Trend Analyzer

Problem statement

It's time consuming to understand the changes happening in competitive / audience / creative landscapes without relying on heave pre-processing and data analysis techniques.

Solution

trend-analyzer uses power of large language models to analyze trends data located to SQL database and make it available for querying with a simple chat bot interface.

Deliverable (implementation)

trend-analyzer is implemented as a:

• library - Use it in your projects with a help of TrendsAnalyzer class.
• CLI tool - trend-analyzer tool is available to be used in the terminal.
• HTTP endpoint - trend-analyzer can be easily exposed as HTTP endpoint.
• Langchain tool - integrated trend-analyzer into your Langchain applications.

Deployment

Prerequisites

• Python 3.11+

• A GCP project with billing account attached

• Service account created and service account key downloaded in order to write data to BigQuery.

  • Once you downloaded service account key export it as an environmental variable

    export GOOGLE_APPLICATION_CREDENTIALS=path/to/service_account.json
    

  • If authenticating via service account is not possible you can authenticate with the following command:

    gcloud auth application-default login
    

• API key to access to access Google Gemini.

  • Once you created API key export it as an environmental variable

    export GOOGLE_API_KEY=<YOUR_API_KEY_HERE>
    

Installation

Install trend-analyzer with pip install trend-analyzer command.

Usage

This section is focused on using trend-analyzer as a CLI tool. Check library, http endpoint, langchain tool sections to learn more.

Once trend-analyzer is installed you can call it:

trend-analyzer 'YOUR_QUESTION_HERE' \
  --llm gemini \
  --llm.model=gemini-1.5-flash \
  --db-uri=<DATABASE_URI> \
  --output-type csv \
  --output-destination sample_results

where:

• YOUR_QUESTION_HERE - trends related question you want to ask.
• --llm gemini - type of large language model (currently only Google Gemini is supported).
• --llm.model=gemini-1.5-flash - any parameters to initialize selected LLM.
• --db-uri=<DATABASE_URI> - location of database with trends data.
• --output-type csv - type of output.
• --output-destination sample_results - name of output table or file.

Disclaimer

This is not an officially supported Google product.