fin-ai-stats-extract 0.1.1

Extract structured AI investment data from earnings call transcripts using OpenAI

fin-ai-stats-extract

Overview

fin-ai-stats-extract extracts structured AI and technology investment data from XML earnings-call transcripts and writes the results to CSV.

It supports:

• a single XML file or a folder tree of XML files via --input
• OpenAI-hosted models
• OpenAI-compatible local endpoints via --base-url
• explicit resume mode via --resume using an existing output CSV
• concurrent async extraction with a live progress bar
• dry-run parsing to validate XML input without making API calls
• a Streamlit UI for drag-and-drop uploads, model discovery, and in-memory CSV download

The output schema is based on required_output.md.

Requirements

• Python 3.14+
• uv

Install And Run

For one-off usage without creating a local environment:

uvx fin-ai-stats-extract --input ./data/Current --output ./output.csv

For local development in this repository:

uv sync

Environment

Create an environment file for API-backed runs:

cp .env.example .env

Example values:

OPENAI_API_KEY=sk-your-key-here
OPENAI_BASE_URL=
OPENAI_MODEL=gpt-4o-mini
CONCURRENCY_LIMIT=100

Basic Usage

Run on a folder of transcripts:

uvx fin-ai-stats-extract --input ./data/Current --output ./output.csv

Run on a single transcript:

uvx fin-ai-stats-extract --input ./data/Current/11473715_T.xml --output ./output.csv

Validate XML parsing only, without calling a model:

uvx fin-ai-stats-extract --input ./data/Current --dry-run

Process only a sample of files:

uvx fin-ai-stats-extract --input ./data/Current --sample 25 --output ./sample.csv

Enable verbose logging:

uvx fin-ai-stats-extract --input ./data/Current --output ./output.csv --verbose

Tune common model sampling settings:

uvx fin-ai-stats-extract \
  --input ./data/Current/11473715_T.xml \
  --output ./output.csv \
  --temperature 0.2 \
  --top-p 0.9 \
  --max-output-tokens 2500 \
  --reasoning-effort medium \
  --verbosity low

Resume an interrupted run from an existing CSV:

uvx fin-ai-stats-extract --input ./data/Current --output ./output.csv --resume

Prompt Selection

The CLI supports a custom prompt file:

uvx fin-ai-stats-extract \
  --input ./data/Current \
  --output ./output.csv \
  --prompt ./my_prompt.md

Prompt resolution works like this:

1. If --prompt PATH is provided, that file is used.
2. Otherwise the CLI looks for ./system_prompt.md in the current working directory.
3. If ./system_prompt.md does not exist, the packaged default prompt is copied there and then used.

The packaged default prompt shipped with the distribution lives at src/fin_ai_stats_extract/resources/system_prompt.md in this repository.

Local OpenAI-Compatible Endpoints

You can point the tool at a local or self-hosted OpenAI-compatible server by passing --base-url.

Example with LM Studio:

uvx fin-ai-stats-extract \
  --input ./data/Current/11473715_T.xml \
  --output ./output.csv \
  --model google/gemma-3-4b \
  --base-url http://127.0.0.1:1234/v1

You can also pass an API key explicitly:

uvx fin-ai-stats-extract \
  --input ./data/Current \
  --output ./output.csv \
  --model gpt-4o-mini \
  --api-key "$OPENAI_API_KEY"

If --base-url is provided and no API key is set, the tool automatically uses lm-studio as a fallback key for local servers that require a non-empty value.

Command-Line Arguments

• --input: Required. Path to one XML file or a folder tree containing XML files. Folder scans are recursive.
• --output: Output CSV path. Defaults to output.csv.
• --prompt: Optional path to a custom system prompt markdown file. Without it, the CLI uses ./system_prompt.md, creating it from the packaged default if needed.
• --model: Model name. Defaults to OPENAI_MODEL or gpt-4o-mini.
• --base-url: Optional OpenAI-compatible API base URL. Defaults to OPENAI_BASE_URL.
• --api-key: Optional API key. Defaults to OPENAI_API_KEY.
• --temperature: Responses API sampling temperature from 0 to 2.
• --top-p: Responses API nucleus sampling mass from 0 to 1.
• --max-output-tokens: Maximum output tokens, including reasoning tokens.
• --reasoning-effort: Reasoning effort for supported gpt-5 and o-series models: none, minimal, low, medium, high, xhigh.
• --verbosity: Output verbosity for supported models: low, medium, or high.
• --max-concurrency: Maximum number of concurrent extraction jobs. Also accepts --max-async-jobs and --concurrency. Defaults to CONCURRENCY_LIMIT or 100.
• --resume: Resume from an existing output CSV. Requires an explicit --output path.csv. Already processed source_file values are skipped.
• --dry-run: Parse XML only. No API calls are made.
• --sample: Randomly process only N files from the input set.
• --yes, -y: Skip the cost confirmation prompt when using OpenAI-hosted models.
• --verbose: Enable debug logging.

For the current OpenAI Responses API path used by this project, the commonly exposed researcher-facing controls are temperature, top_p, max_output_tokens, reasoning_effort, and verbosity. We recommend changing temperature or top_p, but not both at the same time.

Streamlit UI

For repository development, launch the Streamlit app with:

uv run streamlit run src/fin_ai_stats_extract/streamlit_app.py

The UI lets you:

• drag and drop one or more XML files
• choose OpenAI or a custom OpenAI-compatible endpoint
• automatically load available models from the endpoint's /models API
• edit the default system prompt before running extraction
• adjust max concurrency
• view the generated CSV directly in a table
• download the generated CSV directly from memory without writing a CSV file to disk

Output

The tool writes one CSV row per transcript with:

• transcript metadata: event_id, company_name, quarter, date, headline, source_file
• AI infrastructure fields
• AI analytics fields
• AI talent fields
• AI risk fields
• non-AI physical technology investment fields
• non-AI tech talent fields

List-valued fields are serialized using a semicolon-space separator.

When the input is a folder, source_file preserves the relative subfolder path, for example subdir/12345_T.xml.

Notes On Local Models

OpenAI-compatible endpoints vary in how well they support strict structured outputs.

In particular:

• some local models may fail on long transcripts because of context-window limits
• some local models may return non-compliant JSON even when a schema is provided

If you use a local endpoint and see parsing or validation failures, try:

• a model with a larger context window
• smaller inputs using --sample or a single file first
• an OpenAI-hosted model for the most reliable structured-output behavior