docs: add RocketRide framework integration (#2525)

Co-authored-by: Krish Garg <181051779+kgarg2468@users.noreply.github.com>
2026-10-09 21:08:31 +02:00 · 2026-07-23 15:30:41 +05:30
parent a88a274376
commit 3591033da8
2 changed files with 110 additions and 0 deletions
@@ -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.                 |
@@ -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)