Integrate Manual Chat Trigger in your LLM apps and 422+ apps and services

This workflow is designed for dynamic and intelligent conversational capabilities. It incorporates OpenAI's GPT-4o model for natural language understanding and generation. Additional tools include SerpAPI and Wikipedia for enriched, data-driven responses. The workflow is triggered manually, and utilizes a 'Window Buffer Memory' to maintain the context of the last 20 interactions for better conversational continuity. All these components are orchestrated through n8n nodes, ensuring seamless interconnectivity. To use this template, you need to be on n8n version 1.50.0 or later.

The workflow first populates a Pinecone index with vectors from a Bitcoin whitepaper. Then, it waits for a manual chat message. When received, the chat message is turned into a vector and compared to the vectors in Pinecone. The most similar vectors are retrieved and passed to OpenAI for generating a chat response. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow demonstrates how to connect an open-source model to a Basic LLM node. The workflow is triggered when a new manual chat message appears. The message is then run through a Language Model Chain that is set up to process text with a specific prompt to guide the model's responses. Note that open-source LLMs with a small number of parameters require slightly different prompting with more guidance to the model. You can change the default Mistral-7B-Instruct-v0.1 model to any other LLM supported by HuggingFace. You can also connect other nodes, such as Ollama. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow implements a custom tool via JavaScript code which returns a random color to users and excludes the given colors. Note that to use this template, you need to be on n8n version 1.19.4 or later.

This workflow shows how you can get your OpenAI assistant to call an n8n workflow as a tool. Since you can put almost any functionality in an n8n workflow, this means you can give your assistant access to almost any data source. Note that to use this template, you need to be on n8n version 1.19.4 or later.

⚙️🛠️🚀🤖🦾 This template is a PoC of a ReAct AI Agent capable of fetching random pages (not only Wikipedia or Google search results). On the top part there's a manual chat node connected to a LangChain ReAct Agent. The agent has access to a workflow tool for getting page content. The page content extraction starts with converting query parameters into a JSON object. There are 3 pre-defined parameters: url** – an address of the page to fetch method** = full / simplified maxlimit** - maximum length for the final page. For longer pages an error message is returned back to the agent Page content fetching is a multistep process: An HTTP Request mode tries to get the page content. If the page content was successfuly retrieved, a series of post-processing begin: Extract HTML BODY; content Remove all unnecessary tags to recude the page size Further eliminate external URLs and IMG scr values (based on the method query parameter) Remaining HTML is converted to Markdown, thus recuding the page lengh even more while preserving the basic page structure The remaining content is sent back to an Agent if it's not too long (maxlimit = 70000 by default, see CONFIG node). NB: You can isolate the HTTP Request part into a separate workflow. Check the Workflow Tool description, it guides the agent to provide a query string with several parameters instead of a JSON object. Please reach out to Eduard is you need further assistance with you n8n workflows and automations! Note that to use this template, you need to be on n8n version 1.19.4 or later.