embedding-generator

name	embedding-generator
description	Reference documentation for the EmbeddingGenerator operator. Covers the constructor, embedding serving requirements, actual dataframe flow, and runnable pipeline usage. Use when: converting one text column in a dataframe into embedding vectors for retrieval, clustering, similarity search, or downstream vector-based operators.
trigger_keywords	["EmbeddingGenerator","embedding-generator","text embedding","embedding generation","vectorization"]
version	1.0.0

EmbeddingGenerator Operator Reference

EmbeddingGenerator reads one text column from the current dataframe, sends the full column as a batch into an embedding service, writes the returned vectors into output_key, persists the dataframe, and returns [output_key].

1. Imports

from dataflow.operators.core_text import EmbeddingGenerator
from dataflow.serving import APILLMServing_request
from dataflow.serving import LocalEmbeddingServing
from dataflow.serving import LiteLLMServing
from dataflow.serving import LocalModelLLMServing_vllm

2. Embedding Serving Options

Option A: Remote API with APILLMServing_request

APILLMServing_request(
    api_url="https://api.openai.com/v1/embeddings",
    key_name_of_api_key="DF_API_KEY",
    model_name="text-embedding-3-small",
    max_workers=20,
)

This works because APILLMServing_request implements:

generate_embedding_from_input(texts)

Option B: Local embedding model with LocalEmbeddingServing

LocalEmbeddingServing(
    model_name="all-MiniLM-L6-v2",
    device=None,
    max_workers=2,
)

Requires:

pip install "open-dataflow[vectorsql]"

Option C: Provider-agnostic API via LiteLLMServing

LiteLLMServing(
    serving_type="embedding",
    model_name="text-embedding-3-small",
    key_name_of_api_key="DF_API_KEY",
    max_workers=10,
)

This is suitable when you want to route embedding requests through LiteLLM.

Option D: Local vLLM backend with LocalModelLLMServing_vllm

LocalModelLLMServing_vllm(
    hf_model_name_or_path="your-embedding-capable-model",
    vllm_tensor_parallel_size=1,
)

This works only if the selected vLLM model/backend supports embedding through llm.embed(...).

Practical List of Supported Serving Examples

The following classes in dataflow.serving currently expose generate_embedding_from_input(...) and are practical candidates for EmbeddingGenerator:

• APILLMServing_request
• LocalEmbeddingServing
• LiteLLMServing
• LocalModelLLMServing_vllm

The following commonly imported serving is not suitable here:

• LocalModelLLMServing_sglang: its generate_embedding_from_input(...) currently raises NotImplementedError

3. Constructor

EmbeddingGenerator(
    embedding_serving=emb,
)

Parameter	Required	Default	Description
embedding_serving	Yes	None	Service object that provides generate_embedding_from_input(texts)

Important Serving Note

Although the constructor type annotation is LLMServingABC, the operator does not call generate_from_input(...). It specifically calls:

embedding_serving.generate_embedding_from_input(texts)

So the practical requirement is not just “any LLMServingABC”, but a serving object that actually implements generate_embedding_from_input(...).

4. run() Signature

op.run(
    storage=self.storage.step(),
    input_key="text",
    output_key="embeddings",
)
# returns: [output_key]

Parameter	Required	Default	Description
storage	Yes	None	Current operator-step storage object
input_key	No	"text"	Existing dataframe column containing texts
output_key	No	"embeddings"	Column written with embedding vectors

5. Actual Execution Logic

The current implementation behaves as follows:

1. Read the dataframe from storage.
2. Read the column dataframe[input_key].
3. Convert that column into a Python list with tolist().
4. Call:

embedding_serving.generate_embedding_from_input(texts)

5. Write the returned embedding list into dataframe[output_key].
6. Persist the dataframe through storage.write(dataframe).
7. Return [output_key].

6. Important Rules

1. input_key must already exist in the current dataframe.
2. The operator sends the full text column as one batch to generate_embedding_from_input(...).
3. The operator does not perform missing-value filtering or type normalization before sending the texts.
4. output_key is overwritten silently if it already exists.
5. The return value is a list containing the output column name, not the bare string.
6. When using APILLMServing_request, api_url should point to an embedding endpoint such as /v1/embeddings, not /v1/chat/completions.
7. A service being an LLMServingABC subclass is not sufficient by itself; it must actually implement generate_embedding_from_input(...).

7. Typical Usage

from dataflow.operators.core_text import EmbeddingGenerator
from dataflow.serving import APILLMServing_request

embedding_serving = APILLMServing_request(
    api_url="https://api.openai.com/v1/embeddings",
    key_name_of_api_key="DF_API_KEY",
    model_name="text-embedding-3-small",
    max_workers=20,
)

generator = EmbeddingGenerator(
    embedding_serving=embedding_serving,
)

generator.run(
    storage=self.storage.step(),
    input_key="content",
    output_key="embedding",
)