RAG pipeline

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. The flow: a user asks a question, your app searches the web via the Andi API, the results become context for the LLM prompt, and the LLM generates an answer grounded in those results.

Complete example

# Step 1: Search
results=$(curl -s \
  "https://api.andiai.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

import os
import requests

api_key = os.environ["ANDI_API_KEY"]

# Step 1: Search for context
search_response = requests.get(
    "https://api.andiai.com/api/v1/search",
    params={
        "q": "what causes aurora borealis",
        "extracts": "true",
        "limit": 5,
    },
    headers={"x-api-key": api_key},
)
search_data = search_response.json()

# Step 2: Format results as context
context_parts = []
for result in search_data["results"]:
    text = result["desc"]
    if result.get("extracts"):
        text = " ".join(result["extracts"])
    context_parts.append(f"[{result['title']}]({result['link']})\n{text}")

context = "\n\n".join(context_parts)

# Step 3: Build the LLM prompt
prompt = f"""Answer the user's question using only the search results below.
Cite sources by linking to the URLs provided.

Search results:
{context}

Question: What causes the aurora borealis?"""

# Step 4: Send to your LLM (example with OpenAI-compatible API)
# completion = client.chat.completions.create(
#     model="your-model",
#     messages=[{"role": "user", "content": prompt}],
# )
print(prompt)

const apiKey = process.env.ANDI_API_KEY;

// Step 1: Search for context
const url = new URL("https://api.andiai.com/api/v1/search");
url.searchParams.set("q", "what causes aurora borealis");
url.searchParams.set("extracts", "true");
url.searchParams.set("limit", "5");

const searchResponse = await fetch(url, {
  headers: { "x-api-key": apiKey },
});
const searchData = await searchResponse.json();

// Step 2: Format results as context
const context = searchData.results
  .map((r) => {
    const text = r.extracts ? r.extracts.join(" ") : r.desc;
    return `[${r.title}](${r.link})\n${text}`;
  })
  .join("\n\n");

// Step 3: Build the LLM prompt
const prompt = `Answer the user's question using only the search results below.
Cite sources by linking to the URLs provided.

Search results:
${context}

Question: What causes the aurora borealis?`;

// Step 4: Send to your LLM
console.log(prompt);

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://api.andiai.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.

Pinning deep mode for RAG

The default auto mode adapts effort per query and is the right default for most RAG pipelines. For research-heavy queries, set searchMode=deep to force broader source coverage and spell correction on every call:

response = requests.get(
    "https://api.andiai.com/api/v1/search",
    params={
        "q": "what causes aurora borealis",
        "searchMode": "deep",
        "extracts": "true",
        "limit": 10,
    },
    headers={"x-api-key": api_key},
)

Deep mode takes ~2-3 seconds vs ~1 second for fast mode. Pin it when you need guaranteed broad coverage on every call; otherwise auto already applies deeper treatment when a query calls for it.

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

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.

Getting started

Use cases

Complete example

Using `format=context`

Pinning deep mode for RAG

Tips for better RAG results

Next steps

AI agent tool

Research assistant

Response format

Deep search

​Complete example

​Using format=context

​Pinning deep mode for RAG

​Tips for better RAG results

​Next steps

AI agent tool

Research assistant

Response format

Deep search

Complete example

Using `format=context`

Pinning deep mode for RAG

Tips for better RAG results

Next steps