RAG

RAG Skill Overview

This skill provides generic Retrieval-Augmented Generation (RAG) capabilities for building and calling vector search over custom documents in any project.

Typical use cases include:

• Powering knowledge-base retrieval for Q&A systems and chatbots;
• Doing semantic search over technical documentation, codebases, logs, etc.;
• Supplying “retrieved relevant snippets” to downstream LLM generation steps.

Detailed, script-level explanations are split into standalone documents under the reference/ directory. This file focuses on the overall structure and usage guidelines.

Directory Structure

The skill directory is organized as follows:

rag/
├── SKILL.md                 # This file: overview and navigation
├── scripts/                 # Executable scripts (core logic)
│   ├── search.py            # Generic vector search + content retrieval
│   ├── encode.py            # Text encoding utilities (embeddings)
│   ├── build_faiss.py       # Build faiss.index from embeddings.npy (for --use_faiss)
│   └── database.py          # Vector database builder interface (placeholder implementation)
└── reference/               # Detailed docs for each script
    ├── search.md            # Parameters, I/O, and examples for search.py
    ├── encode.md            # Usage and scenarios for encode.py
    ├── build_faiss.md       # Building faiss.index from existing embeddings
    └── database.md          # Interface design and extension notes for database.py

When to Use This Skill

Use the rag skill when you need any of the following:

• Semantic search over your own documents/data, e.g. “find the most relevant snippets in these JSON/Markdown/code files for a given question”;
• Building or maintaining a vector database, and querying it efficiently via vector search (e.g. FAISS when available);
• Combining external knowledge with an LLM, i.e. implementing a RAG (retrieval-augmented generation) workflow;
• Reusing an existing vector store (with embeddings.npy and nodes.jsonl; faiss.index optional) across different tasks or projects.

If you are only doing “single-turn reasoning without external knowledge” (plain Q&A), you do not need this skill.

Quick Start (Command-Line Perspective)

The primary way to interact with this skill is via the Python scripts in the scripts/ directory. Below are conceptual flows for the two most common operations, with explicit embedding parameters so agents can correctly honor upstream configuration (e.g., EvoMaster playground configs).

1. Semantic Search over an Existing Vector Store (scripts/search.py)

Prerequisite: You already have a vector store directory vec_dir that contains at least:

• embeddings.npy: precomputed embedding matrix (used for cosine similarity);
• nodes.jsonl: one JSON per line, each containing fields that identify a node (by default node_id).

Whether to load faiss.index is explicitly controlled by the caller: pass --use_faiss (CLI) or use_faiss=True (API) to load it when FAISS is installed and the file exists; default is off (search uses embeddings.npy only).

The minimal call pattern (only returning node_id and cosine similarity) is for the host system to run search.py with arguments, providing at least:

• vec_dir: path to the vector store directory;
• query: the query text;
• Optional top_k: number of results to return.

When you have an external configuration that already decides which embedding backend/model to use (for example, EvoMaster’s embedding block with type, openai.model, dimensions, etc.), you should forward those values explicitly to this script, instead of relying on its internal defaults.

How to invoke

Invoke via run_script (script_name: search.py). For the full parameter list and semantics, load reference/search.md first (use_skill with action: get_reference, reference_name: search.md); do not rely on this file alone to construct script_args. To retrieve original content per hit, you will need nodes_data and content_path; see reference/search.md.

2. Building faiss.index (scripts/build_faiss.py)

If you already have a vector store with embeddings.npy and want to enable --use_faiss in search (e.g. for larger-scale retrieval), run build_faiss.py via run_script with script_args: --vec_dir <path_to_vec_dir>. This reads vec_dir/embeddings.npy, builds a normalized inner-product index (cosine similarity), and writes vec_dir/faiss.index. The faiss package must be installed (pip install faiss-cpu). See reference/build_faiss.md for details.

3. Standalone Text Embedding Generation (scripts/encode.py)

When you need to manually build a vector store, inspect embeddings, or reuse the “text → vector” capability in other systems, the host can call encode.py and provide at least:

• A model name or path for encoding (local Transformer model or OpenAI embedding model);
• The text(s) to encode (single or multiple);
• Optional output location (e.g. .npy file), whether to normalize embeddings, batch size, etc.

encode.py supports:

• Single-text encoding / batch encoding of multiple texts;
• Local Transformer/HuggingFace models and the OpenAI Embedding API;
• Optional vector normalization and batch-size control.

More examples and parameter details are in reference/encode.md.

Reference Documentation Navigation (reference/)

This skill follows the principle: keep SKILL.md concise and push details into reference/. When using the skill, selectively load only the docs you actually need:

• reference/search.md

  • Scope: Detailed documentation for scripts/search.py.
  • Content: Vector store input requirements, CLI parameters, search and content-retrieval modes, configuration for OpenAI vs. local models, generic schema design, and best practices.

• reference/encode.md

  • Scope: Documentation for scripts/encode.py.
  • Content: Single/batched encoding, output format (.npy), embedding normalization, and integration scenarios with other systems.

• reference/build_faiss.md

  • Scope: Documentation for scripts/build_faiss.py.
  • Content: How to generate faiss.index from embeddings.npy, when to use it, and dependency (faiss package).

• reference/database.md

  • Scope: Interface design for scripts/database.py.
  • Content: The responsibilities of VectorDatabaseBuilder, expected semantics of each method, current placeholder status, and how to gradually implement build/incremental-update/statistics logic in your own project.

When using this skill, load only the reference docs that are directly relevant to the current task, instead of pulling all of them into context at once.

Design Principles and Notes

• Project-Agnostic (Generic) Design

  • Script interfaces do not depend on specific business fields or task definitions.
  • All business-specific fields (such as data_knowledge, model_knowledge, etc.) are accessed via configurable parameters like content_path.

• Pluggable Embedding Models

  • Supports local Transformer models, HuggingFace models, and the OpenAI Embedding API.
  • A unified create_embedder abstraction wraps these backends so callers do not need to handle SDK-specific details.

• Extensible Vector Store Structure

  • Search requires embeddings.npy and nodes.jsonl. Loading faiss.index is controlled by the caller via use_faiss (default off). Other files (e.g. nodes_data.json) are optional for content retrieval.
  • You are free to design the JSON structure of nodes_data according to your own project.

• Script-First, Minimal Documentation

  • Logic that can be implemented in scripts is placed under scripts/ whenever possible.
  • Documentation focuses on “how to call the scripts correctly” and on parameter/I/O descriptions, instead of re-implementing logic in prose.

If you need project-specific “database schema examples” or “custom business field conventions” on top of this skill, document them in your own project, rather than modifying this skill. This keeps rag reusable across multiple, unrelated projects.

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