RagBuilder is a toolkit designed to help you create optimal Production-ready Retrieval-Augmented Generation (RAG) pipeline for your data. Ragbuilder performs hyperparameter tuning on the different RAG parameters (Eg: chunking strategy: semantic, character etc.; chunk size 1000, 2000 etc.), and evaluates them against a test data set, and shows what RAG configurations worked best for your source data. RagBuilder also includes several pre-defined state-of-the-art RAG templates that have shown strong performance across various other datasets.
- Hyperparameter Tuning: Efficiently identify optimal RAG configurations (combination of granular parameters like chunking strategy, chunking size, embedding models, retriever types etc.) using Bayesian optimization
- Pre-defined RAG Templates: Use state-of-the-art templates that have demonstrated strong performance across various datasets.
- Evaluation Dataset Options: Choose to generate a synthetic test dataset or provide your own.
- Automatic Reuse: Automatically re-use previously generated synthetic test data when applicable.
- Easy-to-use Interface: Intuitive UI to guide you through setting up, configuring, and reviewing your RAG configurations.
curl -fsSL https://raw.githubusercontent.com/KruxAI/ragbuilder-readme/main/install.sh |bash
curl -fsSL https://raw.githubusercontent.com/KruxAI/ragbuilder-readme/main/install.bat
Run Install.bat from command prompt
install.bat
Make sure your OpenAI API key is available by setting it as an environment variable. In MacOS and Linux, this is the command:
export OPENAI_API_KEY=XXXXX
and on Windows it is
set OPENAI_API_KEY=XXXXX
Pull docker image from Docker hub
docker pull ashwinzyx/ragbuilder:latest
Run the Docker Container. Create .env as below and use it while running the container. The env file must be in the same directory where the docker command is being run
docker run -it -v "$(pwd):/ragbuilder" --env-file .env -p 55003:8005 ashwinzyx/ragbuilder:latest
docker run -d -v %cd%:/ragbuilder --env-file .env -p 55003:8005 ashwinzyx/ragbuilder
OR
Provide env variables using command line
docker run -p 55003:8005 -e OPENAI_API_KEY=sk-....
Open Ragbuilder http://localhost:55003/
If you are crearting your own synthetic dataset for evaluation, save the csv file in the same directory where the docker run command is being executed and provide the file name only
Now, run ragbuilder on your command line:
ragbuilder
This will start the Ragbuilder Uvicorn app and open the browser. If the browser window doesn't open automatically, go to http://localhost:8005/ in your browser to access the RagBuilder dashboard.
- Click New Project to start building your RAG.
- Description: Describe your use-case. Let's specify "Q&A Chatbot" as the description for our demo.
- Source Data: Specify the path to your source data. This could be a URL, local directory or local file path. For the sake of our demo, let's specify the URL: https://lilianweng.github.io/posts/2023-06-23-agent/
- Select Ragbuilder options:
- Use Pre-defined RAG Templates - When selected, this'll include pre-defined RAG configuration templates that have demonstrated strong performance across various datasets and related use-cases. These templates will be evaluated against your data, providing you with performance metrics for each pre-defined configuration.
- Create Custom RAG Configurations - When selected, this'll generate multiple RAG configurations based on detailed parameters like chunking strategy, chunking size, embedding model, retriever type etc. With this option, it is recommended that you opt for the Bayesian optimization option to efficiently identify the near-optimal RAG configuration for your data. More about this in a bit.*
- Next, in order to tailor your RAG configurations, you can unselect any specific options you wish to exclude (For eg: Unselecting "Chunking Strategy: Character" will exclude all RAG configurations that have the CharacterTextSplitter). For best results, you may want to leave all settings unchanged. But for our Quickstart demo, we will unselect everything except the below:
- Chunking strategy: Markdown
- Embedding model: text-embedding-3-large
- Retriever: Vector DB - Similarity Search
- Top k: 5
- LLM: GPT-3.5 Turbo
- Select optimization approach:
- Bayesian optimization (Recommended): Bayesian optimization is a strategy for the optimization of objective functions that are expensive to evaluate. It is particularly useful in scenarios where the function to be optimized is unknown and expensive to compute, such as in hyperparameter tuning for machine learning models or optimizing engineering designs. This is perfect for RAG where we have multiple moving parts, each with multiple parameters
- Run all Combinations: This option runs all possible combinations of the options selected, offering a comprehensive performance analysis of all RAG configurations for your dataset. This option is appropriate if you have selected fewer number of options. Orherwise, this option can be resource intensive as it may yield hundreds or even thousands of unique configurations to compare. [Note]: This may take several minutes to complete.
- Next, in Evaluation dataset options, you have the option to:
- Use Existing Synthetic Test Data: If synthetic test data was previously generated for your dataset, this option will appear alongside the path of the existing test data.
- Generate Synthetic Test Data from My Dataset: Create a new synthetic test dataset based on your existing data.
- Provide a Manually Created Test Dataset: Use your own test dataset file (CSV format with "question" and "ground_truth" columns). For our demo, let's go ahead and create a synthetic test data by selecting the Generate Synthetic Test Data** option.
- Before running the tool, let's review all your selections:
- Review all the selections and click Confirm
- After processing we should see the dashboard with the results.
- Click the View Code snippet option in the results screen to get the code snippet of the desired RAG configuration. And voila, you've cut down several weeks/months of effort manually creating and evaluating different RAG configuration for your dataset.
This section provides instructions on setting up the environment variables required for the RagBuilder project. These variables need to be configured in a .env file located in the same directory where you run the ragbuilder command.
The environment variables are essential for authenticating and configuring various services used by the RagBuilder project. Below are the variables you need to set in your .env file.
- OPENAI_API_KEY: The API key for OpenAI services.
- MISTRAL_API_KEY: The API key for Mistral services.
- ENABLE_ANALYTICS: A boolean flag to enable or disable analytics. Set to
TrueorFalse. - HUGGINGFACEHUB_API_TOKEN: The API token for HuggingFace Hub.
- COHERE_API_KEY: The API key for Cohere services.
- JINA_API_KEY: The API key for Jina services.
- SINGLESTOREDB_URL: The connection string for SingleStoreDB, formatted as
userid:password@host:port/dbname. - PINECONE_API_KEY: The API key for Pinecone services.
Create a file named .env in the directory where you will run the ragbuilder command and add the following content, replacing the placeholder values with your actual keys and connection string. Ensure not to use quoutes for keys or values
# Environment variables for the RagBuilder project
OPENAI_API_KEY=XXXXXX
MISTRAL_API_KEY=XXXXX
ENABLE_ANALYTICS=True
HUGGINGFACEHUB_API_TOKEN=XXXXXX
COHERE_API_KEY=XXXXXX
JINA_API_KEY=XXXXXX
SINGLESTOREDB_URL=userid:password@host:port/dbname
PINECONE_API_KEY=XXXXXX- Create a new file named .env in your project directory.
- Copy the example content provided above into the .env file.
- Replace the placeholder values with your actual API keys and connection string.
- Save the .env file.
- Ensure that the .env file is located in the same directory where you run the ragbuilder command to ensure the environment variables are properly loaded.
By setting up these environment variables, you enable the RagBuilder project to authenticate and interact with the necessary external services.