LlamaIndex
examples/llamaindex_adapter/pipeline.py — the same nine stages as the
plain-Python reference, wired onto real LlamaIndex constructs:
| Stage | LlamaIndex construct |
|---|---|
document in / cleaned |
Plain Python — no construct exists in LlamaIndex (or LangChain, or Langflow) for either. |
converted |
LlamaIndex's own document readers. |
split |
LlamaIndex's own text splitting. |
embedded |
MockEmbedding — LlamaIndex's own no-credential, no-network embedding stub. |
indexed / retrieved |
VectorStoreIndex — pure in-memory Python, no external service, so this is genuinely native here (unlike Langflow's own knowledge-store component; see the Langflow page). |
answer |
A real CustomLLM subclass — LlamaIndex's own extension point for a model — invoked through .complete(), with deterministic regex extraction behind it: substituted logic behind a native integration point, not a native answering model. |
Running it
Needs the exact pins in examples/llamaindex_adapter/requirements.txt (conflicts with
LangChain's own numpy pin — see examples/PIN_DISCIPLINE.md for running more than one
adapter's requirements in the same environment).
pip install -r examples/llamaindex_adapter/requirements.txt
python examples/llamaindex_adapter/pipeline.py OUT_DIR RUN_ID
Its runs always store each chunk's text in their outputs (chunks.json, and again in vectors.json), along with the documents' converted and cleaned text, so treat its run folders like logs that may contain sensitive data.