WizSearch: The Future of AI is Open
We are thrilled to announce that WizSearch has been awarded the winner of the hackathon THE FUTURE OF AI IS OPEN This event encouraged participants to push the boundaries of what can be achieved with open-source AI, using tools like Streamlit and Snowflake Arctic. Our creation, WizSearch, stood out for its innovation and practical application.
Check Out the Demo!
Inspiration
Inspired by the advanced capabilities of AI assistants like Perplexity, we aimed to create WizSearch, an open-source, customizable, and modular AI assistant. Our goal is to leverage open-source models and tools to develop a robust and intelligent search assistant that can seamlessly integrate internet searches with large language model (LLM) capabilities.
What it Does
WizSearch is a super-smart AI assistant designed to retrieve and synthesize information from the internet. Users can ask questions, and WizSearch utilizes powerful LLMs to generate accurate and relevant answers, complete with summaries and links to sources. This makes finding information as easy and magical as simply asking.
️ How We Built It
We built WizSearch using the following components:
- LLM: Snowflakes Arctic LLM for natural language understanding and generation.
- Embeddings: Snowflakes Arctic Embedding Model to enhance search relevance.
- Intelligent Search: Tavily for advanced search capabilities.
- Vector Databases: Qdrant for efficient data storage and retrieval.
- Observability: Langfuse for monitoring and observability.
- UI: Streamlit for creating an interactive and user-friendly interface.
How It Works
Here’s a diagram that explains the workflow of WizSearch:
Challenges We Ran Into
- Unpredictable LLM Output: Ensuring consistency and accuracy in the responses generated by LLMs.
- Retrieval Issues: Addressing the challenge of the retrieval process not always selecting the most relevant information, leading to potential hallucinations in responses.
- Security: Implementing guardrails to prevent prompt injection and other security vulnerabilities.
Accomplishments That We’re Proud Of
- Successfully integrating multiple open-source tools to create a seamless and efficient search assistant.
- Developing a modular and customizable workflow that allows for easy adjustments and enhancements.
What We Learned
- The importance of modular RAG (retrieval-augmented generation) and LLM routing for maintaining controlled and efficient agentic workflows.
- Effective prompting techniques for open-source models to optimize performance.
What’s Next for WizSearch
In the future, we aim to enhance WizSearch by incorporating more controlled agentic capabilities, such as running code and generating images. We also plan to develop a minimalistic, voice-interactive platform that responds in a human-like voice and displays only the most relevant visual information, such as images and graphs. Technically, we will integrate RAGAS for evaluation and Giskard for testing. This will further streamline the user experience and broaden the range of tasks WizSearch can perform.
暂无评论内容