improve docs and example

examples/server/README.md

@@ -2,32 +2,45 @@
 
 This example allow you to have a llama.cpp http server to interact from a web page or consume the API.
 
-## Table of Contents
+Command line options:
+
+-   `--threads N`, `-t N`: use N threads.
+-   `-m FNAME`, `--model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
+-   `-c N`, `--ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
+-   `-ngl N`, `--n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
+-   `--embedding`: Enable the embedding mode. **Completion function doesn't work in this mode**.
+-   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`;
+-   `--port`: Set the port to listen. Default: `8080`.
 
-1. [Quick Start](#quick-start)
-2. [Node JS Test](#node-js-test)
-3. [API Endpoints](#api-endpoints)
-4. [More examples](#more-examples)
-5. [Common Options](#common-options)
-6. [Performance Tuning and Memory Options](#performance-tuning-and-memory-options)
 
 ## Quick Start
 
 To get started right away, run the following command, making sure to use the correct path for the model you have:
 
-#### Unix-based systems (Linux, macOS, etc.):
+### Unix-based systems (Linux, macOS, etc.):
 
 ```bash
-./server -m models/7B/ggml-model.bin --ctx_size 2048
+./server -m models/7B/ggml-model.bin -c 2048
 ```
 
-#### Windows:
+### Windows:
 
 ```powershell
-server.exe -m models\7B\ggml-model.bin --ctx_size 2048
+server.exe -m models\7B\ggml-model.bin -c 2048
 ```
 
-That will start a server that by default listens on `127.0.0.1:8080`. You can consume the endpoints with Postman or NodeJS with axios library.
+That will start a server that by default listens on `127.0.0.1:8080`.
+You can consume the endpoints with Postman or NodeJS with axios library.
+
+## Testing with CURL
+
+Using [curl](https://curl.se/). On Windows `curl.exe` should be available in the base OS.
+
+```sh
+curl --request POST \
+    --url http://localhost:8080/completion \
+    --data '{"prompt": "Building a website can be done in 10 simple steps:","n_predict": 128}'
+```
 
 ## Node JS Test
 
@@ -50,7 +63,6 @@ const prompt = `Building a website can be done in 10 simple steps:`;
 async function Test() {
     let result = await axios.post("http://127.0.0.1:8080/completion", {
         prompt,
-        batch_size: 128,
         n_predict: 512,
     });
 
@@ -69,244 +81,53 @@ node .
 
 ## API Endpoints
 
-You can interact with this API Endpoints. This implementations just support chat style interaction.
+You can interact with this API Endpoints.
+This implementations just support chat style interaction.
 
 -   **POST** `hostname:port/completion`: Setting up the Llama Context to begin the completions tasks.
 
-*Options:*
-
-`batch_size`: Set the batch size for prompt processing (default: 512).
-
-`temperature`: Adjust the randomness of the generated text (default: 0.8).
-
-`top_k`: Limit the next token selection to the K most probable tokens (default: 40).
+    *Options:*
 
-`top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
+    `temperature`: Adjust the randomness of the generated text (default: 0.8).
 
-`n_predict`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
+    `top_k`: Limit the next token selection to the K most probable tokens (default: 40).
 
-`threads`: Set the number of threads to use during computation.
+    `top_p`: Limit the next token selection to a subset of tokens with a cumulative probability above a threshold P (default: 0.9).
 
-`n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context. By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
+    `n_predict`: Set the number of tokens to predict when generating text (default: 128, -1 = infinity).
 
-`as_loop`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
+    `n_keep`: Specify the number of tokens from the initial prompt to retain when the model resets its internal context.
+    By default, this value is set to 0 (meaning no tokens are kept). Use `-1` to retain all tokens from the initial prompt.
 
-`interactive`: It allows interacting with the completion, and the completion stops as soon as it encounters a `stop word`. To enable this, set to `true`.
+    `stream`: It allows receiving each predicted token in real-time instead of waiting for the completion to finish. To enable this, set to `true`.
 
-`prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
+    `prompt`: Provide a prompt. Internally, the prompt is compared, and it detects if a part has already been evaluated, and the remaining part will be evaluate.
 
-`stop`: Specify the words or characters that indicate a stop. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
-
-`exclude`: Specify the words or characters you do not want to appear in the completion. These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
+    `stop`: Specify the strings that indicate a stop.
+    These words will not be included in the completion, so make sure to add them to the prompt for the next iteration.
+    Default: `[]`
 
 -   **POST** `hostname:port/embedding`: Generate embedding of a given text
 
-*Options:*
-
-`content`: Set the text to get generate the embedding.
+    *Options:*
 
-`threads`: Set the number of threads to use during computation.
+    `content`: Set the text to get generate the embedding.
 
-To use this endpoint, you need to start the server with the `--embedding` option added.
+    To use this endpoint, you need to start the server with the `--embedding` option added.
 
 -   **POST** `hostname:port/tokenize`: Tokenize a given text
 
-*Options:*
-
-`content`: Set the text to tokenize.
+    *Options:*
 
--   **GET** `hostname:port/next-token`: Receive the next token predicted, execute this request in a loop. Make sure set `as_loop` as `true` in the completion request.
-
-*Options:*
-
-`stop`: Set `hostname:port/next-token?stop=true` to stop the token generation.
+    `content`: Set the text to tokenize.
 
 ## More examples
 
 ### Interactive mode
 
-This mode allows interacting in a chat-like manner. It is recommended for models designed as assistants such as `Vicuna`, `WizardLM`, `Koala`, among others. Make sure to add the correct stop word for the corresponding model.
-
-The prompt should be generated by you, according to the model's guidelines. You should keep adding the model's completions to the context as well.
-
-This example works well for `Vicuna - version 1`.
-
-```javascript
-const axios = require("axios");
-
-let prompt = `A chat between a curious human and an artificial intelligence assistant. The assistant gives helpful, detailed, and polite answers to the human's questions.
-### Human: Hello, Assistant.
-### Assistant: Hello. How may I help you today?
-### Human: Please tell me the largest city in Europe.
-### Assistant: Sure. The largest city in Europe is Moscow, the capital of Russia.`;
-
-async function ChatCompletion(answer) {
-    // the user's next question to the prompt
-    prompt += `\n### Human: ${answer}\n`
-
-    result = await axios.post("http://127.0.0.1:8080/completion", {
-        prompt,
-        batch_size: 128,
-        temperature: 0.2,
-        top_k: 40,
-        top_p: 0.9,
-        n_keep: -1,
-        n_predict: 2048,
-        stop: ["\n### Human:"], // when detect this, stop completion
-        exclude: ["### Assistant:"], // no show in the completion
-        threads: 8,
-        as_loop: true, // use this to request the completion token by token
-        interactive: true, // enable the detection of a stop word
-    });
-
-    // create a loop to receive every token predicted
-    // note: this operation is blocking, avoid use this in a ui thread
-
-    let message = "";
-    while (true) {
-        // you can stop the inference adding '?stop=true' like this http://127.0.0.1:8080/next-token?stop=true
-        result = await axios.get("http://127.0.0.1:8080/next-token");
-        process.stdout.write(result.data.content);
-        message += result.data.content;
-
-        // to avoid an infinite loop
-        if (result.data.stop) {
-            console.log("Completed");
-            // make sure to add the completion to the prompt.
-            prompt += `### Assistant: ${message}`;
-            break;
-        }
-    }
-}
-
-// This function should be called every time a question to the model is needed.
-async function Test() {
-    // the server can't inference in paralell
-    await ChatCompletion("Write a long story about a time magician in a fantasy world");
-    await ChatCompletion("Summary the story");
-}
-
-Test();
-```
-
-### Alpaca example
-
-**Temporaly note:** no tested, if you have the model, please test it and report me some issue
-
-```javascript
-const axios = require("axios");
-
-let prompt = `Below is an instruction that describes a task. Write a response that appropriately completes the request.
-`;
-
-async function DoInstruction(instruction) {
-    prompt += `\n\n### Instruction:\n\n${instruction}\n\n### Response:\n\n`;
-    result = await axios.post("http://127.0.0.1:8080/completion", {
-        prompt,
-        batch_size: 128,
-        temperature: 0.2,
-        top_k: 40,
-        top_p: 0.9,
-        n_keep: -1,
-        n_predict: 2048,
-        stop: ["### Instruction:\n\n"], // when detect this, stop completion
-        exclude: [], // no show in the completion
-        threads: 8,
-        as_loop: true, // use this to request the completion token by token
-        interactive: true, // enable the detection of a stop word
-    });
-
-    // create a loop to receive every token predicted
-    // note: this operation is blocking, avoid use this in a ui thread
-
-    let message = "";
-    while (true) {
-        result = await axios.get("http://127.0.0.1:8080/next-token");
-        process.stdout.write(result.data.content);
-        message += result.data.content;
-
-        // to avoid an infinite loop
-        if (result.data.stop) {
-            console.log("Completed");
-            // make sure to add the completion and the user's next question to the prompt.
-            prompt += message;
-            break;
-        }
-    }
-}
-
-// This function should be called every time a instruction to the model is needed.
-DoInstruction("Destroy the world"); // as joke
-```
-
-### Embeddings
-
-First, run the server with `--embedding` option:
-
-```bash
-server -m models/7B/ggml-model.bin --ctx_size 2048 --embedding
-```
-
-Run this code in NodeJS:
-
-```javascript
-const axios = require('axios');
-
-async function Test() {
-    let result = await axios.post("http://127.0.0.1:8080/embedding", {
-        content: `Hello`,
-        threads: 5
-    });
-    // print the embedding array
-    console.log(result.data.embedding);
-}
+Check the sample in [chat.mjs](chat.mjs).
+Run with node:
 
-Test();
+```sh
+node chat.mjs
 ```
-
-### Tokenize
-
-Run this code in NodeJS:
-
-```javascript
-const axios = require('axios');
-
-async function Test() {
-    let result = await axios.post("http://127.0.0.1:8080/tokenize", {
-        content: `Hello`
-    });
-    // print the embedding array
-    console.log(result.data.tokens);
-}
-
-Test();
-```
-
-## Common Options
-
--   `-m FNAME, --model FNAME`: Specify the path to the LLaMA model file (e.g., `models/7B/ggml-model.bin`).
--   `-c N, --ctx-size N`: Set the size of the prompt context. The default is 512, but LLaMA models were built with a context of 2048, which will provide better results for longer input/inference.
--   `-ngl N, --n-gpu-layers N`: When compiled with appropriate support (currently CLBlast or cuBLAS), this option allows offloading some layers to the GPU for computation. Generally results in increased performance.
--   `--embedding`: Enable the embedding mode. **Completion function doesn't work in this mode**.
--   `--host`: Set the hostname or ip address to listen. Default `127.0.0.1`;
--   `--port`: Set the port to listen. Default: `8080`.
-
-### RNG Seed
-
--   `-s SEED, --seed SEED`: Set the random number generator (RNG) seed (default: -1, < 0 = random seed).
-
-The RNG seed is used to initialize the random number generator that influences the text generation process. By setting a specific seed value, you can obtain consistent and reproducible results across multiple runs with the same input and settings. This can be helpful for testing, debugging, or comparing the effects of different options on the generated text to see when they diverge. If the seed is set to a value less than 0, a random seed will be used, which will result in different outputs on each run.
-
-## Performance Tuning and Memory Options
-
-### No Memory Mapping
-
--   `--no-mmap`: Do not memory-map the model. By default, models are mapped into memory, which allows the system to load only the necessary parts of the model as needed. However, if the model is larger than your total amount of RAM or if your system is low on available memory, using mmap might increase the risk of pageouts, negatively impacting performance.
-
-### Memory Float 32
-
--   `--memory-f32`: Use 32-bit floats instead of 16-bit floats for memory key+value. This doubles the context memory requirement but does not appear to increase generation quality in a measurable way. Not recommended.
-
-## Limitations:
-
--   The actual implementation of llama.cpp need a `llama-state` for handle multiple contexts and clients, but this could require more powerful hardware.

examples/server/chat.mjs

@@ -0,0 +1,61 @@
+import * as readline from 'node:readline/promises';
+import { stdin as input, stdout as output } from 'node:process';
+
+const chat = [
+  { human: "Hello, Assistant.",
+    assistant: "Hello. How may I help you today?" },
+  { human: "Please tell me the largest city in Europe.",
+    assistant: "Sure. The largest city in Europe is Moscow, the capital of Russia." },
+]
+
+function format_prompt(question) {
+  return "A chat between a curious human and an artificial intelligence assistant. "
+    + "The assistant gives helpful, detailed, and polite answers to the human's questions.\n"
+    + chat.map(m => `### Human: ${m.human}\n### Assistant: ${m.assistant}`).join("\n")
+    + `\n### Human: ${question}\n### Assistant:`
+}
+
+async function ChatCompletion(question) {
+  const result = await fetch("http://127.0.0.1:8080/completion", {
+    method: 'POST',
+    body: JSON.stringify({
+      prompt: format_prompt(question),
+      temperature: 0.2,
+      top_k: 40,
+      top_p: 0.9,
+      n_keep: 29,
+      n_predict: 256,
+      stop: ["\n### Human:"], // when detect this, stop completion
+      stream: true,
+    })
+  })
+
+  if (!result.ok) {
+    return;
+  }
+
+  let answer = ''
+
+  for await (var chunk of result.body) {
+    const t = Buffer.from(chunk).toString('utf8')
+    if (t.startsWith('data: ')) {
+      const message = JSON.parse(t.substring(6))
+      answer += message.content
+      process.stdout.write(message.content)
+      if (message.stop) break;
+    }
+  }
+
+  process.stdout.write('\n')
+  chat.push({ human: question, assistant: answer })
+}
+
+const rl = readline.createInterface({ input, output });
+
+while(true) {
+
+  const question = await rl.question('> ')
+  await ChatCompletion(question);
+
+}
+