---

name: builder description: "Use this RAGs sub-skill to build a Streamlit RAG agent from files, directories, URLs, optional web search, model settings, and RAG parameters." disable-model-invocation: true metadata: disco-role: operating license: MIT

RAGs Builder

Use this sub-skill when the task is to create a new RAGs bot, diagnose the Home page builder flow, or use the builder APIs directly. RAGs is a Streamlit app that asks a builder agent to create a retrieval-augmented chat agent over a single data source.

Read This When

• The user wants to build a RAG bot from local files, one directory, or URLs.
• The task mentions the Home page, builder agent, task description, system prompt generation, RAGAgentBuilder, or RAGParams.
• The user needs to choose top_k, chunk_size, embedding model, LLM provider, summarization, or optional web_search.
• The user asks why source loading, OpenAI secrets, web search, or multimodal setup fails before the agent exists.

If the bot already exists and the user wants to edit or delete it, read ../configuration/SKILL.md. If the user wants to ask questions to an existing bot, read ../chat/SKILL.md.

Core Workflow

1. Confirm dependencies and secrets first. The app expects a Streamlit secret named openai_key; provider-specific keys are needed only for their routes.
2. Validate the data-source choice with scripts/validate_source_selection.py before invoking builder logic. RAGs accepts exactly one source kind: local files, one directory, or URLs.
3. Use the builder conversation to collect the task description and let the builder create the system prompt.
4. Load data, inspect or set RAG parameters, optionally add web_search when a metaphor_key secret is configured, then create the agent.
5. After creation, route to the configuration and chat sub-skills for later editing and querying.

For detailed step-by-step routes, model identifiers, data-source rules, and validation checks, read references/workflows.md. For signatures and defaults verified from the package inspection environment, read references/api-reference.md. For failure symptoms and fixes, read references/troubleshooting.md.

Important Operating Facts

• RAGParams defaults are include_summarization=False, top_k=2, chunk_size=1024, embed_model="default", and llm="gpt-4-1106-preview".
• load_data requires exactly one of file_names, directory, or urls and raises a ValueError when zero or multiple source kinds are supplied.
• Unprefixed LLM names are treated as OpenAI models. Recognized prefixes are openai:, anthropic:, replicate:, and local:.
• The web-search tool is only exposed when a metaphor_key Streamlit secret is present, and its tool name is web_search.
• The beta multimodal builder path is optional. The minimum verified environment imported multimodal classes but did not install the torch, torchvision, or CLIP runtime stack and did not run external model calls.

Boundaries

Do not claim that a full OpenAI, Anthropic, Replicate, Metaphor, URL-loading, or multimodal chat call was verified unless the user supplies credentials/network and asks for that execution. For safe checks, use the bundled validator and the root installation diagnostic instead of launching a long-running Streamlit server.