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Semantic search and document parsing tools for the command line
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run-llama/semtools
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Semantic search and document parsing tools for the command line
A collection of high-performance CLI tools for document processing and semantic search, built with Rust for speed and reliability.
parse
- Parse documents (PDF, DOCX, etc.) using, by default, the LlamaParse API into markdown formatsearch
- Local semantic keyword search using multilingual embeddings with cosine similarity matching and per-line context matchingworkspace
- Workspace management for accelerating search over large collections
NOTE: By default,parse
uses LlamaParse as a backend. Get your API key today for free athttps://cloud.llamaindex.ai.search
remains local-only.
- Fast semantic search using model2vec embeddings fromminishlab/potion-multilingual-128M
- Reliable document parsing with caching and error handling
- Unix-friendly design with proper stdin/stdout handling
- Configurable distance thresholds and returned chunk sizes
- Multi-format support for parsing documents (PDF, DOCX, PPTX, etc.)
- Concurrent processing for better parsing performance
- Workspace management for efficient document retrieval over large collections
Prerequisites:
- For the
parse
tool: LlamaIndex Cloud API key
Install:
You can installsemtools
via npm:
npm i -g @llamaindex/semtools
Or via cargo:
# install entire cratecargo install semtools# install only parsecargo install semtools --no-default-features --features=parse# install only searchcargo install semtools --no-default-features --features=search
Note: Installing from npm builds the Rust binaries locally during install if a prebuilt binary is not available, which requires Rust and Cargo to be available in your environment. Install fromrustup
if needed:https://www.rust-lang.org/tools/install
.
Basic Usage:
# Parse some filesparse my_dir/*.pdf# Search some (text-based) filessearch"some keywords"*.txt --max-distance 0.3 --n-lines 5# Combine parsing and searchparse my_docs/*.pdf| xargs search"API endpoints"
Advanced Usage:
# Combine with grep for exact-match pre-filtering and distance thresholdingparse*.pdf| xargs cat| grep -i"error"| search"network error" --max-distance 0.3# Pipeline with content search (note the 'cat')find. -name"*.md"| xargs parse| xargs search"installation"# Combine with grep for filtering (grep could be before or after parse/search!)parse docs/*.pdf| xargs search"API"| grep -A5"authentication"# Save search resultsparse report.pdf| xargs cat| search"summary"> results.txt
Using Workspaces:
# Create or select a workspace# Workspaces are stored in ~/.semtools/workspaces/workspace use my-workspace> Workspace'my-workspace' configured.> To activate it, run:>export SEMTOOLS_WORKSPACE=my-workspace>> Or add this to your shell profile (.bashrc, .zshrc, etc.)# Activate the workspaceexport SEMTOOLS_WORKSPACE=my-workspace# All search commands will now use the workspace for caching embeddings# The initial command is used to initialize the workspacesearch"some keywords" ./some_large_dir/*.txt --n-lines 5 --top-k 10# If documents change, they are automatically re-embedded and cachedecho"some new content"> ./some_large_dir/some_file.txtsearch"some keywords" ./some_large_dir/*.txt --n-lines 5 --top-k 10# If documents are removed, you can run prune to clean up stale filesworkspace prune# You can see the stats of a workspace at any timeworkspace status> Active workspace: arxiv> Root: /Users/loganmarkewich/.semtools/workspaces/arxiv> Documents: 3000> Index: Yes (IVF_PQ)
$ parse --helpA CLI toolfor parsing documents using various backendsUsage: parse [OPTIONS]<FILES>...Arguments:<FILES>... Files to parseOptions: -c, --parse-config<PARSE_CONFIG> Path to the config file. Defaults to~/.parse_config.json -b, --backend<BACKEND> The backendtype to usefor parsing. Defaults to`llama-parse` [default: llama-parse] -v, --verbose Verbose outputwhile parsing -h, --help Printhelp -V, --version Print version
$ search --helpA CLI toolfor fast semantic keyword searchUsage: search [OPTIONS]<QUERY> [FILES]...Arguments:<QUERY> Query to searchfor (positional argument) [FILES]... Files or directories to searchOptions: -n, --n-lines<N_LINES> How many lines before/after toreturn as context [default: 3] --top-k<TOP_K> The top-k files or texts toreturn (ignoredif max_distance is set) [default: 3] -m, --max-distance<MAX_DISTANCE> Return all results with distance below this threshold (0.0+) -i, --ignore-case Perform case-insensitive search (default is false) -h, --help Printhelp -V, --version Print version
$ workspace --helpManage semtools workspacesUsage: workspace<COMMAND>Commands: use Use or create a workspace (printsexportcommand to run) status Show active workspace and basic stats prune Remove stale or missing files from storehelp Print this message or thehelp of the given subcommand(s)Options: -h, --help Printhelp -V, --version Print version
By default, theparse
tool uses the LlamaParse API to parse documents.
It will look for a~/.parse_config.json
file to configure the API key and other parameters.
Otherwise, it will fallback to looking for aLLAMA_CLOUD_API_KEY
environment variable and a set of default parameters.
To configure theparse
tool, create a~/.parse_config.json
file with the following content (defaults are shown below):
{"api_key":"your_llama_cloud_api_key_here","num_ongoing_requests":10,"base_url":"https://api.cloud.llamaindex.ai","check_interval":5,"max_timeout":3600,"max_retries":10,"retry_delay_ms":1000,"backoff_multiplier":2.0,"parse_kwargs": {"parse_mode":"parse_page_with_agent","model":"openai-gpt-4-1-mini","high_res_ocr":"true","adaptive_long_table":"true","outlined_table_extraction":"true","output_tables_as_HTML":"true" }}
Or just set via environment variable:
export LLAMA_CLOUD_API_KEY="your_api_key_here"
- More parsing backends (something local-only would be great!)
- Improved search algorithms
- (optional) Persistence for speedups on repeat searches on the same files
We welcome contributions! Please seeCONTRIBUTING.md for guidelines.
This project is licensed under the MIT License - see theLICENSE file for details.
- LlamaIndex/LlamaParse for document parsing capabilities
- model2vec-rsfor fast embedding generation
- minishlab/potion-multilingual-128M for an amazing default static embedding model
- simsimd for efficient similarity computation
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