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Retrieval-augmented generation (RAG) pairs a search retrieval step with an LLM generation step. The Andi API handles retrieval — you pass the results as context to the LLM, which generates answers grounded in real web sources.

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How it works

  1. User asks a question
  2. Your app searches the web via the Andi API
  3. Search results become context for the LLM prompt
  4. The LLM generates an answer grounded in those results

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Complete example

# Step 1: Search
results=$(curl -s \
  "https://search-api.andisearch.com/api/v1/search?q=what+causes+aurora+borealis&extracts=true&limit=5" \
  -H "x-api-key: $ANDI_API_KEY")

# Step 2: Format context (extract titles and descriptions)
context=$(echo "$results" | jq -r '.results[] | "[\(.title)](\(.link))\n\(.desc)\n"')

echo "Context for LLM:"
echo "$context"
# Step 3: Pass $context to your LLM of choice

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Using format=context

For simpler RAG setups, use format=context to get results pre-formatted as markdown. This skips the manual formatting step: 
response = requests.get(
    "https://search-api.andisearch.com/api/v1/search",
    params={
        "q": "what causes aurora borealis",
        "format": "context",
        "limit": 5,
    },
    headers={"x-api-key": api_key},
)

# Response is markdown text — pass directly to your LLM
context = response.text
format=context is the fastest path to a working RAG pipeline. Use format=json with extracts=true when you need more control over how context is structured.

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Using deep search for RAG

For research-heavy queries, deep search provides broader source coverage and spell correction: 
response = requests.get(
    "https://search-api.andisearch.com/api/v1/search",
    params={
        "q": "what causes aurora borealis",
        "depth": "deep",
        "extracts": "true",
        "limit": 10,
    },
    headers={"x-api-key": api_key},
)
Deep search takes ~2-3 seconds vs ~1 second for fast search. Use it when answer quality matters more than latency.

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Tips for better RAG results

  • Use extracts=true to get longer text passages beyond the short desc field
  • Set limit=5 to limit=10 — more results give the LLM more context to draw from, but too many can dilute relevance
  • Include source URLs in the prompt so the LLM can cite them
  • Use includeDomains to restrict to authoritative sources for domain-specific questions
  • Tell the LLM to say “I don’t know” when the search results don’t contain the answer

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Next steps

AI agent tool

Define Andi search as a tool for an AI agent.

Research assistant

Multi-query search with result aggregation.

Response format

Response structure and result types.

Deep search

When to use deep vs fast search.