Processing components

Langflow's processing components process and transform data within a flow. They have many uses, including:

• Feed instructions and context to your LLMs and agents with the Prompt Template component.
• Extract content from larger chunks of data with a Parser component.
• Filter data with natural language with the Smart Function component.
• Save data to your local machine with the Save File component.
• Transform data into a different data type with the Type Convert component to pass it between incompatible components.

Prompt Template

See Prompt Template.

Batch Run

The Batch Run component runs a language model over each row of one text column in a DataFrame, and then returns a new DataFrame with the original text and an LLM response.

The response contains the following columns:

• text_input: The original text from the input DataFrame
• model_response: The model's response for each input
• batch_index: The 0-indexed processing order for all rows in the DataFrame
• metadata (optional): Additional information about the processing

Use the Batch Run component in a flow

If you pass this output to a Parser component, you can use variables in the parsing template to reference these keys, such as {text_input} and {model_response}. This is demonstrated in the following example.

1. Connect a Language Model component to a Batch Run component's Language model port.

2. Connect DataFrame output from another component to the Batch Run component's DataFrame input. For example, you could connect a File component with a CSV file.

3. In the Batch Run component's Column Name field, enter the name of the column in the incoming DataFrame that contains the text to process. For example, if you want to extract text from a name column in a CSV file, enter name in the Column Name field.

4. Connect the Batch Run component's Batch Results output to a Parser component's DataFrame input.

5. Optional: In the Batch Run component's header menu, click Controls, enable the System Message parameter, click Close, and then enter an instruction for how you want the LLM to process each cell extracted from the file. For example, Create a business card for each name.

6. In the Parser component's Template field, enter a template for processing the Batch Run component's new DataFrame columns (text_input, model_response, and batch_index):

   For example, this template uses three columns from the resulting, post-batch DataFrame:

   
   

   _10

   record_number: {batch_index}, name: {text_input}, summary: {model_response}

   
   

7. To test the processing, click the Parser component, and then click Run component, and then click Inspect output to view the final DataFrame.

   You can also connect a Chat Output component to the Parser component if you want to see the output in the Playground.

Batch Run parameters

Some Batch Run component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component's header menu.

Name	Type	Description
model	HandleInput	Input parameter. Connect the 'Language Model' output from your LLM component here. Required.
system_message	MultilineInput	Input parameter. A multi-line system instruction for all rows in the DataFrame.
df	DataFrameInput	Input parameter. The DataFrame whose column is treated as text messages, as specified by 'column_name'. Required.
column_name	MessageTextInput	Input parameter. The name of the DataFrame column to treat as text messages. If empty, all columns are formatted in TOML.
output_column_name	MessageTextInput	Input parameter. Name of the column where the model's response is stored. Default=model_response.
enable_metadata	BoolInput	Input parameter. If True, add metadata to the output DataFrame.
batch_results	DataFrame	Output parameter. A DataFrame with all original columns plus the model's response column.

Data Operations

The Data Operations component performs operations on Data objects, including selecting keys, evaluating literals, combining data, filtering values, appending/updating data, removing keys, and renaming keys.

1. To use the Data Operations component in a flow, you must connect its Data input port to the output port of a component that outputs Data. All operations in the Data Operations component require at least one Data input.

   For this example, add a Webhook component to the flow, and then connect it to a Data Operations component. Assume you'll send requests to the webhook with a consistent payload that has name, username, and email keys.

2. In the Operations field, select the operation you want to perform on the incoming Data. For this example, select the Select Keys operation to extract specific user information.

3. Add keys for name, username, and email to select those values from the incoming request payload.

   To add additional keys, click Add more.

4. Connect a Chat Output component.

   

5. To test the flow, send the following request to your flow's webhook endpoint, and then open the Playground to see the resulting output from processing the payload.

   
   

   _26

   curl -X POST "http://$LANGFLOW_SERVER_URL/api/v1/webhook/$FLOW_ID" \

   _26

   -H "Content-Type: application/json" \

   _26

   -H "x-api-key: $LANGFLOW_API_KEY" \

   _26

   -d '{

   _26

   "id": 1,

   _26

   "name": "Leanne Graham",

   _26

   "username": "Bret",

   _26

   "email": "Sincere@april.biz",

   _26

   "address": {

   _26

   "street": "Main Street",

   _26

   "suite": "Apt. 556",

   _26

   "city": "Springfield",

   _26

   "zipcode": "92998-3874",

   _26

   "geo": {

   _26

   "lat": "-37.3159",

   _26

   "lng": "81.1496"

   _26

   }

   _26

   },

   _26

   "phone": "1-770-736-8031 x56442",

   _26

   "website": "hildegard.org",

   _26

   "company": {

   _26

   "name": "Acme-Corp",

   _26

   "catchPhrase": "Multi-layered client-server neural-net",

   _26

   "bs": "harness real-time e-markets"

   _26

   }

   _26

   }'

   
   

Data Operations parameters

Some Data Operations component input parameters are hidden by default in the visual editor. You can toggle parameters through the Controls in the component's header menu.

Name	Display Name	Info
data	Data	Input parameter. The Data object to operate on.
operations	Operations	Input parameter. The operation to perform on the data. See Data Operations operations
select_keys_input	Select Keys	Input parameter. A list of keys to select from the data.
filter_key	Filter Key	Input parameter. The key to filter by.
operator	Comparison Operator	Input parameter. The operator to apply for comparing values.
filter_values	Filter Values	Input parameter. A list of values to filter by.
append_update_data	Append or Update	Input parameter. The data to append or update the existing data with.
remove_keys_input	Remove Keys	Input parameter. A list of keys to remove from the data.
rename_keys_input	Rename Keys	Input parameter. A list of keys to rename in the data.
data_output	Data	Output parameter. The resulting Data object after the operation.

Data Operations operations

Options for the operations input parameter are as follows. All operations act on an incoming Data object.

Name	Required Inputs	Process
Select Keys	select_keys_input	Selects specific keys from the data.
Literal Eval	None	Evaluates string values as Python literals.
Combine	None	Combines multiple data objects into one.
Filter Values	filter_key, filter_values, operator	Filters data based on key-value pair.
Append or Update	append_update_data	Adds or updates key-value pairs.
Remove Keys	remove_keys_input	Removes specified keys from the data.
Rename Keys	rename_keys_input	Renames keys in the data.

DataFrame operations

The DataFrame component performs operations on DataFrame rows and columns.

To use the DataFrame Operations component in a flow, you must connect its DataFrame input port to the output port of a component that outputs DataFrame. All operations in the DataFrame Operations component require at least one DataFrame input.

The following example fetches JSON data from an API. The Smart Filter component extracts and flattens the results into a tabular DataFrame that is then processed through the DataFrame Operations component.

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 Smart Filter to the API request component, and a Language model to the Smart 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 Smart filter component, in the Instructions field, use natural language to describe how the data should be filtered. For this example, enter:

_10

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 Smart Filter component, click Run component.
6. To inspect the filtered data, in the Smart Filter component, click Inspect output. The result is a structured DataFrame.

_10

id | name | company | username | email | address | zip

_10

---|------------------|----------------------|-----------------|------------------------------------|-------------------|-------

_10

1 | Emily Johnson | ABC Corporation | emily_johnson | emily.johnson@abccorporation.com | 123 Main St | 12345

_10

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.

_10

name

_10

Emily Johnson

_10

Michael Williams

_10

John Smith

_10

...

Operations

This component can perform the following operations on Pandas DataFrame.

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

LLM router

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

The judge LLM analyzed your input message to understand the evaluation context, and then selects the most appropriate model from your LLM pool.

The selected model processes your input and returns the response.

To use the LLM Router component in a flow, do the following:

1. Connect multiple Language Model components to the LLM Router's Language Models input.

2. Connect a Judge LLM component to the Judge LLM input.

3. Connect Chat Input and Chat Output components to the LLM Router. The flow looks like this:

4. In the LLM Router component, set your Optimization preference:

   • Quality: Prioritizes the highest quality response.
   • Speed: Prioritizes the fastest response time.
   • Cost: Prioritizes the most cost-effective option.
   • Balanced: Strikes a balance between quality, speed, and cost.

5. Run the flow. Your input is the task that the LLM router evaluates the models against, such as Write a story about horses or How do I parse data objects out of JSON?.

6. In the LLM Router component, select the Model Selection Decision output to view the router's reasoning.

   
   

   _10

   Model Selection Decision:

   _10

   - Selected Model Index: 0

   _10

   - Selected Langflow Model Name: gpt-4o-mini

   _10

   - Selected API Model ID (if resolved): openai/gpt-4o-mini

   _10

   - Optimization Preference: cost

   _10

   - Input Query Length: 27 characters (~5 tokens)

   _10

   - Number of Models Considered: 2

   _10

   - Specifications Source: OpenRouter API

   
   

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.

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:

_10

# Employee Profile

_10

## Personal Information

_10

- **Name:** {name}

_10

- **ID:** {id}

_10

- **Email:** {email}

7. To run the flow, in the Parser component, click Run component.
8. To view your parsed text, in the Parser component, click Inspect output.
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.

Python Interpreter

This component allows you to execute Python code with imported packages.

Use the Python Interpreter in a flow

1. To use this component in a flow,in the Global Imports field, add the packages you want to import as a comma-separated list, such as math,pandas. At least one import is required.
2. In the Python Code field, enter the Python code you want to execute. Use print() to see the output.
3. Optionally, enable Tool Mode and connect the interpreter to an Agent as a tool. For example, connect a Python Interpreter and a Calculator as tools for an Agent, and test how it chooses different tools to solve math problems. The flow looks like this:
4. Ask the agent an easier math question. The Calculator tool can add, subtract, multiple, divide, or perform exponentiation. The agent executes the evaluate_expression tool to correctly answer the question.

Result:

_10

Executed evaluate_expression

_10

Input:

_10

{

_10

"expression": "2+5"

_10

}

_10

Output:

_10

{

_10

"result": "7"

_10

}

5. Give the agent complete Python code. This example creates a Pandas DataFrame table with the imported pandas packages, and returns the square root of the mean squares.

_12

import pandas as pd

_12

import math

_12

_12

# Create a simple DataFrame

_12

df = pd.DataFrame({

_12

'numbers': [1, 2, 3, 4, 5],

_12

'squares': [x**2 for x in range(1, 6)]

_12

})

_12

_12

# Calculate the square root of the mean

_12

result = math.sqrt(df['squares'].mean())

_12

print(f"Square root of mean squares: {result}")

The Agent correctly chooses the run_python_repl tool to solve the problem.

Result:

_12

Executed run_python_repl

_12

_12

Input:

_12

_12

{

_12

"python_code": "import pandas as pd\nimport math\n\n# Create a simple DataFrame\ndf = pd.DataFrame({\n 'numbers': [1, 2, 3, 4, 5],\n 'squares': [x**2 for x in range(1, 6)]\n})\n\n# Calculate the square root of the mean\nresult = math.sqrt(df['squares'].mean())\nprint(f\"Square root of mean squares: {result}\")"

_12

}

_12

Output:

_12

_12

{

_12

"result": "Square root of mean squares: 3.3166247903554"

_12

}

If you don't include the package imports in the chat, the Agent can still create the table using pd.DataFrame, because the pandas package is imported globally by the Python interpreter component in the Global Imports field.

Python Interpreter parameters

Name	Type	Description
global_imports	String	Input parameter. A comma-separated list of modules to import globally, such as math,pandas,numpy.
python_code	Code	Input parameter. The Python code to execute. Only modules specified in Global Imports can be used.
results	Data	Output parameter. The output of the executed Python code, including any printed results or errors.

Save 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.

_10

curl -X POST "http://127.0.0.1:7860/api/v1/webhook/YOUR_FLOW_ID" \

_10

-H 'Content-Type: application/json' \

_10

-H 'x-api-key: LANGFLOW_API_KEY' \

_10

-d '{

_10

"Name": ["Alex Cruz", "Kalani Smith", "Noam Johnson"],

_10

"Role": ["Developer", "Designer", "Manager"],

_10

"Department": ["Engineering", "Design", "Management"]

_10

}'

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.

_10

| Name | Role | Department |

_10

|:-------------|:----------|:-------------|

_10

| Alex Cruz | Developer | Engineering |

_10

| Kalani Smith | Designer | Design |

_10

| 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.

Smart function

tip

Prior to Langflow 1.5, this component was named the Lambda filter.

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

To use the Smart function 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 Smart function 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.

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 },.

_16

{

_16

"userId": 1,

_16

"id": 1,

_16

"title": "Introduction to Artificial Intelligence",

_16

"body": "Learn the basics of Artificial Intelligence and its applications in various industries.",

_16

"link": "https://example.com/article1",

_16

"comment_count": 8

_16

},

_16

{

_16

"userId": 2,

_16

"id": 2,

_16

"title": "Web Development with React",

_16

"body": "Build modern web applications using React.js and explore its powerful features.",

_16

"link": "https://example.com/article2",

_16

"comment_count": 12

_16

},

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.

_10

First chunk: "title": "Introduction to Artificial Intelligence""

_10

Second chunk: "elligence", "body": "Learn the basics of Artif"

_10

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.

Other text splitters

• LangChain text splitter components

Structured output

This component transforms LLM responses into structured data formats.

In this example from the Financial Support Parser template, the Structured Output component transforms unstructured financial reports into structured data.

The connected LLM model is prompted by the Structured Output component's Format Instructions parameter to extract structured output from the unstructured text. Format Instructions is utilized as the system prompt for the Structured Output component.

In the Structured Output component, click the Open table button to view the Output Schema table. The Output Schema parameter defines the structure and data types for the model's output using a table with the following fields:

• Name: The name of the output field.
• Description: The purpose of the output field.
• Type: The data type of the output field. The available types are str, int, float, bool, list, or dict. The default is text.
• Multiple: This feature is deprecated. Currently, it is set to True by default if you expect multiple values for a single field. For example, a list of features is set to True to contain multiple values, such as ["waterproof", "durable", "lightweight"]. Default: True.

The Parse DataFrame component parses the structured output into a template for orderly presentation in chat output. The template receives the values from the output_schema table with curly braces.

For example, the template EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS_PROFIT: {GROSS_PROFIT} presents the extracted values in the Playground as EBITDA: 900 million , Net Income: 500 million , GROSS_PROFIT: 1.2 billion.

Parameters

Inputs

Name	Type	Description
llm	LanguageModel	The language model to use to generate the structured output.
input_value	String	The input message to the language model.
system_prompt	String	The instructions to the language model for formatting the output.
schema_name	String	The name for the output data schema.
output_schema	Table	The structure and data types for the model's output.
multiple	Boolean	[Deprecated] Always set to True.

Outputs

Name	Type	Description
structured_output	Data	The structured output is a Data object based on the defined schema.

Type convert

This component converts data types between different formats. It can transform data between Data, DataFrame, and Message objects.

• Data: A structured object that contains both text and metadata.

_10

{

_10

"text": "User Profile",

_10

"data": {

_10

"name": "John Smith",

_10

"age": 30,

_10

"email": "john@example.com"

_10

}

_10

}

• DataFrame: A tabular data structure with rows and columns. Keys are columns, and each dictionary (a collection of key-value pairs) in the list is a row.

_12

[

_12

{

_12

"name": "John Smith",

_12

"age": 30,

_12

"email": "john@example.com"

_12

},

_12

{

_12

"name": "Jane Doe",

_12

"age": 25,

_12

"email": "jane@example.com"

_12

}

_12

]

• Message: A string, such as"Name: John Smith, Age: 30, Email: john@example.com".

To use this component in a flow, do the following:

1. Add the Web search component to the Basic prompting flow. In the Search Query field, enter a query, such as environmental news.
2. Connect the Web search component's output to a component that accepts the DataFrame input. This example uses a Prompt component to give the chatbot context, so you must convert the Web search component's DataFrame output to a Message type.
3. Connect a Type Convert component to convert the DataFrame to a Message.
4. In the Type Convert component, in the Output Type field, select Message. Your flow looks like this:

5. In the Language Model component, in the OpenAI API Key field, add your OpenAI API key.
6. Click Playground, and then ask about latest news.

The search results are returned to the Playground as a message.

Result:

_10

Latest news

_10

AI

_10

gpt-4o-mini

_10

Here are some of the latest news articles related to the environment:

_10

Ozone Pollution and Global Warming: A recent study highlights that ozone pollution is a significant global environmental concern, threatening human health and crop production while exacerbating global warming. Read more

_10

...

Parameters

Inputs

Name	Display Name	Info
input_data	Input Data	The data to convert. Accepts Data, DataFrame, or Message objects.
output_type	Output Type	The desired output type. Options: Data, DataFrame, or Message.

Outputs

Name	Display Name	Info
output	Output	The converted data in the specified format.

Legacy processing components

The following processing components are legacy components. You can still use them in your flows, but they are no longer supported and can be removed in a future release.

Replace these components with suggested alternatives as soon as possible.

Alter Metadata

Replace this legacy component with the Data Operations component.

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.

It accepts the following parameters:

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

Combine Data/Merge Data

Replace this legacy component with the Data Operations component or the Loop component.

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

The component iterates through a list of Data objects, merging them into a single Data object (merged_data). 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.

Combine Text

Replace this legacy component with the Data Operations component.

This component concatenates two text inputs into a single text chunk using a specified delimiter, outputting a Message object with the combined text.

Create Data

Replace this legacy component with the Data Operations component.

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

It accepts the following parameters:

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

Extract Key

Replace this legacy component with the Data Operations component.

This component extracts a specific key from a Data object and returns the value associated with that key.

Data to DataFrame/Data to Message

Replace these legacy components with newer processing components, such as the Data Operations component and Type Convert component.

These components converted one or more Data objects into a DataFrame or Message object.

For the Data to DataFrame component, 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.

Filter Data

Replace this legacy component with the Data Operations component.

This component filters a Data object based on a list of keys (filter_criteria), returning a new Data object (filtered_data) that contains only the key-value pairs that match the filter criteria.

Filter Values

Replace this legacy component with the Data Operations component.

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

It accepts the following parameters:

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

JSON Cleaner

Replace this legacy component with the Parser component.

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

It accepts the following parameters:

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

Message to Data

Replace this legacy component with the Type Convert component.

This component converts Message objects to Data objects.

Parse DataFrame

Replace this legacy component with the DataFrame Operations component or Parser component.

This component converts DataFrame objects into plain text using templates.

It accepts the following parameters:

Name	Display Name	Info
df	DataFrame	Input parameter. The DataFrame to convert to text rows.
template	Template	Input parameter. Template for formatting (use {column_name} placeholders).
sep	Separator	Input parameter. String to join rows in output.
text	Text	Output parameter. All rows combined into single text.

Parse JSON

Replace this legacy component with the Parser component.

This component converts and extracts JSON fields in Message and Data objects using JQ queries, then returns filtered_data, which is a list of Data objects.

Python REPL

Replace this legacy component with the Python Interpreter component or another processing or logic component.

This component creates a Python REPL (Read-Eval-Print Loop) tool for executing Python code.

It accepts the following parameters:

Name	Type	Description
name	String	Input parameter. The name of the tool. Default: python_repl.
description	String	Input parameter. A description of the tool's functionality.
global_imports	List[String]	Input parameter. A list of modules to import globally. Default: math.
tool	Tool	Output parameter. A Python REPL tool for use in LangChain.

Python Code Structured

Replace this legacy component with the Python Interpreter component or another processing or logic component.

This component creates a structured tool from Python code using a dataclass.

The component dynamically updates its configuration based on the provided Python code, allowing for custom function arguments and descriptions.

It accepts the following parameters:

Name	Type	Description
tool_code	String	Input parameter. The Python code for the tool's dataclass.
tool_name	String	Input parameter. The name of the tool.
tool_description	String	Input parameter. The description of the tool.
return_direct	Boolean	Input parameter. Whether to return the function output directly.
tool_function	String	Input parameter. The selected function for the tool.
global_variables	Dict	Input parameter. Global variables or data for the tool.
result_tool	Tool	Output parameter. A structured tool created from the Python code.

Regex Extractor

Replace this legacy component with the Parser component.

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

Select Data

Replace this legacy component with the Data Operations component.

This component selects a single Data object from a list.

It accepts the following parameters:

Name	Display Name	Info
data_list	Data List	Input parameter. List of data to select from
data_index	Data Index	Input parameter. Index of the data to select
selected_data	Selected Data	Output parameter. The selected Data object.

Update Data

Replace this legacy component with the Data Operations component.

This component dynamically updates or appends data with specified fields.

It accepts the following parameters:

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