### Install SemTools for Development

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Install the SemTools project locally for development purposes, making it available as a command-line tool.

```bash
cargo install --path .
```

--------------------------------

### Install Required Libraries

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Install the necessary libraries for LlamaIndex and semtools integration. This includes the Anthropic LLM integration and the MCP tool.

```bash
pip install llama-index-llms-anthropic llama-index-tools-mcp
```

--------------------------------

### Install Semtools

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Installs the semtools package using cargo.

```bash
cargo install semtools
```

--------------------------------

### Install NetworkX Package

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Use pip3 to install the networkx package. Note that system-wide installations might be managed externally, and using a virtual environment is recommended.

```bash
pip3 install networkx
```

--------------------------------

### SemTools Unified Configuration File Example

Source: https://github.com/run-llama/semtools/blob/main/README.md

An example JSON configuration file for SemTools, showing settings for parsing and asking tools. This file can be specified using the `-c` or `--config` flag.

```json
{
  "parse": {
    "api_key": "your_llama_cloud_api_key_here",
    "num_ongoing_requests": 10,
    "base_url": "https://api.cloud.llamaindex.ai",
    "parse_kwargs": {
      "tier": "agentic",
      "version": "latest",
      "disable_cache": false
    },
    "check_interval": 5,
    "max_timeout": 3600,
    "max_retries": 10,
    "retry_delay_ms": 1000,
    "backoff_multiplier": 2.0
  },
  "ask": {
    "api_key": "your_openai_api_key_here",
    "base_url": null,
    "model": "gpt-4o-mini",
    "max_iterations": 20,
    "api_mode": "responses"  // Can be responses or chat
  }
}

```

--------------------------------

### Install SemTools via Cargo

Source: https://github.com/run-llama/semtools/blob/main/README.md

Install the SemTools crate using cargo. You can install the entire crate or select features like 'parse'. Ensure Rust and Cargo are installed.

```bash
cargo install semtools
```

```bash
cargo install semtools --no-default-features --features=parse
```

--------------------------------

### Install SemTools via npm

Source: https://github.com/run-llama/semtools/blob/main/README.md

Install the SemTools CLI globally using npm. This method may build Rust binaries locally if prebuilt binaries are unavailable, requiring Rust and Cargo.

```bash
npm i -g @llamaindex/semtools
```

--------------------------------

### Workspace Example with Parse

Source: https://github.com/run-llama/semtools/blob/main/examples/example_CLAUDE.md

Illustrates initializing a workspace named 'my-workspace2' and setting the environment variable to activate it, preparing for subsequent parse and search operations within that workspace.

```bash
# A workspace example if you are using with parse
# create a workspace
semtools workspace use my-workspace2
export SEMTOOLS_WORKSPACE=my-workspace2

```

--------------------------------

### Install fastmcp

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Installs the fastmcp Python package.

```bash
pip install fastmcp
```

--------------------------------

### Install scikit-learn using pip

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Attempts to install the 'scikit-learn' library using 'pip'. This is a common step for enabling machine learning functionalities in Python.

```bash
pip install scikit-learn
```

--------------------------------

### Install scikit-learn using python -m pip

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Installs the 'scikit-learn' library by invoking 'pip' as a module through the Python interpreter. This can resolve issues with 'pip' not being found in the system's PATH.

```bash
python -m pip install scikit-learn
```

--------------------------------

### Integration Test Directory Structure

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Example of how integration tests should be organized within the `tests/` directory of the project. Each file typically corresponds to a specific component or feature.

```bash
# Add integration tests in tests/ directory
tests/
├── parse_integration.rs
└── search_integration.rs
```

--------------------------------

### Pipeline with Content Search

Source: https://github.com/run-llama/semtools/blob/main/examples/example_CLAUDE.md

Finds all markdown files, parses them using `semtools parse`, and then semantically searches for 'installation' using `semtools search`.

```bash
# Pipeline with content search (note the 'cat')
find . -name "*.md" | xargs semtools parse | xargs semtools search "installation"

```

--------------------------------

### Install Python dependencies for analysis

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Use this command to install necessary Python libraries for the abstract similarity analysis script, including numpy, scikit-learn, matplotlib, seaborn, and scipy.

```bash
pip install numpy scikit-learn matplotlib seaborn scipy
```

--------------------------------

### Install Python dependencies using python3 -m pip

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Alternative method to install Python dependencies using the python3 module to invoke pip. This is useful if pip is not directly available in the PATH.

```bash
python3 -m pip install numpy scikit-learn matplotlib seaborn scipy
```

--------------------------------

### Agent Execution Output

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Example output from the agent script, showing the sequence of bash commands executed by the agent to find, parse, and search through PDF documents.

```bash
$ python ./agent.py
Calling tool execute_bash({'command': 'find . -name "*.pdf" | head -10'})
Calling tool execute_bash({'command': 'find . -name "*.pdf" | wc -l'})
Calling tool execute_bash({'command': 'find . -name "*.pdf" | xargs semtools parse'})
Calling tool execute_bash({'command': 'find /Users/loganmarkewich/.parse -name "*.md" | xargs semtools search "large language model, LLM, evaluation, benchmark" --top-k 10 --n-lines 5'})
```

--------------------------------

### Extract all titles chronologically to a file

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Finds all .txt files, sorts them, greps for lines starting with 'TITLE:', and redirects the output to a temporary file for further analysis.

```bash
find /Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000
      _papers/by_date -name "*.txt" | sort | xargs grep -H "^TITLE:" >
      /tmp/all_titles_chronolo…
```

--------------------------------

### Rust Function with Doc Comments and Error Handling

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Example of a well-documented Rust function using `anyhow::Result` for error handling. Doc comments explain the function's purpose, parameters, and return value.

```rust
// Good: Clear function with documentation
/// Searches for semantically similar text in the given documents
pub fn search_documents(
    query: &str,
    documents: &[Document],
    threshold: f64,
) -> Result<Vec<SearchResult>> {
    // Implementation
}
```

--------------------------------

### Combine Search with Grep for Pre-filtering

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/search_CLAUDE.md

Pre-filter files using 'grep' for exact matches before performing a 'search'. This example uses 'grep -i' for case-insensitive matching and then searches for a specific phrase with a maximum distance threshold.

```bash
grep -i "error" some_folder/*.txt | search "network error" --max-distance 0.3 --n-lines 7
```

--------------------------------

### Extract All Titles and Dates

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Use this command to extract all lines starting with 'TITLE:' from text files in the 'full_text' directory. It's a preliminary step to gather data for analysis.

```bash
grep "^TITLE:" full_text/*.txt | head -20
```

--------------------------------

### Find and grep titles chronologically

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Finds all .txt files in a directory, sorts them, and then greps for lines starting with 'TITLE:'. Useful for initial exploration of paper titles.

```bash
find /Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000
      _papers/by_date -name "*.txt" | sort | head -20 | xargs grep -H
      "^TITLE:" | head -20
```

--------------------------------

### Extract author information from initial papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/3.txt

Iterates through the first 10 unique paper filenames found containing 'meta-learning'. For each file, it prints a header and then extracts the first 20 lines, searching for lines that start with an uppercase letter followed by lowercase letters, likely indicating author or title information.

```bash
Bash(for file in $(grep -i "meta-learning" full_text/*.txt | cut -d: -f1 | sort | uniq | head -10); do echo "=== $file ==="; head -20 "$file" | grep -E "^[A-Z].*[a-z…)
```

--------------------------------

### Search for Specific Optimization Topics

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/3.txt

Refine searches with more specific terms like 'neural network optimization' or 'gradient descent optimization' to get more targeted results. The '--top-k' and '--n-lines' parameters control the number of results and the amount of context shown.

```bash
search "neural network optimization" full_text/*.txt --top-k 5 --n-lines 8
```

```bash
search "gradient descent optimization" full_text/*.txt --top-k 5 --n-lines 8
```

```bash
search "hyperparameter optimization" full_text/*.txt --top-k 5 --n-lines 8
```

--------------------------------

### Create or Select a Workspace

Source: https://github.com/run-llama/semtools/blob/main/examples/example_CLAUDE.md

Demonstrates how to use `semtools workspace use` to create or select a workspace, and the subsequent environment variable export to activate it.

```bash
# Create or select a workspace
# Workspaces are stored in ~/.semtools/workspaces/
semtools 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 workspace
export SEMTOOLS_WORKSPACE=my-workspace

# All search commands will now use the workspace for caching embeddings
# The initial command is used to initialize the workspace
semtools search "some keywords" ./some_large_dir/*.txt --n-lines 5 --top-k 10

# If documents change, they are automatically re-embedded and cached
echo "some new content" > ./some_large_dir/some_file.txt
semtools search "some keywords" ./some_large_dir/*.txt --n-lines 5 --top-k 10

```

--------------------------------

### Search CLI Help

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/search_CLAUDE.md

Displays help information for the 'search' CLI tool, outlining its usage, arguments, and options for performing semantic keyword searches.

```bash
search --help
A CLI tool for fast semantic keyword search

Usage: search [OPTIONS] <QUERY> [FILES]...

Arguments:
  <QUERY>     Query to search for (positional argument)
  [FILES]...  Files or directories to search

Options:
  -n, --n-lines <N_LINES>            How many lines before/after to return as context [default: 3]
      --top-k <TOP_K>                The top-k files or texts to return (ignored if 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                         Print help
  -V, --version                      Print version
```

--------------------------------

### SemTools Ask CLI Help

Source: https://github.com/run-llama/semtools/blob/main/README.md

Displays help for the `semtools ask` command, detailing its arguments and options for document-based question-answering.

```bash
$ semtools ask --help
A CLI tool for document-based question-answering

Usage: semtools ask [OPTIONS] <QUERY> [FILES]...

Arguments:
  <QUERY>     Query to prompt the agent with
  [FILES]...  Files to search, optional if using stdin

Options:
  -c, --config <CONFIG>      Path to the config file. Defaults to ~/.semtools_config.json
      --api-key <API_KEY>    OpenAI API key (overrides config file and env var)
      --base-url <BASE_URL>  OpenAI base URL (overrides config file)
  -m, --model <MODEL>        Model to use for the agent (overrides config file)
      --api-mode <API_MODE>  API mode to use: 'chat' or 'responses' (overrides config file)
  -j, --json                 Output results in JSON or text format
  -h, --help                 Print help

```

--------------------------------

### SemTools Workspace CLI Help

Source: https://github.com/run-llama/semtools/blob/main/README.md

Provides help for the `semtools workspace` command, listing available subcommands for managing workspaces.

```bash
$ semtools workspace --help
Manage semtools workspaces

Usage: semtools workspace [OPTIONS] <COMMAND>

Commands:
  use     Use or create a workspace (prints export command to run)
  status  Show active workspace and basic stats
  prune   Remove stale or missing files from store
  help    Print this message or the help of the given subcommand(s)

Options:
  -j, --json  Output results in JSON format
  -h, --help  Print help

```

--------------------------------

### SemTools Search CLI Help

Source: https://github.com/run-llama/semtools/blob/main/README.md

Shows help for the `semtools search` command, detailing its arguments and options for performing semantic keyword searches.

```bash
$ semtools search --help
A CLI tool for fast semantic keyword search

Usage: semtools search [OPTIONS] <QUERY> [FILES]...

Arguments:
  <QUERY>     Query to search for (positional argument)
  [FILES]...  Files to search, optional if using stdin

Options:
  -n, --n-lines <N_LINES>            How many lines before/after to return as context [default: 3]
      --top-k <TOP_K>                The top-k files or texts to return (ignored if 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)
  -j, --json                         Output results in JSON format
  -h, --help                         Print help

```

--------------------------------

### Environment Variables for API Keys

Source: https://github.com/run-llama/semtools/blob/main/README.md

Demonstrates how to set API keys for the parse and ask tools using environment variables. These can supplement or override configuration file settings.

```bash
# For parse tool
export LLAMA_CLOUD_API_KEY="your_llama_cloud_api_key_here"

# For ask tool
export OPENAI_API_KEY="your_openai_api_key_here"

```

--------------------------------

### Get titles of relevant papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/1.txt

Use the `head` command to extract the first line of a file, which is assumed to contain the title when searching for specific papers.

```bash
head -1 full_text/2506.22084v1.txt
```

```bash
head -1 full_text/2505.20280v1.txt
```

```bash
head -1 full_text/2507.21589v1.txt
```

```bash
head -1 full_text/2508.10899v1.txt
```

```bash
head -1 full_text/2506.10140v1.txt
```

--------------------------------

### Launch fastmcp Server

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Launches the configured fastmcp server using the streamable-http transport.

```bash
fastmcp server.py:mcp --transport streamable-http
```

--------------------------------

### SemTools Parse CLI Help

Source: https://github.com/run-llama/semtools/blob/main/README.md

Displays help information for the `semtools parse` command, outlining its usage, arguments, and options for document parsing.

```bash
$ semtools parse --help
A CLI tool for parsing documents using various backends

Usage: semtools parse [OPTIONS] <FILES>...

Arguments:
  <FILES>...  Files to parse

Options:
  -c, --config <CONFIG>    Path to the config file. Defaults to ~/.semtools_config.json
  -b, --backend <BACKEND>  The backend type to use for parsing. Defaults to `llama-parse` [default: llama-parse]
  -v, --verbose            Verbose output while parsing
  -h, --help               Print help

```

--------------------------------

### Count papers for authors A-E with multiple papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Iterates through author directories starting with letters A through E, counting papers for each. Authors with more than one paper are listed.

```bash
for author in $(ls by_author | grep -E "^[A-E]"); do count=$(ls
      by_author/$author/*.txt 2>/dev/null | wc -l); if [ $count -gt 1 ]; then
      echo "$author: $count pa…
```

--------------------------------

### Configure Semtools Ask Subcommand

Source: https://github.com/run-llama/semtools/blob/main/README.md

Override default LLM model and provide API key for the 'ask' subcommand. Ensure your OpenAI API key is kept secure.

```bash
semtools ask "What is this about?" docs/*.txt --model gpt-4o --api-key sk-...
```

--------------------------------

### List Author Papers Bash Command

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/4.txt

Lists all text files in a specified author's directory. Useful for getting an overview of an author's publications.

```bash
for file in /Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers/by_author/Yang_Yang/*.txt; do
      echo "=== $(basename $file) ==="; head -10 "$file"
    done
```

--------------------------------

### Clone SemTools Repository

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Clone the SemTools repository and navigate into the project directory to begin development.

```bash
git clone https://github.com/run-llama/semtools
cd semtools
```

--------------------------------

### List directory contents

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Use 'List' to view the contents of a directory. Use 'ctrl+r' to expand the listing.

```bash
List(~/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers)
```

```bash
List(by_category)
```

--------------------------------

### Search CLI Common Usage

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/search_CLAUDE.md

Demonstrates a common usage pattern for the 'search' CLI tool, including custom context lines and distance thresholds for semantic searches.

```bash
# Search with custom context and thresholds or distance thresholds
search "machine learning" *.txt --n-lines 6 --max-distance 0.4 --ignore-case
```

--------------------------------

### Extract Abstracts from August Papers using Grep

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Uses 'grep' to extract lines starting with 'Abstract' and the subsequent 10 lines from August papers. 'head -500' limits the output to the first 500 matching lines.

```bash
grep -A 10 "^Abstract" /Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers/by_date/2025-08/*.txt | head -500
```

--------------------------------

### Advanced SemTools pipeline with grep and search

Source: https://github.com/run-llama/semtools/blob/main/README.md

Demonstrates a complex pipeline involving parsing, piping to grep for pre-filtering, and then performing a semantic search with distance thresholding.

```bash
semtools parse *.pdf | xargs cat | grep -i "error" | semtools search "network error" --max-distance 0.3
```

--------------------------------

### Extract Abstracts from June Papers using Grep

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Employs 'grep' to extract lines starting with 'Abstract' and the following 10 lines from June papers. 'head -500' limits the output to the first 500 matching lines.

```bash
grep -A 10 "^Abstract" /Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers/by_date/2025-06/*.txt | head -500
```

--------------------------------

### Search for paper titles containing 'TITLE:' in August 2025

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/3.txt

This command searches for lines starting with 'TITLE:' within August 2025 papers. It's useful for extracting or analyzing paper titles directly. The '--top-k' and '--n-lines' parameters can be adjusted.

```bash
search "TITLE:" by_date/2025-08/*.txt --top-k 15 --n-lines 1
```

--------------------------------

### Execute Simple Collaboration Analysis

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Run the simple collaboration analysis script using python3.

```bash
python3 /tmp/simple_collaboration_analysis.py
```

--------------------------------

### List authors in by_author directory

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Lists the first 20 author directories within the 'by_author' directory.

```bash
ls by_author | head -20
```

--------------------------------

### Extract unique paper filenames containing 'meta-learning'

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/3.txt

Filters text files for lines containing 'meta-learning', extracts the filename (part before the first colon), sorts the filenames, and then removes duplicates to get a unique list of papers. This is useful for subsequent processing of each paper.

```bash
Bash(grep -i "meta-learning" full_text/*.txt | cut -d: -f1 | sort | uniq)
```

--------------------------------

### Configure fastmcp Server for Bash Commands

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_mcp.md

Sets up a fastmcp server with a tool to execute bash commands. This approach is not recommended for production due to security implications.

```python
import subprocess
from fastmcp import FastMCP

mcp = FastMCP("My MCP Server")

@mcp.tool
def execute_bash(command: str) -> str:
    """Useful for executing bash commands on your machine."""
    return subprocess.run(command, shell=True, capture_output=True, text=True, timeout=60).stdout

```

--------------------------------

### Find first occurrences of 'reinforcement learning' in titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Prints a header and then searches a chronological title file for the term 'reinforcement' (case-insensitive), displaying the first 3 matches.

```bash
echo "=== REINFORCEMENT LEARNING ===" && grep -i "reinforcement"
      /tmp/all_titles_chronological.txt | head -3
```

--------------------------------

### Pipeline find, parse, and search with SemTools

Source: https://github.com/run-llama/semtools/blob/main/README.md

Use `find` to locate files, pipe to `xargs semtools parse` to process them, and then pipe to `xargs semtools search` to perform semantic search.

```bash
find . -name "*.md" | xargs semtools parse | xargs semtools search "installation"
```

--------------------------------

### Ask questions with SemTools AI agent

Source: https://github.com/run-llama/semtools/blob/main/README.md

Utilize the `semtools ask` command to query documents using an AI agent. It defaults to OpenAI but can be configured for any OpenAI-Compatible API. Requires an OpenAI API key.

```bash
semtools ask "What are the main findings?" papers/*.txt
```

```bash
semtools ask "Some question?" *.txt
```

--------------------------------

### Search for multimodal model mentions

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/1.txt

Find papers mentioning 'multimodal model' and display the first 10 results, showing context lines.

```bash
find by_date/ -name "*.txt" | xargs grep -i
      "multimodal.*model" | head -10)
```

--------------------------------

### Manage SemTools workspace: Use and activate

Source: https://github.com/run-llama/semtools/blob/main/README.md

Commands to select and activate a workspace for SemTools. Workspaces cache embeddings for faster search over large document collections. Activation involves setting the SEMTOOLS_WORKSPACE environment variable.

```bash
semtools workspace use my-workspace
```

```bash
export SEMTOOLS_WORKSPACE=my-workspace
```

--------------------------------

### Search for Machine Learning Optimization Papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/3.txt

Use the 'search' command with a general query to find papers on machine learning optimization. Adjust '--top-k' for the number of results and '--n-lines' for context.

```bash
search "machine learning optimization" full_text/*.txt --top-k 10 --n-lines 5
```

--------------------------------

### Find first occurrences of 'vision-language model' in titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Prints a header and then searches a chronological title file for 'vision-language', 'vision language', or 'vlm' (case-insensitive), displaying the first 3 matches.

```bash
echo "=== VISION-LANGUAGE MODEL ===" && grep -i -E
      "(vision-language|vision language|vlm)"
      /tmp/all_titles_chronological.txt | head -3
```

--------------------------------

### Run Abstract Similarity Analysis Script

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

Command to execute the Python script 'analyze_abstracts.py' for processing and analyzing paper abstracts.

```bash
python analyze_abstracts.py
```

--------------------------------

### Search ArXiv papers for 'attention mechanisms'

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/1.txt

Use the `search` command to find papers discussing a specific topic. Specify the search term, the directory to search in, and optionally parameters like `--top-k` for the number of results and `--n-lines` for context.

```bash
search "attention mechanisms" full_text/*.txt --top-k 10
      --n-lines 5
```

--------------------------------

### Rust Error Handling with Context

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Demonstrates proper error handling in Rust using the `context` method from the `anyhow` crate to add descriptive messages to errors.

```rust
// Good: Error handling
let config = LlamaParseConfig::from_config_file(&config_path)
    .context("Failed to load configuration")?;
```

--------------------------------

### Combine parsing and search with SemTools

Source: https://github.com/run-llama/semtools/blob/main/README.md

Chain `semtools parse` with `xargs` and `semtools search` to parse files and then perform a semantic search on the parsed content.

```bash
semtools parse my_docs/*.pdf | xargs search "API endpoints"
```

--------------------------------

### Search for 'quantum computing' themes in August 2025 papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/3.txt

This command searches for papers related to 'quantum computing' from August 2025. It helps identify emerging topics in advanced computing fields. Adjust '--top-k' and '--n-lines' as needed.

```bash
search "quantum computing" by_date/2025-08/*.txt --top-k 5 --n-lines 3
```

--------------------------------

### Search for 'emerging' themes in August 2025 papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/3.txt

Use the 'search' command to find occurrences of a keyword within papers from a specific month and year. This helps in identifying general emerging themes. Adjust '--top-k' for the number of results and '--n-lines' for context lines.

```bash
search "emerging" by_date/2025-08/*.txt --top-k 10 --n-lines 5
```

--------------------------------

### Basic Search for Computer Vision Applications

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/5.txt

Use this command to perform a broad search for papers on computer vision applications. Adjust `--top-k` and `--max-distance` for result refinement.

```bash
search "computer vision applications" full_text/*.txt --top-k 10
      --max-distance 0.4
```

--------------------------------

### Combine parsing with SemTools ask agent

Source: https://github.com/run-llama/semtools/blob/main/README.md

Pipe the output of `semtools parse` to `xargs` and then to `semtools ask` to summarize parsed documents.

```bash
semtools parse research_papers/*.pdf | xargs ask "Summarize the key methodologies"
```

--------------------------------

### Find first occurrences of 'federated learning' in titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Prints a header and then searches a chronological title file for the term 'federated' (case-insensitive), displaying the first 3 matches.

```bash
echo "=== FEDERATED LEARNING ===" && grep -i "federated"
      /tmp/all_titles_chronological.txt | head -3
```

--------------------------------

### Read paper by 'Baotian_Hu'

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Reads the first 20 lines of a specific paper by 'Baotian_Hu'.

```bash
Read(by_author/Baotian_Hu/2506.09790v1_fulltext.txt)
```

--------------------------------

### Build SemTools Project

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Compile the SemTools project using Cargo, the Rust build tool and package manager.

```bash
cargo build
```

--------------------------------

### Search for Specific Optimization Algorithms

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/3.txt

Search for papers related to specific optimization algorithms like 'Adam optimizer' or techniques such as 'neural architecture search' and 'Bayesian optimization'. Use '--top-k' and '--n-lines' to manage results.

```bash
search "Adam optimizer" full_text/*.txt --top-k 3 --n-lines 5
```

```bash
search "neural architecture search" full_text/*.txt --top-k 3 --n-lines 5
```

```bash
search "Bayesian optimization" full_text/*.txt --top-k 5 --n-lines 5
```

--------------------------------

### Search Arxiv for 'Eric Xing' in cs.CL

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Searches the cs.CL category for papers mentioning 'Eric Xing', retrieving the top 3 results with 3 lines of context.

```bash
search "Eric Xing" by_category/cs.CL/*.txt --top-k 3 --n-lines 3
```

--------------------------------

### List directory contents (by_date)

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Lists the contents of the 'by_date' directory, showing chronological organization of papers. This helps in understanding data structure.

```bash
List(by_date)
```

--------------------------------

### Search for exact term 'meta-learning'

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/3.txt

Uses grep to perform a case-insensitive search for the exact term 'meta-learning' within text files, limiting the output to the first 20 matches. This helps in identifying papers that specifically mention the term.

```bash
Bash(grep -i "meta-learning" full_text/*.txt | head -20)
```

--------------------------------

### List directory contents

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/1.txt

List the contents of a directory, useful for exploring dataset structure.

```bash
List(~/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers)
```

```bash
List(by_date)
```

--------------------------------

### Read Paper Abstract

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/5.txt

Use the `Read` command to fetch the content of a specific paper, useful for examining abstracts and context. Specify the file path and optionally the number of lines to read.

```bash
Read(full_text/2508.00917v1.txt)
```

```bash
Read(full_text/2508.10528v1.txt)
```

```bash
Read(full_text/2506.22174v1.txt)
```

--------------------------------

### Search from Stdin

Source: https://github.com/run-llama/semtools/blob/main/examples/example_CLAUDE.md

Demonstrates searching for 'content' within text piped to `semtools search` via standard input.

```bash
# Search from stdin
echo "some text content" | semtools search "content"

```

--------------------------------

### Arxiv Paper File Sample

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/plain_CLAUDE.md

A sample of the full text content from an Arxiv paper, including title, authors, affiliations, abstract, and introduction. Useful for understanding the data format.

```text
TITLE: Quantization vs Pruning: Insights from the Strong Lottery Ticket Hypothesis

Quantization vs Pruning: Insights from the Strong Lottery Ticket Hypothesis

Aakash Kumar 
Department of Physical Sciences, IISER Kolkata, 
West Bengal, India 741246 
ak20ms209@iiserkol.ac.in
&
Emanuele Natale 
Université Coté d’Azur,
CNRS, Inria, I3S, France
emanuele.natale@univ-cotedazur.fr

Abstract
Quantization is an essential technique for making neural networks more efficient, yet our theoretical understanding of it remains limited. Previous works demonstrated that extremely low-precision networks, such as binary networks, can be constructed by pruning large, randomly-initialized networks, and showed that the ratio between the size of the original and the pruned networks is at most polylogarithmic.
The specific pruning method they employed inspired a line of theoretical work known as the Strong Lottery Ticket Hypothesis (SLTH), which leverages insights from the Random Subset Sum Problem. However, these results primarily address the continuous setting and cannot be applied to extend SLTH results to the quantized setting.
In this work, we build on foundational results by Borgs et al. on the Number Partitioning Problem to derive new theoretical results for the Random Subset Sum Problem in a quantized setting.
Using these results, we then extend the SLTH framework to finite-precision networks. While prior work on SLTH showed that pruning allows approximation of a certain class of neural networks, we demonstrate that, in the quantized setting, the analogous class of target discrete neural networks can be represented exactly, and we prove optimal bounds on the necessary overparameterization of the initial network as a function of the precision of the target network.

1 Introduction

Deep neural networks (DNNs) have become ubiquitous in modern machine-learning systems, yet their ever-growing size quickly collides with the energy, memory, and latency constraints of real-world hardware. Quantization—representing weights with a small number of bits—is arguably the most hardware-friendly compression technique, and recent empirical work shows that aggressive quantization can preserve accuracy even down to the few bits regime. Unfortunately, our theoretical understanding of why and when such extreme precision reduction is possible still lags far behind in practice.
...

```

--------------------------------

### Pipeline Find with Xargs and Search

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/search_CLAUDE.md

Use 'find' to locate files and 'xargs' to pass them as arguments to the 'search' command. This enables searching within files identified by 'find', with options for context lines and distance.

```bash
find . -name "*.md" | xargs search "installation" --n-lines 10 --max-distance 0.5
```

--------------------------------

### Search Arxiv for Institutions in cs.CV and cs.CL

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Searches the cs.CV and cs.CL categories for papers mentioning specific universities and companies, retrieving the top 8 results with 2 lines of context.

```bash
search "University of Washington\|Stanford
      University\|Google\|Microsoft\|OpenAI" by_category/cs.CV/*.txt
      by_category/cs.CL/*.txt --top-k 8 --n-lines 2
```

--------------------------------

### Run Linting and Formatting Checks

Source: https://github.com/run-llama/semtools/blob/main/CONTRIBUTING.md

Ensure code quality and consistency by running clippy for linting and fmt for formatting. These checks are part of the pull request validation process.

```bash
cargo clippy
    cargo fmt
```

--------------------------------

### Search for 'GAN', 'GPT', or 'BERT' in Titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Filters titles for terms like 'GAN', 'GPT', or 'BERT' (case-insensitive) and sorts them numerically. This command is useful for tracking the emergence of specific influential models or architectures.

```bash
grep "^TITLE:" full_text/*.txt | sed
      's/full_text\/\([0-9]*\)\.[0-9]*v[0-9]*\.txt:TITLE: /\1: /' | grep -i -E
       "(GAN|GPT|BERT)" | sort -n
```

--------------------------------

### Set Llama Cloud API Key

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_coding_agents.md

Configure your environment by exporting your Llama Cloud API key. This is necessary for Semtools to authenticate with the Llama API.

```bash
export LLAMA_CLOUD_API_KEY=your_api_key
```

--------------------------------

### Ask questions based on stdin content with SemTools

Source: https://github.com/run-llama/semtools/blob/main/README.md

Feed content directly into `semtools ask` via standard input using `cat` and a pipe.

```bash
cat README.md | semtools ask "How do I install SemTools?"
```

--------------------------------

### Execute Python Script for Simple Topic Analysis

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/5.txt

This bash command runs a Python script that analyzes topic shifts for authors who published in multiple months. It provides feedback on the analysis progress.

```bash
python3 simple_topic_analysis.py
```

--------------------------------

### Read file content

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Use 'Read' to display the content of a specified file. Reads up to 50 lines.

```bash
Read(by_category/cs.CV/2507.22264v1_fulltext.txt)
```

```bash
Read(by_category/cs.CL/2506.22385v1_fulltext.txt)
```

--------------------------------

### Update and re-embed documents in SemTools workspace

Source: https://github.com/run-llama/semtools/blob/main/README.md

Demonstrates how changing a document (e.g., adding content) triggers automatic re-embedding and caching when performing a search in an activated workspace.

```bash
echo "some new content" > ./some_large_dir/some_file.txt
semtools search "some keywords" ./some_large_dir/*.txt --n-lines 5 --top-k 10
```

--------------------------------

### Combine Parse, Grep, and Search

Source: https://github.com/run-llama/semtools/blob/main/examples/example_CLAUDE.md

Combines `semtools parse`, `grep` for exact-match pre-filtering, and `semtools search` with a distance threshold to find 'network error' in parsed PDF documents.

```bash
# Combine with grep for exact-match pre-filtering and distance thresholding
semtools parse *.pdf | xargs cat | grep -i "error" | semtools search "network error" --max-distance 0.3

```

--------------------------------

### Execute simple abstract analysis script

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/4.txt

This command executes the simple Python script for abstract analysis after navigating to the specified directory. It loads and processes papers from June and August.

```bash
cd "/Users/loganmarkewich/semtools/benchmarks/arxiv/arxiv_dataset_1000_papers" && python3 simple_abstract_analysis.py
```

--------------------------------

### Search Arxiv for 'Eric P. Xing' or 'Eric Xing' in cs.CL

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/5.txt

Searches the cs.CL category for papers mentioning 'Eric P. Xing' or 'Eric Xing', retrieving the top 5 results with 3 lines of context.

```bash
search "Eric P. Xing\|Eric Xing" by_category/cs.CL/*.txt --top-k 5 \
      --n-lines 3
```

--------------------------------

### Find first occurrences of 'GAN' or 'generative adversarial' in titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Prints a header and then searches a chronological title file for 'gan' or 'generative adversarial' (case-insensitive), displaying the first 3 matches.

```bash
echo "=== GAN/GENERATIVE ADVERSARIAL ===" && grep -i -E
      "(gan|generative adversarial)" /tmp/all_titles_chronological.txt | head
      -3
```

--------------------------------

### Claude Code Interaction Trace

Source: https://github.com/run-llama/semtools/blob/main/examples/use_with_coding_agents.md

This trace shows a simulated interaction with Claude Code, demonstrating its ability to understand a task, explore a directory, parse PDFs, and search for information using semtools commands.

```bash
╭────────────────────────────────────────────────────────────────╮
│ ✻ Welcome to Claude Code!                                      │
│                                                                │
│   /help for help, /status for your current setup               │
│                                                                │
│   cwd: /Users/loganmarkewich/filesystem-index/acl_papers_2023  │
╰────────────────────────────────────────────────────────────────╯

> Currently you are in a directory full of PDF files containing papers from ACL 2023. Can you summarize the findings from these papers that 
  discuss LLMs and evals?

⏺ I'll help you summarize findings from ACL 2023 papers about LLMs and evaluations. Let me first explore the directory structure and then
  search for relevant papers.

⏺ Update Todos
  ⎿  ☐ Explore directory structure to understand the PDF files
     ☐ Parse PDF files to make them searchable
     ☐ Search for papers discussing LLMs and evaluations
     ☐ Analyze and summarize key findings

⏺ List(~/filesystem-index/acl_papers_2023)
  ⎿  Listed 914 paths (ctrl+r to expand)

⏺ Update Todos
  ⎿  ☒ Explore directory structure to understand the PDF files
     ☐ Parse PDF files to make them searchable
     ☐ Search for papers discussing LLMs and evaluations
     ☐ Analyze and summarize key findings

⏺ I can see there are 910 ACL 2023 papers in PDF format. Now I'll parse all these PDFs to make them searchable, then look for papers
  discussing LLMs and evaluations.

⏺ Bash(semtools parse *.pdf)
  ⎿  /Users/loganmarkewich/.parse/2023.acl-long.0.pdf.md 
     /Users/loganmarkewich/.parse/2023.acl-long.1.pdf.md
     /Users/loganmarkewich/.parse/2023.acl-long.10.pdf.md
     … +556 lines (ctrl+r to expand)

⏺ Update Todos
  ⎿  ☒ Explore directory structure to understand the PDF files
     ☒ Parse PDF files to make them searchable
     ☐ Search for papers discussing LLMs and evaluations
     ☐ Analyze and summarize key findings

⏺ Great! Now I have parsed all the PDFs. Let me search for papers related to LLMs and evaluations using the search tool.

⏺ Bash(semtools parse *.pdf | xargs cat | semtools search "large language model, evaluation, assessment, benchmark" --n-lines 5 --max-distance 0.4)
  ⎿  <stdin>:447094::447105 (0.16900031210055844) 
     447095: for Computational Linguistics.
     447096:
     … +415 lines (ctrl+r to expand)

⏺ Now let me search for more specific terms to get a broader view of LLM evaluation research:

⏺ Bash(semtools parse *.pdf | xargs cat | semtools search "GPT, BERT, language model evaluation, performance evaluation, model comparison" --n-lines 5
      --max-distance 0.35)
  ⎿  <stdin>:244769::244780 (0.2719643874147169)                                                             
     244770: <td>
     244771: - Reference-free and reference-based human evaluation results have a near-zero correlation.<br/>
     … +630 lines (ctrl+r to expand)

⏺ Now let me search for specific types of evaluation studies and findings:

⏺ Bash(semtools parse *.pdf | xargs cat | semtools search "robustness, bias, fairness, hallucination, reliability" --n-lines 4 --max-distance 0.3)
  ⎿  Using cached result for: 2023.acl-long.0.pdf 
     Using cached result for: 2023.acl-long.1.pdf
     Using cached result for: 2023.acl-long.10.pdf
     … +639 lines (ctrl+r to expand)

⏺ Now let me focus on a few specific papers to get key titles and detailed findings:

⏺ Update Todos
  ⎿  ☒ Explore directory structure to understand the PDF files
     ☒ Parse PDF files to make them searchable
     ☒ Search for papers discussing LLMs and evaluations
     ☐ Analyze and summarize key findings

⏺ Let me search for paper titles and abstracts to get a better understanding of the specific papers discussing LLM evaluation:

⏺ Bash(semtools parse *.pdf | xargs cat | semtools search "Abstract" --n-lines 10 | grep -A 10 -B 2 -i "large language model\|evaluation\|benchmark\|GPT\|BERT")
  ⎿  <stdin>:94456::94477 (0.00000000009809730805202874)                    
     94457:   Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought
     94458:                 Reasoning by Large Language Models
     … +628 lines (ctrl+r to expand)

⏺ Update Todos
  ⎿  ☒ Explore directory structure to understand the PDF files

```

--------------------------------

### Find first occurrences of 'LLM' or 'large language model' in titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Prints a header and then searches a chronological title file for 'llm' or 'large language model' (case-insensitive), displaying the first 3 matches.

```bash
echo "=== LLM/LARGE LANGUAGE MODEL ===" && grep -i -E "(llm|large
      language model)" /tmp/all_titles_chronological.txt | head -3
```

--------------------------------

### Read Paper Abstract (Command)

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/1.txt

Initiates reading the abstract of a specified paper file. Reads a default number of lines.

```command
Read(full_text/2505.20291v1.txt)
```

```command
Read(full_text/2505.20292v4.txt)
```

```command
Read(full_text/2506.09736v1.txt)
```

```command
Read(full_text/2506.10116v1.txt)
```

```command
Read(full_text/2507.09617v1.txt)
```

```command
Read(full_text/2508.21720v1.txt)
```

--------------------------------

### Search for Object Detection Papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/a_search_and_filter/5.txt

Use this command to find papers specifically on object detection, a key area within computer vision. Adjust `--top-k` as needed.

```bash
search "object detection" full_text/*.txt --top-k 5
```

--------------------------------

### Search for 'generative AI' themes in August 2025 papers

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/3.txt

Use this command to search for 'generative AI' related content in August 2025 papers. This helps in identifying trends in AI content creation. Adjust '--top-k' and '--n-lines' for specific results.

```bash
search "generative AI" by_date/2025-08/*.txt --top-k 8 --n-lines 3
```

--------------------------------

### Search for 'LLM' or 'language model' in Titles

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/c_temporal_analysis/2.txt

Filters titles for 'LLM' or 'language model' (case-insensitive) and displays the first 15 results sorted numerically. This is useful for tracking the discourse around large language models.

```bash
grep "^TITLE:" full_text/*.txt | sed
      's/full_text\/\([0-9]*\)\.[0-9]*v[0-9]*\.txt:TITLE: /\1: /' | grep -i -E
       "(LLM|language model)" | sort -n | head -15
```

--------------------------------

### Find papers with similar abstracts to a target paper

Source: https://github.com/run-llama/semtools/blob/main/benchmarks/arxiv/answers/b_cross_reference_and_relationships/1.txt

Use this command to initiate a search for papers with abstracts semantically similar to a specified paper. The tool first locates and reads the target paper's abstract before performing the search.

```bash
> Find papers with similar abstracts to paper 2506.09738v1
```