Add quickstart and RAG MCP starter docs.

Made-with: Cursor

docs/rag-mcp-starter/index.py

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+#!/usr/bin/env python3
+import os
+import uuid
+from pathlib import Path
+
+import requests
+from qdrant_client import QdrantClient
+from qdrant_client.models import Distance, PointStruct, VectorParams
+
+DOCS_DIR = Path(os.getenv("RAG_DOCS_DIR", "./docs"))
+QDRANT_URL = os.getenv("QDRANT_URL", "http://127.0.0.1:6333")
+COLLECTION = os.getenv("QDRANT_COLLECTION", "docs")
+EMBED_MODEL = os.getenv("EMBED_MODEL", "nomic-embed-text")
+CHUNK_SIZE = int(os.getenv("CHUNK_SIZE", "1000"))
+CHUNK_OVERLAP = int(os.getenv("CHUNK_OVERLAP", "120"))
+
+
+def read_text(path: Path) -> str:
+    if path.suffix.lower() == ".pdf":
+        from pypdf import PdfReader
+
+        reader = PdfReader(str(path))
+        return "\n".join((p.extract_text() or "") for p in reader.pages)
+    return path.read_text(encoding="utf-8", errors="ignore")
+
+
+def chunk_text(text: str, size: int, overlap: int) -> list[str]:
+    chunks = []
+    i = 0
+    step = max(1, size - overlap)
+    while i < len(text):
+        chunks.append(text[i : i + size])
+        i += step
+    return chunks
+
+
+def embed(text: str) -> list[float]:
+    r = requests.post(
+        "http://127.0.0.1:11434/api/embeddings",
+        json={"model": EMBED_MODEL, "prompt": text},
+        timeout=120,
+    )
+    r.raise_for_status()
+    return r.json()["embedding"]
+
+
+def main() -> None:
+    if not DOCS_DIR.exists():
+        print(f"Docs directory not found: {DOCS_DIR}")
+        return
+
+    client = QdrantClient(url=QDRANT_URL)
+    test_vec = embed("ping")
+    dim = len(test_vec)
+
+    if not client.collection_exists(COLLECTION):
+        client.create_collection(
+            collection_name=COLLECTION,
+            vectors_config=VectorParams(size=dim, distance=Distance.COSINE),
+        )
+
+    points: list[PointStruct] = []
+    supported = {".md", ".txt", ".pdf"}
+
+    for path in DOCS_DIR.rglob("*"):
+        if not path.is_file() or path.suffix.lower() not in supported:
+            continue
+        raw = read_text(path).strip()
+        if not raw:
+            continue
+        for idx, chunk in enumerate(chunk_text(raw, CHUNK_SIZE, CHUNK_OVERLAP)):
+            vec = embed(chunk)
+            points.append(
+                PointStruct(
+                    id=str(uuid.uuid4()),
+                    vector=vec,
+                    payload={
+                        "source": str(path),
+                        "chunk_id": idx,
+                        "text": chunk[:4000],
+                    },
+                )
+            )
+
+    if points:
+        client.upsert(collection_name=COLLECTION, points=points)
+        print(f"Indexed chunks: {len(points)}")
+    else:
+        print("No documents indexed.")
+
+
+if __name__ == "__main__":
+    main()