retrieval-generator

name	retrieval-generator
description	Reference documentation for the RetrievalGenerator operator. [Purpose] Reads one text column from storage, forwards every non-empty row to `llm_serving.generate_from_input(...)`, and writes the returned list into a new output column. [Default backend] Use `LightRAGServing` by default. [Important] `run()` is async. The operator itself does not initialize the serving object; it only awaits `llm_serving.generate_from_input(llm_inputs, system_prompt)`.
trigger_keywords	["RetrievalGenerator","retrieval-generator","LightRAG","RAG generation"]
version	1.0.0

RetrievalGenerator Operator Reference

RetrievalGenerator is an async operator. It reads text from one column, collects only truthy values, calls await self.llm_serving.generate_from_input(llm_inputs, self.system_prompt), and writes the returned list into output_key.

See examples/good.md for a valid usage pattern and examples/bad.md for common failure cases.

---

1. Import

from dataflow.operators.core_text import RetrievalGenerator
from dataflow.serving import LightRAGServing

---

2. Constructor

RetrievalGenerator(
    llm_serving=serving,
    system_prompt="You are a helpful agent.",
)

Parameter	Required	Default	Description
llm_serving	Yes	None	Stored on self.llm_serving without validation. run() later awaits self.llm_serving.generate_from_input(llm_inputs, self.system_prompt).
system_prompt	No	"You are a helpful agent."	Stored on self.system_prompt and forwarded unchanged into generate_from_input(...).

Notes

1. The operator does not initialize the serving backend for you.
2. Any serving object used here must already be ready before run() starts.
3. Default recommendation: use LightRAGServing.

---

3. Default LightRAGServing Initialization

If you use the default backend, initialize it like this before constructing RetrievalGenerator:

llm_serving = await LightRAGServing.create(
    api_url="https://api.openai.com/v1",
    llm_model_name="gpt-4o",
    embed_model_name="bge-m3:latest",
    embed_binding_host="http://localhost:11434",
    document_list=["knowledge_base.txt"],
)
if llm_serving is None:
    raise RuntimeError("LightRAGServing initialization failed.")

• LightRAGServing.__init__() accepts api_url, key_name_of_api_key, llm_model_name, embed_model_name, embed_binding_host, embedding_dim, max_embed_tokens, and document_list.
• LightRAGServing.create(...) builds self.rag and loads documents.
• DF_API_KEY must exist in the environment, otherwise construction raises ValueError.
• If document loading fails inside create(...), it logs the error and returns None.

---

4. run() Signature

await op.run(
    storage=storage,
    input_key="raw_content",
    output_key="generated_content",
)

Parameter	Required	Default	Description
storage	Yes	None	Used as storage.read("dataframe") and storage.write(df).
input_key	No	"raw_content"	Column name read from each row via row.get(input_key, ""). Only truthy values are appended to llm_inputs.
output_key	No	"generated_content"	Column name assigned as df[output_key] = generated_outputs.

Return Value

On success, the method returns the string output_key.

If generate_from_input(...) raises an exception, the operator logs the error and returns None.

---

5. Actual Runtime Logic

1. Save input_key and output_key onto self.
2. Read the DataFrame from storage.read("dataframe").
3. Iterate row by row.
4. Read row.get(input_key, "").
5. Append str(raw_content) only when the value is truthy.
6. Call generated_outputs = await self.llm_serving.generate_from_input(llm_inputs, self.system_prompt).
7. Assign generated_outputs to df[output_key].
8. Write the updated DataFrame back with storage.write(df).
9. Return output_key.

There is no placeholder output for skipped rows.

---

6. Critical Constraints

1. run() is async. You must call it with await.
2. Empty or falsy values in input_key are skipped before generation.