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Integrate with Azure AI Search (vectorDB) #6749
Labels
2.x
Related to Haystack v2.0
P2
Medium priority, add to the next sprint if no P1 available
type:feature
New feature or request
Comments
Can you assign this to me? I already have an implementation for the document store following the protocol, and the retrievers. I closely followed the OpenSearch implementation. |
hey there, is there an update on this? +1 to the OP point on "enterprise use case" :) |
+1 if any update on it? high demanding with Azure-stack RAG system in enterprise :) |
mrm1001
added
P1
High priority, add to the next sprint
P2
Medium priority, add to the next sprint if no P1 available
and removed
P1
High priority, add to the next sprint
labels
Jun 24, 2024
This was referenced Jul 31, 2024
Closing this issue as it is being tracked in #950. |
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Labels
2.x
Related to Haystack v2.0
P2
Medium priority, add to the next sprint if no P1 available
type:feature
New feature or request
Is your feature request related to a problem? Please describe.
Integrate with Azure AI Search (formerly known as Azure Cognitive Search) as a vectorDB
Llamaindex: https://docs.llamaindex.ai/en/stable/examples/vector_stores/CognitiveSearchIndexDemo.html
Langchain: https://python.langchain.com/docs/integrations/vectorstores/azuresearch
Describe the solution you'd like
Azure AI Search is fundamentally a vectorDB with its own default embedder.
Additional context
Useful for enterprise use cases that rely on Azure ecosystem.
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