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    AI Semantic Search & RAG

    1

    AI-powered semantic search and Retrieval-Augmented Generation (RAG) for intelligent document retrieval, knowledge discovery and context-aware answers with source references.

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    AI Semantic Search & RAG

    AI Semantic Search & RAG

    Example session with this skill installed

    Check the health of my local search engine and search the 'chroma' provider for 'How does the chunking logic work?' with natural language answers enabled.

    • Read your context and instructions
    • Compiled the ai semantic search
    • Generated the document

    I have checked the system status and performed the requested semantic search. The service is healthy and currently using the Chroma vector provider.

    Search Results: Chunking Logic

    SourceFilenameScoreContent Preview
    [Source 1]indexing_logic.md0.924The system splits documents into overlapping chunks of 1000 characters...
    [Source 2]api_ref.pdf0.881Text extraction occurs via PDFMiner before being passed to the recursive splitter...

    Answer
    Based on the indexed documents, the chunking logic utilizes a recursive character splitter that creates segments of 1000 characters with a defined overlap to ensure semantic continuity. This process occurs immediately after text extraction from PDF or Markdown files and before the vectors are sent to the Chroma database.

    Next steps

    • If the answer is too brief, I can re-run the search with a higher top_k value.
    • I can list all currently indexed documents in the chroma provider to verify if other relevant files are missing.
    • If you want to compare this result, I can run the same query against Qdrant or Pinecone.

    api_ref.pdf

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    AI Semantic Search & RAG

    Transform your document collections into a searchable AI-powered knowledge base.

    AI Semantic Search & RAG helps developers build intelligent search and question-answering workflows using semantic embeddings, vector databases and Retrieval-Augmented Generation (RAG).

    Key Features

    • Semantic Search: Find relevant information based on meaning rather than exact keywords.
    • Document Processing: Work with PDF, TXT and Markdown documents.
    • RAG Workflows: Retrieve relevant content to support AI-generated answers.
    • Source References: Help users trace answers back to retrieved information.
    • Vector Database Integration: Support workflows using Chroma, Pinecone or Qdrant, depending on configuration.
    • Developer-Friendly: Designed for Python-based AI projects and knowledge-management applications.

    Ideal For

    • Developers building AI assistants.
    • Researchers searching large document collections.
    • Teams developing internal knowledge bases.
    • Students exploring semantic search and RAG.
    • AI automation and document-analysis workflows.

    Requirements

    • Python and the dependencies specified in the package.
    • An OpenAI API key.
    • A configured vector database or supported local storage.
    • Internet access for external API calls.

    Important Notes

    API usage costs are not included in the purchase price. Setup and integration requirements depend on your environment and the supplied package.

    The Skill is intended to assist with document retrieval and grounded question answering. Always verify important answers against their original sources.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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    Verified clean 3 days ago

    • Passed all security checks, Safe to install

    Requires

    OpenAI
    Python 3.12+
    pip
    Git (optional)
    ChromaDB (local) or a configured Pinecone/Qdrant service
    Python
    macOS
    Git

    Listed3 days ago

    What's inside

    Frequently Asked Questions