index.md

Built-in Components

Component	Description
aggregate	Aggregate messages into one message.
assembly	Assembles messages till criteria is met, the output will be the assembled message
broker_input	Connect to a messaging broker and receive messages from it. The component will output the payload, topic, and user properties of the message.
broker_output	Connect to a messaging broker and send messages to it. Note that this component requires that the data is transformed into the input schema.
broker_request_response	Connect to a messaging broker, send request messages, and receive responses. This component combines the functionality of broker_input and broker_output with additional request-response handling.
delay	A simple component that simply passes the input to the output, but with a configurable delay.
error_input	Receive processing errors from the Solace AI Event Connector. Note that the input_selection configuration is ignored. This component should be used to create a flow that handles errors from other flows.
file_output	File output component
iterate	Take a single message that is a list and output each item in that list as a separate message
langchain_chat_model	Provide access to all the LangChain chat models via configuration
langchain_chat_model_with_history	A chat model based on LangChain that includes keeping per-session history of the conversation. Note that this component will only take the first system message and the first human message in the messages array.
langchain_embeddings	Provide access to all the LangChain Text Embeddings components via configuration
langchain_vector_store_delete	This component allows for entries in a LangChain Vector Store to be deleted. This is needed for the continued maintenance of the vector store. Due to the nature of langchain vector stores, you need to specify an embedding component even though it is not used in this component.
langchain_vector_store_embedding_index	Use LangChain Vector Stores to index text for later semantic searches. This will take text, run it through an embedding model and then store it in a vector database.
langchain_vector_store_embedding_search	Use LangChain Vector Stores to search a vector store with a semantic search. This will take text, run it through an embedding model with a query embedding and then find the closest matches in the store.
litellm_chat_model	LiteLLM chat component
litellm_chat_model_with_history	LiteLLM model handler component with conversation history
litellm_embeddings	Embed text using a LiteLLM model
message_filter	A filtering component. This will apply a user configurable expression. If the expression evaluates to True, the message will be passed on. If the expression evaluates to False, the message will be discarded. If the message is discarded, any previous components that require an acknowledgement will be acknowledged.
openai_chat_model	OpenAI chat model component
openai_chat_model_with_history	OpenAI chat model component with conversation history
parser	Parse input from the given type to output type.
pass_through	What goes in comes out
stdin_input	STDIN input component. The component will prompt for input, which will then be placed in the message payload using the output schema below. The component will wait for its output message to be acknowledged before prompting for the next input.
stdout_output	STDOUT output component
timer_input	An input that will generate a message at a specified interval.
user_processor	A component that allows the processing stage to be defined in the configuration file.
web_scraper	Scrape javascript based websites.
websearch_bing	Perform a search query on Bing.
websearch_duckduckgo	Perform a search query on DuckDuckGo.
websearch_google	Perform a search query on Google.
websocket_input	Listen for incoming messages on a websocket connection.
websocket_output	Send messages to a websocket connection.