Creating a support chat bot for my business

API
1

Hello

I am trying to create a kind of Support Bot to answer my clients about specific technical details about WordPress plugins that I sell.

The goal is that the /completitions api would be feeded a prompt which could be something general like a CSS styling change which the davinci engine knows without any specific data about my business, but the customer might ask something specific for which I have about a data set of 3000 questions and answers ( prompts / completions ? input / output ? ) on which the bot can feed on. - exactly like this awesome example here

I am a web developer and I don’t have experience with AI. I am just scratching the surface trying to put this bot together learning concepts like Machine Learning, Training Data, Validation Set, Plotting, Neural Network. So bear with me cause it’s a lot to grasp :pleading_face:

So first of all, I did a lot of documenting and getting an API key from OPENAI is certainly the first step.

Then I told ChatGPT my story and what I tried to achieve, I asked him to write in PHP preferably , but it always ends up hallucinating so could not really use anything he generated without adjusting. And the further I asked him about specifics, the further he hallucinated.

So I read a lot of documentation and extrapolating with what I got from ChatGPT I think there are 3 ways to achieve this:

  • fine tuned model
  • uploading a training set and a validation set
  • embeddings api ( which the example that I linked uses )

Understanding that most examples are on Python, I started mlq.ai/gpt-3-fine-tuning-key-concepts/ tutorial - then I prepared with fine-tunes.prepare DATA_UNDER_COMMENT into the json by line to be categorised in prompt and completions

Then, I used openai api fine_tunes.create -t to create my fine-tune

and now I have my fine-tune created and I run

openai.Completion.create(
    model=FINE_TUNED_MODEL,
    prompt=YOUR_PROMPT)

this looked like the way to go, but even if put a basic question that was actually in the JSONL, it’s like the engine forgot to talk and outputs random characters

so I tried another approach from the cookbook which seems pretty great - following this which seems exactly like what I want to achieve .

Blockquote The GPT models have picked up a lot of general knowledge in training, but we often need to ingest and use a large library of more specific information.

I tried to use the code there, but with my CSV hosted online I got a 406 response when trying to load
Then I stored the CSV locally, and it complained that a column ( tokens ) was not available for converting to int ( 10 )
Then from what I could understand I switched from using load_embeddings to compute_doc_embeddings because it says from the documentation that they already have the embedding generated for that CSV

I did that, but now it asks for a json instated of CSV

Of course, I am able to provide my data in any format, so when I tried to load my data it says that the token limit of 8000 is exceeded for this request

I now try to input a small json here - under a comment

and try to run a prompt. And, kind of amazing, after hours of work, it seems to work. I provide a question from the data, but under a different structure and it replies to me correctly, using a different wording than the one from the json data.

he could have not known this from the general knowledge.

so this is what I want to achieve, but my data set is much larger.

I need help to understand if my approach is correct . And if Embedding is the way to go , how to feed data into OpenAI and reference that embeddings set when doing api calls to completions. Ideally I would have those embeddings stored somehow with the possibility to add to them. Just like I have fine tune sets or files under my API account.

2 Likes
2

Welcome to the forum.

If you search, you’ll find a lot of gems regarding what you’re asking.

Sounds like you’re on the right track. Good luck!

1 Like
3

It seems like you might need some help with the infrastructure necessary to integrate Embeddings and GPT. LlamaIndex/GPT Index is an open-source project you might find helpful in that case.

3 Likes
4

I have a similar question, I’m trying to use llama index in a commercial app. I’m using it to create an index object from my custom data and query it. I’m assuming chatgpt is actually creating the embeddings for the index, and I have a paid openai account who’s api key I provide to make the code work. I’m wondering if llama index can be used in commercial app like this. my example function is below:

def index_response(api_key,text_path,query):

    # api key you generate in your openai account

    import os

    # add your openai api key here
    os.environ['OPENAI_API_KEY'] = api_key

    # Load you data into 'Documents' a custom type by LlamaIndex
    from llama_index import SimpleDirectoryReader

    documents = SimpleDirectoryReader(text_path).load_data()

    from llama_index import GPTVectorStoreIndex

    index = GPTVectorStoreIndex.from_documents(documents)

    query_engine = index.as_query_engine()
    response = query_engine.query(query)

    return response.response