RAGFlow

In this catalog it is the RAG option when you need to ground model answers in retrieved company documents. Keep RAGFlow on the observe/propose side of the loop: useful signals and drafts, with human or policy authority still required before anything irreversible runs.

Ingest parsers chunk and embed content into a store; at query time the pipeline retrieves, optionally reranks, and feeds context into the LLM.

1. Ingest a sample document through the product’s parser/index path.
2. Embed and store chunks in the configured vector backend.
3. Ask a grounded question; confirm citations/hits come from the fixture.
4. Wire the agent host to retrieve before it proposes actions.

Related: llamaindex · model-context-protocol · neo4j · topics/04-data · topics/35-context-engineering

• Use when: RAGFlow is an open-source RAG and agent platform with deep document parsing, hybrid retrieval, knowledge graphs, visual workflows, tools, and MCP matches the bottleneck and you specifically need to ground model answers in retrieved company documents.
• Skip when: another pack already covers the need with less ops surface, or RAGFlow would become a second place people retype truth.
• Prefer instead: compare LlamaIndex, Model Context Protocol, Neo4j before locking the cell.

• Ops: confirm license, hosting, and release cadence for RAGFlow (infiniflow/ragflow) before relying on it in a live corridor.
• Integration: bad credentials, scopes, or schema drift produce empty retrievals or runaway tool/model spend without an obvious UI alarm.
• Scope: RAG components support agents; they are not the company ledger or the approval authority.