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## Description Adds a content-type-aware path for **tabular data** — CSV/TSV, markdown tables, fixed-width text, and binary `.xlsx`/`.xls` spreadsheets — by routing them through the existing, battle-tested `SmartCrusher` instead of letting them fall through to `PLAIN_TEXT → Kompress`. The pipeline already compressed tables losslessly when handed a JSON array of records. This wires up the missing front door: detect tabular text (and ingest binary spreadsheets), convert to JSON records, and reuse `SmartCrusher.crush()`. No new compression algorithm. Closes # ## Type of Change - [ ] Bug fix (non-breaking change that fixes an issue) - [x] New feature (non-breaking change that adds functionality) - [ ] Breaking change (fix or feature that would cause existing functionality to change) - [ ] Documentation update - [ ] Performance improvement - [ ] Code refactoring (no functional changes) ## Changes Made - **Detection** (`content_detector.py`): new `ContentType.TABULAR` + `_try_detect_tabular()` for CSV/TSV, markdown tables, and fixed-width columns. Ordered after search/log (which also look "delimited") and before code, with a prose-rejection guard so it never steals `file:line:content` search output, `key: value` logs, or sentences with incidental commas. Rust backend returns `plain_text` for unknown types and the router already falls back to the Python detector, so **no Rust change**. - **Bridge** (`tabular_ingest.py`): stdlib parsers + `to_records()` + a `TabularCompressor` that parses → JSON records → `SmartCrusher` (lossless `csv-schema` first; lossy row-drop with reversible `<<ccr:HASH>>` markers stays SmartCrusher's built-in fallback). Only adopts a result when it actually saves bytes. - **Spreadsheets** (`spreadsheet_ingest.py`): `.xlsx`/`.xls` → per-sheet CSV text at the SDK boundary. Optional deps (`pip install headroom-ai[spreadsheet]`) fail loudly with an install hint, never silently degrade. - **Routing** (`content_router.py`): `CompressionStrategy.TABULAR`, `enable_tabular_compressor` flag, lazy getter, apply branch, strategy maps, Kompress fallback eligibility. - **SDK** (`compress.py`): `compress_spreadsheet(path, ...)` helper (one message per sheet). - **Packaging** (`pyproject.toml`): new `[spreadsheet]` extra; `openpyxl` added to `[dev]` so the xlsx path is exercised in CI. - **Docs/demo**: `examples/tabular_compression_demo.py` + README entry. ### Design note: lossless-only Compact, all-unique tables with no query yield ~0 savings — this is correct, not a bug. SmartCrusher returns `skip:unique_entities_no_signal` and won't drop unique rows without a duplicate/relevance signal. Real wins come from verbose/redundant tables and query-driven selection. A pressure-driven lossy row sampler was considered and intentionally not added. ## Testing - [x] Unit tests pass (`pytest`) - [x] Linting passes (`ruff check .`) - [x] Type checking passes (`mypy headroom`) - [x] New tests added for new functionality - [x] Manual testing performed ### Test Output ```text $ python -m pytest tests/test_transforms_tabular.py -q collected 20 items tests/test_transforms_tabular.py .................... [100%] ============================== 20 passed in 7.15s ============================== $ ruff check headroom/transforms/tabular_ingest.py headroom/transforms/spreadsheet_ingest.py All checks passed! $ mypy headroom/transforms/tabular_ingest.py headroom/transforms/spreadsheet_ingest.py Success: no issues found in 2 source files ``` `tests/test_transforms_tabular.py` (20 tests): detection true positives + no-misroute negatives (search/log/JSON/prose), parser units (incl. fixed-width), the CSV→SmartCrusher bridge, router routing + disable flag, and `.xlsx` ingestion (skipif openpyxl missing) + error paths. `spreadsheet_ingest` 100% / `tabular_ingest` 90% line coverage. ## Real Behavior Proof - **Environment:** local checkout of `feat/tabular-compression`, Python 3.x, `pip install -e ".[dev]"`. - **Exact command / steps:** `python examples/tabular_compression_demo.py` (no API key required). - **Observed result:** ```text === Raw tabular text (ContentRouter, char-level) === compact unique CSV strat=tabular chars 1306 -> 1072 ( 17.9% saved) redundant CSV strat=tabular chars 2661 -> 1350 ( 49.3% saved) verbose markdown strat=tabular chars 2019 -> 1580 ( 21.7% saved) === Full pipeline (real tokenizer) === redundant CSV tokens 768 -> 394 ( 48.7% saved) === Binary spreadsheet (.xlsx) === 2-sheet workbook tokens 1092 -> 683 ( 37.5% saved) ``` - **Not tested:** legacy `.xls` binary path (needs optional `xlrd` + binary fixture; `# pragma: no cover`); base64-embedded `.xlsx` inside multimodal blocks (out of scope, noted as a follow-up). ## Review Readiness - [x] I have performed a self-review - [x] This PR is ready for human review ## Checklist - [x] My code follows the project's style guidelines - [x] I have performed a self-review of my code - [x] I have commented my code, particularly in hard-to-understand areas - [x] I have made corresponding changes to the documentation - [x] My changes generate no new warnings - [x] I have added tests that prove my fix is effective or that my feature works - [x] New and existing unit tests pass locally with my changes - [ ] I have updated the CHANGELOG.md if applicable ## Additional Notes - CHANGELOG/version are intentionally untouched: this repo uses **release-please**, which bumps the version and CHANGELOG via automated `chore: release main` PRs, not per-feature PRs. - The `.xls` path is `# pragma: no cover` (legacy, needs optional `xlrd` + a binary fixture). - Follow-up (out of scope): base64-embedded `.xlsx` inside tool-result/multimodal blocks; porting tabular parsers into the Rust core for parity. --------- Co-authored-by: Claude Opus 4.8 <noreply@anthropic.com>
200 lines
4.5 KiB
Markdown
200 lines
4.5 KiB
Markdown
# Headroom Examples
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This directory contains examples demonstrating Headroom's capabilities.
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## Quick Start Examples
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### basic_usage.py
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Basic integration with OpenAI client:
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```bash
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export OPENAI_API_KEY='your-key'
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python examples/basic_usage.py
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```
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### anthropic_example.py
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Integration with Anthropic Claude:
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```bash
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export ANTHROPIC_API_KEY='your-key'
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python examples/anthropic_example.py
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```
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### streaming_example.py
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Streaming responses with optimization:
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```bash
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export OPENAI_API_KEY='your-key'
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python examples/streaming_example.py
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```
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### tabular_compression_demo.py
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Tabular + spreadsheet compression on generated sample data (no API key needed).
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Shows where CSV/markdown tables and `.xlsx` workbooks compress and where compact,
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all-unique data correctly passes through:
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```bash
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python examples/tabular_compression_demo.py # run all scenarios
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python examples/tabular_compression_demo.py --write DIR # also save the sample files
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```
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## Evaluation Examples
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### smart_vs_naive_eval.py
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Compare SmartCrusher against naive truncation:
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```bash
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export OPENAI_API_KEY='your-key'
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python examples/smart_vs_naive_eval.py
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```
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### real_world_eval.py
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Comprehensive evaluation with Anthropic models:
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```bash
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export ANTHROPIC_API_KEY='your-key'
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python examples/real_world_eval.py
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```
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### real_world_openai_eval.py
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Comprehensive evaluation with OpenAI models:
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```bash
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export OPENAI_API_KEY='your-key'
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python examples/real_world_openai_eval.py
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```
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## Demo Directories
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### langchain_demo/
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Full LangChain agent integration demo:
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```bash
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# No API key needed for compression demo
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PYTHONPATH=. python -m examples.langchain_demo.show_compression
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# Full comparison (requires API key)
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export OPENAI_API_KEY='your-key'
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PYTHONPATH=. python -m examples.langchain_demo.run_comparison
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```
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See [langchain_demo/README.md](langchain_demo/README.md) for details.
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### mcp_demo/
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MCP (Model Context Protocol) integration demo:
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```bash
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export OPENAI_API_KEY='your-key'
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PYTHONPATH=. python -m examples.mcp_demo.run_agent_eval
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```
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### strands_bedrock_demo.py
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AWS Strands Agents + Bedrock integration demo. Showcases two Headroom integration patterns:
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1. **HeadroomHookProvider** - Compresses tool outputs in real-time
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2. **HeadroomStrandsModel** - Optimizes entire conversation context
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```bash
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# Configure AWS credentials
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export AWS_ACCESS_KEY_ID='your-access-key'
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export AWS_SECRET_ACCESS_KEY='your-secret-key'
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export AWS_DEFAULT_REGION='us-west-2' # Optional, defaults to us-west-2
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# Or use AWS profile
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export AWS_PROFILE='your-profile-name'
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# Run the full demo (both integration patterns)
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python examples/strands_bedrock_demo.py
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# Run only the hook provider demo
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python examples/strands_bedrock_demo.py --hook
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# Run only the model wrapper demo
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python examples/strands_bedrock_demo.py --model
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# Specify a different AWS region
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python examples/strands_bedrock_demo.py --region us-east-1
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```
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The demo uses Claude 3 Haiku via Bedrock for cost efficiency. It creates agents with
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4 tools that return verbose JSON output (search results, logs, database records, metrics)
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and displays compression statistics with visual comparisons.
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**Requirements:**
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- AWS account with Bedrock enabled
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- Claude 3 Haiku model access in your region
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- `pip install strands-agents headroom-ai[strands]`
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## Running Examples
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All examples can be run from the repository root:
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```bash
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# Install dependencies
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pip install -e ".[dev]"
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# Run any example
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python examples/<example_name>.py
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```
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## Expected Results
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| Example | Token Savings | Notes |
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|---------|---------------|-------|
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| basic_usage | 50-70% | Simple tool output compression |
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| langchain_demo | 70-85% | Real agent with multiple tools |
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| mcp_demo | 60-80% | MCP tool outputs |
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| strands_bedrock_demo | 60-85% | Strands + Bedrock with verbose tools |
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| real_world_eval | 50-90% | Varies by scenario |
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## Troubleshooting
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**ModuleNotFoundError: No module named 'headroom'**
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Run from the repository root with PYTHONPATH:
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```bash
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PYTHONPATH=. python examples/basic_usage.py
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```
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Or install in development mode:
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```bash
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pip install -e .
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```
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**API Key Errors**
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Ensure your API keys are set:
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```bash
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export OPENAI_API_KEY='sk-...'
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export ANTHROPIC_API_KEY='sk-ant-...'
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```
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**AWS Credentials Errors (for Strands demo)**
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Ensure AWS credentials are configured:
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```bash
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# Option 1: Environment variables
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export AWS_ACCESS_KEY_ID='your-access-key'
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export AWS_SECRET_ACCESS_KEY='your-secret-key'
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# Option 2: AWS profile
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export AWS_PROFILE='your-profile-name'
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# Option 3: AWS credentials file (~/.aws/credentials)
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```
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Also ensure Bedrock and the Claude 3 Haiku model are enabled in your AWS account.
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