Milvus Adventures Jan 5, 2023
Published: 2024-01-06 01:05:47

##COMMUNITY ٩(＾◡＾)۶
2024 is here so join us at our next one in San Francisco is on Jan 16, 2024. Please [register](https://lu.ma/vq31dazy) early because they fill up fast! There are already 245 people registered! We will be joined by

* Christy Bergman, Developer Advocate, Zilliz, ​Vector Search in the Age of OpenAI Assistants using Milvus
* George Williams, Organizer, [Big-ANN NeurIPS 2023](https://big-ann-benchmarks.com/neurips23.html), Are CPUs Enough? A Review Of Vector Search Running On Novel Hardware
* 3rd speaker to be announced next week
	 

##Open Source Advent d(⌒ー⌒)
We wrapped up the Open Source Advent by showcasing different open source projects in December. People got a chance to check out the projects and earned points for a chance to win an exclusive swag pack from Zilliz and the participating projects! We will be announcing the winners next week! I created 2 blogs that list out all the projects here ([First 15](https://dev.to/chrischurilo/first-15-open-source-advent-projects-7d) and [Final](https://dev.to/chrischurilo/open-source-advent-fun-wraps-up-1i0d)).


##ARTICLES To check out ໒(＾ᴥ＾)७
**RAG**

* [OpenAI RAG vs. Your Customized RAG: Which One Is Better?](https://zilliz.com/blog/openai-rag-vs-customized-rag-which-one-is-better)
* [Optimizing RAG Applications: A Guide to Methodologies, Metrics, and Evaluation Tools for Enhanced Reliability](https://zilliz.com/blog/how-to-evaluate-retrieval-augmented-generation-rag-applications)

**Vector Search & NLP**

* [Sparse and Dense Embeddings](https://zilliz.com/learn/sparse-and-dense-embeddings)
* [Similarity Metrics for Vector Search](https://zilliz.com/blog/similarity-metrics-for-vector-search)


##TUTORIALS ٩(◕‿◕｡)۶
**Getting Started with a Milvus Connection**
Milvus has four SDKs: Go, Java, Python, React, and Ruby. In this [blog](https://zilliz.com/blog/getting-started-with-a-milvus-connection), we’ll show steps for Python.

**Building an Open Source Chatbot Using LangChain and Milvus in Under 5 Minutes**
This [tutorial](https://zilliz.com/blog/building-open-source-chatbot-using-milvus-and-langchain-in-5-minutes) uses a completely open-source RAG (Retrieval Augmented Generation) stack with LangChain to answer questions about Milvus using our product documentation web pages.

**Metadata Filtering with Zilliz Cloud Pipelines**
This [tutorial](https://zilliz.com/blog/metadata-filtering-with-zilliz-cloud-pipelines) discuss scalar or metadata filtering and how you can perform metadata filtering in Zilliz Cloud. This blog continues on the previous blog on [Getting started with RAG in just 5 minutes](https://zilliz.com/blog/building-open-source-chatbot-using-milvus-and-langchain-in-5-minutes). You can find its code in this [notebook](https://github.com/milvus-io/bootcamp/blob/master/bootcamp/RAG/readthedocs_zilliz_langchain.ipynb) and scroll down to Cell #27.


##VIDEOS (＾▽＾)

* [Evaluation Builds Better Retrieval Augmented Generation Applications](https://www.youtube.com/watch?v=Kp_AGKtql_U)
* [Effective RAG: Generate and Evaluate High-Quality Content for Your LLMs](https://www.youtube.com/watch?v=7dcfB8nXmds)
* [RAG Evals: Statistical Analysis of Retrieval Strategies with Arize](https://www.youtube.com/watch?v=fdDaR9XkMKo)


##GITHUB REPOS ᕙ(‾̀◡‾́)ᕗ
**[Milvus Vector Database](https://github.com/milvus-io/milvus)**. Milvus is an open source vector database used to store, index, and manage massive embedding vectors generated by deep neural networks and other machine learning (ML) models.

**[Akcio](https://github.com/zilliztech/akcio): Enhancing LLM-Powered ChatBot with CVP Stack** A full chatbot app all open-source for you to try out for your self!


**[GPT Cache.](https://github.com/zilliztech/GPTCache)** GPTCache is an open-source tool designed to improve the efficiency and speed of GPT-based applications by implementing a cache to store the responses generated by language models.

**[VectorDBBench](https://github.com/zilliztech/VectorDBBench)**. VectorDBBench is an open-source benchmarking tool to help you evaluate the performance of mainstream vector databases and cloud services with yoru specific use case.