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How to load documents into Milvus for retrieval in a workflow with Goal Flow

Extract, chunk, and embed your documents into Milvus, then point a workflow at the retriever.

Short answer: Run git clone on the goalflow repository and cd goalflow; then run pip install -e . and set Milvus connection values in .env from .env.example; read docs/knowledge-retrieval.md to confirm the ingestion CLI name and options. All steps.

Who it is forA researcher or support engineer who has a set of internal documents and wants a workflow to answer questions from them.

The problemAn LLM workflow cannot answer from private documents until they are indexed in a vector store.

The outcomeA Milvus collection holding embedded chunks of your documents, which a workflow can query through the pluggable retriever.

What you need first

  • Python 3.12
  • Redis (cluster or standalone) and MySQL
  • A cloned copy of the goalflow repository and a filled-in .env file copied from .env.example
  • A Dify DSL export (.yml) if you want to transpile a visual flow

Steps

  1. Run git clone on the goalflow repository and cd goalflow.
  2. Run pip install -e . and set Milvus connection values in .env from .env.example.
  3. Read docs/knowledge-retrieval.md to confirm the ingestion CLI name and options.
  4. Run the document ingestion CLI on your document folder to extract, chunk, embed, and write to Milvus. (inferred, check the README)
  5. Set the retriever to Milvus in your workflow configuration and run one test question.

Prompt for your agent

In the goalflow repo, follow docs/knowledge-retrieval.md to ingest the documents in <document-folder> into Milvus. Expected output is a Milvus collection with embedded chunks. Verify the ingestion count matches the number of input documents and run one retrieval query for <question>.

Caveat. The README does not state the ingestion command or the Milvus settings, so the exact invocation must come from docs/knowledge-retrieval.md.

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