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LibreChat is an open-source, self-hosted AI chat platform that brings together multiple LLM providers, agents, and assistants behind a single interface. It also supports the Model Context Protocol (MCP), so you can connect external tools and data sources directly to the chat and pull in live data from the systems you already work with.
By integrating LibreChat with CData Connect AI through the built-in MCP Server, LibreChat gains governed, real-time access to live Elasticsearch data. This enables you to list catalogs, explore schemas, and query records from Elasticsearch data using natural language prompts, with all data access running securely against authorized sources.
This article explains how to configure Elasticsearch connectivity in Connect AI, generate the required personal access token, install LibreChat, register the Connect AI MCP Server, configure an LLM provider, and verify the integration by querying live Elasticsearch data from the LibreChat interface.
Accessing and integrating live data from Elasticsearch has never been easier with CData. Customers rely on CData connectivity to:
Users frequently integrate Elasticsearch data with analytics tools such as Crystal Reports, Power BI, and Excel, and leverage our tools to enable a single, federated access layer to all of their data sources, including Elasticsearch.
For more information on CData's Elasticsearch solutions, check out our Knowledge Base article: CData Elasticsearch Driver Features & Differentiators.
Connectivity to Elasticsearch from LibreChat is made possible through Connect AI's Remote MCP Server. To interact with Elasticsearch data from LibreChat, start by creating and configuring a Elasticsearch connection in Connect AI.
Set the Server and Port connection properties to connect. To authenticate, set the User and Password properties, PKI (public key infrastructure) properties, or both. To use PKI, set the SSLClientCert, SSLClientCertType, SSLClientCertSubject, and SSLClientCertPassword properties.
The data provider uses X-Pack Security for TLS/SSL and authentication. To connect over TLS/SSL, prefix the Server value with 'https://'. Note: TLS/SSL and client authentication must be enabled on X-Pack to use PKI.
Once the data provider is connected, X-Pack will then perform user authentication and grant role permissions based on the realms you have configured.
π Configuring a connection (Salesforce is shown)A Personal Access Token (PAT) is used to authenticate the connection to Connect AI from LibreChat. It is best practice to create a separate PAT for each integration to maintain granular access control.
With the Elasticsearch connection configured and a PAT generated, LibreChat can now connect to Elasticsearch data through Connect AI.
Next, install LibreChat locally and configure the Connect AI Remote MCP Server so that the chat interface can discover and call live data tools through Connect AI.
Note: LibreChat will use Basic authentication with Connect AI. Combine your Connect AI user email and the PAT you created earlier in the format email:PAT, then base64 encode the combined string and paste it in the API Key field. For example, [email protected]:ABC123...XYZ789 base64-encoded becomes something like: dXNlckBkb21haW4uY29tOkFCQzEyMy4uLlhZWjc4OQ==
π Configuring the Connect AI MCP ServerLibreChat requires at least one LLM provider to power the chat. Enable the MCP server in the chat input and add an API key for your preferred provider so the model can interpret prompts and call MCP tools through Connect AI.
With the MCP server and an LLM provider configured, LibreChat is ready to query live Elasticsearch data through Connect AI.
With the integration complete, use the LibreChat chat input to interact with live Elasticsearch data through natural language prompts handled by the configured LLM.
At this point, your LibreChat instance communicates with the Connect AI MCP Server and retrieves live Elasticsearch data through remote MCP tools directly from the chat interface.
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