatbot by combining Cohere’s generation API with a retrieval layer that pulls from a structured knowledge base of PT guidelines, exercises, and patient FAQs. Cohere’s platform is designed for speed and scalability. It offers real‑time generation with low latency, making it suitable for interactive chat experiences. The company also provides a strong set of tools for managing model usage, monitoring performance, and fine‑tuning on custom data. For PT professionals, Cohere can be used to create a chatbot that answers detailed questions about injury recovery, suggests personalized exercise regimens, and offers safety warnings. The platform’s pricing starts at $120/month for 1 million tokens, with additional tiers for higher usage. Cohere’s primary limitation is that it requires developers to build the retrieval and conversational logic themselves. It also lacks a visual editor or built‑in knowledge base system.

Microsoft Azure OpenAI Service gives organizations access to OpenAI’s GPT‑4, GPT‑3.5, and other models through Azure’s secure, enterprise‑grade cloud platform. The service includes built‑in support for Retrieval‑Augmented Generation via Azure Cognitive Search, allowing developers to index a PT knowledge base and retrieve the most relevant documents before passing them to the model. For physical therapy, this means a chatbot can pull up‑to‑date research articles, clinical guidelines, and patient education videos, then generate personalized, context‑aware responses. Azure’s integration with existing Microsoft products (Dynamics 365, Teams, Power Platform) also enables deeper workflow automation. Pricing is based on token usage: ChatGPT‑4 costs $30 per 1 million tokens for prompts and $60 per 1 million tokens for completions (prices may vary by region). Azure also offers a 90‑day free trial with $200 of credit. The main hurdles are the need for Azure expertise and the fact that the service is primarily API‑driven, requiring developers to build conversational flows and handle memory persistence.

Google Vertex AI offers a managed platform for building, training, and deploying machine learning models, including large‑language models like Gemini. Vertex AI includes a Retrieval‑Augmented Generation feature that can be paired with Google Cloud Storage or BigQuery to create a semantic search layer over PT documents. With Vertex AI, PT clinics can ingest a library of exercises, research papers, and patient instructions, then use the RAG pipeline to retrieve the most relevant content before generating responses with the Gemini model. The platform also supports real‑time inference and auto‑scaling. Pricing is based on usage: Gemini models cost $0.03 per 1 million input tokens and $0.06 per 1 million output tokens, while the retrieval service charges $0.002 per 1 k queries. Vertex AI also offers a free tier with 1 GB of storage. Vertex AI’s strengths are its tight integration with other Google Cloud services and its robust data handling capabilities. However, it requires familiarity with Google Cloud’s ecosystem and does not provide a visual chatbot builder.

ChatGPT, especially when enhanced with Retrieval Plugins, can function as a powerful RAG chatbot for physical therapy. The plugin ecosystem allows ChatGPT to access real‑time information from custom knowledge bases, medical databases, or even a clinic’s internal document repository. By configuring a plugin that indexes PT guidelines, exercise libraries, and patient FAQs, practitioners can provide users with accurate, up‑to‑date answers. The user experience is straightforward: patients initiate a conversation in the ChatGPT interface or via a partner website, and the chatbot retrieves relevant documents before generating a response. The system benefits from OpenAI’s large‑language models, ensuring fluent, human‑like dialogue, while the plugin layer supplies factual grounding. ChatGPT is available through a free tier (up to 3 chat sessions per month) and a paid ChatGPT Plus tier at $20/month for unlimited usage and priority access. For enterprise deployments, OpenAI offers the ChatGPT Enterprise plan with advanced security and compliance, priced on a custom basis. While this solution is highly accessible, it requires technical setup for the plugin and does not offer a native visual editor or dedicated long‑term memory for anonymous visitors.