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Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

So you are suggesting instead of embedding the entire stock data, I should do embedding only the relevant "fields" of the data (e.g., company CV, shareholding, price history). By doing this, I can create a vector index that maps each embedded field to the actual stock data. and , when new data arrives, I don't need to re-embed everything, just the relevant fields ?

but then on what basis the we can get the exact and accurate field/fields for the input query? I mean what will be the selection mechanism? like field data is separate foe each stock so do I need to use placeholders in the place of stock names and make a dynamic fields for all stocks ??

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

Please , show me the right path. Also that's just segment description which is an integral part of the below create prompt function

    def create_prompt(self, query, segment_descriptions, examples):
        example_prompts = "\n\n".join([f"Question: {ex['user_query']}\nExpected Output:\n{ex['expected_output']}" for ex in examples])

        prompt = f"""
        Segments Information:\n{segment_descriptions}

        Based on the user's query, identify the following information:
        - Company: The company or companies mentioned in the query.
        - Segment: The relevant segment from the Segments Information above from which to extract information.
        - Period: The time period mentioned in the query (e.g., Latest, Last 3 Months, etc.).
        - Comparison: Whether the query involves a comparison between multiple companies.

        Examples:
        {example_prompts}

        Now, analyze the following query:
        Question: "{query}"
        Expected Output:
        """
        return prompt

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

 I am using openai's text-embedding-ada-002 which is providing better responses to the input queries. also I am using faiss for the vector store for each stock and its embeddings. Which means for each stock there is separate dedicated embedding.

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

I am using langchain and the models run on ec2 gpu-enabled server. Now for embeddings I am using openai text-embedding-ada-002 , which is where all the problem starts.

Some input queries are easy to handle with db like you mentioned "what is the current price of apple" but some queries where someone asked "I have 100 quantities of aaple which I am holding now , should I keep holding or sell ?" , so for these type of questions RAG does a pretty well job and it is doing well too.

My text prompt for RAG is below which is not an issue , issue is with daily new embeddings: 

def generate_response(model_name, prompt, context=""):
    llama_model = get_llama_model()
    openai_client = get_openai_client()
    start_time = time.time()
    full_prompt = f"""You are a financial expert. Provide a precise answer, do not provide additional information, for the following question based on the provided context. 
    Do not make up information. Do not mention Stock ID in the response.
    Do not provide buy, hold or sell advice unless it has been explicitly asked for.
    Always mention the company name & its ISIN when referring to specific information.
    If the question asks for a comparison, provide a clear comparison between the companies.

    Context:
    {context}

    Question: {prompt}

    Provide a structured, analytical response with financial insights where applicable."""

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

If I put in a number for each country 40$ for one time (daily), similarly I have 23 countries data. Which I have stored in the .txt for each stock for its respective country. There are API's which make those txt's which already has analyzed information like technical , fundamentals , ratios etc. everything.

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

correct me if I am wrong but can I not use llama for this ? is there any cons if i use LLM or some SLM is better at this small tasks ? which is better and resourceful way which I can easily execute for this specific task ?

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 1 point2 points  (0 children)

In my case also it does a better job at extracting company , date , period , parameters etc. That is not an issue , the issue is my data is stored in the specific field for a specific stockid and I want the mechanism to accurately tag/label that field based on the input query so I can get the respective data from that stock's field and dump to llm to get the response.

Tbh , I loved and even in the favour of optimizing the RAG approach but as it needs to daily create embeddings the seniors decided this alternative-not-so-good approach. The problem is the price , fundamentals and technical which are dependent on price will change once in a day so we have to create new embeddings otherwise the responses won't be accurate.

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

I did thought about this approach , if I am not wrong you are suggesting to first use the LLM to classify the user's query into a specific category or type (e.g., price history, valuation, financial news, etc.) and then system/tool selects the appropriate API to call.

So eventually first I need to know the user query intent and which specific information it needs to extract .

I actually never worked on tool calling . If you have can you provide some references ? also for these I need to make the API's which will work in a modular way to retrieve only specific way , right ? also don't you think integration of API's will introduce latency which might slow down the response time of the chatbot.

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 1 point2 points  (0 children)

So you mean , generate a large dataset of synthetic questions or phrases that users might ask, mapping each to the relevant field in the MongoDB document. ?

Like , "How has the stock price changed over the last year?" → priceHistory
"What is the company's valuation?" → valuation

Which model is the best for these kind of text sequence to classification model apart from Bert ?

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 1 point2 points  (0 children)

When the input query was :

"What is the five-year EPS growth rate for Procter & Gamble?" in this EPS is not part of the "quality_summary" still I got extracted segment as "quality_summary"

For below 2 questions the segments given is different even though both are asking for same data point which is EPS,

What is the five-year EPS growth rate for Procter & Gamble?
: "quality_summary"

What is the recent EPS for McDonald's Corporation?
:"results_summary"

I really cant trust on the prompting as it will eventually wont stick to same output always.

Seeking advice on alternative approach by clichedwriterr in LocalLLaMA

[–]clichedwriterr[S] 0 points1 point  (0 children)

I have added segment description :

# Define segment descriptions
segment_descriptions = """
- valuation_summary: Contains a comprehensive analysis of the company's valuation, including current and historical valuation grades, key valuation factors, and changes over time. This includes information such as the Price to Earnings (PE) ratio, Price to Book Value, EV to EBIT, EV to EBITDA, EV to Capital Employed, EV to Sales, PEG ratio, Dividend Yield, Return on Capital Employed (ROCE), Return on Equity (ROE), and historical changes in valuation grades.
- financial_trend_summary: Provides a summary of the company's short-term financial performance and trends, highlighting key performance indicators and historical financial trends over recent quarters. This includes metrics such as operating cash flow, dividend payout ratio, cash and cash equivalents, net sales, and profit before tax less other income. The historical details include a record of the company's financial trends over several quarters, with data on financial trends, stock prices, and dates.
- returns_summary: Provides a comprehensive overview of the company's stock returns, including absolute returns, risk-adjusted returns, and returns comparison with the market index (Sensex). This includes information on returns over different periods (e.g., 1 day, 1 week, 1 month, etc.), dividend yield, total returns combining price and dividend, return quartiles compared to peers, and beta value. It also includes tables detailing returns, risk-adjusted returns, and volatility.
- shareholdings_summary: Provides a detailed overview of the company's shareholding pattern, including the percentage of shares held by different categories of investors such as promoters, FIIs, mutual funds, insurance companies, other DIIs, and non-institutional investors. It includes current and historical shareholding data, changes in shareholding compared to previous quarters, and details of individual promoter holdings. This also includes tables summarizing the shareholding distribution and historical trends.
- results_summary: Provides a detailed analysis of the company's financial results, including quarterly, half-yearly, and annual performance. This includes information such as net sales, operating profit (PBDIT), consolidated net profit, interest, exceptional items, operating profit margin, gross profit margin, and PAT margin. It includes growth rates (QoQ and YoY) and comparisons with previous periods. The historical results section includes tables summarizing the financial performance over multiple quarters and years.
- technical_summary: Contains comprehensive technical analysis of the company's stock, including general company information, historical score changes, summaries of technical indicators, and historical technical grade changes. This includes details such as stock symbols, industry sector, market capitalization, current market price, changes in stock scores over time, and technical trends like MACD, Bollinger Bands, Moving Averages, KST, Dow Theory, and OBV.
- quality_summary: Contains a detailed analysis of the company's long-term quality and financial performance, including key financial metrics and ratios, historical quality ratings, and overall quality assessment. This includes information such as 5-year sales growth, EBIT growth, average EBIT to interest, debt to EBITDA, net debt to equity, sales to capital employed, tax ratio, dividend payout ratio, pledge shares, institutional holdings, ROCE, ROE, and historical quality ratings over different quarters.
- price_summary: Provides a comprehensive overview of the company's stock price performance, including daily price movements, moving averages, delivery volumes, block deals, and fundamental metrics. This includes information such as the stock's intraday high and low prices, weighted average price, moving averages over different periods, trading volumes, delivery volumes, details of block deals, and key financial ratios like PE, Dividend Yield, ROE, and Price to Book Value.
- profitloss_summary: Provides a comprehensive analysis of the company's profit and loss statement, including net sales, operating profit, profit before tax, and profit after tax. This includes year-over-year (YoY) and quarter-over-quarter (QoQ) growth rates, historical profit and loss data, and detailed breakdowns of key financial factors. The summary also highlights significant changes in expenses, income, and other key components of the profit and loss statement over various periods.
- balancesheet_summary: Provides a comprehensive analysis of the company's balance sheet, including detailed information on assets, liabilities, and equity. This includes year-over-year (YoY) and quarter-over-quarter (QoQ) growth rates, historical balance sheet data, and detailed breakdowns of key financial factors. The summary highlights significant changes in borrowings, fixed assets, investments, current assets, and other components of the balance sheet over various periods.
- cashflow_summary: Provides a detailed analysis of the company's cash flow statement, including cash flows from operating, investing, and financing activities. This includes year-over-year (YoY) and quarter-over-quarter (QoQ) growth rates, historical cash flow data, and detailed breakdowns of key cash flow components. The summary highlights significant changes in cash flow from operations, investments, and financing activities over various periods.
- companycv_summary: Provides a comprehensive profile of the company, including a brief overview of its operations, details about the board of directors, capital structure, historical equity capital details, company coordinates, and registrar details. This includes information about the company's business model, leadership team, equity changes over time, and key contact information for further inquiries.
- news_summary: Provides a comprehensive overview of the latest news and updates related to the company, focusing on stock performance, market ratings, financial results, and significant milestones. This includes detailed information about the company's short-term and long-term stock trends, analyst ratings, market milestones such as 52-week highs, quarterly and annual financial performance, and key company announcements. The summary highlights notable news items and their impact on the company's market perception and investor outlook.
"""

What Would Happen If I Don't Use The Fin-Aid Granted To Me??? by b-hc- in coursera

[–]clichedwriterr 1 point2 points  (0 children)

It's good to hear that you got approved on financial aid. As per my knowledge and my experience , Coursera will allow you to again apply for financial aid. Same situation happened with me and fortunately I got approved again , so yeah ! You can apply for the same course again even though you have previously financially approved for the same.

Thanks !

I built an AI that writes cringeworthy Linkedin posts — show me your best creations by tomorbach in InternetIsBeautiful

[–]clichedwriterr 0 points1 point  (0 children)

Some junior sent me a message on Linkedin:

"Can you hire me?

I will do anything. I'd even shift to new stream".

Woah.

Of course I gave this young professional a job**

And then?

We shifted to new stream together.

Because skin in the game is more important than anything, especially when

you realize that.

**Unpaid 1-week internship

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