The Astra DB Chat Memory component retrieves and stores chat messages using an Astra DB database.
Chat memories are passed between memory storage components as the Memory data type.
Specifically, the component creates an instance of AstraDBChatMessageHistory, which is a LangChain chat message history class that uses Astra DB for storage.
The Astra DB Chat Memory component isn't recommended for most memory storage because memories tend to be long JSON objects or strings, often exceeding the maximum size of a document or object supported by Astra DB.
However, Langflow's Agent and Language Model components include built-in chat memory that is enabled by default. -Your flows don't need an external database to store chat memory. +
The Astra DB Chat Memory component isn't recommended for most memory storage because memories tend to be long JSON objects or strings, often exceeding the maximum size of a document or object supported by Astra DB.
However, Langflow's Agent component includes built-in chat memory that is enabled by default. +Your agentic flows don't need an external database to store chat memory. For more information, see Memory management options.
For more information about using external chat memory in flows, see the Message History component.
The Message History component provides combined chat history and message storage functionality. It can store and retrieve chat messages from either Langflow storage or a dedicated chat memory database like Mem0 or Redis.
-It replaces the legacy Chat History and Message Store components.
-The Language Model and Agent components have built-in chat memory that is enabled by default and uses Langflow storage.
This built-in chat memory functionality is sufficient for most use cases.
Use the Message History component only if you need to access chat memories outside the chat context, such as sentiment analysis flow that retrieves and analyzes recently stored memories, or you want to store memories in a specific database, separate from Langflow storage.
For more information, see Store chat memory.
The Agent component has built-in chat memory that is enabled by default and uses Langflow storage. +This built-in chat memory functionality is sufficient for most use cases.
Use the Message History component for the following use cases:
For more information, see Store chat memory.
The Message History component has two modes, depending on where you want to use it in your flow:
Connect the Prompt Template component's output to a Language Model component's System Message input.
-This example uses a Language Model component as the central chat driver, but you can also use an Agent component.
+This example uses the Language Model core component as the central chat driver, but you can also use another language model component or the Agent component.
Add a Chat Input component, and then connect it to the Language Model component's Input input.
+Add a Chat Input component, and then connect it to the Language Model component's Input field.
Connect the Language Model component's output to a Chat Output component.
@@ -108,8 +112,8 @@ The template can include any text and variables that you want to supply to the LConnect the Chat Output component's output to the Message History component's Message input.
Each response from the LLM is output from the Language Model component to the Chat Output component, and then stored in chat memory by the final Message History component.
To store and retrieve chat memory from a dedicated, external chat memory database, use the Message History component and a provider-specific chat memory component.
Available provider-specific chat memory components include Cassandra Chat Memory component, Mem0 Chat Memory component, and Redis Chat Memory component. -For all provider-specific chat memory components, see Bundles.
The following steps explain how to create a flow that stores and retrieves chat memory from Redis chat memory:
To store and retrieve chat memory from a dedicated, external chat memory database, use the Message History component and a provider-specific chat memory component.
The following steps explain how to create a flow that stores and retrieves chat memory with a Redis Chat Memory component. +Other options include the Mem0 Chat Memory component and Cassandra Chat Memory component.
Create or edit a flow where you want to use chat memory.
Some If-Else 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 |
|---|---|---|
| input_text | String | Input parameter. The primary text input for the operation. |
| match_text | String | Input parameter. The text to compare against. |
| operator | Dropdown | Input parameter. The operator used to compare texts. Options include equals, not equals, contains, starts with, ends with, and regex. The default is equals. |
| case_sensitive | Boolean | Input parameter. When true, the comparison is case sensitive. The default is false. This setting doesn't apply to regex comparisons. |
| message | Message | Input parameter. The message to pass through either route. |
| max_iterations | Integer | Input parameter. The maximum number of iterations allowed for the conditional router. The default is 10. |
| default_route | Dropdown | Input parameter. The route to take when max iterations are reached. Options include true_result or false_result. The default is false_result. |
| true_result | Message | Output parameter. The output produced when the condition is true. |
| false_result | Message | Output parameter. The output produced when the condition is false. |
| Name | Type | Description |
|---|---|---|
| input_text | String | Input parameter. The primary text input for the operation. |
| match_text | String | Input parameter. The text to compare against. |
| operator | Dropdown | Input parameter. The operator used to compare texts. Options include equals, not equals, contains, starts with, ends with, and regex. The default is equals. |
| case_sensitive | Boolean | Input parameter. When true, the comparison is case sensitive. The default is false. This setting doesn't apply to regex comparisons. |
| max_iterations | Integer | Input parameter. The maximum number of iterations allowed for the conditional router. The default is 10. |
| default_route | Dropdown | Input parameter. The route to take when max iterations are reached. Options include true_result or false_result. The default is false_result. |
| true_result | Message | Output parameter. The output produced when the condition is true. |
| false_result | Message | Output parameter. The output produced when the condition is false. |
The Loop component iterates over a list of input by passing individual items to other components attached at the Item output port until there are no items left to process. Then, the Loop component passes the aggregated result of all looping to the component connected to the Done port.
diff --git a/components-memories.html b/components-memories.html index 01f35d9156..5e1452954c 100644 --- a/components-memories.html +++ b/components-memories.html @@ -23,7 +23,7 @@ - + diff --git a/components-models.html b/components-models.html index 028725085a..cc2bc3d748 100644 --- a/components-models.html +++ b/components-models.html @@ -23,7 +23,7 @@ - + diff --git a/components-processing.html b/components-processing.html index 14b8c28aa0..e30e3a8a2d 100644 --- a/components-processing.html +++ b/components-processing.html @@ -23,7 +23,7 @@ - + diff --git a/components-prompts.html b/components-prompts.html index a6382e96bf..aaeb517dba 100644 --- a/components-prompts.html +++ b/components-prompts.html @@ -23,7 +23,7 @@ - + @@ -63,7 +63,7 @@ For example:Variables in a Prompt Template component dynamically add fields to the Prompt Template component so that your flow can receive definitions for those values from other components, Langflow global variables, or fixed input.
For example, with the Message History component, you can use a {memory} variable to pass chat history to the prompt.
-However, the Language Model and Agent components include built-in chat memory that is enabled by default.
+However, the Agent component includes built-in chat memory that is enabled by default.
For more information, see Memory management options.
The following steps demonstrate how to add variables to a Prompt Template component:
For other options, see the LANGFLOW_CACHE_TYPE environment variable.
Chat-based flows with a Language Model or Agent component have built-in chat memory that is enabled by default. -This memory allows them to retrieve and reference messages from previous conversations associated with the same session ID.
-Built-in chat memory stores memories in the Langflow messages table.
Chat memory is a cache for the LLM or agent to preserve past conversations to retain and reference that context in future interactions. -For example, if a user has already told the LLM their name, the LLM can retrieve that information from chat memory rather than asking the user to repeat themselves in future conversations or messages.
Chat memory is distinct from vector store memory because it is built specifically for storing and retrieving chat messages from databases.
Components that support chat memory (such as the Agent, Language Model, Message History, or third-party Chat Memory components) provide access to their respective databases as memory. +
Chat-based flows with a Chat Input or Chat Output component produce chat history that is stored in the Langflow messages table.
+At minimum, this serves as a chat log, but it isn't functionally the same as chat memory that provides historical context to an LLM.
To store and retrieve chat memories in flows, you can use a Message History component or the Agent component's built-in chat memory.
+Chat memory is a cache for an LLM or agent to preserve past conversations to retain and reference that context in future interactions. +For example, if a user has already told the LLM their name, the LLM can retrieve that information from chat memory rather than asking the user to repeat themselves in future conversations or messages.
Chat memory is distinct from vector store memory because it is built specifically for storing and retrieving chat messages from databases.
Components that support chat memory (such as the Agent and Message History components) provide access to their respective databases as memory. Retrieval as memory is an important distinction for LLMs and agents because this storage and retrieval mechanism is specifically designed to recall context from past conversations. Unlike vector stores, which are designed for semantic search and retrieval of text chunks, chat memory is designed to store and retrieve chat messages in a way that is optimized for conversation history.
Chat memories are grouped by session ID (session_id).
Chat history and memories are grouped by session ID (session_id).
The default session ID is the flow ID, which means that all chat messages for a flow are stored under the same session ID as one large chat session.
For better segregation of chat memory, especially in flows used by multiple users, consider using custom session IDs. For example, if you use user IDs as session IDs, then each user's chat history is stored separately, isolating the context of their chats from other users' chats.
@@ -113,18 +113,19 @@ For example, if you use user IDs as session IDs, then each user's chat histWhere and how chat memory is stored depends on the components used in your flow:
Language Model and Agent components: All messages are stored in Langflow storage. -The Agent component provides some memory configuration options, such as Number of Chat History Messages.
-The Language Model and Agent component's built-in chat memory are sufficient for most use cases.
-If you prefer to use dedicated, external chat memory storage, or you need to retrieve memories outside the context of a chat, you can add Message History and Chat Memory components to your flow.
+Agent component: This component has built-in chat memory that is enabled by default. +This memory allows the agent to retrieve and reference messages from previous conversations associated with the same session ID. +All messages are stored in Langflow storage, and the component provides minimal memory configuration options, such as the number of messages to retrieve.
+The Agent component's built-in chat memory is sufficient for most use cases.
+If you want to use external chat memory storage, retrieve memories outside the context of a chat, or use chat memory with a language model component (not an agent), you must use the Message History component (with or without a third-party chat memory component).
Message History component: By default, this component stores and retrieves memories from Langflow storage, unless you attach a Chat Memory component. It provides a few more options for sorting and filtering memories, although most of these options are built-in to the Agent component as configurable or fixed parameters.
-You can use the Message History component with or without a Language Model or Agent component. +
Message History component: By default, this component stores and retrieves memories from Langflow storage, unless you attach a third-party chat memory component. It provides a few more options for sorting and filtering memories, although most of these options are also built-in to the Agent component as configurable or fixed parameters.
+You can use the Message History component with or without a language model or agent. For example, if you need to retrieve data from memories outside of chat, you can use the Message History component to fetch that data directly from your chat memory database without feeding it into a chat.
Third-party Chat Memory components: Use one of these components only if you need to specifically store or retrieve chat memories from a dedicated chat memory database. +
Third-party chat memory components: Use one of these components only if you need to store or retrieve chat memories from a dedicated external chat memory database. Typically, this is necessary only if you have specific storage needs that aren't met by Langflow storage. For example, if you want to manage chat memory data by directly working with the database, or if you want to use a different database than the default Langflow storage.