Processing

Processing components transform, filter, parse, and route data as it moves through the flow.

Combine Data

Merges outputs from multiple upstream components into a single data object.

Use in a flow

1. Add a Combine Data node to the canvas.
2. Connect at least two Data Inputs from upstream components.
3. Choose an Operation Type.
4. Connect DataFrame output downstream.

Component A ──► Data Inputs ──┐
Component B ──► Data Inputs ──┼──► Combine Data (DataFrame) ──► ...
Component C ──► Data Inputs ──┘

Parameters

Operation types

Outputs

Limitations

• Minimum inputs — Requires at least two Data inputs to produce a result.
• Single output type — Output is always a DataFrame, not a raw Data object.

Data → DataFrame

Converts raw data into a tabular DataFrame structure for further processing.

Use in a flow

1. Add a Data → DataFrame node to the canvas.
2. Connect one or more Data objects to Data or Data List.
3. Connect DataFrame output to downstream steps.

API Request (Data) ──► Data → DataFrame ──► DataFrame Operations

Parameters

Outputs

Limitations

• Data input only — Input must be Data objects; other types raise an error.
• Column names — Column names come from keys in each Data object’s data fields.

Data Operations

Applies transformations to structured data — select keys, combine, filter values, append or update fields, remove keys, rename keys, and evaluate literal values.

Use in a flow

1. Add a Data Operations node to the canvas.
2. Connect a Data input.
3. Select one Operation from the list.
4. Fill in the fields that appear for that operation.
5. Connect Data output downstream.

Webhook (Data) ──► Data Operations ──► Agent / Output

Parameters

Fields below appear based on the selected operation (open Controls to access them):

Outputs

Limitations

• One operation at a time — Only a single operation runs per execution.
• Combine — Requires multiple Data inputs wired to Data.
• Filter Values — The target key must contain a list of dictionaries.

DataFrame Operations

Applies operations on a DataFrame — sort, filter, select columns, add or drop columns, rename columns, replace values, and take head or tail rows.

Use in a flow

1. Add a DataFrame Operations node to the canvas.
2. Connect a DataFrame input.
3. Select an Operation.
4. Fill in the fields that appear for that operation.
5. Connect DataFrame output downstream.

Data → DataFrame ──► DataFrame Operations ──► Agent / Output

Parameters

Available operations (open Controls to access operation-specific fields):

Outputs

Limitations

• One operation at a time — Each run applies a single selected operation.
• Exact match filter — Filter uses equality comparison only.

Document Parser

Extracts text and structure from documents (PDF, DOCX, PPTX, XLSX, Markdown, and images) using configurable parsing strategies.

Use in a flow

1. Add a Document Parser node to the canvas.
2. Connect an Input Message that includes file attachments (for example, from Input).
3. Choose a Parsing Strategy.
4. Connect Parsed Content downstream.

Input (Message with files) ──► Document Parser ──► Agent / Split Text

Parameters

When LLM is selected, model fields appear:

Supported file types by strategy:

Outputs

Limitations

• File attachments — The input message must include files; empty input returns an empty result.
• Strategy-specific formats — Unsupported extensions fail for the selected strategy.
• LLM strategy — Requires a configured or connected language model.

Ingest Approved Response

Captures the approved response from a Human In The Loop step and injects it back into the flow by embedding and indexing the post/response pair for future retrieval.

Use in a flow

1. Add an Ingest Approved Response node after a Human In The Loop approval step.
2. Wire Post (original user message) and Post Response (approved bot reply).
3. Configure embedding model and OpenSearch connection details.
4. Connect Result output downstream.

Human In The Loop ──► Ingest Approved Response ──► Output / downstream processing

Parameters

Outputs

Limitations

• OpenSearch required — Valid host, credentials, and network access are needed.
• Embedding model — A workspace-configured embedding model must be selected.
• Index naming — Index name is derived from Customer Name with a _confidence_score suffix.

Lambda Filter (Beta)

Filters a list of items using a custom expression. A connected language model generates a Python lambda function from your natural-language instructions and applies it to the input data.

Use in a flow

1. Add a Lambda Filter (Beta) node to the canvas.
2. Connect Data from an upstream component.
3. Connect a Language Model from an LLM component.
4. Write Instructions describing how to filter or transform the data.
5. Connect Filtered Data or DataFrame output downstream.

Data source ──► Lambda Filter ──► downstream processing
LLM ──► Language Model

Example instruction: Filter the data to only include items where the status is active.

Parameters

Outputs

Limitations

• Beta — Behavior may change; lambda generation depends on the connected LLM.
• LLM required — A Language Model must be connected; instructions are not executed directly.
• Large data — Very large payloads are sampled before the LLM generates the lambda.

LLM Router

Routes the flow to one of several downstream paths based on the output of an LLM classification. A judge model selects the best language model for the input, then runs that model and returns its response.

Use in a flow

1. Add an LLM Router node to the canvas.
2. Connect multiple LLM components to Language Models.
3. Connect a judge LLM to Judge LLM.
4. Connect the user message to Input.
5. Connect Output to downstream steps.

                ┌── LLM A ──┐
Input ──► LLM Router ── Judge LLM ──► Output
                └── LLM B ──┘

Parameters

Outputs

Limitations

• All inputs required — Language Models, Input, and Judge LLM must all be connected.
• Fallback — If the judge returns an invalid index, the first model in the list is used.

Message to Data (Beta)

Converts a conversation message object into a structured data record.

Use in a flow

1. Add a Message to Data (Beta) node to the canvas.
2. Connect a Message from an upstream component (for example, Input or an Agent).
3. Connect Data output downstream.

Input (Message) ──► Message to Data ──► Data Operations / Agent

Parameters

Outputs

Limitations

• Beta — Behavior may change.
• Message input only — Input must be a Message object; other types produce an error in the output.

Parser

Formats structured data into text using a template, or converts input into a readable string. Use it to turn Data or DataFrame content into prompt-ready text for downstream components.

Use in a flow

1. Add a Parser node to the canvas.
2. Connect Data or DataFrame from an upstream component.
3. Choose Mode — Parser (template) or Stringify (plain text conversion).
4. Connect Parsed Text downstream.

SQL Query (DataFrame) ──► Parser ──► Prompt / Agent

Template example:

Name: {Name}, Age: {Age}, Country: {Country}

Parameters

Outputs

Limitations

• Template variables — Placeholders must match column or key names in the input.
• Stringify mode — DataFrames are converted to markdown-style text, not parsed into new fields.

Regex Extractor

Extracts values from text using a regular expression pattern.

Use in a flow

1. Add a Regex Extractor node to the canvas.
2. Enter Input Text or connect text from an upstream component.
3. Enter a Regex Pattern.
4. Connect Data or Message output downstream.

Input (Message) ──► Regex Extractor ──► Data Operations / Agent

Parameters

Outputs

Limitations

• Valid regex — Invalid patterns return an error in the output.
• findall behavior — Uses findall; capture groups affect what is returned per match.
• No matches — Returns an empty Data list and a “No matches found” message.

Split Text

Splits text or a DataFrame into chunks using configurable size and overlap settings.

Use in a flow

1. Add a Split Text node to the canvas.
2. Connect Data or DataFrame containing text.
3. Set Chunk Size, Chunk Overlap, and Separator.
4. Connect Chunks or DataFrame output downstream.

File (Data) ──► Split Text ──► Embeddings / Vector Store

Parameters

Outputs

Limitations

• Empty input — Empty DataFrame or missing data raises an error.
• Separator — Splits larger than Chunk Size are not subdivided further.
• Text key — For DataFrames, the column named by Text Key is used as the source text.

Streaming Data to Message

Converts a streaming data response into a standard message format.

Use in a flow

1. Add a Streaming Data to Message node to the canvas.
2. Connect streaming or static Text input (Data, DataFrame, Message, string, list, or generator).
3. Connect Message output downstream (for example, Output).

LLM (streaming) ──► Streaming Data to Message ──► Output
Agent ──► Streaming Data to Message

Parameters

Outputs

Limitations

• Supported input types — Data, DataFrame, Message, string, list, and generator inputs are supported.
• Streaming — Generator inputs are fully consumed before the message is returned.
• Empty Data — Data objects with no text content raise an error.