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Unverified Commit 84a23144 authored by Parth Sareen's avatar Parth Sareen Committed by GitHub
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examples: remove codified examples (#8267)

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examples/typescript-mentors/mentors.ts deleted 100644 → 0
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import { Ollama } from 'ollama-node';
const mentorCount = 3;
const ollama = new Ollama();
type Mentor = { ns: string, char: string };
function getMentors(): Mentor[] {
const mentors = [{ ns: 'mattw', char: 'Gary Vaynerchuk' }, { ns: 'mattw', char: 'Kanye West'}, {ns: 'mattw', char: 'Martha Stewart'}, {ns: 'mattw', char: 'Neil deGrasse Tyson'}, {ns: 'mattw', char: 'Owen Wilson'}, {ns: 'mattw', char: 'Ronald Reagan'}, {ns: 'mattw', char: 'Donald Trump'}, {ns: 'mattw', char: 'Barack Obama'}, {ns: 'mattw', char: 'Jeff Bezos'}];
const chosenMentors: Mentor[] = [];
for (let i = 0; i < mentorCount; i++) {
const mentor = mentors[Math.floor(Math.random() * mentors.length)];
chosenMentors.push(mentor);
mentors.splice(mentors.indexOf(mentor), 1);
}
return chosenMentors;
}
function getMentorFileName(mentor: Mentor): string {
const model = mentor.char.toLowerCase().replace(/\s/g, '');
return `${mentor.ns}/${model}`;
}
async function getSystemPrompt(mentor: Mentor, isLast: boolean, question: string): Promise<string> {
ollama.setModel(getMentorFileName(mentor));
const info = await ollama.showModelInfo()
let SystemPrompt = info.system || '';
SystemPrompt += ` You should continue the conversation as if you were ${mentor} and acknowledge the people before you in the conversation. You should adopt their mannerisms and tone, but also not use language they wouldn't use. If they are not known to know about the concept in the question, don't offer an answer. Your answer should be no longer than 1 paragraph. And definitely try not to sound like anyone else. Don't repeat any slang or phrases already used. And if it is a question the original ${mentor} wouldn't have know the answer to, just say that you don't know, in the style of ${mentor}. And think about the time the person lived. Don't use terminology that they wouldn't have used.`
if (isLast) {
SystemPrompt += ` End your answer with something like I hope our answers help you out`;
} else {
SystemPrompt += ` Remember, this is a conversation, so you don't need a conclusion, but end your answer with a question related to the first question: "${question}".`;
}
return SystemPrompt;
}
async function main() {
const mentors = getMentors();
const question = process.argv[2];
let theConversation = `Here is the conversation so far.\nYou: ${question}\n`
for await (const mentor of mentors) {
const SystemPrompt = await getSystemPrompt(mentor, mentor === mentors[mentorCount - 1], question);
ollama.setModel(getMentorFileName(mentor));
ollama.setSystemPrompt(SystemPrompt);
let output = '';
process.stdout.write(`\n${mentor.char}: `);
for await (const chunk of ollama.streamingGenerate(theConversation + `Continue the conversation as if you were ${mentor.char} on the question "${question}".`)) {
if (chunk.response) {
output += chunk.response;
process.stdout.write(chunk.response);
} else {
process.stdout.write('\n');
}
}
theConversation += `${mentor.char}: ${output}\n\n`
}
}
main();
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examples/typescript-mentors/package.json deleted 100644 → 0
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{
"scripts": {
"charactergen": "tsx character-generator.ts",
"start": "tsx mentors.ts"
},
"dependencies": {
"fs": "^0.0.1-security",
"ollama-node": "^0.0.3",
"path": "^0.12.7"
},
"devDependencies": {
"tsx": "^4.6.2",
"typescript": "^5.3.3"
}
}
examples/typescript-simplechat/client.ts deleted 100644 → 0
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import * as readline from "readline";
const model = "llama3.2";
type Message = {
role: "assistant" | "user" | "system";
content: string;
}
const messages: Message[] = [{
role: "system",
content: "You are a helpful AI agent."
}]
const rl = readline.createInterface({
input: process.stdin,
output: process.stdout
})
async function chat(messages: Message[]): Promise<Message> {
const body = {
model: model,
messages: messages
}
const response = await fetch("http://localhost:11434/api/chat", {
method: "POST",
body: JSON.stringify(body)
})
const reader = response.body?.getReader()
if (!reader) {
throw new Error("Failed to read response body")
}
let content = ""
while (true) {
const { done, value } = await reader.read()
if (done) {
break;
}
const rawjson = new TextDecoder().decode(value);
const json = JSON.parse(rawjson)
if (json.done === false) {
process.stdout.write(json.message.content);
content += json.message.content
}
}
return { role: "assistant", content: content };
}
async function askQuestion(): Promise<void> {
return new Promise<void>((resolve) => {
rl.question("\n\nAsk a question: (press enter alone to quit)\n\n", async (user_input) => {
if (user_input.trim() === "") {
rl.close();
console.log("Thankyou. Goodbye.\n")
console.log("=======\nHere is the message history that was used in this conversation.\n=======\n")
messages.forEach(message => {
console.log(message)
})
resolve();
} else {
console.log();
messages.push({ role: "user", content: user_input });
messages.push(await chat(messages));
await askQuestion(); // Ask the next question
}
});
});
}
async function main() {
await askQuestion();
}
main();
examples/typescript-simplechat/package.json deleted 100644 → 0
View file @ 17fcdea6
{
"scripts": {
"start": "tsx client.ts"
},
"dependencies": {
"@types/node": "^20.10.4",
"prompt-sync": "^4.2.0",
"readline": "^1.3.0",
"tsx": "^4.6.2",
"typescript": "^5.3.3"
}
}
\ No newline at end of file
examples/typescript-simplechat/readme.md deleted 100644 → 0
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# Simple Chat Example
The **chat** endpoint, available as of v0.1.14, is one of two ways to generate text from an LLM with Ollama. At a high level, you provide the endpoint an array of message objects with a role and content specified. Then with each output and prompt, you add more messages, which builds up the history.
## Run the Example
`npm start`
## Review the Code
You can see in the **chat** function that is actually calling the endpoint is simply done with:
```typescript
const body = {
model: model,
messages: messages
}
const response = await fetch("http://localhost:11434/api/chat", {
method: "POST",
body: JSON.stringify(body)
})
```
With the **generate** endpoint, you need to provide a `prompt`. But with **chat**, you provide `messages`. And the resulting stream of responses includes a `message` object with a `content` field.
The final JSON object doesn't provide the full content, so you will need to build the content yourself. In this example, **chat** takes the full array of messages and outputs the resulting message from this call of the chat endpoint.
In the **askQuestion** function, we collect `user_input` and add it as a message to our messages, and that is passed to the chat function. When the LLM is done responding, the output is added as another message to the messages array.
At the end, you will see a printout of all the messages.
## Next Steps
In this example, all generations are kept. You might want to experiment with summarizing everything older than 10 conversations to enable longer history with less context being used.
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