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We’re opening this thread to collect ideas, proposals, and feedback from the community about which popular open-source projects DocumentDB should integrate with to boost usability, ecosystem compatibility, and real-world adoption.
📌 About DocumentDB DocumentDB is an open-source, MIT-licensed document database engine that:
Supports both PostgreSQL and MongoDB wire protocols.
Enables native vector search for AI/semantic use cases.
Runs locally, in containers, or in Kubernetes cluster.
Designed for use as both a relational and document store.
🌟 Integration Goals
We believe that integrating DocumentDB with high-impact open-source tools will help make it easier for developers and teams to adopt it as a core data engine — especially for AI applications, content management, data analytics, and data pipelines.
Our goal is to enable DocumentDB to be:
A backend for popular developer tools.
A plug-in or drop-in for AI/BI ecosystems.
A deployable service in modern infrastructure stacks (K8s, containers, cloud-native).
🧪 Initial Integration Plan
We’re starting by exploring integrations with the following projects:
LangChain – DocumentDB as a vector store and retriever for retrieval-augmented generation (RAG)
LlamaIndex – DocumentDB as an embedding-aware doc store with hybrid query capabilities
Semantic Kernel – Storage backend for memory and skills with vector support
🛠️ Possible Integration Approaches
We envision several ways DocumentDB can be integrated into these ecosystems:
As a Build-Time Dependency
DocumentDB can be directly compiled or embedded into host projects as a pluggable storage engine (via protocol compatibility or SDK bindings).
As a Standalone On-Premise Service
Run DocumentDB as a separate local or cloud service, and connect it via standard Mongo or Postgres clients or vector store interfaces (e.g., LangChain, LlamaIndex).
As a Containerized Service
Deploy DocumentDB as a containerized service using Docker images from GHCR, Helm charts for Kubernetes or Kubernetes Operator.
🙋 We Want Your Ideas
We're looking for community feedback on:
What open-source tools/projects should DocumentDB integrate with?
What formats or deployment models would help you use DocumentDB in your stack?
Are there other AI, CMS, or BI tools where DocumentDB would be a good fit?
Feel free to suggest projects, share use cases, or even start contributing integration code!
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We’re opening this thread to collect ideas, proposals, and feedback from the community about which popular open-source projects DocumentDB should integrate with to boost usability, ecosystem compatibility, and real-world adoption.
📌 About DocumentDB
DocumentDB is an open-source, MIT-licensed document database engine that:
🌟 Integration Goals
We believe that integrating DocumentDB with high-impact open-source tools will help make it easier for developers and teams to adopt it as a core data engine — especially for AI applications, content management, data analytics, and data pipelines.
Our goal is to enable DocumentDB to be:
🧪 Initial Integration Plan
We’re starting by exploring integrations with the following projects:
🛠️ Possible Integration Approaches
We envision several ways DocumentDB can be integrated into these ecosystems:
As a Build-Time Dependency
DocumentDB can be directly compiled or embedded into host projects as a pluggable storage engine (via protocol compatibility or SDK bindings).
As a Standalone On-Premise Service
Run DocumentDB as a separate local or cloud service, and connect it via standard Mongo or Postgres clients or vector store interfaces (e.g., LangChain, LlamaIndex).
As a Containerized Service
Deploy DocumentDB as a containerized service using Docker images from GHCR, Helm charts for Kubernetes or Kubernetes Operator.
🙋 We Want Your Ideas
We're looking for community feedback on:
Feel free to suggest projects, share use cases, or even start contributing integration code!
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