Smithery Logo
MCPsSkillsDocsPricing
Login
Smithery Logo

Accelerating the Agent Economy

Resources

DocumentationPrivacy PolicySystem Status

Company

PricingAboutBlog

Connect

© 2026 Smithery. All rights reserved.

    run-llama

    retrieve-relevant-information-through-rag

    run-llama/retrieve-relevant-information-through-rag
    AI & ML
    176
    1 installs

    About

    SKILL.md

    Install

    Install via Skills CLI

    or add to your agent
    • Claude Code
      Claude Code
    • Codex
      Codex
    • OpenClaw
      OpenClaw
    • Cursor
      Cursor
    • Amp
      Amp
    • GitHub Copilot
      GitHub Copilot
    • Gemini CLI
      Gemini CLI
    • Kilo Code
      Kilo Code
    • Junie
      Junie
    • Replit
      Replit
    • Windsurf
      Windsurf
    • Cline
      Cline
    • Continue
      Continue
    • OpenCode
      OpenCode
    • OpenHands
      OpenHands
    • Roo Code
      Roo Code
    • Augment
      Augment
    • Goose
      Goose
    • Trae
      Trae
    • Zencoder
      Zencoder
    • Antigravity
      Antigravity
    ├─
    ├─
    └─

    About

    Leverage Retrieval Augmented Generation to retrieve relevant information from a a LlamaCloud Index. Requires the llama_cloud_services package and LLAMA_CLOUD_API_KEY as an environment variable.

    SKILL.md

    Information Retrieval

    Quick start

    You can create an index on LlamaCloud using the following code. By default, new indexes use managed embeddings (OpenAI text-embedding-3-small, 1536 dimensions, 1 credit/page):

    import os
    
    from llama_index.core import SimpleDirectoryReader
    from llama_cloud_services import LlamaCloudIndex
    
    # create a new index (uses managed embeddings by default)
    index = LlamaCloudIndex.from_documents(
        documents,
        "my_first_index",
        project_name="default",
        api_key="llx-...",
        verbose=True,
    )
    
    # connect to an existing index
    index = LlamaCloudIndex("my_first_index", project_name="default")
    

    You can also configure a retriever for managed retrieval:

    # from the existing index
    index.as_retriever()
    
    # from scratch
    from llama_cloud_services import LlamaCloudRetriever
    
    retriever = LlamaCloudRetriever("my_first_index", project_name="default")
    
    # perform retrieval
    result = retriever.retrieve("What is the capital of France?")
    

    And of course, you can use other index shortcuts to get use out of your new managed index:

    query_engine = index.as_query_engine(llm=llm)
    
    # perform retrieval and generation
    result = query_engine.query("What is the capital of France?")
    

    Retriever Settings

    A full list of retriever settings/kwargs is below:

    • dense_similarity_top_k: Optional[int] -- If greater than 0, retrieve k nodes using dense retrieval
    • sparse_similarity_top_k: Optional[int] -- If greater than 0, retrieve k nodes using sparse retrieval
    • enable_reranking: Optional[bool] -- Whether to enable reranking or not. Sacrifices some speed for accuracy
    • rerank_top_n: Optional[int] -- The number of nodes to return after reranking initial retrieval results
    • alpha Optional[float] -- The weighting between dense and sparse retrieval. 1 = Full dense retrieval, 0 = Full sparse retrieval.

    Requirements

    The llama_cloud_services and llama-index-core packages must be installed in your environment:

    pip install llama-index-core llama_cloud_services
    

    And the LLAMA_CLOUD_API_KEY must be available as an environment variable:

    export LLAMA_CLOUD_API_KEY="..."
    
    Recommended Servers
    Linkup
    Linkup
    Repository
    run-llama/vibe-llama
    Files