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docs: message history and message store example (#7697)

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140
docs/docs/Components/components-helpers.md
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@ -68,27 +68,6 @@ record_number: {batch_index}, name: {text_input}, summary: {model_response}
|------|--------------|--------|------|
| batch_results | Batch Results | run_batch | A DataFrame with columns: 'text_input', 'model_response', 'batch_index', and optional 'metadata' containing processing information. |
## Create List
:::important
This component is in **Legacy**, which means it is no longer in active development as of Langflow version 1.3.
:::
This component dynamically creates a record with a specified number of fields.
### Inputs
| Name | Display Name | Info |
|------|--------------|------|
| n_fields | Number of Fields | Number of fields to be added to the record. |
| text_key | Text Key | Key used as text. |
### Outputs
| Name | Display Name | Info |
|------|--------------|------|
| list | List | The dynamically created list with the specified number of fields. |
## Current date
The Current Date component returns the current date and time in a selected timezone. This component provides a flexible way to obtain timezone-specific date and time information within a Langflow pipeline.
@ -127,7 +106,13 @@ This component generates a unique ID.
Prior to Langflow 1.1, this component was known as the Chat Memory component.
:::
This component retrieves and manages chat messages from Langflow tables or an external memory.
This component retrieves chat messages from Langflow tables or external memory.
In this example, the **Message Store** component stores the complete chat history in a local Langflow table, which the **Message History** component retrieves as context for the LLM to answer each question.
![Message store and history components](/img/component-message-history-message-store.png)
For more information on configuring memory in Langflow, see [Memory](/memory).
### Inputs
@ -151,9 +136,13 @@ This component retrieves and manages chat messages from Langflow tables or an ex
## Message store
This component stores chat messages or text into Langflow tables or an external memory.
This component stores chat messages or text in Langflow tables or external memory.
It provides flexibility in managing message storage and retrieval within a chat system.
In this example, the **Message Store** component stores the complete chat history in a local Langflow table, which the **Message History** component retrieves as context for the LLM to answer each question.
![Message store and history components](/img/component-message-history-message-store.png)
For more information on configuring memory in Langflow, see [Memory](/memory).
### Inputs
@ -171,48 +160,6 @@ It provides flexibility in managing message storage and retrieval within a chat
|------|--------------|------|
| stored_messages | Stored Messages | The list of stored messages after the current message has been added. |
## Output Parser
:::important
This component is in **Legacy**, which means it is no longer in active development as of Langflow version 1.3.
:::
This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's `CommaSeparatedListOutputParser`.
:::note
This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.
:::
Both the **Output Parser** and **Structured Output** components format LLM responses, but they have different use cases.
The **Output Parser** is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example `["item1", "item2", "item3"]`.
The **Structured Output** is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.
To use this component:
1. Create a Prompt component and connect the Output Parser's `format_instructions` output to it. This ensures the LLM knows how to format its response.
2. Write your actual prompt text in the Prompt component, including the `{format_instructions}` variable.
For example, in your Prompt component, the template might look like:
```
{format_instructions}
Please list three fruits.
```
3. Connect the `output_parser` output to your LLM model.
4. The output parser converts this into a Python list: `["apple", "banana", "orange"]`.
### Inputs
| Name | Display Name | Info |
|------|--------------|------|
| parser_type | Parser | Select the parser type. Currently supports "CSV". |
### Outputs
| Name | Display Name | Info |
|------|--------------|------|
| format_instructions | Format Instructions | Pass to a prompt template to include formatting instructions for LLM responses. |
| output_parser | Output Parser | The constructed output parser that can be used to parse LLM responses. |
## Structured output
This component transforms LLM responses into structured data formats.
@ -251,4 +198,63 @@ For example, the template `EBITDA: {EBITDA} , Net Income: {NET_INCOME} , GROSS
| Name | Display Name | Info |
|------|--------------|------|
| structured_output | Structured Output | The structured output is a Data object based on the defined schema. |
| structured_output_dataframe | DataFrame | The structured output converted to a [DataFrame](/concepts-objects#dataframe-object) format. |
| structured_output_dataframe | DataFrame | The structured output converted to a [DataFrame](/concepts-objects#dataframe-object) format. |
## Legacy components
Legacy components are no longer in active development but are backward compatible.
### Create List
This component dynamically creates a record with a specified number of fields.
#### Inputs
| Name | Display Name | Info |
|------|--------------|------|
| n_fields | Number of Fields | Number of fields to be added to the record. |
| text_key | Text Key | Key used as text. |
#### Outputs
| Name | Display Name | Info |
|------|--------------|------|
| list | List | The dynamically created list with the specified number of fields. |
### Output Parser
This component transforms the output of a language model into a specified format. It supports CSV format parsing, which converts LLM responses into comma-separated lists using Langchain's `CommaSeparatedListOutputParser`.
:::note
This component only provides formatting instructions and parsing functionality. It does not include a prompt. You'll need to connect it to a separate Prompt component to create the actual prompt template for the LLM to use.
:::
Both the **Output Parser** and **Structured Output** components format LLM responses, but they have different use cases.
The **Output Parser** is simpler and focused on converting responses into comma-separated lists. Use this when you just need a list of items, for example `["item1", "item2", "item3"]`.
The **Structured Output** is more complex and flexible, and allows you to define custom schemas with multiple fields of different types. Use this when you need to extract structured data with specific fields and types.
To use this component:
1. Create a Prompt component and connect the Output Parser's `format_instructions` output to it. This ensures the LLM knows how to format its response.
2. Write your actual prompt text in the Prompt component, including the `{format_instructions}` variable.
For example, in your Prompt component, the template might look like:
```
{format_instructions}
Please list three fruits.
```
3. Connect the `output_parser` output to your LLM model.
4. The output parser converts this into a Python list: `["apple", "banana", "orange"]`.
#### Inputs
| Name | Display Name | Info |
|------|--------------|------|
| parser_type | Parser | Select the parser type. Currently supports "CSV". |
#### Outputs
| Name | Display Name | Info |
|------|--------------|------|
| format_instructions | Format Instructions | Pass to a prompt template to include formatting instructions for LLM responses. |
| output_parser | Output Parser | The constructed output parser that can be used to parse LLM responses. |

8
docs/docs/Develop/memory.md
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@ -15,7 +15,13 @@ This page details the following memory configuration options in Langflow.
## Local Langflow database tables
The default storage option in Langflow is a [SQLite](https://www.sqlite.org/) database located at `langflow/src/backend/base/langflow/langflow.db`. The following tables are stored in `langflow.db`:
The default storage option in Langflow is a [SQLite](https://www.sqlite.org/) database stored in your system's cache directory:
- Linux/WSL: `~/.cache/langflow/langflow.db`
- macOS: `/Users/<username>/Library/Caches/langflow/langflow.db`
- Windows: `%LOCALAPPDATA%\langflow\langflow\Cache\langflow.db`
The following tables are stored in `langflow.db`:
• **User** - Stores user account information including credentials, permissions, and profiles. For more information, see [Authentication](/configuration-authentication).

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