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refactor: move to apps

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This commit is contained in:
Andy Lee
2025-07-22 22:18:17 -07:00
parent a97d3ada1c
commit b92ec04178
31 changed files with 2926 additions and 23618 deletions
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README.md
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@@ -145,12 +145,11 @@ Above we showed the Python API, while this CLI script demonstrates the same conc
The following scripts use Ollama `qwen3:8b` by default, so you need `ollama pull qwen3:8b` first. For other models: `--llm openai --model gpt-4o` (requires `OPENAI_API_KEY` environment variable) or `--llm hf --model Qwen/Qwen3-4B`.
```bash
# Drop your PDFs, .txt, .md files into examples/data/
uv run ./examples/main_cli_example.py
# Drop your PDFs, .txt, .md files into apps/documents/data/
python -m apps.documents
# Or use python directly
source .venv/bin/activate
python ./examples/main_cli_example.py
# Or with uv
uv run python -m apps.documents
```
@@ -159,7 +158,7 @@ python ./examples/main_cli_example.py
### Search Your Entire Life
```bash
python examples/mail_reader_leann.py
python -m apps.email
# "What's the number of class recommend to take per semester for incoming EECS students?"
```
**90K emails → 14MB.** Finally, search your email like you search Google.
@@ -169,19 +168,19 @@ python examples/mail_reader_leann.py
```bash
# Use default mail path (works for most macOS setups)
python examples/mail_reader_leann.py
python -m apps.email
# Run with custom index directory
python examples/mail_reader_leann.py --index-dir "./my_mail_index"
python -m apps.email --index-dir "./my_mail_index"
# Process all emails (may take time but indexes everything)
python examples/mail_reader_leann.py --max-emails -1
python -m apps.email --max-emails -1
# Limit number of emails processed (useful for testing)
python examples/mail_reader_leann.py --max-emails 1000
python -m apps.email --max-emails 1000
# Run a single query
python examples/mail_reader_leann.py --query "What did my boss say about deadlines?"
python -m apps.email --query "What did my boss say about deadlines?"
```
</details>
@@ -197,7 +196,7 @@ Once the index is built, you can ask questions like:
### Time Machine for the Web
```bash
python examples/google_history_reader_leann.py
python -m apps.browser
# "Tell me my browser history about machine learning system stuff?"
```
**38K browser entries → 6MB.** Your browser history becomes your personal search engine.
@@ -207,16 +206,16 @@ python examples/google_history_reader_leann.py
```bash
# Use default Chrome profile (auto-finds all profiles)
python examples/google_history_reader_leann.py
python -m apps.browser
# Run with custom index directory
python examples/google_history_reader_leann.py --index-dir "./my_chrome_index"
python -m apps.browser --index-dir "./my_chrome_index"
# Limit number of history entries processed (useful for testing)
python examples/google_history_reader_leann.py --max-entries 500
python -m apps.browser --max-entries 500
# Run a single query
python examples/google_history_reader_leann.py --query "What websites did I visit about machine learning?"
python -m apps.browser --query "What websites did I visit about machine learning?"
```
</details>
@@ -252,7 +251,7 @@ Once the index is built, you can ask questions like:
### WeChat Detective
```bash
python examples/wechat_history_reader_leann.py
python -m apps.wechat
# "Show me all group chats about weekend plans"
```
**400K messages → 64MB.** Search years of chat history in any language.
@@ -274,19 +273,19 @@ sudo packages/wechat-exporter/wechattweak-cli install
```bash
# Use default settings (recommended for first run)
python examples/wechat_history_reader_leann.py
python -m apps.wechat
# Run with custom export directory and wehn we run the first time, LEANN will export all chat history automatically for you
python examples/wechat_history_reader_leann.py --export-dir "./my_wechat_exports"
python -m apps.wechat --export-dir "./my_wechat_exports"
# Run with custom index directory
python examples/wechat_history_reader_leann.py --index-dir "./my_wechat_index"
python -m apps.wechat --index-dir "./my_wechat_index"
# Limit number of chat entries processed (useful for testing)
python examples/wechat_history_reader_leann.py --max-entries 1000
python -m apps.wechat --max-entries 1000
# Run a single query
python examples/wechat_history_reader_leann.py --query "Show me conversations about travel plans"
python -m apps.wechat --query "Show me conversations about travel plans"
```
</details>
@@ -388,7 +387,7 @@ Options:
Run the comparison yourself:
```bash
python examples/compare_faiss_vs_leann.py
python -m apps.benchmarks
```
| System | Storage |
@@ -430,8 +429,8 @@ Same dataset, same hardware, same embedding model. LEANN just works better.
```bash
uv pip install -e ".[dev]" # Install dev dependencies
python examples/run_evaluation.py data/indices/dpr/dpr_diskann # DPR dataset
python examples/run_evaluation.py data/indices/rpj_wiki/rpj_wiki.index # Wikipedia
python -m apps.evaluation data/indices/dpr/dpr_diskann # DPR dataset
python -m apps.evaluation data/indices/rpj_wiki/rpj_wiki.index # Wikipedia
```
The evaluation script downloads data automatically on first run. The last three results were tested with partial personal data, and you can reproduce them with your own data!