Building a Real-Time Reddit RAG & LLM Grounding Pipeline in Python

import os, requests

API_KEY = os.environ["REDDIT_API_KEY"]
HEADERS = {"x-api-key": API_KEY}

def build_reddit_rag_chunks(subreddit: str = "saas") -> list[dict]:
    posts = requests.get(
        "https://api.forumdataapi.com/v1/subreddit/posts",
        params={"subreddit": subreddit, "sort": "hot", "limit": 5},
        headers=HEADERS,
        timeout=15,
    ).json()["data"]["items"]

    chunks = []
    for post in posts:
        comments = requests.get(
            "https://api.forumdataapi.com/v1/posts/comments",
            params={"post_id": post["id"], "sort": "top", "limit": 20},
            headers=HEADERS,
            timeout=15,
        ).json()["data"]["items"]
        top_replies = [f"- ({c['score']} pts) u/{c['author']}: {c['body']}" for c in comments if c["score"] >= 5][:10]
        chunks.append({
            "id": post["post_id"],
            "url": post["permalink"],
            "subreddit": post["subreddit_name_prefixed"],
            "text": f"# {post['title']}\n\n{post['selftext'][:600]}\n\n## Top Community Replies\n" + "\n".join(top_replies),
        })
    return chunks

const headers = { "x-api-key": process.env.REDDIT_API_KEY };
const searchRes = await fetch(
  "https://api.forumdataapi.com/v1/subreddit/posts?subreddit=saas&sort=hot&limit=5",
  { headers },
);
const { data } = await searchRes.json();
console.log(`Fetched ${data.items.length} canonical Reddit threads`);

curl "https://api.forumdataapi.com/v1/subreddit/posts?subreddit=saas&sort=hot&limit=5" \
  -H "x-api-key: rd_live_your_api_key"

{
  "id": "t3_1u0z4vz",
  "url": "https://reddit.com/r/SaaS/comments/1u0z4vz/new_rule_banning_a_saas_product_category_no/",
  "subreddit": "r/SaaS",
  "score": 570,
  "num_comments": 326,
  "top_reply_sample": "- (86 pts) u/reallehnert: Great News!"
}