• SEARCH
  • CONTEXT
  • DATA
  • HUMAN

How to build RAG search pipelines with your AI

Connect DeepInfra to your AI client to turn text into high-dimensional vectors. This enables semantic search that understands meaning instead of just matching keywords.

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Short answer

How do I build RAG search pipelines with my AI?

Use the create_embedding tool to convert your documents into numerical vectors. These vectors allow your AI to find relevant information based on context rather than exact word matches. You'll get a structured way to feed specific data into your agent's context window.

The results of vectorization

Where your data goes

Once your text is converted, your AI can use those vectors for these specific outcomes.

SEARCH

Semantic retrieval

Your AI finds the most relevant text chunks by comparing vector distances. This replaces old-school keyword searches with actual meaning.

CONTEXT

Smart context injection

The agent pulls only the necessary snippets into its prompt. This keeps your context window clean and focused on the right data.

DATA

Vector database prep

The output is ready to be stored in a vector database. You get the exact numerical representations needed for indexing.

HUMAN

Manual review

You decide which chunks are high-quality enough to be indexed. The AI provides the vectors, but you control the final knowledge base.

The workflow

What your AI does when text arrives.

The AI handles the heavy math of converting language into coordinates.

  1. Text processing

    Your agent takes raw text from your files or database and prepares it for the model.

    create_embedding

  2. Vector generation

    The AI calls the embedding model to turn text strings into long lists of numbers.

    create_embedding

  3. Similarity calculation

    Your agent compares these new vectors against existing ones to find matches.

    create_embedding

  4. Context assembly

    The agent selects the best matches to answer your specific question.

    create_embedding

Try it

Copy these to start.

Use these prompts to begin building your pipeline.

Starting points

Replace the bracketed text with your actual file names or data sources.

DeepInfra Connector

You're all set. Choose your MCP client and follow the setup instructions.

Connector linkhttps://edge.vinkius.com/vk_preview_RMvS2UlIMhdShOjNbZfqar6XAZE9QfzTMbaejxJY/mcp

Claude Desktop

Follow the steps below to connect in seconds.

  1. 1In Claude Desktop, open Settings → Connectors.
  2. 2Click “Add custom connector” and paste the connector link above as the remote MCP server URL.
  3. 3Click Add and start a new chat — DeepInfra capabilities are ready to use.
Configuration · claude_desktop_config.jsonCopy
{
  "mcpServers": {
    "deepinfra-serverless-llm-inference-mcp": {
      "url": "https://edge.vinkius.com/vk_preview_RMvS2UlIMhdShOjNbZfqar6XAZE9QfzTMbaejxJY/mcp"
    }
  }
}
Copy into chat04

  • Create embeddings for the contents of my project_notes.txt file.

  • Convert this list of customer feedback entries into vectors for my search index.

  • Generate embeddings for the text in my documentation folder so I can perform semantic search.

  • Turn the data in my sales_report.csv into vectors to help you answer questions about it.

  • Claude
  • ChatGPT
  • Cursor
  • VS Code
  • Windsurf
  • Claude Code
  • JetBrains
  • Cline

Start here

Connect DeepInfra once, then ask.

Just link your account. Your credentials stay encrypted, and you can immediately start calling embedding tools from your favorite AI client.

Connect DeepInfra to your AI

FAQ

How this task behaves

  • 01

    Can the AI store these vectors for me?

    No. The tool generates the vectors, but you are responsible for saving them into a vector database.

  • 02

    Does this tool clean my text before embedding?

    No. You should handle text cleaning and chunking before sending the data to the embedding tool.

  • 03

    Can I use this to search through my emails?

    Yes, if you provide the email text to the agent to be vectorized.

  • 04

    Will the AI delete my original documents?

    No. This tool only reads text to create vectors; it cannot modify or delete your source files.

  • 05

    Can the AI perform the actual search itself?

    The AI uses the vectors to find matches, but the actual mathematical comparison happens through the embedding process.

  • More questions about DeepInfra? The Connector page answers them. See everything the DeepInfra Connector can do

Connect DeepInfra to Claude, Cursor, ChatGPT & more