What features do you want❓ #6302
Replies: 27 comments 87 replies
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Hi, |
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Voting for So
This is second in the list i think, at least for me. SQL and GPU extended support are important as well, but not as first two. |
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How about embedding clustering in real time? I have a flow of embeddings that arrive in real-time. The following embedding must be compared with ahead embeddings to check if two embeddings can group into a cluster (based on a threshold distance). As the result, we have a collection that contains many clusters, and when a new embedding arrives, it will be assigned to one of those clusters or init a new cluster as a center. I already implemented it with python but that's not efficient when the number of embeddings grows up. |
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Another key feature that I think shoudl be worked on is the ability to increase replication factor on the go. As a system grows it makes sense to be able to increase the replication without having to shutdown the system to release and reload. |
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One more feature that might be very useful is the ability to only search indexed segments. Many users like having the most up to date data as possible without bogging down the system. Currently the only way to get around this is hack-y and not guaranteed to work. |
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One feature that would be useful is the ability to search multi-vector in one search |
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I would like to have folowing features:
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sparse-dense hybrid search would be helpful too! |
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Would be cool to have a functionality to automatically group vectors by similarities. We could have a specific type of collections for that. For example:
That way we can have different app implementations at scale. I have described DeepSORT internals. If we could move DeepSORT logic into Milvus and make YOLO detections and features extraction services distributed/stateless, that would be the next step for our company. |
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Support groupby + top |
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Would it be beneficial or even practical to incorporate an integrated text embedding functionality within Milvus? The essence of this feature would be to automatically translate any inserted text into its corresponding vector representation using a pre-selected embedding technique. Following this transformation, a search query composed of text could result in a return of the 'n' most similar texts, based on their proximity in the vector space. This idea sounds appealing as it could streamline the process of text-to-vector conversion, and improve the overall efficiency of the text search within Milvus. However, it raises several considerations. How would different embedding methods affect the quality and relevancy of search results? How could this feature accommodate the broad range of languages and semantic complexities in text data? Furthermore, embedding processes can be computationally heavy; could the real-time transformation of text into vectors impact system performance? While the concept presents a potential advancement in handling text data within Milvus, it warrants thorough exploration of these issues to determine its practicality and potential benefits. |
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Good suggestion! We actually open sourced another project towhee, for embedding generation https://github.com/towhee-io/towhee And yes, I'm thinking of how to integrate embedding and reranking model into Milvus |
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Hi I think ipv6 is definitely a trend to overcome the shortage of ipv4 addresses, especially when using cloud service. Do the community has plan to support it? I think the ROI should be high. BTW, aws is about to charge for ipv4 public addresses from Feb. 2024 |
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I hope to improve the restful related functions, align them with other SDK interface functions, and make it convenient for other languages to call related interfaces。 |
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Milvus is integrated into huggingface. If huggingface spaces does not provide a way to share vector databases for retrieval augmentation, some way of having a hub for vector databases would be helpful. |
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If vectors representing arguments that are made in conversations and debates are stored in a vector database, we might need a higher layer of continual training of a model that learns the strongest weights between vectors that are best supported by reason and evidence. |
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I just watched "How to add conversational memory to an LLM using langchain : vector database fundamentals" in the zilliz channel at YouTube. It would be nice to have a milvus lite that runs on smartphones to store vector representations of conversational history on device. |
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@xiaofan-luan restful typescript client library. support for milvus and zilliz. And feature parity for restful api |
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Topic: Access Control Request for 'rbac_user' User in Milvus Vector Database Example Sceanario:
The 'rbac_user' user should not be allowed to log in to the other databases like 'default' database. This access control is essential for maintaining the security and privacy of our database. Please ensure that the necessary configurations are in place to restrict 'rbac_user' from accessing the 'default' database and its associated collections. Thanks |
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@xiaofan-luan in general would be very useful to have ingestion streams and output streams out of Milvus/zilliz Specifically, it would be great to have the zilliz confluent Kafka stream work with any kakfka instance. i’d like to use it with upstash serverless kafka https://upstash.com/docs/kafka/overall/get-started |
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SQL & JDBC/ODBC library(Java is preferred) |
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I am trying to deploy Milvus db on IPV6 only Kubernetes cluster and getting some error ? Can Milvus db have IPV6 support in next release? |
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Hello, I would like to ask if milvus provides an LRU(Least Recently Used) method? How to use it? |
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It may be interesting to have:
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Hello @xiaofan-luan How can I do it? |
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@xiaofan-luan last year you mentioned "We will support count/min/max in 2.3.X and GroupBy/Having will in 2.4 release."; |
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Hi, It'd be good to have indexing of JSON fields. |
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Hi All!
Milvus 2.2.X is stable and the search performance improved tremendously.
We're gonna to release 2.2.3 soon, with rolling upgrade and resource group features and bunch of other performance improvement.
Milvus 2.3 will be released by the end of Feb, with enhanced partition functionalities, new velox execution engine, new knowhere 2.0 vector search engine and range search. see detailes at https://wiki.lfaidata.foundation/display/MIL/Feature+plans+for+Milvus+2.3+and+timeline.
Now we are looking for feature list of Milvus 2.4 and Milvus 3.0, pls give us advices.
Some of the possible feature list:
Restful API
SQL support
Graph QL
Cpp/Rust SDK
GPU support
OpenAI/Hugging face/Spark connector
ScaNN index
Multi vector field support
Change data capture
Primary/Backup milvus cluster across dc
Aggregations
Data Iteration
BM25 index
Other datatype support
HDFS storage
PrimaryKey Deduplication
Delete with complicated expression
Bulkload with more data format
Migrate from Milvus 1.0/Faiss/HNSW
Ranking/Reranking
... and anything you are looking for
Thanks
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