# Logos - Your personal knowledge base AI assistant

We are what we read but also what we watch, listen and converse with. With that in mind, our team embarked on the [Cohere and Qdrant Multilingual Semantic Search Hackathon](https://lablab.ai/event/multilingual-semantic-search-hackathon) with a vision to revolutionize the way people interact with their sources of knowledge. And that is how [Logos](https://lablab.ai/event/multilingual-semantic-search-hackathon/abacates-voadores/logos) was born.

![Logos](/assets/portfolio/logos_logo.png)

We created Logos with the goal of enhancing people's experiences in their relationship with their sources of knowledge: books and articles they read, podcasts and songs they listen, videos they watch and spaces where they discuss. Logos achieves this with the use of cutting-edge technologies to index and search for relevant content, assemble and interpret it in a semantically meaningful way. The multi-language feature of our solution grants super powers to our users, and our specially created expert agents are capable of answering very complex questions. We believe Logos will help heighten people’s passion for knowledge and thought, and that it will be a great tool for students, professionals and curious people alike!

To build Logos, we utilized cutting-edge technologies to ensure its efficacy and performance. Our tech stack included:

- **Dash:** We used [Plotly Dash](https://dash.plotly.com) for creating a beautiful and intuitive user interface.
- **Cohere:** [Cohere](https://cohere.com)'s LLM multilingual model was responsible for the embeddings of our system, playing a pivotal role in enabling Logos to support multiple languages, ensuring inclusivity and accessibility for users worldwide.
- **Qdrant:** [Qdrant](https://qdrant.tech)'s vector database and search engine facilitated efficient indexing and retrieval of relevant content, enabling users to access information quickly and accurately.
- **OpenAI:** Integration with the [OpenAI API](https://openai.com/index/openai-api) allowed Logos to generate multi-language text, further enhancing its capabilities in processing diverse linguistic inputs and generating rich and meaningful responses.
- **LangChain:** [LangChain](https://www.langchain.com) served as the backbone for Logos expert agent, enabling advanced question-answering capabilities and facilitating insightful interactions with the knowledge base.

![Logos schematic](/assets/portfolio/logos_schematic.png)

Through the strategic integration of these technologies, Logos is able to significantly enhance the way people engage with and derive insights from their sources of knowledge, providing users with quick access to specific information and insights over vast volumes of data. Logos is a powerful tool that empowers users to explore and interact with their sources of knowledge in a more meaningful and engaging way, fostering a deeper understanding and appreciation for the content they consume.

Logos was the winner of the most popular project award at the [Cohere and Qdrant Multilingual Semantic Search Hackathon](https://lablab.ai/event/multilingual-semantic-search-hackathon), and we are excited to continue developing and refining this innovative solution to further enhance the user experience and provide even greater value to our users.

 

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Do you want to empower your business with cutting edge Large Language Models capabilities? We can help you! [Get in touch with us](/#contact)!

We are what we read but also what we watch, listen and converse with. With that in mind, our team embarked on the <a href="https://lablab.ai/event/multilingual-semantic-search-hackathon">Cohere and Qdrant Multilingual Semantic Search Hackathon</a> with a vision to revolutionize the way people interact with their sources of knowledge. And that is how <a href="https://lablab.ai/event/multilingual-semantic-search-hackathon/abacates-voadores/logos">Logos</a> was born.
<img src="/assets/portfolio/logos_logo.png" alt="Logos">
We created Logos with the goal of enhancing people's experiences in their relationship with their sources of knowledge: books and articles they read, podcasts and songs they listen, videos they watch and spaces where they discuss. Logos achieves this with the use of cutting-edge technologies to index and search for relevant content, assemble and interpret it in a semantically meaningful way. The multi-language feature of our solution grants super powers to our users, and our specially created expert agents are capable of answering very complex questions. We believe Logos will help heighten people’s passion for knowledge and thought, and that it will be a great tool for students, professionals and curious people alike!
To build Logos, we utilized cutting-edge technologies to ensure its efficacy and performance. Our tech stack included:
<ul>
<li>Dash: We used <a href="https://dash.plotly.com">Plotly Dash</a> for creating a beautiful and intuitive user interface.</li>
<li>Cohere: <a href="https://cohere.com">Cohere</a>'s LLM multilingual model was responsible for the embeddings of our system, playing a pivotal role in enabling Logos to support multiple languages, ensuring inclusivity and accessibility for users worldwide.</li>
<li>Qdrant: <a href="https://qdrant.tech">Qdrant</a>'s vector database and search engine facilitated efficient indexing and retrieval of relevant content, enabling users to access information quickly and accurately.</li>
<li>OpenAI: Integration with the <a href="https://openai.com/index/openai-api">OpenAI API</a> allowed Logos to generate multi-language text, further enhancing its capabilities in processing diverse linguistic inputs and generating rich and meaningful responses.</li>
<li>LangChain: <a href="https://www.langchain.com">LangChain</a> served as the backbone for Logos expert agent, enabling advanced question-answering capabilities and facilitating insightful interactions with the knowledge base.</li>
</ul>
<img src="/assets/portfolio/logos_schematic.png" alt="Logos schematic">
Through the strategic integration of these technologies, Logos is able to significantly enhance the way people engage with and derive insights from their sources of knowledge, providing users with quick access to specific information and insights over vast volumes of data. Logos is a powerful tool that empowers users to explore and interact with their sources of knowledge in a more meaningful and engaging way, fostering a deeper understanding and appreciation for the content they consume.
Logos was the winner of the most popular project award at the <a href="https://lablab.ai/event/multilingual-semantic-search-hackathon">Cohere and Qdrant Multilingual Semantic Search Hackathon</a>, and we are excited to continue developing and refining this innovative solution to further enhance the user experience and provide even greater value to our users.
<hr>
Do you want to empower your business with cutting edge Large Language Models capabilities? We can help you! <a href="/#contact">Get in touch with us</a>!