> ## Documentation Index > Fetch the complete documentation index at: https://docs.getmetal.io/llms.txt > Use this file to discover all available pages before exploring further. # Create a Retrieval Pipeline > Build a LLM retrieval pipeline with Metal (RAG) ## What is RAG (Retrieval-Augmented Generation)? RAG combines LLMs with external collections of data, like the Datasources we'll create next. It works in two steps: * Retrieval: The model taps into an external source (a Datasource) to find relevant data based on a query. * Augmented Generation: With the data it found, the model crafts a more informed response. ## 1. Create a Datasource A Datasource is the collection of files that need to be preprocessed to feed our application. To start, go to your organization [Dashboard](https://app.getmetal.io), navigate to the Datasources tab, and click on the `Add Datasource` button to create a new Datasource. rag-1 ## 2. Add an Index and Connect it to a Datasource Indexes are where your preprocessed data will be transformed and made queryable for your application. To set up an Index, go to the Indexes tab, and click on the `Add Index` button to create a new Index. You can then connect your Index to the existing Datasource. rag-2 ## 3. Add Files to your Datasource Go to the Datasource tab, and click on the `Upload File` button to upload your files. Accepted file formats are PDF, DOC, DOCX, XLSX, and CSV. This will now become `Data Entities`. After you've added a file, Metal preprocess the data, create the chunks and generate the embeddings that will be used for retrieval. rag-3