docs: remove Guides, add pages on other categories, add Types (#4881)
* Adds Chat Memory on Components * Removes Guides * Adds Types * Fix links that referenced Guides * combine-files * redirects * content * fix(docs): update sidebar position and redirect path in configuration files * feat(docs): update memory components documentation and sidebar links * feat(docs): enhance configuration documentation with ContentBlock and DataFrame examples * add-link-for-pydantic-baseclass * update-links --------- Co-authored-by: Mendon Kissling <59585235+mendonk@users.noreply.github.com>
This commit is contained in:
Lucas Oliveira
GitHub
parent
d790761ff0
commit
a6bebd5fcb
15 changed files with 246 additions and 254 deletions
206
docs/docs/Configuration/configuration-objects.md
Normal file
206
docs/docs/Configuration/configuration-objects.md
Normal file
|
|
@ -0,0 +1,206 @@
|
|||
---
|
||||
title: Langflow objects
|
||||
slug: /configuration-objects
|
||||
---
|
||||
|
||||
In Langflow, the Data and Message objects are Pydantic models that serve as structured, functional representations of data.
|
||||
|
||||
## Data object
|
||||
|
||||
The `Data` object is a [Pydantic](https://docs.pydantic.dev/latest/api/base_model/) model that serves as a container for storing and manipulating data. It carries `data`—a dictionary that can be accessed as attributes—and uses `text_key` to specify which key in the dictionary should be considered the primary text content.
|
||||
|
||||
- **Main Attributes:**
|
||||
- `text_key`: Specifies the key to retrieve the primary text data.
|
||||
- `data`: A dictionary to store additional data.
|
||||
- `default_value`: default value when the `text_key` is not present in the `data` dictionary.
|
||||
|
||||
### Create a Data Object
|
||||
|
||||
Create a `Data` object by directly assigning key-value pairs to it. For example:
|
||||
|
||||
```python
|
||||
from langflow.schema import Data
|
||||
|
||||
# Creating a Data object with specified key-value pairs
|
||||
data = Data(text="my_string", bar=3, foo="another_string")
|
||||
|
||||
# Outputs:
|
||||
print(data.text) # Outputs: "my_string"
|
||||
print(data.bar) # Outputs: 3
|
||||
print(data.foo) # Outputs: "another_string"
|
||||
```
|
||||
|
||||
The `text_key` specifies which key in the `data` dictionary should be considered the primary text content. The `default_value` provides a fallback if the `text_key` is not present.
|
||||
|
||||
```python
|
||||
# Creating a Data object with a specific text_key and default_value
|
||||
data = Data(data={"title": "Hello, World!"}, text_key="content", default_value="No content available")
|
||||
|
||||
# Accessing the primary text using text_key and default_value
|
||||
print(data.get_text()) # Outputs: "No content available" because "content" key is not in the data dictionary
|
||||
|
||||
# Accessing data keys by calling the attribute directly
|
||||
print(data.title) # Outputs: "Hello, World!" because "title" key is in the data dictionary
|
||||
```
|
||||
|
||||
The `Data` object is also convenient for visualization of outputs, since the output preview has visual elements to inspect data as a table and its cells as pop ups for basic types. The idea is to create a unified way to work and visualize complex information in Langflow.
|
||||
|
||||
To receive `Data` objects in a component input, use the `DataInput` input type.
|
||||
|
||||
```python
|
||||
inputs = [
|
||||
DataInput(name="data", display_name="Data", info="Helpful info about the incoming data object.", is_list=True),
|
||||
]
|
||||
```
|
||||
|
||||
## Message object
|
||||
|
||||
The `Message` object extends the functionality of `Data` and includes additional attributes and methods for chat interactions.
|
||||
|
||||
- **Core message data:**
|
||||
|
||||
- `text`: The main text content of the message
|
||||
- `sender`: Identifier for the sender ("User" or "AI")
|
||||
- `sender_name`: Name of the sender
|
||||
- `session_id`: Identifier for the chat session (`string` or `UUID`)
|
||||
- `timestamp`: Timestamp when the message was created (UTC)
|
||||
- `flow_id`: Identifier for the flow (`string` or `UUID`)
|
||||
- `id`: Unique identifier for the message
|
||||
|
||||
- **Content and files:**
|
||||
|
||||
- `files`: List of files or images associated with the message
|
||||
- `content_blocks`: List of structured content block objects
|
||||
- `properties`: Additional properties including visual styling and source information
|
||||
|
||||
- **Message state:**
|
||||
- `error`: Boolean indicating if there was an error
|
||||
- `edit`: Boolean indicating if the message was edited
|
||||
- `category`: Message category ("message", "error", "warning", "info")
|
||||
|
||||
The `Message` object can be used to send, store, and manipulate chat messages within Langflow.
|
||||
|
||||
### Create a Message object
|
||||
|
||||
You can create a `Message` object by directly assigning key-value pairs to it. For example:
|
||||
|
||||
```python
|
||||
from langflow.schema.message import Message
|
||||
|
||||
message = Message(text="Hello, AI!", sender="User", sender_name="John Doe")
|
||||
```
|
||||
|
||||
To receive `Message` objects in a component input, you can use the `MessageInput` input type or `MessageTextInput` when the goal is to extract just the `text` field of the `Message` object.
|
||||
|
||||
## ContentBlock object
|
||||
|
||||
The `ContentBlock` object is a list of multiple `ContentTypes`. It allows you to include multiple types of content within a single `Message`, including images, videos, and text.
|
||||
|
||||
Content types are Pydantic base classes constructed from the types in [content_types.py](https://github.com/langflow-ai/langflow/blob/main/src/backend/base/langflow/schema/content_types.py).
|
||||
|
||||
Each content type has specific fields related to its data type. For example:
|
||||
|
||||
* `TextContent` has a `text` field for storing strings of text
|
||||
* `MediaContent` has a `urls` field for storing media file URLs
|
||||
* `CodeContent` has `code` and `language` fields for code snippets
|
||||
* `JSONContent` has a `data` field for storing arbitrary JSON data
|
||||
* `ToolContent` has a `tool_input` field for storing input parameters for the tool
|
||||
|
||||
### Create a ContentBlock object
|
||||
|
||||
Create a `ContentBlock` object with a list of different content types.
|
||||
|
||||
```python
|
||||
content_block = ContentBlock(
|
||||
title="Mixed Content Example",
|
||||
contents=[
|
||||
TextContent(text="This is a text content"),
|
||||
MediaContent(urls=["http://example.com/image.jpg"]),
|
||||
JSONContent(data={"key": "value"}),
|
||||
CodeContent(code="print('Hello')", language="python")
|
||||
],
|
||||
media_url=["http://example.com/additional_image.jpg"]
|
||||
)
|
||||
```
|
||||
|
||||
### Add ContentBlocks objects to a message
|
||||
|
||||
In this example, a text and a media `ContentBlock` are added to a message.
|
||||
|
||||
```python
|
||||
from langflow.schema.message import Message
|
||||
from langflow.schema.content_block import ContentBlock
|
||||
from langflow.schema.content_types import TextContent, MediaContent
|
||||
|
||||
message = Message(
|
||||
text="Main message text",
|
||||
sender="User",
|
||||
sender_name="John Doe",
|
||||
content_blocks=[
|
||||
ContentBlock(
|
||||
title="Text Block",
|
||||
contents=[
|
||||
TextContent(type="text", text="This is some text content")
|
||||
]
|
||||
),
|
||||
ContentBlock(
|
||||
title="Media Block",
|
||||
contents=[
|
||||
MediaContent(type="media", urls=["http://example.com/image.jpg"])
|
||||
]
|
||||
)
|
||||
]
|
||||
)
|
||||
```
|
||||
|
||||
## DataFrame object
|
||||
|
||||
The `DataFrame` class is a custom extension of the pandas' [DataFrame](https://pandas.pydata.org/docs/reference/api/pandas.DataFrame.html) class, specifically designed to work seamlessly with Langflow's `Data` objects. The class includes methods for converting between `DataFrame` and lists of `Data` objects.
|
||||
|
||||
A `DataFrame` object accepts various input formats, including lists of `Data` objects, dictionaries, and existing pandas DataFrames.
|
||||
|
||||
### Create a DataFrame object
|
||||
|
||||
You can create a DataFrame object using different data formats:
|
||||
|
||||
```python
|
||||
from langflow.schema import Data
|
||||
from langflow.schema.data import DataFrame
|
||||
|
||||
# From a list of Data objects
|
||||
data_list = [Data(data={"name": "John"}), Data(data={"name": "Jane"})]
|
||||
df = DataFrame(data_list)
|
||||
|
||||
# From a list of dictionaries
|
||||
dict_list = [{"name": "John"}, {"name": "Jane"}]
|
||||
df = DataFrame(dict_list)
|
||||
|
||||
# From a dictionary of lists
|
||||
data_dict = {"name": ["John", "Jane"], "age": [30, 25]}
|
||||
df = DataFrame(data_dict)
|
||||
Key Methods
|
||||
to_data_list(): Converts the DataFrame back to a list of Data objects.
|
||||
add_row(data): Adds a single row (either a Data object or a dictionary) to the DataFrame.
|
||||
add_rows(data): Adds multiple rows (list of Data objects or dictionaries) to the DataFrame.
|
||||
Usage Example
|
||||
python
|
||||
# Create a DataFrame
|
||||
df = DataFrame([Data(data={"name": "John"}), Data(data={"name": "Jane"})])
|
||||
|
||||
# Add a new row
|
||||
df = df.add_row({"name": "Alice"})
|
||||
|
||||
# Convert back to a list of Data objects
|
||||
data_list = df.to_data_list()
|
||||
|
||||
# Use pandas functionality
|
||||
filtered_df = df[df["name"].str.startswith("J")]
|
||||
```
|
||||
|
||||
To use DataFrame objects in a component input,use the DataFrameInput input type.
|
||||
|
||||
```python
|
||||
DataFrameInput(
|
||||
name="dataframe_input", display_name="DataFrame Input", info="Input for DataFrame objects.", tool_mode=True
|
||||
),
|
||||
```
|
||||
Loading…
Add table
Add a link
Reference in a new issue