From 3591033da83191a888d09497365ee77b36065e92 Mon Sep 17 00:00:00 2001 From: Krish Garg Date: Thu, 23 Jul 2026 03:00:41 -0700 Subject: [PATCH] docs: add RocketRide framework integration (#2525) Co-authored-by: Krish Garg <181051779+kgarg2468@users.noreply.github.com> --- .../documentation/frameworks/_index.md | 1 + .../documentation/frameworks/rocketride.md | 109 ++++++++++++++++++ 2 files changed, 110 insertions(+) create mode 100644 qdrant-landing/content/documentation/frameworks/rocketride.md diff --git a/qdrant-landing/content/documentation/frameworks/_index.md b/qdrant-landing/content/documentation/frameworks/_index.md index b44343044..fd1423ce3 100644 --- a/qdrant-landing/content/documentation/frameworks/_index.md +++ b/qdrant-landing/content/documentation/frameworks/_index.md @@ -39,6 +39,7 @@ aliases: ["/documentation/frameworks/memgpt/"] | [Neo4j GraphRAG](/documentation/frameworks/neo4j-graphrag/) | Package to build graph retrieval augmented generation (GraphRAG) applications using Neo4j and Python. | | [NLWeb](/documentation/frameworks/nlweb/) | A framework to turn websites into chat-ready data using schema.org and associated data formats. | | [Rig-rs](/documentation/frameworks/rig-rs/) | Rust library for building scalable, modular, and ergonomic LLM-powered applications. | +| [RocketRide](/documentation/frameworks/rocketride/) | Open-source AI development environment and C++ runtime for building RAG pipelines and agents with Qdrant. | | [Semantic Router](/documentation/frameworks/semantic-router/) | Python library to build a decision-making layer for AI applications using vector search. | | [SmolAgents](/documentation/frameworks/smolagents/) | Barebones library for agents. Agents write python code to call tools and orchestrate other agent. | | [Spring AI](/documentation/frameworks/spring-ai/) | Java AI framework for building with Spring design principles such as portability and modular design. | diff --git a/qdrant-landing/content/documentation/frameworks/rocketride.md b/qdrant-landing/content/documentation/frameworks/rocketride.md new file mode 100644 index 000000000..ce22f56bf --- /dev/null +++ b/qdrant-landing/content/documentation/frameworks/rocketride.md @@ -0,0 +1,109 @@ +--- +title: RocketRide +short_description: "Build RAG pipelines and AI agents with RocketRide's multithreaded C++ runtime and Qdrant for vector search, retrieval, and memory." +description: "Use Qdrant in RocketRide to ingest embedded documents, retrieve context for RAG, and expose vector search, upsert, and delete operations to AI agents." +--- + +# RocketRide + +[RocketRide](https://github.com/rocketride-org/rocketride-server) is an open-source AI development environment and runtime for building, running, and integrating AI systems. Pipelines are portable JSON and execute on a multithreaded C++ engine. You can compose them in the visual IDE, run them from the CLI, integrate them through Python or TypeScript SDKs, or expose them as tools over MCP. + +RocketRide's native Qdrant node stores embedded documents, retrieves context for retrieval-augmented generation (RAG), and gives agents tools for searching and updating a Qdrant collection. It supports both Qdrant Cloud and self-hosted deployments. + +## Run the RAG example + +RocketRide includes a complete [Qdrant RAG pipeline](https://github.com/rocketride-org/rocketride-server/blob/develop/examples/rag-pipeline.pipe) with separate ingestion and query flows: + +```text +Ingestion: webhook -> parse -> chunk -> embed -> Qdrant +Query: chat -> embed -> Qdrant -> prompt -> LLM -> response +``` + +First, install RocketRide by following its [Quick Start](https://github.com/rocketride-org/rocketride-server#quick-start), and start Qdrant by following the [Qdrant Quickstart](/documentation/quickstart/). Then download the example pipeline: + +```bash +curl -O https://raw.githubusercontent.com/rocketride-org/rocketride-server/develop/examples/rag-pipeline.pipe +``` + +Set the values used by the example: + +```bash +export ROCKETRIDE_QDRANT_HOST=localhost +export ROCKETRIDE_COLLECTION_NAME=rocketride_docs +export ROCKETRIDE_OPENAI_KEY=your-openai-api-key +``` + +The example uses a local MiniLM embedding model and OpenAI for answer generation. Start it from the RocketRide CLI: + +```bash +rocketride start --pipeline ./rag-pipeline.pipe +``` + +You can also open the same `.pipe` file in the RocketRide IDE extension or start it through the [Python](https://docs.rocketride.org/sdk/python-sdk/) or [TypeScript](https://docs.rocketride.org/sdk/node-sdk/) SDK. + +## Configure the Qdrant node + +For a self-hosted Qdrant server, use the `local` profile. This is the ingestion-side Qdrant node from the example: + +```json +{ + "id": "qdrant_1", + "provider": "qdrant", + "config": { + "profile": "local", + "local": { + "host": "${ROCKETRIDE_QDRANT_HOST}", + "port": 6333, + "collection": "${ROCKETRIDE_COLLECTION_NAME}" + }, + "parameters": {} + }, + "input": [ + { "lane": "documents", "from": "embedding_transformer_1" } + ] +} +``` + +For Qdrant Cloud, select the `cloud` profile and supply your cluster host and API key: + +```json +{ + "profile": "cloud", + "cloud": { + "host": "${ROCKETRIDE_QDRANT_HOST}", + "port": 6333, + "apikey": "${ROCKETRIDE_QDRANT_API_KEY}", + "collection": "${ROCKETRIDE_COLLECTION_NAME}" + } +} +``` + +The Qdrant node creates the collection on the first write and infers its vector dimensions from the first batch of embeddings. Use the same embedding model for every write to a collection. + +## How retrieval works + +The example uses two Qdrant nodes pointed at the same collection: + +- The ingestion node receives document chunks after the embedding step and writes them to Qdrant. +- The query node receives an embedded question, retrieves matching chunks, and sends both the question and retrieved context to the prompt and LLM nodes. + +The pipeline can be edited visually without hand-writing its JSON, but the same file is version-controllable and can be run from the [CLI](https://docs.rocketride.org/cli/) or either SDK. Once running, it can also be exposed as a tool to an MCP-compatible assistant. + +## Use Qdrant from an agent + +The Qdrant node can also connect to a RocketRide agent through its tool interface. By default, it exposes three namespaced tools: + +| Tool | Purpose | +| --- | --- | +| `qdrant.search` | Run semantic search and return matching content, scores, and metadata. | +| `qdrant.upsert` | Add or update documents, using the node's configured embedding provider when vectors are not supplied. | +| `qdrant.delete` | Delete documents by object ID. | + +This lets an agent decide when to retrieve context or update its knowledge base during a reasoning loop. The tool namespace can be changed when a pipeline contains more than one Qdrant connection. + +## Further reading + +- [RocketRide Qdrant node reference](https://docs.rocketride.org/nodes/qdrant/) +- [Complete RocketRide RAG example](https://docs.rocketride.org/examples/rag-pipeline/) +- [RocketRide MCP integration](https://docs.rocketride.org/protocols/mcp/) +- [RocketRide source code](https://github.com/rocketride-org/rocketride-server)