
# SimpleChat

by Humans for All.

## quickstart

To run from the build dir

bin/llama-server -m path/model.gguf --path ../tools/server/public_simplechat --jinja

Continue reading for the details.

## overview

This simple web frontend, allows triggering/testing the server's /completions or /chat/completions endpoints
in a simple way with minimal code from a common code base. Additionally it also allows end users to have
single or multiple independent chat sessions with back and forth chatting to an extent, with the ai llm model
at a basic level, with their own system prompts.

This allows seeing the generated text / ai-model response in oneshot at the end, after it is fully generated,
or potentially as it is being generated, in a streamed manner from the server/ai-model.

![Chat and Settings (old) screens](./simplechat_screens.webp "Chat and Settings (old) screens")

Auto saves the chat session locally as and when the chat is progressing and inturn at a later time when you
open SimpleChat, option is provided to restore the old chat session, if a matching one exists. In turn if
any of those chat sessions were pending wrt user triggering a tool call or submitting a tool call response,
the ui is setup as needed for end user to continue with those previously saved sessions, from where they
left off.

The UI follows a responsive web design so that the layout can adapt to available display space in a usable
enough manner, in general.

Allows developer/end-user to control some of the behaviour by updating gMe members from browser's devel-tool
console. Parallely some of the directly useful to end-user settings can also be changed using the provided
settings ui.

For GenAi/LLM models supporting tool / function calling, allows one to interact with them and explore use of
ai driven augmenting of the knowledge used for generating answers as well as for cross checking ai generated
answers logically / programatically and by checking with other sources and lot more by making using of the
simple yet useful predefined tools / functions provided by this client web ui. The end user is provided full
control over tool calling and response submitting.

For GenAi/LLM models which support reasoning, the thinking of the model will be shown to the end user as the
model is running through its reasoning.

NOTE: As all genai/llm web service apis may or may not expose the model context length directly, and also
as using ai out of band for additional parallel work may not be efficient given the loading of current systems
by genai/llm models, so client logic doesnt provide any adaptive culling of old messages nor of replacing them
with summary of their content etal. However there is a optional sliding window based chat logic, which provides
a simple minded culling of old messages from the chat history before sending to the ai model.

NOTE: Wrt options sent with the request, it mainly sets temperature, max_tokens and optionaly stream as well
as tool_calls mainly for now. However if someone wants they can update the js file or equivalent member in
gMe as needed.

NOTE: One may be able to use this to chat with openai api web-service /chat/completions endpoint, in a very
limited / minimal way. One will need to set model, openai url and authorization bearer key in settings ui.


## usage

One could run this web frontend directly using server itself or if anyone is thinking of adding a built in web
frontend to configure the server over http(s) or so, then run this web frontend using something like python's
http module.

### running directly using tools/server

./llama-server -m path/model.gguf --path tools/server/public_simplechat [--port PORT]

### running using python3's server module

first run tools/server
* ./llama-server -m path/model.gguf

next run this web front end in tools/server/public_simplechat
* cd ../tools/server/public_simplechat
* python3 -m http.server PORT

### for tool calling

remember to

* pass --jinja to llama-server to enable tool calling support from the server ai engine end.

* set tools.enabled to true in the settings page of the client side gui.

* use a GenAi/LLM model which supports tool calling.

* if fetch web page or web search tool call is needed remember to run bundled local.tools/simpleproxy.py
  helper along with its config file, before using/loading this client ui through a browser

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

  * remember that this is a relatively minimal dumb proxy logic along with optional stripping of non textual
  content like head, scripts, styles, headers, footers, ... Be careful when accessing web through this and
  use it only with known safe sites.

  * look into local.tools/simpleproxy.json for specifying

    * the white list of allowed.domains
    * the shared bearer token between server and client ui

* other builtin tool / function calls like calculator, javascript runner, DataStore dont require the
  simpleproxy.py helper.



### using the front end

Open this simple web front end from your local browser

* http://127.0.0.1:PORT/index.html

Once inside

* If you want to, you can change many of the default global settings
  * the base url (ie ip addr / domain name, port)
  * chat (default) vs completion mode
  * try trim garbage in response or not
  * amount of chat history in the context sent to server/ai-model
  * oneshot or streamed mode.
  * use built in tool calling or not and its related params.

* In completion mode >> note: most recent work has been in chat mode <<
  * one normally doesnt use a system prompt in completion mode.
  * logic by default doesnt insert any role specific "ROLE: " prefix wrt each role's message.
    If the model requires any prefix wrt user role messages, then the end user has to
    explicitly add the needed prefix, when they enter their chat message.
    Similarly if the model requires any prefix to trigger assistant/ai-model response,
    then the end user needs to enter the same.
    This keeps the logic simple, while still giving flexibility to the end user to
    manage any templating/tagging requirement wrt their messages to the model.
  * the logic doesnt insert newline at the begining and end wrt the prompt message generated.
    However if the chat being sent to /completions end point has more than one role's message,
    then insert newline when moving from one role's message to the next role's message, so
    that it can be clearly identified/distinguished.
  * given that /completions endpoint normally doesnt add additional chat-templating of its
    own, the above ensures that end user can create a custom single/multi message combo with
    any tags/special-tokens related chat templating to test out model handshake. Or enduser
    can use it just for normal completion related/based query.

* If you want to provide a system prompt, then ideally enter it first, before entering any user query.
  Normally Completion mode doesnt need system prompt, while Chat mode can generate better/interesting
  responses with a suitable system prompt.
  * if chat.add_system_begin is used
    * you cant change the system prompt, after it is has been submitted once along with user query.
    * you cant set a system prompt, after you have submitted any user query
  * if chat.add_system_anytime is used
    * one can change the system prompt any time during chat, by changing the contents of system prompt.
    * inturn the updated/changed system prompt will be inserted into the chat session.
    * this allows for the subsequent user chatting to be driven by the new system prompt set above.

* Enter your query and either press enter or click on the submit button.
  If you want to insert enter (\n) as part of your chat/query to ai model, use shift+enter.

* Wait for the logic to communicate with the server and get the response.
  * the user is not allowed to enter any fresh query during this time.
  * the user input box will be disabled and a working message will be shown in it.
  * if trim garbage is enabled, the logic will try to trim repeating text kind of garbage to some extent.

* any reasoning / thinking by the model is shown to the end user, as it is occuring, if the ai model
  shares the same over the http interface.

* tool calling flow when working with ai models which support tool / function calling
  * if tool calling is enabled and the user query results in need for one of the builtin tools to be
    called, then the ai response might include request for tool call.
  * the SimpleChat client will show details of the tool call (ie tool name and args passed) requested
    and allow the user to trigger it as is or after modifying things as needed.
    NOTE: Tool sees the original tool call only, for now
  * inturn returned / generated result is placed into user query entry text area with approriate tags
    ie <tool_response> generated result with meta data </tool_response>
  * if user is ok with the tool response, they can click submit to send the same to the GenAi/LLM.
    User can even modify the response generated by the tool, if required, before submitting.
  * ALERT: Sometimes the reasoning or chat from ai model may indicate tool call, but you may actually
    not get/see a tool call, in such situations, dont forget to cross check that tool calling is
    enabled in the settings.

* just refresh the page, to reset wrt the chat history and or system prompt and start afresh.
  This also helps if you had forgotten to start the bundled simpleproxy.py server before hand.
  Start the simpleproxy.py server and refresh the client ui page, to get access to web access
  related tool calls.
  * if you refreshed unknowingly, you can use the Restore feature to try load the previous chat
    session and resume that session. This uses a basic local auto save logic that is in there.

* Using NewChat one can start independent chat sessions.
  * two independent chat sessions are setup by default.

* When you want to print, switching ChatHistoryInCtxt to Full and clicking on the chat session button of
  interest, will display the full chat history till then wrt same, if you want full history for printing.


## Devel note

### Reason behind this

The idea is to be easy enough to use for basic purposes, while also being simple and easily discernable
by developers who may not be from web frontend background (so inturn may not be familiar with template /
end-use-specific-language-extensions driven flows) so that they can use it to explore/experiment things.

And given that the idea is also to help explore/experiment for developers, some flexibility is provided
to change behaviour easily using the devel-tools/console or provided minimal settings ui (wrt few aspects).
Skeletal logic has been implemented to explore some of the end points and ideas/implications around them.


### General

Me/gMe consolidates the settings which control the behaviour into one object.
One can see the current settings, as well as change/update them using browsers devel-tool/console.
It is attached to the document object. Some of these can also be updated using the Settings UI.

  * baseURL - the domain-name/ip-address and inturn the port to send the request.

  * chatProps - maintain a set of properties which manipulate chatting with ai engine

    * apiEP - select between /completions and /chat/completions endpoint provided by the server/ai-model.

    * stream - control between oneshot-at-end and live-stream-as-its-generated collating and showing of the generated response.

      the logic assumes that the text sent from the server follows utf-8 encoding.

      in streaming mode - if there is any exception, the logic traps the same and tries to ensure that text generated till then is not lost.

      * if a very long text is being generated, which leads to no user interaction for sometime and inturn the machine goes into power saving mode or so, the platform may stop network connection, leading to exception.

    * iRecentUserMsgCnt - a simple minded SlidingWindow to limit context window load at Ai Model end. This is set to 10 by default. So in addition to latest system message, last/latest iRecentUserMsgCnt user messages after the latest system prompt and its responses from the ai model will be sent to the ai-model, when querying for a new response. Note that if enabled, only user messages after the latest system message/prompt will be considered.

      This specified sliding window user message count also includes the latest user query.

      * less than 0 : Send entire chat history to server

      * 0 : Send only the system message if any to the server

      * greater than 0 : Send the latest chat history from the latest system prompt, limited to specified cnt.

    * bCompletionFreshChatAlways - whether Completion mode collates complete/sliding-window history when communicating with the server or only sends the latest user query/message.

    * bCompletionInsertStandardRolePrefix - whether Completion mode inserts role related prefix wrt the messages that get inserted into prompt field wrt /Completion endpoint.

    * bTrimGarbage - whether garbage repeatation at the end of the generated ai response, should be trimmed or left as is. If enabled, it will be trimmed so that it wont be sent back as part of subsequent chat history. At the same time the actual trimmed text is shown to the user, once when it was generated, so user can check if any useful info/data was there in the response.

      One may be able to request the ai-model to continue (wrt the last response) (if chat-history is enabled as part of the chat-history-in-context setting), and chances are the ai-model will continue starting from the trimmed part, thus allows long response to be recovered/continued indirectly, in many cases.

      The histogram/freq based trimming logic is currently tuned for english language wrt its is-it-a-alpabetic|numeral-char regex match logic.

  * tools - contains controls related to tool calling

    * enabled - control whether tool calling is enabled or not

      remember to enable this only for GenAi/LLM models which support tool/function calling.

    * proxyUrl - specify the address for the running instance of bundled local.tools/simpleproxy.py

    * proxyAuthInsecure - shared token between simpleproxy.py server and client ui, for accessing service provided by it.

      Shared token is currently hashed with the current year and inturn handshaked over the network. In future if required one could also include a dynamic token provided by simpleproxy server during /aum handshake and running counter or so into hashed token. ALERT: However do remember that currently the handshake occurs over http and not https, so others can snoop the network and get token. Per client ui running counter and random dynamic token can help mitigate things to some extent, if required in future.

    * searchUrl - specify the search engine's search url template along with the tag SEARCHWORDS in place where the search words should be substituted at runtime.

    * toolCallResponseTimeoutMS - specifies the time (in msecs) for which the logic should wait for a tool call to respond
    before a default timed out error response is generated and control given back to end user, for them to decide whether
    to submit the error response or wait for actual tool call response further.

    * auto - the amount of time in seconds to wait before the tool call request is auto triggered and generated response is auto submitted back.

      setting this value to 0 (default), disables auto logic, so that end user can review the tool calls requested by ai and if needed even modify them, before triggering/executing them as well as review and modify results generated by the tool call, before submitting them back to the ai.

    the builtin tools' meta data is sent to the ai model in the requests sent to it.

    inturn if the ai model requests a tool call to be made, the same will be done and the response sent back to the ai model, under user control, by default.

    as tool calling will involve a bit of back and forth between ai assistant and end user, it is recommended to set iRecentUserMsgCnt to 10 or more, so that enough context is retained during chatting with ai models with tool support.

  * apiRequestOptions - maintains the list of options/fields to send along with api request, irrespective of whether /chat/completions or /completions endpoint.

    If you want to add additional options/fields to send to the server/ai-model, and or modify the existing options value or remove them, for now you can update this global var using browser's development-tools/console.

    For string, numeric and boolean fields in apiRequestOptions, including even those added by a user at runtime by directly modifying gMe.apiRequestOptions, setting ui entries will be auto created.

    cache_prompt option supported by example/server is allowed to be controlled by user, so that any caching supported wrt system-prompt and chat history, if usable can get used. When chat history sliding window is enabled, cache_prompt logic may or may not kick in at the backend wrt same, based on aspects related to model, positional encoding, attention mechanism etal. However system prompt should ideally get the benefit of caching.

  * headers - maintains the list of http headers sent when request is made to the server. By default

    * Content-Type is set to application/json.

    * Additionally Authorization entry is provided, which can be set if needed using the settings ui.


By using gMe's chatProps.iRecentUserMsgCnt and apiRequestOptions.max_tokens/n_predict one can try to
control the implications of loading of the ai-model's context window by chat history, wrt chat response
to some extent in a simple crude way. You may also want to control the context size enabled when the
server loads ai-model, on the server end. One can look at the current context size set on the server
end by looking at the settings/info block shown when ever one switches-to/is-shown a new session.


Sometimes the browser may be stuborn with caching of the file, so your updates to html/css/js
may not be visible. Also remember that just refreshing/reloading page in browser or for that
matter clearing site data, dont directly override site caching in all cases. Worst case you may
have to change port. Or in dev tools of browser, you may be able to disable caching fully.


Currently the settings are maintained globally and not as part of a specific chat session, including
the server to communicate with. So if one changes the server ip/url in setting, then all chat sessions
will auto switch to this new server, when you try using those sessions.


By switching between chat.add_system_begin/anytime, one can control whether one can change
the system prompt, anytime during the conversation or only at the beginning.


### Default setup

By default things are setup to try and make the user experience a bit better, if possible.
However a developer when testing the server of ai-model may want to change these value.

Using chatProps.iRecentUserMsgCnt reduce chat history context sent to the server/ai-model to be
just the system-prompt, few prev-user-requests-and-ai-responses and cur-user-request, instead of
full chat history. This way if there is any response with garbage/repeatation, it doesnt
mess with things beyond the next few question/request/query, in some ways. The trim garbage
option also tries to help avoid issues with garbage in the context to an extent.

Set max_tokens to 2048 or as needed, so that a relatively large previous reponse doesnt eat up
the space available wrt next query-response. While parallely allowing a good enough context size
for some amount of the chat history in the current session to influence future answers. However
dont forget that the server when started should also be started with a model context size of
2k or more, as needed.

  The /completions endpoint of tools/server doesnt take max_tokens, instead it takes the
  internal n_predict, for now add the same here on the client side, maybe later add max_tokens
  to /completions endpoint handling code on server side.

NOTE: One may want to experiment with frequency/presence penalty fields in apiRequestOptions
wrt the set of fields sent to server along with the user query, to check how the model behaves
wrt repeatations in general in the generated text response.

A end-user can change these behaviour by editing gMe from browser's devel-tool/console or by
using the provided settings ui (for settings exposed through the ui). The logic uses a generic
helper which autocreates property edit ui elements for the specified set of properties. If the
new property is a number or text or boolean or a object with properties within it, autocreate
logic will try handle it automatically. A developer can trap this autocreation flow and change
things if needed.


### OpenAi / Equivalent API WebService

One may be abe to handshake with OpenAI/Equivalent api web service's /chat/completions endpoint
for a minimal chatting experimentation by setting the below.

* the baseUrl in settings ui
  * https://api.openai.com/v1 or similar

* Wrt request body - gMe.apiRequestOptions
  * model (settings ui)
  * any additional fields if required in future

* Wrt request headers - gMe.headers
  * Authorization (available through settings ui)
    * Bearer THE_OPENAI_API_KEY
  * any additional optional header entries like "OpenAI-Organization", "OpenAI-Project" or so

NOTE: Not tested, as there is no free tier api testing available. However logically this might
work.


### Tool Calling

Given that browsers provide a implicit env for not only showing ui, but also running logic,
simplechat client ui allows use of tool calling support provided by the newer ai models by
end users of llama.cpp's server in a simple way without needing to worry about seperate mcp
host / router, tools etal, for basic useful tools/functions like calculator, code execution
(javascript in this case).

Additionally if users want to work with web content as part of their ai chat session, Few
functions related to web access which work with a included python based simple proxy server
have been implemented.

This can allow end users to use some basic yet useful tool calls to enhance their ai chat
sessions to some extent. It also provides for a simple minded exploration of tool calling
support in newer ai models and some fun along the way as well as occasional practical use
like

* verifying mathematical or logical statements/reasoning made by the ai model during chat
sessions by getting it to also create and execute mathematical expressions or code to verify
such stuff and so.

* access content from internet and augment the ai model's context with additional data as
needed to help generate better responses. this can also be used for
  * generating the latest news summary by fetching from news aggregator sites and collating
  organising and summarising the same
  * searching for specific topics and summarising the results
  * or so

* save collated data or generated analysis or more to the provided data store and retrieve
them later to augment the analysis / generation then. Also could be used to summarise chat
session till a given point and inturn save the summary into data store and later retrieve
the summary and continue the chat session using the summary and thus with a reduced context
window to worry about.

* use your imagination and ai models capabilities as you see fit, without restrictions from
others.

The tool calling feature has been tested with Gemma3N, Granite4 and GptOss.

ALERT: The simple minded way in which this is implemented, it provides some minimal safety
mechanism like running ai generated code in web workers and restricting web access to user
specified whitelist and so, but it can still be dangerous in the worst case, So remember
to verify all the tool calls requested and the responses generated manually to ensure
everything is fine, during interaction with ai models with tools support. One could also
always run this from a discardable vm, just in case if one wants to be extra cautious.

#### Builtin Tools

The following tools/functions are currently provided by default

##### directly in browser

* simple_calculator - which can solve simple arithmatic expressions

* run_javascript_function_code - which can be used to run ai generated or otherwise javascript code using browser's js capabilities.

* data_store_get/set/delete/list - allows for a basic data store to be used.

All of the above are run from inside web worker contexts. Currently the ai generated code / expression
is run through a simple minded eval inside a web worker mechanism. Use of WebWorker helps avoid exposing
browser global scope to the generated code directly. However any shared web worker scope isnt isolated.
Either way always remember to cross check the tool requests and generated responses when using tool calling.

##### using bundled simpleproxy.py (helps bypass browser cors restriction, ...)

* fetch_web_url_raw - fetch contents of the requested url through a proxy server

* fetch_web_url_text - fetch text parts of the content from the requested url through a proxy server.
  Related logic tries to strip html response of html tags and also head, script, style, header,footer,
  nav, ... blocks.

* search_web_text - search for the specified words using the configured search engine and return the
plain textual content from the search result page.

the above set of web related tool calls work by handshaking with a bundled simple local web proxy
(/caching in future) server logic, this helps bypass the CORS restrictions applied if trying to
directly fetch from the browser js runtime environment.

Depending on the path specified wrt the proxy server, it executes the corresponding logic. Like if
urltext path is used (and not urlraw), the logic in addition to fetching content from given url, it
tries to convert html content into equivalent plain text content to some extent in a simple minded
manner by dropping head block as well as all scripts/styles/footers/headers/nav blocks and inturn
dropping the html tags.

The client ui logic does a simple check to see if the bundled simpleproxy is running at specified
proxyUrl before enabling these web and related tool calls.

The bundled simple proxy

* can be found at
  * tools/server/public_simplechat/local.tools/simpleproxy.py

* it provides for a basic white list of allowed domains to access, to be specified by the end user.
  This should help limit web access to a safe set of sites determined by the end user. There is also
  a provision for shared bearer token to be specified by the end user.

* it tries to mimic the client/browser making the request to it by propogating header entries like
  user-agent, accept and accept-language from the got request to the generated request during proxying
  so that websites will hopefully respect the request rather than blindly rejecting it as coming from
  a non-browser entity.

In future it can be further extended to help with other relatively simple yet useful tool calls like
fetch_rss and so.

  * for now fetch_rss can be indirectly achieved using fetch_web_url_raw.

#### Extending with new tools

This client ui implements the json schema based function calling convention supported by gen ai
engines over http.

Provide a descriptive meta data explaining the tool / function being provided for tool calling,
as well as its arguments.

Provide a handler which
* implements the specified tool / function call or
* rather in some cases constructs the code to be run to get the tool / function call job done,
  and inturn pass the same to the provided web worker to get it executed. Use console.log while
  generating any response that should be sent back to the ai model, in your constructed code.
* once the job is done, return the generated result as needed, along with tool call related meta
  data like chatSessionId, toolCallId, toolName which was passed along with the tool call.

Update the tc_switch to include a object entry for the tool, which inturn includes
* the meta data wrt the tool call
* a reference to the handler - handler should take chatSessionId, toolCallId, toolName and toolArgs.
  It should pass these along to the tools web worker, if used.
* the result key (was used previously, may use in future, but for now left as is)

Look into tooljs.mjs and tooldb.mjs for javascript and inturn web worker based tool calls and
toolweb.mjs for the simpleproxy.py based tool calls.

#### OLD: Mapping tool calls and responses to normal assistant - user chat flow

Instead of maintaining tool_call request and resultant response in logically seperate parallel
channel used for requesting tool_calls by the assistant and the resulstant tool role response,
the SimpleChatTC pushes it into the normal assistant - user chat flow itself, by including the
tool call and response as a pair of tagged request with details in the assistant block and inturn
tagged response in the subsequent user block.

This allows GenAi/LLM to be still aware of the tool calls it made as well as the responses it got,
so that it can incorporate the results of the same in the subsequent chat / interactions.

NOTE: This flow tested to be ok enough with Gemma-3N-E4B-it-Q8_0 LLM ai model for now. Logically
given the way current ai models work, most of them should understand things as needed, but need
to test this with other ai models later.

TODO:OLD: Need to think later, whether to continue this simple flow, or atleast use tool role wrt
the tool call responses or even go further and have the logically seperate tool_calls request
structures also.

DONE: rather both tool_calls structure wrt assistant messages and tool role based tool call
result messages are generated as needed now.

#### Related stuff

Promise as well as users of promise (for now fetch) have been trapped wrt their then and catch flow,
so that any scheduled asynchronous code or related async error handling using promise mechanism also
gets executed, before tool calling returns and thus data / error generated by those async code also
get incorporated in result sent to ai engine on the server side.


### Progress

#### Done

Tool Calling support added, along with a bunch of useful tool calls as well as a bundled simple proxy
if one wants to access web as part of tool call usage.

Reasoning / thinking response from Ai Models is shown to the user, as they are being generated/shared.

Chat Messages/Session and UI handling have been moved into corresponding Classes to an extent, this
helps ensure that
* switching chat sessions or loading a previous auto saved chat session will restore state including
  ui such that end user can continue the chat session from where they left it, even if in the middle
  of a tool call handshake.
* new fields added to http handshake in oneshot or streaming mode can be handled in a structured way
  to an extent.

Chat message parts seperated out and tagged to allow theming chat message as needed in future.
The default Chat UI theme/look changed to help differentiate between different messages in chat
history as well as the parts of each message in a slightly better manner. Change the theme slightly
between normal and print views (beyond previous infinite height) for better printed chat history.

A builtin data store related tool calls, inturn built on browser's indexedDB, without needing any
proxy / additional helper to handle the store. One could use the ai assistant to store ones (ie end
users) own data or data of ai model.

Trap http response errors and inform user the specific error returned by ai server.


#### ToDo

Is the tool call promise land trap deep enough, need to think through and explore around this once later.

Handle multimodal handshaking with ai models.

Add fetch_rss and may be different document formats processing related tool calling, in turn through
the simpleproxy.py if and where needed.

Save used config entries along with the auto saved chat sessions and inturn give option to reload the
same when saved chat is loaded.

MAYBE make the settings in general chat session specific, rather than the current global config flow.

Provide tool to allow for specified pdf files to be converted to equivalent plain text form, so that ai
can be used to work with the content in those PDFs.

### Debuging the handshake and beyond

When working with llama.cpp server based GenAi/LLM running locally, to look at the handshake directly
from the commandline, you could run something like below

* sudo tcpdump -i lo -s 0 -vvv -A host 127.0.0.1 and port 8080 | tee /tmp/td.log
* or one could also try look at the network tab in the browser developer console

One could always remove message entries or manipulate chat sessions by accessing document['gMe']
in devel console of the browser

* if you want the last tool call response you submitted to be re-available for tool call execution and
  resubmitting of response fresh, for any reason, follow below steps
  * remove the assistant response from end of chat session, if any, using
    * document['gMe'].multiChat.simpleChats['SessionId'].xchat.pop()
  * reset role of Tool response chat message to TOOL.TEMP from tool
    * toolMessageIndex = document['gMe'].multiChat.simpleChats['SessionId'].xchat.length - 1
    * document['gMe'].multiChat.simpleChats['SessionId'].xchat[toolMessageIndex].role = "TOOL.TEMP"
  * clicking on the SessionId at top in UI, should refresh the chat ui and inturn it should now give
    the option to control that tool call again
  * this can also help in the case where the chat session fails with context window exceeded
    * you restart the GenAi/LLM server after increasing the context window as needed
    * edit the chat session history as mentioned above, to the extent needed
    * resubmit the last needed user/tool response as needed


## At the end

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