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The current examples/quickstart package does not cater for normal light mode users that well enough. It defaults to using the white background which then renders incorrectly the rest of the text on the Chatbot page.

Quick fix by commenting the code in the ui/index.css file Line 6 and 56-67

/* @media (prefers-color-scheme: light) {
  :root {
    color: #213547;
    background-color: #ffffff;
  }
  a:hover {
    color: #747bff;
  }
  button {
    background-color: #f9f9f9;
  }
} */

I know the UI is documented as "focused on internal MongoDB use cases", but it's already very customizable, the one exception being the chat icons (notably the MongoDB leaf). It would be great to have a property to override this with something else.

from:
https://mongodb.github.io/chatbot/server/rag/retrieve

to add a booster, to filter for a given data source, the field needs to be added to the Query index
as seen here
https://www.mongodb.com/docs/atlas/atlas-vector-search/vector-search-overview/

index definition (to be inline with mongodb-ingest)

{
  "fields": [
    {
      "numDimensions": 1536,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    },
    {
      "path": "sourceName",
      "type": "filter"
    }
  ]
}

• When creating a conversation, make so can include the project and group
  in the DB.
• api key should not be persisted in server. keep in client.

The following packages in mongodb-chatbot-ui are not on npm:

    "@lg-chat/avatar": "^2.0.6",
    "@lg-chat/chat-disclaimer": "^2.0.0",
    "@lg-chat/chat-window": "^1.0.4",
    "@lg-chat/fixed-chat-window": "^1.1.1",
    "@lg-chat/input-bar": "^3.2.6",
    "@lg-chat/leafygreen-chat-provider": "^1.0.2",
    "@lg-chat/message": "^2.0.8",
    "@lg-chat/message-feed": "^2.0.7",
    "@lg-chat/message-prompts": "^1.0.2",
    "@lg-chat/message-rating": "^1.1.3",

This results in auth errors during npm install

.nprc has these commented out lines:

//artifactory.corp.mongodb.com/artifactory/api/npm/leafygreen-ui/:_authToken=${LG_ARTIFACTORY_TOKEN}
//artifactory.corp.mongodb.com/artifactory/api/npm/leafygreen-ui/:email=${LG_ARTIFACTORY_EMAIL}
//artifactory.corp.mongodb.com/artifactory/api/npm/leafygreen-ui/:always-auth=true

//registry.npmjs.org/:_authToken=${MONGODB_EAI_NPM_TOKEN}

Do you need this, and if so where do you get these tokens??

I successfully added a function calling to my LLM (using mongo-chatbot-server)
On the call to /conversations (and /conversations/:id/messages), I pass a user ID parameter.
I store it as customData in the conversations collection.
When the user's query is about a personal request, I am able to have the LLM invoke my function.
However, I would like to know the context (user ID) so I can make a query from another MongoDB collection (or a SQL DB).
Do you have a best practice approach?

when installing the sample code, i get the following:
npm ERR! code E404
npm ERR! 404 Not Found - GET https://registry.npmjs.org/@lg-chat%2fchat-disclaimer - Not found
npm ERR! 404
npm ERR! 404 '@lg-chat/chat-disclaimer@^2.0.0' is not in this registry.
npm ERR! 404
npm ERR! 404 Note that you can also install from a
npm ERR! 404 tarball, folder, http url, or git url.

npm ERR! A complete log of this run can be found in: /Users/willesclusa/.npm/_logs/2024-02-07T03_17_36_566Z-debug-0.log

This applies to all the @lg dependancies.

When deploying mongodb-chatbot-server (onto Azure app service), the server deploys fine.
But it generates this error when calling /conversations API:

"error": "The request has an invalid IP address: xxx.xxx.xxx.xxx:54295"

xxx.xxx.xxx.xxx being my public IP address

Hi,

is mongodb-chatbot-evaluation ready to use?
I installed you and tried an "out-of-the-box"

tsc --module commonjs --target es2017 --outDir build src/eval/eval.config.ts

but eval.config.ts can not import most functions from 'mongodb-chatbot-evaluation'
Module '"mongodb-chatbot-evaluation"' has no exported member 'makeGenerateConversationData'.ts(2305)

We might implement a limit to the number of calls being made (based on user account).
Also, we would like to measure/track the usage of LLM calls.
For that, I would like to get the usage response (number of tokens used by LLM) from the LLM call, accessible in the API response, or possibly stored in the /conversations collection.

when using mongodb-chatbot-ui
as
<Chatbot
user={{ name: 'eric' }}
isExperimental={false}
darkMode={false}
serverBaseUrl="http://localhost:3000/api/v1"
>

how can I pass parameters (like userID, or app parameters) to the mongodb-chatbot-server ?
I am using middleware to extract parameters, like
const conversation = await res.locals.conversations.findById( req.params.conversationId )

but req.params is always empty.
how can I add my own property?

I redefined the fetch function to add my own header, but it feels a little "hacky"

I wanted to test with Claude3 LLM.
Out-of-the-box, framework didn't work; just wanted to share my changes:

I had to change LangchainChatLlm.ts for answerQuestionAwaited, with

...
      const prompts = ChatPromptTemplate.fromMessages(
        messages.map(m => messageBaseMessagePromptTemplateLike(m))
      )
      const chain = prompts.pipe(chatModel)
      const res = await chain.invoke({})
...

function messageBaseMessagePromptTemplateLike(
  message: OpenAiChatMessage
): BaseMessagePromptTemplateLike {
  return [message.role, message.content ?? '']
}

Some feedback:
I am looking into LangSmith as a tool to monitor and audit the queries.
One of the steps is to retrieve the result from openAiClient.getChatCompletions, and send it to LangSmith.

In my project, I have now copied all the mongodb-server package files so I can customize/debug as needed.
So I added code into the answerQuestionAwaited method to interact with LangSmith.

Wondering if this could be done another way (so I could eventually just use the mongodb-server package "as is")?

same exact logic as current POST /api-conversations/:conversationId/messages/:messageId/rating endpoint, but for api conversations

doc refers to InputBarTrigger accepting a placeholder prop.
https://mongodb.github.io/chatbot/ui
But the latest version of the code does not.

create an ApiConversations service that manages the interface between the server and database to persist api conversations.

similar to docs chat ConversationsService. though, will need to take into account the OpenAI functions + (and maybe system prompt changes?) that are available to following messages.

mongodb-chatbot-server uses VECTOR_SEARCH_INDEX_NAME from .env variables.
but mongodb-rag-ingest does not.
I wanted to setup 2 environment: one with text-embedding-ada-002 and one with text-embedding-3-large, so I could test the difference in performance. I was wondering if I could use VECTOR_SEARCH_INDEX_NAME for that purpose (basically having 2 "embedded_content" collections, each with its search index).

My system is properly working when using:
OPENAI_EMBEDDING_MODEL="text-embedding-ada-002"
for ingest and server.

However, when I use:
OPENAI_EMBEDDING_MODEL="text-embedding-3-small"

I am always getting the message associated to NO_RELEVANT_CONTENT.
Log shows

{"level":"info","message":"No matching content found","reqId":"65cc405d974b84e18cac0060"}

Do I need to change any setting in the Config?