MuleSoft AI Chain Connector 1.0 | MuleSoft Documentation
# MuleSoft AI Chain Connector 1.0
Anypoint Connector for MuleSoft AI Chain (MuleSoft AI Chain Connector) helps developers design, build, and manage AI agents within Anypoint Platform. It provides the tools and support to integrate Large Language Models (LLMs), vector stores, and other advanced AI services into MuleSoft applications.
MuleSoft AI Chain Connector supports 15 operations, categorized into:
  * Agent
  * Embeddings
  * Image generation
  * Sentiment
  * Tools


For information about compatibility and fixed issues, see the MuleSoft AI Chain Connector release notes.
## Before You Begin
To use this connector, you must be familiar with:
  * Anypoint Connectors
  * Mule runtime engine (Mule)
  * Elements and global elements in a Mule flow
  * How to create a Mule app using Anypoint Code Builder or Anypoint Studio


Before creating an app, you must have:
  * Java 17
  * Apache Maven
  * Credentials to access the MuleSoft AI Chain Connector target resource
  * Anypoint Platform
  * The latest versions of Anypoint Code Builder or Anypoint Studio


## Common Use Cases for the Connector
### Agent Operations
Some common use cases for Agent operations include:
  * Customer Service Agents
Enhance customer service by providing case summaries, case classifications, large dataset summaries, and more.
  * Sales Operation Agents
Assist sales teams in writing sales emails, summarizing cases for specific accounts, assessing the probability of closing deals, and more.
  * Marketing Agents
Support marketing teams in generating product descriptions, creating newsletters, planning social media campaigns, and more.


### Chat Operations
Some common use cases for Chat operations include:
  * Basic chatbots that answer simple user prompts.
  * Customer service queries that provide direct answers to frequently asked questions.
  * Customer support chats that retain the context of the ongoing support conversations.
  * Multi-user chat applications that maintain the conversation history for different users.
  * Personal assistants that keep track of user interactions to provide more relevant responses.


### Embeddings Operations
Some common use cases for Embeddings operations include:
  * Creating a new in-memory vector store for storing document embeddings.
  * Exporting an in-memory embedding to a physical file for persistence across sessions.
  * Querying a knowledge store with a plain text prompt and receiving a refined response powered by an LLM.
  * Retrieving and interpreting data from documents in an embedding store, with enhanced context from the LLM.
  * Adding documents, such as PDFs and text files, into an embedding store for future retrieval.
  * Adding a folder for documents, such as PDFs and text files, into an embedding store for future retrieval.
  * Ingesting documents from a specific directory into an in-memory embedding store for contextual analysis.
  * Querying a vector store for specific information using semantic search.
  * Retrieving multiple relevant documents or text segments from an embedding store based on a given prompt.


### Image Generation Operations
Some common use cases for Image operations include:
  * Analyzing images in business reports, presentations, or customer service scenarios.
  * Generating content describing images for blog posts, articles, or social media.
  * Extracting visual insights from images in research or design projects.
  * Creating visuals for marketing and advertising campaigns based on specific descriptions.
  * Generating images for blog posts, articles, or social media based on given prompts.
  * Prototyping and design, such as quickly generating concept images from textual descriptions.


### RAG Operations
Use Retrieval-Augmented Generation (RAG) operations to retrieve information from a document based on a plain text prompt.
Some common use cases for Retrieval-Augmented Generation (RAG) operations include:
  * Knowledge Management
Extract specific information from documents stored in a knowledge base.
  * Customer Support
Retrieve relevant data from customer service documents to assist with inquiries.
  * Research
Access information from research papers or documents based on specific queries.


### Sentiment Operations
Use sentiment operations to analyze the sentiment of text, such as for:
  * Customer Feedback Analysis
Determine whether customer feedback is positive, negative, or neutral.
  * Social Media Monitoring
Analyze the sentiment of social media posts or comments to gauge public opinion.
  * Market Research
Assess the sentiment of survey responses or market research data.


### Tools Operations
Some common use cases for Tools operations include:
  * Automating Routine Tasks
Create autonomous agents that handle routine tasks by calling appropriate APIs.
  * Customer Support
Automate responses to common queries by integrating tools that provide necessary information.
  * Inventory Management
Check inventory levels or order status based on user prompts.
  * Employee Management
Retrieve employee information or manage employee-related tasks through API calls.
  * Sales and Marketing
Access CRM data or manage leads and accounts efficiently using predefined tools.


## Next Step
After you complete the prerequisites, you are ready to create an app and configure the connector using Anypoint Studio or Anypoint Code Builder.
## See Also
  * Introduction to Anypoint Connectors
  * Using Anypoint Code Builder to Configure a Connector
  * Using Studio to Configure a Connector
  * MuleSoft AI Chain Connector Reference
  * MuleSoft AI Chain - Additional Configuration Information
  * MuleSoft Help Center