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RHIDP-16107: Switch Lightspeed RAG Container to OKP #2540
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RHIDP-16107: Switch Lightspeed RAG Container to OKP
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28 changes: 21 additions & 7 deletions
28
...on-retrieval-augmented-generation-rag-embeddings-for-grounded-ai-responses.adoc
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| :_mod-docs-content-type: CONCEPT | ||
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| [id="retrieval-augmented-generation-rag-embeddings-for-grounded-ai-responses_{context}"] | ||
| = Retrieval augmented generation (RAG) embeddings for grounded AI responses | ||
| = Retrieval-augmented generation for grounded AI responses | ||
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| [role="_abstract"] | ||
| Use retrieval-augmented generation (RAG) embeddings to ground artificial intelligence (AI) responses in your internal documentation and provide verified citations during user interactions. | ||
| Use retrieval-augmented generation (RAG) to ground artificial intelligence (AI) responses in {product-very-short} product documentation and provide verified citations during user interactions. | ||
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| The {product-very-short} documentation serves as the primary data source for RAG operations. To provide accurate citations to production documentation during inference, the system uses RAG embeddings stored within a vector database. | ||
| {product-very-short} {ia-name} uses the Offline Knowledge Portal (OKP) as the RAG backend for product documentation. OKP is a knowledge base curated by Red Hat that contains {product-very-short} product documentation optimized for RAG operations. OKP runs as a standalone service alongside the {ia-brand-name} installation. | ||
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| The system processes RAG data through the following sequence: | ||
| The {lcs-name} ({lcs-short}) uses OKP as a RAG tool during inference to retrieve relevant {product-very-short} documentation and attaches documentation references to chat responses. | ||
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| * An initialization container copies the RAG data to a shared volume. | ||
| * The {lcs-name} ({lcs-short}) sidecar container mounts the shared volume to access the data. | ||
| * The sidecar layer uses the embeddings to attach precise documentation references to the chat responses. | ||
| OKP supports the following search modes: | ||
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| `hybrid`:: The default mode, which combines semantic and keyword search. This mode requires an embedding model. | ||
| `semantic`:: Uses vector-based semantic search. This mode requires an embedding model. | ||
| `keyword`:: Uses BM25 text search. This mode does not require downloading an embedding model, which makes it suitable for disconnected deployments. | ||
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| OKP is optional and disabled by default on all platforms. The OKP image is large, so enabling it can significantly increase the time of the first installation while the image is downloaded. How you enable OKP depends on your deployment method: | ||
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| * With the {product} Operator, enable the `intelligent-assistant-okp` add-on flavor alongside the base `intelligent-assistant` flavor. On {ocp-short}, the Operator automatically creates the OKP `Deployment`, `Service`, and `Route`, and sets the OKP service URL for the {lcs-short} sidecar. On other Kubernetes platforms, the Operator deploys OKP, but you must provide the registry credentials, Ingress, TLS, and OKP service URL yourself, because the Operator does not manage Ingress. | ||
| * With the Helm chart, set `intelligentAssistant.okp.enabled` to `true`. On {ocp-short}, the chart creates the OKP `Route` automatically. On other Kubernetes platforms, you must also enable and configure `intelligentAssistant.okp.ingress`. | ||
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| When OKP is not enabled, the assistant runs without a knowledge base: general chat still works, but product-specific responses might be less accurate. | ||
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| [NOTE] | ||
| ==== | ||
| The Notebooks feature, which enables you to upload and query your own documents, uses a separate RAG mechanism and is not affected by the OKP integration. | ||
| ==== |
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24 changes: 15 additions & 9 deletions
24
modules/shared/proc-mirror-images-for-operator-deployments.adoc
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