Build RAG in 3 steps. Focus on improving rather than building.
You don't have to build components like indexing and retrieval by writing codes. The things you have to do are only 3 steps below. Build RAG simply and allocate resources to increase accuracy, to mitigate hallucinations and enhance reliability of your app.
1. Add data sources without code
Upload from local by TXT, JSON, PDF, HTML or OCR. You can also add data from object storages like Amazon S3, Google Cloud Storage and Azure Blob Storage, or by pasting webpage URL.
2. Choose the best search strategies & models on your use case
Select search strategies, chunking strategies, embedding and reranking models such as Cohere, OpenAI, Voyage AI, BAAI, WhereIsAI, Multilingual E5, etc. You can easily switch between them to increase accuracy.
Enter a query in the playground, search, and verify if it is correctly summarized. If the summary is incorrect, review a part of document that generated the answer, and improve by changing the data source or search strategy.
Developers are always in pressure of guaranteeing the accuracy and quality of GenAI outputs. The dev platform that natively supports "iterative" framework with "observability" brings you a long-term success.
Integrate RAG into the prompt chain seamlessly
After building RAG in Teammate Aug, you can integrate it as a node in Teammate Lang. This allows you to check the final output on the playground when combined with prompts.
Check the accuracy & relevancy scores based on Ragas
Teammate Intel is compatible with Ragas (evaluation framework for RAG), so you can monitor accuracy score & relevancy score, and mitigate hallucinations.
Prompt & RAG open source community
We stand for open-sourcing the wisdom of developers. We build the new platform of "prompts and RAG" work for the community, where prompt engineers & GenAI DevOps share and showcase their expertises for free, aiming we build the future of advanced GenAI apps.
*Coming soon...
Reduce costs by paying only for what you use
Teammate Aug does not take fixed sever fee because it's severless platform. This allows you to pay only for queries you use and storage cost.
Compile RAG into a single API
Our platform is designed to be developer-friendly. Your logics and programs should be easily manageable even at RAG. In Teammate Aug, your RAG (including search strategies, chunking strategies, embedding and reranking models) is compiled into a single API, so your RAG entire flow is availabe everywhere at a simple manner.
Choose the data Residency
You can choose the data residency such as cloud platform (AWS /GCP /Azure) and region to keep control of your data. Teammate AI Services is also enterprise-friendly platform.
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