Compare AI RAG Tooling (2025)

RAG systems combine embedding, storage, and retrieval with LLM reasoning. This guide compares popular stacks and shows how to wire calls through a unified API.

At‑a‑glance

Feature comparison

Unified API examples

Create embeddings — JavaScript

import axios from "axios";

const res = await axios.post(
  "https://api.coreapi.com/v1/openai/embeddings",
  {
    model: "text-embedding-3-large",
    input: [
      "RAG connects your data to an LLM via retrieval.",
      "Use chunking and metadata to improve recall.",
    ],
  },
  { headers: { Authorization: `Bearer ${process.env.CORE_API_KEY}` } }
);
console.log(res.data);

Retrieve → answer — Python

import requests, os

# 1) embed the query
e = requests.post(
    "https://api.coreapi.com/v1/openai/embeddings",
    json={"model": "text-embedding-3-large", "input": "How do fallbacks work?"},
    headers={"Authorization": f"Bearer {os.environ['CORE_API_KEY']}"},
).json()
query_vec = e["data"][0]["embedding"]

# 2) search your vector DB (pseudo‑code)
# matches = vector_db.search(query_vec, top_k=5, filter={"tag": "docs"})
contexts = [m["text"] for m in []]  # replace with results

# 3) ask LLM with contexts
r = requests.post(
    "https://api.coreapi.com/v1/openai/chat/completions",
    json={
        "model": "gpt-4o-mini",
        "messages": [
            {"role": "system", "content": "Answer with provided context."},
            {"role": "user", "content": f"Context: {'\n'.join(contexts)}\nQuestion: How do fallbacks work?"},
        ],
    },
    headers={"Authorization": f"Bearer {os.environ['CORE_API_KEY']}"},
)
print(r.json())

Implementation tips

• Chunk by structure (headings, sections) not only tokens.
• Store metadata (source, section, updated_at) for filters and recency.
• Evaluate with golden‑set queries; watch recall@k and answer utility.