Welcome to Verba: The Golden RAGtriever, an open-source application designed to offer an end-to-end, streamlined, and user-friendly interface for Retrieval-Augmented Generation (RAG) out of the box. In just a few easy steps, explore your datasets and extract insights with ease, either locally with HuggingFace and Ollama or through LLM providers such as OpenAI, Cohere, and Google.
pip install goldenverba
- Verba
- ✨ Getting Started with Verba
- 🔑 API Keys
- Quickstart: Deploy with pip
- Quickstart: Build from Source
- Quickstart: Deploy with Docker
- 💾 Verba Walkthrough
- 💖 Open Source Contribution
- 🚩 Known Issues
- ❔FAQ
Verba is a fully-customizable personal assistant for querying and interacting with your data, either locally or deployed via cloud. Resolve questions around your documents, cross-reference multiple data points or gain insights from existing knowledge bases. Verba combines state-of-the-art RAG techniques with Weaviate's context-aware database. Choose between different RAG frameworks, data types, chunking & retrieving techniques, and LLM providers based on your individual use-case.
| 🤖 Model Support | Implemented | Description |
|---|---|---|
| Ollama (e.g. Llama3) | ✅ | Local Embedding and Generation Models powered by Ollama |
| HuggingFace (e.g. MiniLMEmbedder) | ✅ | Local Embedding Models powered by HuggingFace |
| Cohere (e.g. Command R+) | ✅ | Embedding and Generation Models by Cohere |
| Google (e.g. Gemini) | ✅ | Embedding and Generation Models by Google |
| OpenAI (e.g. GPT4) | ✅ | Embedding and Generation Models by OpenAI |
| 📁 Data Support | Implemented | Description |
|---|---|---|
| PDF Ingestion | ✅ | Import PDF into Verba |
| CSV/XLSX Ingestion | ✅ | Import Table Data into Verba |
| Multi-Modal | planned ⏱️ | Import Multi-Modal Data into Verba |
| UnstructuredIO | ✅ | Import Data through Unstructured |
| ✨ RAG Features | Implemented | Description |
|---|---|---|
| Hybrid Search | ✅ | Semantic Search combined with Keyword Search |
| Semantic Caching | ✅ | Results saved and retrieved based on semantic meaning |
| Autocomplete Suggestion | ✅ | Verba suggests autocompletion |
| Filtering | planned ⏱️ | Apply Filters (e.g. documents, document types etc.) before performing RAG |
| Advanced Querying | planned ⏱️ | Task Delegation Based on LLM Evaluation |
| Reranking | planned ⏱️ | Rerank results based on context for improved results |
| RAG Evaluation | planned ⏱️ | Interface for Evaluating RAG pipelines |
| Customizable Metadata | planned ⏱️ | Free control over Metadata |
| 🆒 Cool Bonus | Implemented | Description |
|---|---|---|
| Docker Support | ✅ | Verba is deployable via Docker |
| Customizable Frontend | ✅ | Verba's frontend is fully-customizable via the frontend |
| 🤝 RAG Libraries | Implemented | Description |
|---|---|---|
| Haystack | planned ⏱️ | Implement Haystack RAG pipelines |
| LlamaIndex | planned ⏱️ | Implement LlamaIndex RAG pipelines |
| LangChain | planned ⏱️ | Implement LangChain RAG pipelines |
Something is missing? Feel free to create a new issue or discussion with your idea!
You have three deployment options for Verba:
- Install via pip
pip install goldenverba
- Build from Source
git clone https://github.com/weaviate/Verba
pip install -e .
- Use Docker for Deployment
Prerequisites: If you're not using Docker, ensure that you have Python >=3.10.0 installed on your system.
If you're unfamiliar with Python and Virtual Environments, please read the python tutorial guidelines.
Before starting Verba you'll need to configure access to various components depending on your chosen technologies, such as OpenAI, Cohere, and HuggingFace via an .env file. Create this .env in the same directory you want to start Verba in. You can find an .env.example file in the goldenverba directory.
Make sure to only set environment variables you intend to use, environment variables with missing or incorrect values may lead to errors.
Below is a comprehensive list of the API keys and variables you may require:
| Environment Variable | Value | Description |
|---|---|---|
| WEAVIATE_URL_VERBA | URL to your hosted Weaviate Cluster | Connect to your WCS Cluster |
| WEAVIATE_API_KEY_VERBA | API Credentials to your hosted Weaviate Cluster | Connect to your WCS Cluster |
| OPENAI_API_KEY | Your API Key | Get Access to OpenAI Models |
| OPENAI_BASE_URL | URL to OpenAI instance | Models |
| COHERE_API_KEY | Your API Key | Get Access to Cohere Models |
| OLLAMA_URL | URL to your Ollama instance (e.g. http://localhost:11434 ) | Get Access to Ollama Models |
| OLLAMA_MODEL | Model Name (e.g. llama) | Get Access to a specific Ollama Model |
| UNSTRUCTURED_API_KEY | Your API Key | Get Access to Unstructured Data Ingestion |
| UNSTRUCTURED_API_URL | URL to Unstructured Instance | Get Access to Unstructured Data Ingestion |
| GITHUB_TOKEN | Your GitHub Token | Get Access to Data Ingestion via GitHub |
| GOOGLE_APPLICATION_CREDENTIALS | Google Credentials | Get Access to Google Models |
| GOOGLE_CLOUD_PROJECT | Google Cloud Project | Get Access to Google Models |
| GOOGLE_API_KEY | Your API Key | Get Access to Google Models |
| VERBA_PRODUCTION | True | Run Verba in Production Mode |
Verba provides flexibility in connecting to Weaviate instances based on your needs. By default, Verba opts for Weaviate Embedded if it doesn't detect the WEAVIATE_URL_VERBA and WEAVIATE_API_KEY_VERBA environment variables. This local deployment is the most straightforward way to launch your Weaviate database for prototyping and testing.
However, you also have other options:
🌩️ Weaviate Cloud Service (WCS)
If you prefer a cloud-based solution, Weaviate Cloud Service (WCS) offers a scalable, managed environment. Learn how to set up a cloud cluster and get the API keys by following the Weaviate Cluster Setup Guide.
🐳 Docker Deployment Another robust local alternative is deploying Weaviate using Docker. For more details, consult the Weaviate Docker Guide.
Verba supports Ollama models. Download and Install Ollama on your device (https://ollama.com/download). Make sure to install your preferred LLM using ollama run <model>.
Tested with llama3, llama3:70b and mistral. The bigger models generally perform better, but need more computational power.
Make sure Ollama Server runs in the background and that you don't ingest documents with different ollama models since their vector dimension can vary that will lead to errors
If you want to use the Google Features, make sure to install the Google Verba package.
pip install goldenverba[google]
or
pip install `.[google]`If you're using Docker, modify the Dockerfile accordingly
For the Google Embeddings, Verba is using Vertex AI Studio inside Google Cloud. You can find instructions for obtaining a key here. If you have the gcloud CLI installed, you can run the following command: gcloud auth print-access-token. At the moment, this access token must be renewed every hour.
You also need to set the GOOGLE_CLOUD_PROJECT environment variable to the name of your project.
To use Google Gemini, you need a service account key, which is a JSON file. To obtain this, go to "project settings" in your Google Cloud console, then to "service accounts". Create a new service account, then create a new key. Download this key and place it in the route of Verba. Name it gemini_secrets.json to have it excluded from git automatically. Set the environment variable GOOGLE_APPLICATION_CREDENTIALS to the location of this file, e.g. gemini_secrets.json.
You also need to set the GOOGLE_CLOUD_PROJECT environment variable to the name of your project.
Verba supports importing documents through Unstructured IO (e.g plain text, .pdf, .csv, and more). To use them you need the UNSTRUCTURED_API_KEY and UNSTRUCTURED_API_URL environment variable. You can get it from Unstructured
UNSTRUCTURED_API_URL is set to
https://api.unstructured.io/general/v0/generalby default
Verba supports OpenAI Models such as Ada, GPT3, and GPT4. To use them, you need to specify the OPENAI_API_KEY environment variable. You can get it from OpenAI
You can also add a OPENAI_BASE_URL to use proxies such as LiteLLM (https://github.com/BerriAI/litellm)
OPENAI_BASE_URL=YOUR-OPENAI_BASE_URL
To use Azure OpenAI, you need to set
- The API type:
OPENAI_API_TYPE="azure"
- The key and the endpoint:
OPENAI_API_KEY=<YOUR_KEY>
OPENAI_BASE_URL=http://XXX.openai.azure.com
- Azure OpenAI resource name, which is XXX if your endpoint is XXX.openai.azure.com
AZURE_OPENAI_RESOURCE_NAME=<YOUR_AZURE_RESOURCE_NAME>
- You need to set the models, for the embeddings and for the query.
AZURE_OPENAI_EMBEDDING_MODEL="text-embedding-ada-002"
OPENAI_MODEL="gpt-4"
- Finally, as Azure is using per-minute quota, you might need to add a waiting time between each chunk upload. For example, if you have a limit of 240k tokens per minute, if your chunks are 400 tokens max, then 100ms between queries should be fine. If you get error 429 from weaviate, then increase this value.
WAIT_TIME_BETWEEN_INGESTION_QUERIES_MS="100"
If you want to use the HuggingFace Features, make sure to install the correct Verba package.
pip install goldenverba[huggingface]
or
pip install `.[huggingface]`If you're using Docker, modify the Dockerfile accordingly
Python >=3.10.0
- Initialize a new Python Environment
python3 -m virtualenv venv
- Install Verba
pip install goldenverba
- Launch Verba
verba start
You can specify the --port and --host via flags
- Access Verba
Visit localhost:8000
- Create .env file and add environment variables
- Clone the Verba repos
git clone https://github.com/weaviate/Verba.git
- Initialize a new Python Environment
python3 -m virtualenv venv
- Install Verba
pip install -e .
- Launch Verba
verba start
You can specify the --port and --host via flags
- Access Verba
Visit localhost:8000
- Create .env file and add environment variables
Docker is a set of platform-as-a-service products that use OS-level virtualization to deliver software in packages called containers. To get started with deploying Verba using Docker, follow the steps below. If you need more detailed instructions on Docker usage, check out the Docker Curriculum.
- Clone the Verba repos Ensure you have Git installed on your system. Then, open a terminal or command prompt and run the following command to clone the Verba repository:
git clone https://github.com/weaviate/Verba.git
-
Set neccessary environment variables Make sure to set your required environment variables in the
.envfile. You can read more about how to set them up in the API Keys Section -
Adjust the docker-compose file You can use the
docker-compose.ymlto add required environment variables under theverbaservice and can also adjust the Weaviate Docker settings to enable Authentification or change other settings of your database instance. You can read more about the Weaviate configuration in our docker-compose documentation
Please make sure to only add environment variables that you really need. If you have no authentifcation enabled in your Weaviate Cluster, make sure to not include the
WEAVIATE_API_KEY_VERBAenviroment variable
- Deploy using Docker With Docker installed and the Verba repository cloned, navigate to the directory containing the Docker Compose file in your terminal or command prompt. Run the following command to start the Verba application in detached mode, which allows it to run in the background:
docker compose up -d
docker compose --env-file .env up -d
This command will download the necessary Docker images, create containers, and start Verba. Remember, Docker must be installed on your system to use this method. For installation instructions and more details about Docker, visit the official Docker documentation.
- Access Verba
-
You can access your local Weaviate instance at
localhost:8080 -
You can access the Verba frontend at
localhost:8000
If you want your Docker Instance to install a specific version of Verba you can edit the Dockerfile and change the installation line.
RUN pip install -e '.'
Once you have access to Verba, you can use the Overview Page to validate if all environments and libraries were correctly set and installed. You can use the Admin Console, to see all data stored in the Weaviate Collections and reset certain parts of Verba (e.g. Documents, Cache, Configuration, etc.)
With Verba configured, you're ready to import your data and start exploring. Use the Add Documents Page to ingest your data. You can choose between Readers that support different data types, chunking techniques, and embedding model.
With Data imported, you can use the Chat page to ask any related questions. You will receive relevant chunks that are semantically relevant to your question and an answer generated by your choosen model. You can configure the RAG pipeline under the RAG page.
Your contributions are always welcome! Feel free to contribute ideas, feedback, or create issues and bug reports if you find any! Before contributing, please read the Contribution Guide. Visit our Weaviate Community Forum if you need any help!
You can learn more about Verba's architecture and implementation in its technical documentation and frontend documentation. It's recommended to have a look at them before making any contributions.
- Weaviate Embeeded currently not working on Windows yet
- Will be fixed in future versions, until then please use the Docker or WCS Deployment
-
Is Verba Multi-Lingual?
- This depends on your choosen Embedding and Generation Model whether they support multi-lingual data.
-
Can I use my Ollama Server with the Verba Docker?
- Yes, you can! Make sure the URL is set to:
OLLAMA_URL=http://host.docker.internal:11434
- Yes, you can! Make sure the URL is set to:
-
How to clear Weaviate Embedded Storage?
- Remove the directory
rm ~/.local/share/weaviate
- Remove the directory
-
How can I specify the port?
- You can use the port and host flag
verba start --port 9000 -host 0.0.0.0
- You can use the port and host flag