LangRAG Knowledge Base

Use this reference when validating LangRAG creation, document ingestion, retrieval, and local-agent RAG behavior.

Setup

1. Install langbot-team/LangRAG from Marketplace if /api/v1/knowledge/engines has no LangRAG engine.
2. Confirm LANGBOT_BACKEND_URL/api/v1/knowledge/engines contains plugin id langbot-team/LangRAG.
3. Prefer local Chroma embedding for offline/free tests:
   • Provider requester: chroma-embedding
   • Embedding model name: chroma-all-MiniLM-L6-v2

Important: a Chroma embedding entry must exist under embedding_models. A model accidentally created as an LLM model will appear in the wrong model selector and will not satisfy LangRAG's embedding-model field.

Parser Golden Case

Use cases/langrag-parser-golden-e2e.yaml when validating the LangRAG + GeneralParsers integration on the current master worktree.

Fixture:

fixtures/rag/parser-golden.html

Golden intent:

• Start LangBot from LANGBOT_REPO, which should point at the master worktree for this run.
• Build and install/update local LANGBOT_RAG_PLUGIN_REPO and LANGBOT_PARSER_PLUGIN_REPO.
• Upload the HTML fixture and select GeneralParsers when the parser chooser is shown.
• Confirm retrieval returns aurora-parser-rag-9137, GeneralParsers, LangRAG, and the Markdown table header | Parser field | Golden value |.
• Confirm logs show LangRAG used external pre-parsed content instead of the internal fallback parser.

Local install pitfall:

• If GeneralParsers fails while installing PyMuPDF>=1.24.0, read troubleshooting/plugin-dependency-install-offline.yaml.
• The golden case can continue after the active LangBot master venv can import fitz and python -m pip install --dry-run 'PyMuPDF>=1.24.0' reports the requirement is satisfied.

Browser Flow

1. Open LANGBOT_FRONTEND_URL.
2. Navigate to Knowledge.
3. Create a knowledge base.
4. Select engine LangRAG.
5. Select embedding model chroma-all-MiniLM-L6-v2 or another known working embedding model.
6. Keep the index type as Chunk for smoke/regression tests.
7. Upload a small sentinel document.
8. Wait until the document row status is Completed.
9. Open Retrieve Test and query for the sentinel.

Recommended fixture:

fixtures/rag/sentinel-doc.txt

Pass Criteria

• The created knowledge base appears in the sidebar.
• The uploaded document reaches Completed.
• Retrieve Test returns the uploaded document with the sentinel text.
• Browser console has no unexpected errors.

Local-Agent RAG Check

After retrieval passes:

1. Open the target pipeline.
2. In Configuration > AI, add the knowledge base to Knowledge Bases.
3. Save.
4. Open Debug Chat.
5. Ask for the sentinel.
6. Confirm the bot response contains the exact sentinel.