llama.cpp/tools/server/public_simplechat

SimpleChat / AnveshikaSallap

by Humans for All.

Quickstart

Server

From the root directory of llama.cpp source code repo containing build / tools / ... sub directories

Start ai engine / server using

build/bin/llama-server -m <path/to/model.gguf> \
  --path tools/server/public_simplechat --jinja

• --jinja enables tool‑calling support
• --mmproj <path/to/mmproj.gguf> enables vision support
• --port <port number> use if a custom port is needed

If one needs web related access / tool calls dont forget to run

cd tools/server/public_simplechat/local.tools; python3 ./simpleproxy.py --config simpleproxy.json

• --debug True enables debug mode which captures internet handshake data.

Client

1. Open http://127.0.0.1:PORT/index.html in a browser

2. Select / Create a chat session

   • set a suitable system prompt, if needed
   • modify settings, if needed
   • Restore loads last autosaved session with same name

3. Enter the query, press Enter

   • use Shift‑Enter for newline
   • include images if required (ai vision models)

4. View any streamed ai response (if enabled and supported)

5. If a tool call is requested

   • verify / edit the tool call details before triggering the same
     • one can even ask ai to rethink on the tool call requested, by sending a appropriate user response instead of a tool call response.
   • tool call is executed using browser's web worker or simpleproxy
   • tool call response is placed in user input area (with color coding)
   • verify / edit the tool call response, before submit same back to ai
     • tool response initially assigned TOOL-TEMP role, promoted to TOOL upon submit
   • based on got response, if needed one can rerun tool call with modified arguments
   • at any time there can be one pending tool call wrt a chat session

6. Delete / Copy available via popover menu for each message

7. Clear / + New chat with provided buttons, as needed

Overview

A lightweight ai chat client with a web front-end that supports multiple chat sessions, vision, reasoning and tool calling.

• Supports multiple independent chat sessions with

  • One‑shot or streamed (default) responses
  • Custom settings and system prompts per session
  • Automatic local autosave (restorable on next load)
  • can handshake with /completions or /chat/completions (default) endpoints

• Supports peeking at model's reasoning live

  • if model streams the same and
  • streaming mode is enabled in settings (default)

• Supports vision / image / multimodal ai models

  • attach image files as part of user chat messages
    • handshaked as image_urls in chat message content array along with text
    • supports multi-image uploads per message
    • images displayed inline in the chat history
  • specify mmproj file via -mmproj or using -hf
  • specify -batch-size and -ubatch-size if needed

• Built-in support for GenAI models that expose tool calling

  • includes a bunch of useful builtin tool calls, without needing any additional setup

  • direct browser based tool calls include

    • sys_date_time, simple_calculator, run_javascript_function_code, data_store_*, external_ai
    • except for external_ai, these are run from within a web worker context to isolate main context from them
    • data_store brings in browser IndexedDB based persistant key/value storage across sessions

  • along with included python based simpleproxy.py

    • search_web_text, fetch_web_url_raw, fetch_html_text, fetch_pdf_as_text, fetch_xml_filtered
      • these built‑in tool calls (via SimpleProxy) help fetch PDFs, HTML, XML or perform web search
      • PDF tool also returns an outline with numbering
      • result is truncated to iResultMaxDataLength (default 128 kB)
    • helps isolate these functionality into a separate vm running locally or otherwise, if needed
    • supports whitelisting of allowed.schemes and allowed.domains through simpleproxy.json
    • supports a bearer token shared between server and client for auth
      • needs https support, for better security wrt this flow, avoided now given mostly local use

  • follows a safety first design and lets the user

    • verify and optionally edit the tool call requests, before executing the same
    • verify and optionally edit the tool call response, before submitting the same
    • user can update the settings for auto executing these actions, if needed

  • external_ai allows invoking a separate fresh ai instance

    • ai could run self modified targeted versions of itself/... with custom system prompts and user messages as needed
    • user can bring in an ai instance with additional compute access, which should be used only if needed
    • tool calling is currently kept disabled in such a instance

• Client side Sliding window Context control, using iRecentUserMsgCnt, helps limit context sent to ai model

• Optional auto trimming of trailing garbage from model outputs

• Follows responsive design to try adapt to any screen size

• built using plain html + css + javascript and python

  • no additional dependencies that one needs to keep track of
    • except for pypdf, if pdf support needed. automaticaly drops pdf tool call if pypdf missing
  • fits within ~260KB even in uncompressed source form (including simpleproxy.py)
  • easily extend with additional tool calls using either javascript or python, for additional functionality

Start exploring / experimenting with your favorite ai models and thier capabilities.

Configuration / Settings

One can modify the session configuration using Settings UI. All the settings and more are also exposed in the browser console via document['gMe'].

Settings Groups

Group	Purpose
chatProps	ApiEndpoint, streaming, sliding window, ...
tools	enabled, proxyUrl, proxyAuthInsecure, search URL/template & drop rules, max data length, timeouts
apiRequestOptions	temperature, max_tokens, frequency_penalty, presence_penalty, cache_prompt, ...
headers	Content-Type, Authorization, ...

Some specific settings

• Ai Server (baseURL)
  • ai server (llama-server) address
  • default is http://127.0.0.1:PORT
• Stream (stream)
  • true for live streaming, false for oneshot
• Client side Sliding Window (iRecentUserMsgCnt)
  • -1 : send full history
  • 0 : only system prompt
  • >0 : last N user messages after the most recent system prompt
• Cache Prompt (cache_prompt)
  • enables server‑side caching of system prompt and history
• Tool Call Timeout (toolCallResponseTimeoutMS)
  • 200s by default
• Tool call Auto (autoSecs)
  • seconds to wait before auto-triggering tool calls and auto-submitting tool responses
  • default is 0 ie manual
• Trim Garbage (bTrimGarbage)
  • Removes repeated trailing text

Debugging Tips

• Local TCPdump
  • sudo tcpdump -i lo -s 0 -vvv -A host 127.0.0.1 and port 8080
• Browser DevTools
  • inspect document['gMe'] for session state
• Reset Tool Call
  • delete any assistant response after the tool call handshake
  • next wrt the last tool message
    • set role back to TOOL-TEMP
      • edit the response as needed
    • delete the same
      • user will be given option to edit and retrigger the tool call
    • submit the new response

At the end

A thank you to all open source and open model developers, who strive for the common good.