#vectordb — Public Fediverse posts

Bases de Datos y Cómputo

Vector Databases (bases de datos vectoriales) son el pilar invisible de la IA moderna. A diferencia de las bases de datos relacionales tradicionales, permiten almacenar y buscar embeddings para encontrar similitud semántica en segundos. Es la tecnología que hace posibles los buscadores inteligentes y los sistemas RAG.

#DataScience #AI #Databases #VectorDB #Tech #Backend #Programming

Bases de Datos y Cómputo

Vector Databases (bases de datos vectoriales) son el pilar invisible de la IA moderna. A diferencia de las bases de datos relacionales tradicionales, permiten almacenar y buscar embeddings para encontrar similitud semántica en segundos. Es la tecnología que hace posibles los buscadores inteligentes y los sistemas RAG.

#DataScience #AI #Databases #VectorDB #Tech #Backend #Programming

Bases de Datos y Cómputo

Vector Databases (bases de datos vectoriales) son el pilar invisible de la IA moderna. A diferencia de las bases de datos relacionales tradicionales, permiten almacenar y buscar embeddings para encontrar similitud semántica en segundos. Es la tecnología que hace posibles los buscadores inteligentes y los sistemas RAG.

#DataScience #AI #Databases #VectorDB #Tech #Backend #Programming

Bases de Datos y Cómputo

Vector Databases (bases de datos vectoriales) son el pilar invisible de la IA moderna. A diferencia de las bases de datos relacionales tradicionales, permiten almacenar y buscar embeddings para encontrar similitud semántica en segundos. Es la tecnología que hace posibles los buscadores inteligentes y los sistemas RAG.

#DataScience #AI #Databases #VectorDB #Tech #Backend #Programming

Bases de Datos y Cómputo

Vector Databases (bases de datos vectoriales) son el pilar invisible de la IA moderna. A diferencia de las bases de datos relacionales tradicionales, permiten almacenar y buscar embeddings para encontrar similitud semántica en segundos. Es la tecnología que hace posibles los buscadores inteligentes y los sistemas RAG.

#DataScience #AI #Databases #VectorDB #Tech #Backend #Programming

💡 Vector databases explained simply:

Traditional DB: "Find me rows WHERE name = 'cats'"
Vector DB: "Find me the 10 most similar things to THIS cat"

Under the hood:
→ Text → Embedding (1536-dim float array)
→ Store in pgvector / Pinecone / Qdrant
→ Query = cosine similarity search

For most apps: pgvector (Postgres extension) is enough. No new infra needed.

#VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

💡 Vector databases explained simply:

Traditional DB: "Find me rows WHERE name = 'cats'"
Vector DB: "Find me the 10 most similar things to THIS cat"

Under the hood:
→ Text → Embedding (1536-dim float array)
→ Store in pgvector / Pinecone / Qdrant
→ Query = cosine similarity search

For most apps: pgvector (Postgres extension) is enough. No new infra needed.

#VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

💡 Vector databases explained simply:

Traditional DB: "Find me rows WHERE name = 'cats'"
Vector DB: "Find me the 10 most similar things to THIS cat"

Under the hood:
→ Text → Embedding (1536-dim float array)
→ Store in pgvector / Pinecone / Qdrant
→ Query = cosine similarity search

For most apps: pgvector (Postgres extension) is enough. No new infra needed.

#VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

💡 Vector databases explained simply:

Traditional DB: "Find me rows WHERE name = 'cats'"
Vector DB: "Find me the 10 most similar things to THIS cat"

Under the hood:
→ Text → Embedding (1536-dim float array)
→ Store in pgvector / Pinecone / Qdrant
→ Query = cosine similarity search

For most apps: pgvector (Postgres extension) is enough. No new infra needed.

#VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

💡 Vector databases explained simply:

Traditional DB: "Find me rows WHERE name = 'cats'"
Vector DB: "Find me the 10 most similar things to THIS cat"

Under the hood:
→ Text → Embedding (1536-dim float array)
→ Store in pgvector / Pinecone / Qdrant
→ Query = cosine similarity search

For most apps: pgvector (Postgres extension) is enough. No new infra needed.

#VectorDB #AI #RAG #LLM #GenerativeAI #FullStack #MachineLearning

Escape the proprietary AI cloud tax!

Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

Our SRE guide on making the switch:
🔹 In-graph filtering > post-filtering
🔹 INT8 cuts RAM footprint by 400%
🔹 Direct NVMe > Network Storage

Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

Read the playbook here:
https://www.servermo.com/howto/migrate-pinecone-to-qdrant/

#OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

Escape the proprietary AI cloud tax!

Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

Our SRE guide on making the switch:
🔹 In-graph filtering > post-filtering
🔹 INT8 cuts RAM footprint by 400%
🔹 Direct NVMe > Network Storage

Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

Read the playbook here:
https://www.servermo.com/howto/migrate-pinecone-to-qdrant/

#OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

Escape the proprietary AI cloud tax!

Moving your vector search from Pinecone to open-source Qdrant on Bare Metal is a massive performance & cost upgrade.

Our SRE guide on making the switch:
🔹 In-graph filtering > post-filtering
🔹 INT8 cuts RAM footprint by 400%
🔹 Direct NVMe > Network Storage

Includes full Docker configs & Linux kernel tuning tips to squeeze every drop of IOPS.

Read the playbook here:
https://www.servermo.com/howto/migrate-pinecone-to-qdrant/

#OpenSource #SelfHosted #Qdrant #DevOps #VectorDB

Interessante Erklärung von Databricks zu Vektordatenbanken.
🔑 **Kern-Einblick:** Traditionelle DBs finden exakte Treffer, Vektordatenbanken verstehen *Bedeutung* durch Embeddings – essenziell für semantische Suche & KI-Anwendungen.
- Handhabt hochdimensionale Vektoreffizient
- Beschleunigt Retrieval-Augmented Generation (RAG)
- Unterstützt komplexe Ähnlichkeitssuchen

#VectorDB #MachineLearning #KünstlicheIntelligenz #DataEngineering #Databricks

🔗 https://news.google.com/rss/articles/CBMiakFVX3lxTE9rWndNaVdDamV2VVlEQVFvWTVxaV92V0ZmODNvajdYdVFrZ1g1b1AtOFkwaW1QSzZuLWw0QTdUU1BmblVHOVBYazlzUXJDSHN4bmtYS0JaZkVCMjNBaGd6NFBrcHVFdjc1b0E?oc=5

https://github.com/spotify/annoy #ai #vectordb

https://github.com/spotify/annoy #ai #vectordb

https://github.com/spotify/annoy #ai #vectordb

https://github.com/spotify/annoy #ai #vectordb

https://github.com/spotify/annoy #ai #vectordb

Have pushed 0.9.5-dev branch to codeberg of foxing ( https://codeberg.org/aenertia/foxing/src/branch/0.9.5-dev ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

Have pushed 0.9.5-dev branch to codeberg of foxing ( https://codeberg.org/aenertia/foxing/src/branch/0.9.5-dev ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

Have pushed 0.9.5-dev branch to codeberg of foxing ( https://codeberg.org/aenertia/foxing/src/branch/0.9.5-dev ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

Have pushed 0.9.5-dev branch to codeberg of foxing ( https://codeberg.org/aenertia/foxing/src/branch/0.9.5-dev ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

Have pushed 0.9.5-dev branch to codeberg of foxing ( https://codeberg.org/aenertia/foxing/src/branch/0.9.5-dev ) in preparation for release tagging. A LOT of features and a couple of bug-fixes now the packet/file processing engine has stabilized ; including Semantic Routing to Parsers for Metadata Extraction and in-path Binary analysis using local ORT/BERT models ; letting you get semantic search powers for free when you copy something with foxingd/fxcp #linux #filesystem #bert #vectordb #postgres #xfs #stratis #blake3 #localllm

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

@OpenSearchProj was named a Leader and Fast Mover in the 2025 GigaOm Radar for Vector Databases 🏆

My #OpenSearch report highlights:
✅ Platform play
✅ Search variety
✅ Business criteria
✅ Security
And I'd add - it's OPEN SOURCE @linuxfoundation !!
https://opensearch.org/gigaom-radar-vector-report-2025/

#gigaom #vectorDB

ICYMI, #OpenSearch 3.5 is here! 🤩
I shared some of my personal highlights in this short clip.
Hope you enjoy the format 👍
#OpenSearchAmbassador @OpenSearchProject @linuxfoundation #observability #analytics #search #vectorDB #kubecon #kubeconEU #o11yDay

ICYMI, #OpenSearch 3.5 is here! 🤩
I shared some of my personal highlights in this short clip.
Hope you enjoy the format 👍
#OpenSearchAmbassador @OpenSearchProject @linuxfoundation #observability #analytics #search #vectorDB #kubecon #kubeconEU #o11yDay

ICYMI, #OpenSearch 3.5 is here! 🤩
I shared some of my personal highlights in this short clip.
Hope you enjoy the format 👍
#OpenSearchAmbassador @OpenSearchProject @linuxfoundation #observability #analytics #search #vectorDB #kubecon #kubeconEU #o11yDay

ICYMI, #OpenSearch 3.5 is here! 🤩
I shared some of my personal highlights in this short clip.
Hope you enjoy the format 👍
#OpenSearchAmbassador @OpenSearchProject @linuxfoundation #observability #analytics #search #vectorDB #kubecon #kubeconEU #o11yDay

ICYMI, #OpenSearch 3.5 is here! 🤩
I shared some of my personal highlights in this short clip.
Hope you enjoy the format 👍
#OpenSearchAmbassador @OpenSearchProject @linuxfoundation #observability #analytics #search #vectorDB #kubecon #kubeconEU #o11yDay

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

My talk from #NDCLondon is now available on YouTube! 🇬🇧

http://youtube.com/watch?v=N2kn4UybFBI&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=1&t=9s

@OpenSearchProject @linuxfoundation
#vectorDB #OpenSearch #opensource #search #GenAI #OpenSearchAmbassador

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

Stoked seeing the OpenSearch Project featured by Jensen Huang on #NVIDIA #GTC keynote! 😍

One of the innovations in #OpenSearch V3 has been adding GPU acceleration based on NVIDIA's cuVS. Our #VectorSearch benchmarks, using CAGRA algorithm integrated through Facebook's Faiss library, showed:
✅ 9.3x faster index builds
✅ 3.75x lower cost
✅ 2x higher throughput
✅ 2.5x lower CPU usage

https://www.linkedin.com/feed/update/urn:li:activity:7439600547852189697/

#OpenSearchAmbassador #opensource #gtc2026 #gtc26 #cuvs #vectordb

310% throughput increase and 300% latency reduction!
Great work by the AWS #opensearch engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
And it's all #opensource under @linuxfoundation 🤩
https://opensearch.org/blog/accelerating-fp16-vector-search-performance-using-bulk-simd-in-opensearch-3-5/
#vectorDB #search #OpenSearchAmbassador

310% throughput increase and 300% latency reduction!
Great work by the AWS #opensearch engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
And it's all #opensource under @linuxfoundation 🤩
https://opensearch.org/blog/accelerating-fp16-vector-search-performance-using-bulk-simd-in-opensearch-3-5/
#vectorDB #search #OpenSearchAmbassador

310% throughput increase and 300% latency reduction!
Great work by the AWS #opensearch engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
And it's all #opensource under @linuxfoundation 🤩
https://opensearch.org/blog/accelerating-fp16-vector-search-performance-using-bulk-simd-in-opensearch-3-5/
#vectorDB #search #OpenSearchAmbassador

310% throughput increase and 300% latency reduction!
Great work by the AWS #opensearch engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
And it's all #opensource under @linuxfoundation 🤩
https://opensearch.org/blog/accelerating-fp16-vector-search-performance-using-bulk-simd-in-opensearch-3-5/
#vectorDB #search #OpenSearchAmbassador

310% throughput increase and 300% latency reduction!
Great work by the AWS #opensearch engineers with bulk SIMD brings these performance gains in @OpenSearchProject 's vector search 👏
And it's all #opensource under @linuxfoundation 🤩
https://opensearch.org/blog/accelerating-fp16-vector-search-performance-using-bulk-simd-in-opensearch-3-5/
#vectorDB #search #OpenSearchAmbassador

Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.



#OpenSource #devops #vectordb #programming #vector #search

Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.



#OpenSource #devops #vectordb #programming #vector #search

Chunking: an essential concept to understand for Retrieval-Augmented Generation (#RAG). It is the process of dividing large documents into smaller, manageable segments called “chunks.” Effective chunking preserves semantic meaning while ensuring content fits within model context limits.

Proper chunking is essential, as it directly affects retrieval quality. Well-structured chunks improve precision and support more accurate responses.



#OpenSource #devops #vectordb #programming #vector #search

Can't wait to to have a great ride at #JavaLand ! 🇩🇪

#OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

See you 11th March in @JavaLandConf 🎡

🔸Agenda: https://meine.doag.org/events/javaland/2026/agenda/#agendaId.6918

🔸Tickets: https://www.javaland.eu/

Can't wait to to have a great ride at #JavaLand ! 🇩🇪

#OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

See you 11th March in @JavaLandConf 🎡

🔸Agenda: https://meine.doag.org/events/javaland/2026/agenda/#agendaId.6918

🔸Tickets: https://www.javaland.eu/

Can't wait to to have a great ride at #JavaLand ! 🇩🇪

#OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

See you 11th March in @JavaLandConf 🎡

🔸Agenda: https://meine.doag.org/events/javaland/2026/agenda/#agendaId.6918

🔸Tickets: https://www.javaland.eu/

Can't wait to to have a great ride at #JavaLand ! 🇩🇪

#OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

See you 11th March in @JavaLandConf 🎡

🔸Agenda: https://meine.doag.org/events/javaland/2026/agenda/#agendaId.6918

🔸Tickets: https://www.javaland.eu/

Can't wait to to have a great ride at #JavaLand ! 🇩🇪

#OpenSearch sucht nicht mehr nach Wörtern, sondern nach Bedeutungen. 🔍

If you're there and want to learn about #vectorDB search and about the @OpenSearchProject, check out my talk 🙂

See you 11th March in @JavaLandConf 🎡

🔸Agenda: https://meine.doag.org/events/javaland/2026/agenda/#agendaId.6918

🔸Tickets: https://www.javaland.eu/

I joined InstaBlinks podcast to talk about vector search, difference from lexical search, and how the @OpenSearchProject facilitates both in a hybrid model.
Thanks NetApp Instaclustr for having me!
https://www.youtube.com/watch?v=buKXHi6kFwY&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=63

#OpenSearch #vectorSearch #vectorDB #opensource #search

I joined InstaBlinks podcast to talk about vector search, difference from lexical search, and how the @OpenSearchProject facilitates both in a hybrid model.
Thanks NetApp Instaclustr for having me!
https://www.youtube.com/watch?v=buKXHi6kFwY&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=63

#OpenSearch #vectorSearch #vectorDB #opensource #search

I joined InstaBlinks podcast to talk about vector search, difference from lexical search, and how the @OpenSearchProject facilitates both in a hybrid model.
Thanks NetApp Instaclustr for having me!
https://www.youtube.com/watch?v=buKXHi6kFwY&list=PLPFMHjhoDntsPHFHTN1d4H-U3G8zRlJ-p&index=63

#OpenSearch #vectorSearch #vectorDB #opensource #search