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RAG Configuration Guide

This document explains how to configure and customize your RAG pipeline using the llama-stack configuration YAML file. You will:

  • Initialize a vector store
  • Download and point to a local embedding model
  • Configure an inference provider (LLM)
  • Enable Agent-based RAG querying

Table of Contents

Introduction

RAG in Lightspeed Core Stack (LCS) is yet only supported via the Agents API. The agent is responsible for planning and deciding when to query the vector index.

The system operates a chain of command. The Agent is the orchestrator, using the LLM as its reasoning engine. When a plan requires external information, the Agent queries the Vector Store. This is your database of indexed knowledge, which you are responsible for creating before running the stack. The Embedding Model is used to convert the queries to vectors.

Note

The same Embedding Model should be used to both create the store and to query it.

Prerequisites

Set Up the Vector Database

Use the rag-content repository to build a compatible vector database.

Important

The resulting DB must be compatible with Llama Stack (e.g., FAISS with SQLite metadata, SQLite-vec). This can be configured when using the tool to generate the index.

Download an Embedding Model

Download a local embedding model such as sentence-transformers/all-mpnet-base-v2 by using the script in rag-content or manually download and place in your desired path.

Note

Llama Stack can also download a model for you, which will make the first start-up slower. In the YAML configuration file run.yaml specify a supported model name as provider_model_id instead of a path. LLama Stack will then download the model to the ~/.cache/huggingface/hub folder.

Configure Vector Store and Embedding Model

Update the run.yaml file used by Llama Stack to point to:

  • Your downloaded embedding model
  • Your generated vector database

FAISS example

providers:
  inference:
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}

  # FAISS vector store
  vector_io:
  - provider_id: custom-index
    provider_type: inline::faiss
    config:
      persistence:
        namespace: vector_io::faiss
        backend: rag_backend  # References storage.backends.rag_backend

storage:
  backends:
    rag_backend:
      type: kv_sqlite
      db_path: <path-to-vector-index>  # e.g. /home/USER/vector_db/faiss_store.db

registered_resources:
  models:
  - model_id: <embedding-model-name> # e.g. sentence-transformers/all-mpnet-base-v2
    metadata:
        embedding_dimension: <embedding-dimension> # e.g. 768
    model_type: embedding
    provider_id: sentence-transformers
    provider_model_id: <path-to-embedding-model> # e.g. /home/USER/embedding_model

  vector_stores:
  - embedding_dimension: <embedding-dimension> # e.g. 768
    embedding_model: <embedding-model-name> # e.g. sentence-transformers/all-mpnet-base-v2
    provider_id: custom-index
    vector_store_id: <index-id>

Where:

  • provider_model_id is the path to the folder of the embedding model (or alternatively, the supported embedding model to download)
  • db_path is the path to the vector index (.db file in this case)
  • vector_store_id is the index ID used to generate the db

See the full working config example for more details.

pgvector example

This example shows how to configure a remote PostgreSQL database with the pgvector extension for storing embeddings.

You will need to install PostgreSQL with a matching version to pgvector, then log in with psql and enable the extension with:

CREATE EXTENSION IF NOT EXISTS vector;

Update the connection details (host, port, db, user, password) to match your PostgreSQL setup.

Each pgvector-backed table follows this schema:

  • id (text): UUID identifier of the chunk
  • document (jsonb): json containing content and metadata associated with the embedding
  • embedding (vector(n)): the embedding vector, where n is the embedding dimension and will match the model's output size (e.g. 768 for all-mpnet-base-v2)

Note

The vector_store_id (e.g. rhdocs) is used to point to the table named vector_store_rhdocs in the specified database, which stores the vector embeddings.

[...]
providers:
  [...]
  vector_io:
  - provider_id: pgvector-example 
    provider_type: remote::pgvector
    config:
      host: localhost
      port: 5432
      db: pgvector_example # PostgreSQL database (psql -d pgvector_example)
      user: lightspeed # PostgreSQL user
      password: password123
      kvstore:
        type: sqlite
        db_path: .llama/distributions/pgvector/pgvector_registry.db

vector_stores:
- embedding_dimension: 768
  embedding_model: sentence-transformers/all-mpnet-base-v2
  provider_id: pgvector-example 
  # A unique ID that becomes the PostgreSQL table name, prefixed with 'vector_store_'.
  # e.g., 'rhdocs' will create the table 'vector_store_rhdocs'.
  # If the table was already created, this value must match the ID used at creation.
  vector_store_id: rhdocs

See the full working config example for more details.

Add an Inference Model (LLM)

vLLM on RHEL AI (Llama 3.1) example

Note

The following example assumes that podman's CDI has been properly configured to enable GPU support.

The vllm-openai Docker image is used to serve the Llama-3.1-8B-Instruct model.
The following example shows how to run it on RHEL AI with podman:

podman run \
  --device "${CONTAINER_DEVICE}" \
  --gpus ${GPUS} \
  -v ~/.cache/huggingface:/root/.cache/huggingface \
  --env "HUGGING_FACE_HUB_TOKEN=${HF_TOKEN}" \
  -p ${EXPORTED_PORT}:8000 \
  --ipc=host \
  docker.io/vllm/vllm-openai:latest \
  --model meta-llama/Llama-3.1-8B-Instruct \
  --enable-auto-tool-choice \
  --tool-call-parser llama3_json --chat-template examples/tool_chat_template_llama3.1_json.jinja

The example command above enables tool calling for Llama 3.1 models. For other supported models and configuration options, see the vLLM documentation: vLLM: Tool Calling

After starting the container edit your run.yaml file, matching model_id with the model provided in the podman run command.

[...]
models:
[...]
- model_id: meta-llama/Llama-3.1-8B-Instruct # Same as the model name in the 'podman run' command
  provider_id: vllm
  model_type: llm
  provider_model_id: null

providers:
  [...]
  inference:
  - provider_id: vllm
    provider_type: remote::vllm
    config:
      url: http://localhost:${env.EXPORTED_PORT:=8000}/v1/ # Replace localhost with the url of the vLLM instance
      api_token: <your-key-here> # if any

See the full working config example for more details.

OpenAI example

Add a provider for your language model (e.g., OpenAI):

models:
[...]
- model_id: my-model 
  provider_id: openai
  model_type: llm
  provider_model_id: <model-name> # e.g. gpt-4o-mini

providers:
[...]
  inference:
  - provider_id: openai
    provider_type: remote::openai
    config:
      api_key: ${env.OPENAI_API_KEY}

Make sure to export your API key:

export OPENAI_API_KEY=<your-key-here>

Note

When experimenting with different models, providers and vector_dbs, you might need to manually unregister the old ones with the Llama Stack client CLI (e.g. llama-stack-client vector_dbs list)

See the full working config example for more details.

Azure OpenAI

Not yet supported.

Ollama

The remote::ollama provider can be used for inference. However, it does not support tool calling, including RAG.
While Ollama also exposes an OpenAI compatible endpoint that supports tool calling, it cannot be used with llama-stack due to current limitations in the remote::openai provider.

There is an ongoing discussion about enabling tool calling with Ollama.
Currently, tool calling is not supported out of the box. Some experimental patches exist (including internal workarounds), but these are not officially released.

vLLM Mistral

The RAG tool calls where not working properly when experimenting with mistralai/Mistral-7B-Instruct-v0.3 on vLLM.

Solr Vector IO

The OKP (Offline Knowledge Portal) Solr Vector IO is a read-only vector search provider that integrates with Apache Solr for enhanced vector search capabilities. It enables retrieving contextual information from Solr-indexed Red Hat documents to enhance query responses with support for hybrid search and chunk window expansion.

How to Enable Solr Vector IO

1. Configure Llama Stack (run.yaml):

providers:
  vector_io:
  - provider_id: solr-vector
    provider_type: remote::solr_vector_io
    config:
      solr_url: http://localhost:8983/solr
      collection_name: portal-rag
      vector_field: chunk_vector
      content_field: chunk
      embedding_dimension: 384
      embedding_model: ${env.EMBEDDING_MODEL_DIR}
      chunk_window_config:
        chunk_parent_id_field: "parent_id"
        chunk_content_field: "chunk_field"
        chunk_index_field: "chunk_index"
        chunk_token_count_field: "num_tokens"
        parent_total_chunks_field: "total_chunks"
        parent_total_tokens_field: "total_tokens"
        chunk_filter_query: "is_chunk:true" 
      persistence:
        namespace: portal-rag
        backend: kv_default

registered_resources:
  vector_stores:
  - vector_store_id: portal-rag
    provider_id: solr-vector
    embedding_model: granite-embedding-30m
    embedding_dimension: 384

Note: if the vector database (portal-rag) is not in the persistent data store within the vector_io provider (e.g. after deleting the llama stack cache) you will need to register the vector database under registered resources:

  vector_stores:
    - embedding_dimension: 384
      embedding_model: sentence-transformers/${env.EMBEDDING_MODEL_DIR}
      provider_id: solr-vector
      vector_store_id: portal-rag

2. Configure Lightspeed Stack (lightspeed-stack.yaml):

solr:
  enabled: true     # Enable Solr vector IO functionality
  offline: true     # Use parent_id for document URLs (offline mode)
                   # Set to false to use reference_url (online mode)

Query Request Example:

curl -sX POST http://localhost:8080/v1/query \
    -H "Content-Type: application/json" \
    -d '{"query" : "how do I secure a nodejs application with keycloak?", "no_tools":true}' | jq .

Note: Solr does not currently work with RAG tools. You will need to specify "no_tools": true in request.

Query Processing:

  1. When Solr is enabled, queries use the portal-rag vector store
  2. Vector search is performed with configurable parameters:
    • k: Number of results (default: 5)
    • score_threshold: Minimum similarity score (default: 0.0)
    • mode: Search mode (default: "hybrid")
  3. Results include document metadata and source URLs
  4. Document URLs are built based on the offline setting:
    • Offline mode: Uses parent_id with Mimir base URL
    • Online mode: Uses reference_url from document metadata

Query Filtering:

To filter the Solr context edit the chunk_filter_query field in the Solr vector_io provider in the run.yaml. Filters should follow the key:value format: ex. "product:*openshift*"

Note: This static filter is a temporary work-around.

Prerequisites:

Limitations:

  • This is a read-only provider - no insert/delete operations

Complete Configuration Reference

To enable RAG functionality, make sure the agents, tool_runtime, and safety APIs are included and properly configured in your YAML.

Below is a real example of a working config, with:

  • A local all-mpnet-base-v2 embedding model
  • A FAISS-based vector store
  • OpenAI as the inference provider
  • Agent-based RAG setup

Tip

We recommend starting with a minimal working configuration (one is automatically generated by the rag-content tool when generating the database) and extending it as needed by adding more APIs and providers.

version: 2
image_name: rag-configuration

apis:
- agents
- inference
- vector_io
- tool_runtime
- safety

providers:
  inference:
  - provider_id: sentence-transformers
    provider_type: inline::sentence-transformers
    config: {}
  - provider_id: openai
    provider_type: remote::openai
    config:
      api_key: ${env.OPENAI_API_KEY}

  agents:
  - provider_id: meta-reference
    provider_type: inline::meta-reference
    config:
      persistence:
        agent_state:
          namespace: agents_state
          backend: kv_default
        responses:
          table_name: agents_responses
          backend: sql_default

  safety:
  - provider_id: llama-guard
    provider_type: inline::llama-guard
    config:
      excluded_categories: []

  vector_io:
  - provider_id: ocp-docs
    provider_type: inline::faiss
    config:
      persistence:
        namespace: vector_io::faiss
        backend: ocp_docs_backend  # References storage.backends

  tool_runtime:
  - provider_id: rag-runtime
    provider_type: inline::rag-runtime
    config: {}

storage:
  backends:
    kv_default:
      type: kv_sqlite
      db_path: ~/.llama/storage/kv_store.db
    sql_default:
      type: sql_sqlite
      db_path: ~/.llama/storage/sql_store.db
    ocp_docs_backend:
      type: kv_sqlite
      db_path: /home/USER/lightspeed-stack/vector_dbs/ocp_docs/faiss_store.db

registered_resources:
  models:
  - model_id: gpt-test
    provider_id: openai
    model_type: llm
    provider_model_id: gpt-4o-mini
  - model_id: sentence-transformers/all-mpnet-base-v2
    model_type: embedding
    provider_id: sentence-transformers
    provider_model_id: /home/USER/lightspeed-stack/embedding_models/all-mpnet-base-v2
    metadata:
      embedding_dimension: 768
  vector_stores:
  - vector_store_id: openshift-index  # This ID was defined during index generation
    provider_id: ocp-docs  # References providers.vector_io
    embedding_model: sentence-transformers/all-mpnet-base-v2
    embedding_dimension: 768
  tool_groups:
  - toolgroup_id: builtin::rag
    provider_id: rag-runtime

System Prompt Guidance for RAG (as a tool)

When using RAG, the knowledge_search tool must be explicitly referenced in your system prompt. Without clear instructions, models may inconsistently use the tool.

Tool-Aware sample instruction:

You are a helpful assistant with access to a 'knowledge_search' tool. When users ask questions, ALWAYS use the knowledge_search tool first to find accurate information from the documentation before answering.

References