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MODELFILE

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+FROM llama3
+
+PARAMETER temperature 0
+PARAMETER num_ctx 4096
+
+SYSTEM """
+
+You are an AI agent. Your jobs is to figgure out what the user wants from and return a function, including but not limited to for:
+
+* Searching online for information, and grabbing said information.
+* Open a jitsi call
+
+If you are asked to do any of these things, choose and respond with the relevant function code from the following:
+search("userQuery")
+jitsi()
+
+Be sure to actually replace userQuery with the user's search query.
+
+If you are asked to analyze text, analyze it and respond as shown in this pattern:
+console.log(`analyzedText`)
+
+Replace "analyzedText" with the summary/answer/result of the analaysis of text.
+
+If you are asked to write something (examples: blog posts/emails/READMEs/etc), simply respond as shown in this pattern:
+console.log(`writtenTextInMarkdown`)
+
+Replace "writtenTextInMarkdown" with the written text you have generated for the user in markdown format, including a title, body, and optionally styling and subtitles.
+
+If the user does not specify to do any of these things listed in the list, or asks you to generate code, simply respond as shown in this pattern:
+console.log(`botResponse`)
+
+Replace "botResponse" with YOUR response (yes yours, ai person.)
+
+
+Note that you should replace the Camel Cased strings with YOUR generated responses.
+Also note that you should respond with just a response like "OK, I will search that for you now", "Ok, here's a blog post", "Here's your jitsi link", followed by the function you generated in a new line, unless you are running console.log, then just return the function.
+But be sure to wrap the function in "<functioncall>" and "</functioncall>". Besides that, respond with nothing else.
+
+
+REMEMBER TO: replace the Camel Cased strings with YOUR generated responses, write a simple "ok" message if relevant, and wrap each function defined above in "<functioncall>" and "</functioncall>".
+"""

index.mjs

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+import readline from 'readline';
+import Ollama from 'ollama-js-client';
+import fs from 'fs';
+
+let DEBUG_MODE = true
+
+async function ollamaInteraction() {
+  const rl = await readline.createInterface({
+    input: process.stdin,
+    output: process.stdout
+  });
+
+  function extractFunctionName(response) {
+    const match = response.match(/<functioncall>([^<]+)<\/functioncall>/);
+    return match ? match[1] : '';
+  }
+
+
+  function generateRandomString(length = 16) {
+    const characters = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789";
+    let result = "";
+    for (let i = 0; i < length; i++) {
+      result += characters.charAt(Math.floor(Math.random() * characters.length));
+    }
+    return result;
+  }
+
+  //INTERNAL FUNCTIONS MAGIC BEGIN
+  async function jitsi() {
+    const id = generateRandomString()
+    const jitsiURL = `https://meet.jit.si/${id}`;
+    console.log(jitsiURL);
+    return jitsiURL;
+  }
+
+  async function search(q) {
+    q = q.replaceAll(" ", "+")
+    const searchURL = `https://www.google.com/search?q=${q}&sca_upv=1`
+    console.log(searchURL);
+    return searchURL;
+  }
+  //END OF INTERNAL FUNCTIONS MAGIC
+
+  return new Promise(async (resolve) => {
+    rl.question("User: ", async (userInput) => {
+      rl.close();
+
+      const ollama = new Ollama({
+        model: "sneedgroup-llama3-agent",
+        url: "http://127.0.0.1:11434/api/",
+      }); // Ensure the model name is correct
+
+      const responsePreParse = await ollama.prompt(userInput)
+      const response = responsePreParse.response;
+      const functionName = extractFunctionName(response);
+      const responseWithoutFunctionCall = response.replace(/<functioncall>.*?<\/functioncall>/, '');
+
+      console.log(responseWithoutFunctionCall);
+
+      let contentToAppend = `<USER>: ${userInput}
+                         
+                          <AI AGENT>: ${responseWithoutFunctionCall}`;
+
+      await fs.appendFile('journal.txt', contentToAppend, async (err) => {
+        if (err) {
+          await console.error(err);
+        } else {
+          await console.log('Content appended to journal file successfully!');
+        }
+      });
+
+      if (DEBUG_MODE) {
+        console.log(`DEBUG: RUN ${functionName}`)
+      }
+
+      eval(function() {
+        if (typeof functionName == 'undefined' || functionName == null) {
+          return "";
+        } else {
+          return functionName;
+        }
+      })
+
+      resolve(); // Resolve the promise after processing
+    });
+  });
+}
+
+(async () => {
+  while (true) {
+    try {
+      await ollamaInteraction();
+    } catch (error) {
+      console.error('Error occurred:', error);
+    }
+  }
+})();

package.json

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+{
+  "name": "sneedgroup-agent",
+  "version": "1.0.0",
+  "description": "",
+  "main": "index.mjs",
+  "scripts": {
+    "test": "echo \"Error: no test specified\" && exit 1"
+  },
+  "author": "Samuel Lord",
+  "license": "SEE LICENSE IN LICENSE",
+  "dependencies": {
+    "ollama": "^0.5.0",
+    "ollama-js-client": "^1.0.1",
+    "readline": "^1.3.0"
+  }
+}