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+# RAG Studio 
+
+### What is Rag Studio?
+
+TBD - An AMP that provides a no-code tool to build RAG applications
+
+### Pre-requisites
+
+RAG Studio requires AWS for access to both LLM and embedding models.  Please complete the following steps before using the RAG Studio:
+
+- A S3 bucket to store the documents
+- The following models configured and accessible via AWS Bedrock.  Any of the models not enabled will not function in the UI.
+  - Llama3.1 8b Instruct V1 (`meta.llama3-1-8b-instruct-v1:0`) - This model is required for the RAG Studio to function
+  - Llama3.1 70b Instruct V1 (`meta.llama3-1-70b-instruct-v1:0`)
+  - Llama3.1 405b Instruct V1 (`meta.llama3-1-405b-instruct-v1:0`)
+- For Embedding, you will need to enable the following model in AWS Bedrock:
+  - Cohere English Embedding v3 (`meta.cohere-english-embedding-v3:0`)
+
+### Cloudera DataFlow (Nifi) Setup:
+
+Rag Studio provides a Nifi template that can be downloaded for a given Knowledge Base from the `Connections` tab.  
+The Nifi template can then be imported into your Cloudera DataFlow (CDF) environment and used to setup a pipeline into Rag Studio.  
+
+IMPORTANT: In order to inject data from CDF, users must disable authentication of the AMP Project from their Cloudera Machine Learning (CML) workspace.
+This carries a security risk and should be carefully considered before proceeding.
+
+### Updating RAG Studio
+
+The Rag Studio UI will show a banner at the top of the page when a new version of the AMP is available.  
+To update the Rag Studio, click on the banner and follow the instructions.  If any issues are encountered, please contact 
+Cloudera for assistance.  Additionally, further details on the AMP status can be found from the CML workspace.
+
+### Common Issues
+
+- Both the Llama and Cohere models must be enabled in AWS Bedrock for the Rag Studio to function properly.  If the models are not enabled, Rag Studio will not function as expected.
+
+## Installation
+
+Follow the [standard instructions](https://docs.cloudera.com/machine-learning/cloud/applied-ml-prototypes/topics/ml-amp-add-catalog.html) for installing this AMP into your CML workspace.
+The "File Name" to use is `catalog-entry.yaml`.
+
+For configuring the project, you will need to provide some environment variables.
+
+- `S3_RAG_BUCKET_PREFIX` - A prefix added to all S3 paths used by Rag Studio
+- `S3_RAG_DOCUMENT_BUCKET` - The S3 bucket where uploaded documents are stored
+- `AWS_DEFAULT_REGION` - defaults to `us-west-2`
+- `AWS_ACCESS_KEY_ID`
+- `AWS_SECRET_ACCESS_KEY`
+
+## Developer Information
+
+Ignore this section unless you are working on developing or enhancing this AMP.
+
+### Environment Variables
+
+Make a copy of the `.env.example` file and rename it to `.env`. Fill in the values for the environment variables.
+
+### Local Development
+
+Every service can be started locally for development by running `./local-dev.sh`.  Once started, the UI can be accessed 
+at `http://localhost:5173`.  Additionally, each service can be started individually by following the instructions below.
+
+#### FE Setup
+
+- Navigate to the FE subdirectory (`cd ./ui`)
+- Make sure node is installed (if not, run `brew install node@20`)
+- Run `pnpm install` (if pnpm is not installed on your system, install globally `brew install pnpm`)
+- Start the dev server (`pnpm dev`) [if you want to run the dev server standalone, for debugging, for instance?]
+
+#### Node Setup
+
+The Node Service is used as a proxy and to serve static assets. For local development, the proxying and static 
+asset serving is handled by the FE service.  The Node service is only used in production.  However, if you want to run 
+the Node service locally, you can do so by following these steps:
+
+- Build the FE service (`cd ./ui` and then `pnpm build`)
+- Navigate to the Node subdirectory (`cd ./express`)
+- Start the Node server (`node index.js`)
+
+#### Python Setup
+
+- Install Python (via [pyenv](https://github.com/pyenv/pyenv), probably) (directly via brew, if you must)
+- `cd llm-service`
+- Create a virtual environment (`python -m venv venv; source venv/bin/activate`)
+- Install dependencies (`python -m pip install -r requirements.txt`)
+- `fastapi dev`
+    - ends up running on port 8000
+
+#### Java Setup
+
+- Install Java 21 and make default JDK
+- `cd ./api`
+- `./gradlew bootRun`
+
+#### To run quadrant locally
+```
+docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/databases/qdrant_storage:/qdrant/storage:z qdrant/qdrant
+```
+
+## The Fine Print
+
+IMPORTANT: Please read the following before proceeding. This AMP includes or otherwise depends on certain third party software packages. Information about such third party software packages are made available in the notice file associated with this AMP. By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites. For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms. If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP. By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.

.history/README_20241108133701.md

@@ -0,0 +1,102 @@
+# RAG Studio 
+
+### What is Rag Studio?
+
+An AMP that provides a no-code tool to build RAG applications
+
+### Pre-requisites
+
+RAG Studio requires AWS for access to both LLM and embedding models.  Please complete the following steps before using the RAG Studio:
+
+- A S3 bucket to store the documents
+- The following models configured and accessible via AWS Bedrock.  Any of the models not enabled will not function in the UI.
+  - Llama3.1 8b Instruct V1 (`meta.llama3-1-8b-instruct-v1:0`) - This model is required for the RAG Studio to function
+  - Llama3.1 70b Instruct V1 (`meta.llama3-1-70b-instruct-v1:0`)
+  - Llama3.1 405b Instruct V1 (`meta.llama3-1-405b-instruct-v1:0`)
+- For Embedding, you will need to enable the following model in AWS Bedrock:
+  - Cohere English Embedding v3 (`meta.cohere-english-embedding-v3:0`)
+
+### Cloudera DataFlow (Nifi) Setup:
+
+Rag Studio provides a Nifi template that can be downloaded for a given Knowledge Base from the `Connections` tab.  
+The Nifi template can then be imported into your Cloudera DataFlow (CDF) environment and used to setup a pipeline into Rag Studio.  
+
+IMPORTANT: In order to inject data from CDF, users must disable authentication of the AMP Project from their Cloudera Machine Learning (CML) workspace.
+This carries a security risk and should be carefully considered before proceeding.
+
+### Updating RAG Studio
+
+The Rag Studio UI will show a banner at the top of the page when a new version of the AMP is available.  
+To update the Rag Studio, click on the banner and follow the instructions.  If any issues are encountered, please contact 
+Cloudera for assistance.  Additionally, further details on the AMP status can be found from the CML workspace.
+
+### Common Issues
+
+- Both the Llama and Cohere models must be enabled in AWS Bedrock for the Rag Studio to function properly.  If the models are not enabled, Rag Studio will not function as expected.
+
+## Installation
+
+Follow the [standard instructions](https://docs.cloudera.com/machine-learning/cloud/applied-ml-prototypes/topics/ml-amp-add-catalog.html) for installing this AMP into your CML workspace.
+The "File Name" to use is `catalog-entry.yaml`.
+
+For configuring the project, you will need to provide some environment variables.
+
+- `S3_RAG_BUCKET_PREFIX` - A prefix added to all S3 paths used by Rag Studio
+- `S3_RAG_DOCUMENT_BUCKET` - The S3 bucket where uploaded documents are stored
+- `AWS_DEFAULT_REGION` - defaults to `us-west-2`
+- `AWS_ACCESS_KEY_ID`
+- `AWS_SECRET_ACCESS_KEY`
+
+## Developer Information
+
+Ignore this section unless you are working on developing or enhancing this AMP.
+
+### Environment Variables
+
+Make a copy of the `.env.example` file and rename it to `.env`. Fill in the values for the environment variables.
+
+### Local Development
+
+Every service can be started locally for development by running `./local-dev.sh`.  Once started, the UI can be accessed 
+at `http://localhost:5173`.  Additionally, each service can be started individually by following the instructions below.
+
+#### FE Setup
+
+- Navigate to the FE subdirectory (`cd ./ui`)
+- Make sure node is installed (if not, run `brew install node@20`)
+- Run `pnpm install` (if pnpm is not installed on your system, install globally `brew install pnpm`)
+- Start the dev server (`pnpm dev`) [if you want to run the dev server standalone, for debugging, for instance?]
+
+#### Node Setup
+
+The Node Service is used as a proxy and to serve static assets. For local development, the proxying and static 
+asset serving is handled by the FE service.  The Node service is only used in production.  However, if you want to run 
+the Node service locally, you can do so by following these steps:
+
+- Build the FE service (`cd ./ui` and then `pnpm build`)
+- Navigate to the Node subdirectory (`cd ./express`)
+- Start the Node server (`node index.js`)
+
+#### Python Setup
+
+- Install Python (via [pyenv](https://github.com/pyenv/pyenv), probably) (directly via brew, if you must)
+- `cd llm-service`
+- Create a virtual environment (`python -m venv venv; source venv/bin/activate`)
+- Install dependencies (`python -m pip install -r requirements.txt`)
+- `fastapi dev`
+    - ends up running on port 8000
+
+#### Java Setup
+
+- Install Java 21 and make default JDK
+- `cd ./api`
+- `./gradlew bootRun`
+
+#### To run quadrant locally
+```
+docker run -p 6333:6333 -p 6334:6334 -v $(pwd)/databases/qdrant_storage:/qdrant/storage:z qdrant/qdrant
+```
+
+## The Fine Print
+
+IMPORTANT: Please read the following before proceeding. This AMP includes or otherwise depends on certain third party software packages. Information about such third party software packages are made available in the notice file associated with this AMP. By configuring and launching this AMP, you will cause such third party software packages to be downloaded and installed into your environment, in some instances, from third parties' websites. For each third party software package, please see the notice file and the applicable websites for more information, including the applicable license terms. If you do not wish to download and install the third party software packages, do not configure, launch or otherwise use this AMP. By configuring, launching or otherwise using the AMP, you acknowledge the foregoing statement and agree that Cloudera is not responsible or liable in any way for the third party software packages.

README.md

@@ -2,7 +2,7 @@
 
 ### What is Rag Studio?
 
-TBD - An AMP that provides a no-code tool to build RAG applications
+An AMP that provides a no-code tool to build RAG applications
 
 ### Pre-requisites