langflow/docs/docs/Components/components-processing.md

title	slug
Processing	/components-processing

import Icon from "@site/src/components/icon";

Processing components process and transform data within a flow.

Use a processing component in a flow

The Split Text processing component in this flow splits the incoming Data into chunks to be embedded into the vector store component.

The component offers control over chunk size, overlap, and separator, which affect context and granularity in vector store retrieval results.

Combine data

:::important Prior to Langflow version 1.1.3, this component was named Merge Data. :::

This component combines multiple data sources into a single unified Data object.

The component iterates through the input list of data objects, merging them into a single data object. If the input list is empty, it returns an empty data object. If there's only one input data object, it returns that object unchanged. The merging process uses the addition operator to combine data objects.

Parameters

Inputs

Name	Display Name	Info
data	Data	A list of data objects to be merged.

Outputs

Name	Display Name	Info
merged_data	Merged Data	A single Data object containing the combined information from all input data objects.

Combine text

This component concatenates two text sources into a single text chunk using a specified delimiter.

1. To use this component in a flow, connect two components that output Messages to the Combine Text component's First Text and Second Text inputs. This example uses two Text Input components.

2. In the Combine Text component, in the Text fields of both Text Input components, enter some text to combine.
3. In the Combine Text component, enter an optional Delimiter value. The delimiter character separates the combined texts. This example uses \n\n **end first text** \n\n **start second text** \n\n to label the texts and create newlines between them.
4. Connect a Chat Output component to view the text combination.
5. Click Playground, and then click Run Flow. The combined text appears in the Playground.

This is the first text. Let's combine text!
end first text
start second text
Here's the second part. We'll see how combining text works.

Parameters

Inputs

Name	Display Name	Info
first_text	First Text	The first text input to concatenate.
second_text	Second Text	The second text input to concatenate.
delimiter	Delimiter	A string used to separate the two text inputs. The default is a space.

Outputs

Name	Display Name	Info
message	Message	A Message object containing the combined text.

DataFrame operations

This component performs operations on DataFrame rows and columns.

To use this component in a flow, connect a component that outputs DataFrame to the DataFrame Operations component.

This example fetches JSON data from an API. The Lambda filter component extracts and flattens the results into a tabular DataFrame. The DataFrame Operations component can then work with the retrieved data.

1. The API Request component retrieves data with only source and result fields. For this example, the desired data is nested within the result field.
2. Connect a Lambda Filter to the API request component, and a Language model to the Lambda Filter. This example connects a Groq model component.
3. In the Groq model component, add your Groq API key.
4. To filter the data, in the Lambda filter component, in the Instructions field, use natural language to describe how the data should be filtered. For this example, enter:

I want to explode the result column out into a Data object

:::tip Avoid punctuation in the Instructions field, as it can cause errors. ::: 5. To run the flow, in the Lambda Filter component, click . 6. To inspect the filtered data, in the Lambda Filter component, click . The result is a structured DataFrame.

id | name             | company               | username        | email                              | address           | zip
---|------------------|----------------------|-----------------|------------------------------------|-------------------|-------
1  | Emily Johnson    | ABC Corporation      | emily_johnson   | emily.johnson@abccorporation.com   | 123 Main St       | 12345
2  | Michael Williams | XYZ Corp             | michael_williams| michael.williams@xyzcorp.com       | 456 Elm Ave       | 67890

7. Add the DataFrame Operations component, and a Chat Output component to the flow.
8. In the DataFrame Operations component, in the Operation field, select Filter.
9. To apply a filter, in the Column Name field, enter a column to filter on. This example filters by name.
10. Click Playground, and then click Run Flow. The flow extracts the values from the name column.

name
Emily Johnson
Michael Williams
John Smith
...

Operations

This component can perform the following operations on Pandas DataFrame.

Operation	Description	Required Inputs
Add Column	Adds a new column with a constant value	new_column_name, new_column_value
Drop Column	Removes a specified column	column_name
Filter	Filters rows based on column value	column_name, filter_value
Head	Returns first n rows	num_rows
Rename Column	Renames an existing column	column_name, new_column_name
Replace Value	Replaces values in a column	column_name, replace_value, replacement_value
Select Columns	Selects specific columns	columns_to_select
Sort	Sorts DataFrame by column	column_name, ascending
Tail	Returns last n rows	num_rows

Parameters

Inputs

Name	Display Name	Info
df	DataFrame	The input DataFrame to operate on.
operation	Operation	The DataFrame operation to perform. Options include Add Column, Drop Column, Filter, Head, Rename Column, Replace Value, Select Columns, Sort, and Tail.
column_name	Column Name	The column name to use for the operation.
filter_value	Filter Value	The value to filter rows by.
ascending	Sort Ascending	Whether to sort in ascending order.
new_column_name	New Column Name	The new column name when renaming or adding a column.
new_column_value	New Column Value	The value to populate the new column with.
columns_to_select	Columns to Select	A list of column names to select.
num_rows	Number of Rows	The number of rows to return for head/tail operations. The default is 5.
replace_value	Value to Replace	The value to replace in the column.
replacement_value	Replacement Value	The value to replace with.

Outputs

Name	Display Name	Info
output	DataFrame	The resulting DataFrame after the operation.

Data to DataFrame

This component converts one or multiple Data objects into a DataFrame. Each Data object corresponds to one row in the resulting DataFrame. Fields from the .data attribute become columns, and the .text field (if present) is placed in a 'text' column.

1. To use this component in a flow, connect a component that outputs Data to the Data to Dataframe component's input. This example connects a Webhook component to convert text and data into a DataFrame.
2. To view the flow's output, connect a Chat Output component to the Data to Dataframe component.

3. Send a POST request to the Webhook containing your JSON data. Replace YOUR_FLOW_ID with your flow ID. This example uses the default Langflow server address.

curl -X POST "http://127.0.0.1:7860/api/v1/webhook/YOUR_FLOW_ID" \
-H 'Content-Type: application/json' \
-d '{
    "text": "Alex Cruz - Employee Profile",
    "data": {
        "Name": "Alex Cruz",
        "Role": "Developer",
        "Department": "Engineering"
    }
}'

4. In the Playground, view the output of your flow. The Data to DataFrame component converts the webhook request into a DataFrame, with text and data fields as columns.

| text                         | data                                                                    |
|:-----------------------------|:------------------------------------------------------------------------|
| Alex Cruz - Employee Profile | {'Name': 'Alex Cruz', 'Role': 'Developer', 'Department': 'Engineering'} |

5. Send another employee data object.

curl -X POST "http://127.0.0.1:7860/api/v1/webhook/YOUR_FLOW_ID" \
-H 'Content-Type: application/json' \
-d '{
    "text": "Kalani Smith - Employee Profile",
    "data": {
        "Name": "Kalani Smith",
        "Role": "Designer",
        "Department": "Design"
    }
}'

6. In the Playground, this request is also converted to DataFrame.

| text                            | data                                                                 |
|:--------------------------------|:---------------------------------------------------------------------|
| Kalani Smith - Employee Profile | {'Name': 'Kalani Smith', 'Role': 'Designer', 'Department': 'Design'} |

Parameters

Inputs

Name	Display Name	Info
data_list	Data or Data List	One or multiple Data objects to transform into a DataFrame.

Outputs

Name	Display Name	Info
dataframe	DataFrame	A DataFrame built from each Data object's fields plus a text column.

Filter data

:::important This component is in Beta as of Langflow version 1.1.3, and is not yet fully supported. :::

This component filters a Data object based on a list of keys.

Parameters

Inputs

Name	Display Name	Info
data	Data	The Data object to filter.
filter_criteria	Filter Criteria	A list of keys to filter by.

Outputs

Name	Display Name	Info
filtered_data	Filtered Data	A new Data object containing only the key-value pairs that match the filter criteria.

Filter values

:::important This component is in Beta as of Langflow version 1.1.3, and is not yet fully supported. :::

The Filter values component filters a list of data items based on a specified key, filter value, and comparison operator.

Parameters

Inputs

Name	Display Name	Info
input_data	Input data	The list of data items to filter.
filter_key	Filter Key	The key to filter on.
filter_value	Filter Value	The value to filter by.
operator	Comparison Operator	The operator to apply for comparing the values.

Outputs

Name	Display Name	Info
filtered_data	Filtered data	The resulting list of filtered data items.

Lambda filter

This component uses an LLM to generate a Lambda function for filtering or transforming structured data.

To use the Lambda filter component, you must connect it to a Language Model component, which the component uses to generate a function based on the natural language instructions in the Instructions field.

This example gets JSON data from the https://jsonplaceholder.typicode.com/users API endpoint. The Instructions field in the Lambda filter component specifies the task extract emails. The connected LLM creates a filter based on the instructions, and successfully extracts a list of email addresses from the JSON data.

Parameters

Inputs

Name	Display Name	Info
data	Data	The structured data to filter or transform using a Lambda function.
llm	Language Model	The connection port for a Model component.
filter_instruction	Instructions	The natural language instructions for how to filter or transform the data using a Lambda function, such as Filter the data to only include items where the 'status' is 'active'.
sample_size	Sample Size	For large datasets, the number of characters to sample from the dataset head and tail.
max_size	Max Size	The number of characters for the data to be considered "large", which triggers sampling by the sample_size value.

Outputs

Name	Display Name	Info
filtered_data	Filtered Data	The filtered or transformed Data object.
dataframe	DataFrame	The filtered data as a DataFrame.

LLM router

This component routes requests to the most appropriate LLM based on OpenRouter model specifications.

Parameters

Inputs

Name	Display Name	Info
models	Language Models	A list of LLMs to route between.
input_value	Input	The input message to be routed.
judge_llm	Judge LLM	The LLM that evaluates and selects the most appropriate model.
optimization	Optimization	The optimization preference between quality, speed, cost, or balanced.

Outputs

Name	Display Name	Info
output	Output	The response from the selected model.
selected_model	Selected Model	The name of the chosen model.

Message to data

This component converts Message objects to Data objects.

Parameters

Inputs

Name	Display Name	Info
message	Message	The Message object to convert to a Data object.

Outputs

Name	Display Name	Info
data	Data	The converted Data object.

Parser

This component formats DataFrame or Data objects into text using templates, with an option to convert inputs directly to strings using stringify.

To use this component, create variables for values in the template the same way you would in a Prompt component. For DataFrames, use column names, for example Name: {Name}. For Data objects, use {text}.

To use the Parser component with a Structured Output component, do the following:

1. Connect a Structured Output component's DataFrame output to the Parser component's DataFrame input.
2. Connect the File component to the Structured Output component's Message input.
3. Connect the OpenAI model component's Language Model output to the Structured Output component's Language Model input.

The flow looks like this:

4. In the Structured Output component, click Open Table. This opens a pane for structuring your table. The table contains the rows Name, Description, Type, and Multiple.
5. Create a table that maps to the data you're loading from the File loader. For example, to create a table for employees, you might have the rows id, name, and email, all of type string.
6. In the Template field of the Parser component, enter a template for parsing the Structured Output component's DataFrame output into structured text. Create variables for values in the template the same way you would in a Prompt component. For example, to present a table of employees in Markdown:

# Employee Profile
## Personal Information
- **Name:** {name}
- **ID:** {id}
- **Email:** {email}

7. To run the flow, in the Parser component, click .
8. To view your parsed text, in the Parser component, click .
9. Optionally, connect a Chat Output component, and open the Playground to see the output.

For an additional example of using the Parser component to format a DataFrame from a Structured Output component, see the Market Research template flow.

Parameters

Inputs

Name	Display Name	Info
mode	Mode	The tab selection between "Parser" and "Stringify" modes. "Stringify" converts input to a string instead of using a template.
pattern	Template	The template for formatting using variables in curly brackets. For DataFrames, use column names, such as Name: {Name}. For Data objects, use {text}.
input_data	Data or DataFrame	The input to parse. Accepts either a DataFrame or Data object.
sep	Separator	The string used to separate rows or items. The default is a newline.
clean_data	Clean Data	When stringify is enabled, this option cleans data by removing empty rows and lines.

Outputs

Name	Display Name	Info
parsed_text	Parsed Text	The resulting formatted text as a Message object.

Regex extractor

This component extracts patterns from text using regular expressions. It can be used to find and extract specific patterns or information from text data.

To use this component in a flow:

1. Connect the Regex Extractor to a URL component and a Chat Output component.

2. In the Regex Extractor tool, enter a pattern to extract text from the URL component's raw output. This example extracts the first paragraph from the "In the News" section of https://en.wikipedia.org/wiki/Main_Page:

In the news\s*\n(.*?)(?=\n\n)

Result:

Peruvian writer and Nobel Prize in Literature laureate Mario Vargas Llosa (pictured) dies at the age of 89.

Save to File

This component saves DataFrames, Data, or Messages to various file formats.

1. To use this component in a flow, connect a component that outputs DataFrames, Data, or Messages to the Save to File component's input. The following example connects a Webhook component to two Save to File components to demonstrate the different outputs.

2. In the Save to File component's Input Type field, select the expected input type. This example expects Data from the Webhook.
3. In the File Format field, select the file type for your saved file. This example uses .md in one Save to File component, and .xlsx in another.
4. In the File Path field, enter the path for your saved file. This example uses ./output/employees.xlsx and ./output/employees.md to save the files in a directory relative to where Langflow is running. The component accepts both relative and absolute paths, and creates any necessary directories if they don't exist. :::tip If you enter a format in the file_path that is not accepted, the component appends the proper format to the file. For example, if the selected file_format is csv, and you enter file_path as ./output/test.txt, the file is saved as ./output/test.txt.csv so the file is not corrupted. :::
5. Send a POST request to the Webhook containing your JSON data. Replace YOUR_FLOW_ID with your flow ID. This example uses the default Langflow server address.

curl -X POST "http://127.0.0.1:7860/api/v1/webhook/YOUR_FLOW_ID" \
-H 'Content-Type: application/json' \
-d '{
    "Name": ["Alex Cruz", "Kalani Smith", "Noam Johnson"],
    "Role": ["Developer", "Designer", "Manager"],
    "Department": ["Engineering", "Design", "Management"]
}'

6. In your local filesystem, open the outputs directory. You should see two files created from the data you've sent: one in .xlsx for structured spreadsheets, and one in Markdown.

| Name         | Role      | Department   |
|:-------------|:----------|:-------------|
| Alex Cruz    | Developer | Engineering  |
| Kalani Smith | Designer  | Design       |
| Noam Johnson | Manager   | Management   |

File input format options

For DataFrame and Data inputs, the component can create:

• csv
• excel
• json
• markdown
• pdf

For Message inputs, the component can create:

• txt
• json
• markdown
• pdf

Parameters

Inputs

Name	Display Name	Info
input_text	Input Text	The text to analyze and extract patterns from.
pattern	Regex Pattern	The regular expression pattern to match in the text.
input_type	Input Type	The type of input to save.
df	DataFrame	The DataFrame to save.
data	Data	The Data object to save.
message	Message	The Message to save.
file_format	File Format	The file format to save the input in.
file_path	File Path	The full file path including filename and extension.

Outputs

Name	Display Name	Info
data	Data	A list of extracted matches as Data objects.
text	Message	The extracted matches formatted as a Message object.
confirmation	Confirmation	The confirmation message after saving the file.

Split text

This component splits text into chunks based on specified criteria. It's ideal for chunking data to be tokenized and embedded into vector databases.

The Split Text component outputs Chunks or DataFrame. The Chunks output returns a list of individual text chunks. The DataFrame output returns a structured data format, with additional text and metadata columns applied.

1. To use this component in a flow, connect a component that outputs Data or DataFrame to the Split Text component's Data port. This example uses the URL component, which is fetching JSON placeholder data.

2. In the Split Text component, define your data splitting parameters.

This example splits incoming JSON data at the separator },, so each chunk contains one JSON object.

The order of precedence is Separator, then Chunk Size, and then Chunk Overlap. If any segment after separator splitting is longer than chunk_size, it is split again to fit within chunk_size.

After chunk_size, Chunk Overlap is applied between chunks to maintain context.

3. Connect a Chat Output component to the Split Text component's DataFrame output to view its output.
4. Click Playground, and then click Run Flow. The output contains a table of JSON objects split at },.

{
"userId": 1,
"id": 1,
"title": "Introduction to Artificial Intelligence",
"body": "Learn the basics of Artificial Intelligence and its applications in various industries.",
"link": "https://example.com/article1",
"comment_count": 8
},
{
"userId": 2,
"id": 2,
"title": "Web Development with React",
"body": "Build modern web applications using React.js and explore its powerful features.",
"link": "https://example.com/article2",
"comment_count": 12
},

5. Clear the Separator field, and then run the flow again. Instead of JSON objects, the output contains 50-character lines of text with 10 characters of overlap.

First chunk:  "title": "Introduction to Artificial Intelligence""
Second chunk: "elligence", "body": "Learn the basics of Artif"
Third chunk:  "s of Artificial Intelligence and its applications"

Parameters

Inputs

Name	Display Name	Info
data_inputs	Input Documents	The data to split. The component accepts Data or DataFrame objects.
chunk_overlap	Chunk Overlap	The number of characters to overlap between chunks. Default: 200.
chunk_size	Chunk Size	The maximum number of characters in each chunk. Default: 1000.
separator	Separator	The character to split on. Default: newline.
text_key	Text Key	The key to use for the text column. Default: text.

Outputs

Name	Display Name	Info
chunks	Chunks	A list of split text chunks as Data objects.
dataframe	DataFrame	A list of split text chunks as DataFrame objects.

Update data

This component dynamically updates or appends data with specified fields.

Parameters

Inputs

Name	Display Name	Info
old_data	Data	The records to update.
number_of_fields	Number of Fields	The number of fields to add. The maximum is 15.
text_key	Text Key	The key for text content.
text_key_validator	Text Key Validator	Validates the text key presence.

Outputs

Name	Display Name	Info
data	Data	The updated Data objects.

Legacy components

Legacy components are available for use but are no longer supported.

Alter metadata

This component modifies metadata of input objects. It can add new metadata, update existing metadata, and remove specified metadata fields. The component works with both Message and Data objects, and can also create a new Data object from user-provided text.

Parameters

Inputs

Name	Display Name	Info
input_value	Input	Objects to which Metadata should be added
text_in	User Text	Text input; the value is contained in the 'text' attribute of the Data object. Empty text entries are ignored.
metadata	Metadata	Metadata to add to each object
remove_fields	Fields to Remove	Metadata fields to remove

Outputs

Name	Display Name	Info
data	Data	List of Input objects, each with added metadata

Create data

:::important This component is in Legacy, which means it is no longer in active development as of Langflow version 1.1.3. :::

This component dynamically creates a Data object with a specified number of fields.

Parameters

Inputs

Name	Display Name	Info
number_of_fields	Number of Fields	The number of fields to be added to the record.
text_key	Text Key	Key that identifies the field to be used as the text content.
text_key_validator	Text Key Validator	If enabled, checks if the given Text Key is present in the given Data.

Outputs

Name	Display Name	Info
data	Data	A Data object created with the specified fields and text key.

JSON cleaner

The JSON cleaner component cleans JSON strings to ensure they are fully compliant with the JSON specification.

Parameters

Inputs

Name	Display Name	Info
json_str	JSON String	The JSON string to be cleaned. This can be a raw, potentially malformed JSON string produced by language models or other sources that may not fully comply with JSON specifications.
remove_control_chars	Remove Control Characters	If set to True, this option removes control characters (ASCII characters 0-31 and 127) from the JSON string. This can help eliminate invisible characters that might cause parsing issues or make the JSON invalid.
normalize_unicode	Normalize Unicode	When enabled, this option normalizes Unicode characters in the JSON string to their canonical composition form (NFC). This ensures consistent representation of Unicode characters across different systems and prevents potential issues with character encoding.
validate_json	Validate JSON	If set to True, this option attempts to parse the JSON string to ensure it is well-formed before applying the final repair operation. It raises a ValueError if the JSON is invalid, allowing for early detection of major structural issues in the JSON.

Outputs

Name	Display Name	Info
output	Cleaned JSON String	The resulting cleaned, repaired, and validated JSON string that fully complies with the JSON specification.

Parse DataFrame

:::important This component is in Legacy, which means it is no longer in active development as of Langflow version 1.3. Instead, use the Parser component. :::

This component converts DataFrames into plain text using templates.

Parameters

Inputs

Name	Display Name	Info
df	DataFrame	The DataFrame to convert to text rows.
template	Template	Template for formatting (use {column_name} placeholders).
sep	Separator	String to join rows in output.

Outputs

Name	Display Name	Info
text	Text	All rows combined into single text.

Parse JSON

:::important This component is in Legacy, which means it is no longer in active development as of Langflow version 1.1.3. :::

This component converts and extracts JSON fields using JQ queries.

Parameters

Inputs

Name	Display Name	Info
input_value	Input	Data object to filter (Message or Data).
query	JQ Query	JQ Query to filter the data

Outputs

Name	Display Name	Info
filtered_data	Filtered Data	Filtered data as list of Data objects.

Select data

:::important This component is in Legacy, which means it is no longer in active development as of Langflow version 1.1.3. :::

This component selects a single Data item from a list.

Parameters

Inputs

Name	Display Name	Info
data_list	Data List	List of data to select from
data_index	Data Index	Index of the data to select

Outputs

Name	Display Name	Info
selected_data	Selected Data	The selected Data object.