> ## Documentation Index
> Fetch the complete documentation index at: https://prompt-lockbox.mintlify.site/llms.txt
> Use this file to discover all available pages before exploring further.
# Advanced Search & Discovery
> Learn how to enable fast, intent-based prompt discovery using advanced search and indexing.
When your project has dozens or even hundreds of prompts, finding the exact one you need with grep or a simple file search becomes impossible. You need a search that understands intent, not just keywords.
This guide will walk you through Prompt Lockbox's advanced search capabilities. You'll learn **how to build a search index** and use powerful **hybrid and SPLADE search** methods to find any prompt in seconds, no matter how large your library grows.
**The Goal**: To move beyond simple fuzzy search and learn how to:
Build a dedicated search index for your project.
* Use **Hybrid** Search for a balance of keyword and semantic matching.
* Use **SPLADE** Search for the a balanced mix of keyword and conceptual matching.
## Building Your Search Index
To enable advanced search, you first need to **create an index**. This is a **one-time process** that scans all your prompts and creates optimized files for fast lookups. The `plb index` command handles this for you.
**Dependencies!**
Advanced search methods require extra Python libraries. If you haven't installed them, the command will guide you. You can pre-install them with:
pip install prompt-lockbox\[search]
### How to Build the Index
You can choose which type of index to build. We recommend starting with **hybrid**.
```bash CLI theme={null}
# Build the index for Hybrid Search (TF-IDF + FAISS)
plb index --method=hybrid
```
```python Python theme={null}
from prompt_lockbox import Project
project = Project()
# Call the index() method to build the search index
project.index(method="hybrid")
```
This command will create several files inside your project's hidden `.plb/` directory. You only need to **run this command again when you've added or significantly changed many prompts**.
## Choosing the Right Search Method
Now that your index is built, you can use the advanced search commands. Which one should you use?
`plb search fuzzy "query"`
* **How it Works**: Simple string matching on names, descriptions, and tags.
* **Pros**: Very fast, requires no index.
* **Best For**: Quick lookups when you already know part of the prompt's name or a unique keyword in its description.
`plb search hybrid "query"`
* **How it Works**: Combines classic keyword search (TF-IDF) with modern semantic search (FAISS).
* **Pros**: The best of both worlds. It finds prompts that contain your exact keywords and prompts that are just conceptually similar.
* **Best For**: General-purpose, high-quality searching in any prompt library. This is the recommended "power search" for most users.
`plb search splade "query"`
* **How it Works**: Uses a state-of-the-art "sparse vector" model that is exceptionally good at understanding the context and importance of words.
* **Pros**: Often provides the most relevant results, especially for complex or vague queries.
* **Best For**: When you need the absolute highest quality results and want to find prompts based on their meaning, even if they don't share any keywords.
## Performing an Advanced Search
**Note**
Search index needs to be built before using search!
Now that your index is built, you can use the advanced search methods through the `search()` method in the SDK or the `plb search` command.
Let's imagine you have this prompt in your library and you want to find it.
```yaml prompts/code-gen.v1.0.0.yml theme={null}
name: python-function-writer
description: "Writes a complete Python function based on a docstring."
tags: [python, code-generation]
...
```
A simple fuzzy search for "python code" might work, but an advanced search for "create a python function" is much more powerful because it understands that "create," "writer," and "generation" are conceptually related. Examples:
**Hybrid Search (CLI)**:
```bash theme={null}
# Search using the 'hybrid' engine
plb search hybrid "create a python function"
```
**Hybrid Search (Python SDK)**:
```python theme={null}
from prompt_lockbox import Project
from rich import print
project = Project()
results = project.search(
query="create a python function",
method="hybrid"
)
print(results)
# Expected output:
# [
# {'score': 0.85, 'name': 'python-function-writer', ...},
# ...
# ]
```
**SPLADE Search (Python SDK)**:
```python theme={null}
from prompt_lockbox import Project
from rich import print
project = Project()
# Using the SPLADE engine is as simple as changing the method
results = project.search(
query="create a python function",
method="splade",
limit=5 # You can also change the limit
)
print(results)
```
### Tuning your Search
A unique feature of the hybrid search method is the ability to **balance between keyword and semantic matching** using the `alpha` parameter.
* **alpha=0.0**: Purely keyword-based (like a classic search engine).
* **alpha=1.0**: Purely semantic-based (finds conceptually similar prompts).
* **alpha=0.5** (Default): A balanced mix of both.
This is available in both the CLI and SDK.
```bash CLI theme={null}
# Prioritize semantic meaning over keywords
plb search hybrid "email analysis" --alpha 0.9
```
```python Python theme={null}
from prompt_lockbox import Project
project = Project()
# Emphasize semantic results by setting a high alpha
semantic_results = project.search(
query="email analysis",
method="hybrid",
alpha=0.9
)
# Emphasize keyword results by setting a low alpha
keyword_results = project.search(
query="email analysis",
method="hybrid",
alpha=0.1
)
```
By building an `index` and using the `hybrid` or `splade` search methods, you transform your prompt library from a simple collection of files into a powerful, discoverable knowledge base.