revert: "chore(cleanup)!: remove tool_runtime.rag_tool" (#3877)

Reverts llamastack/llama-stack#3871

This PR broke RAG (even from Responses -- there _is_ a dependency)

llama_stack/providers/utils/memory/vector_store.py

@@ -12,14 +12,17 @@ from dataclasses import dataclass
 from typing import Any
 from urllib.parse import unquote
 
+import httpx
 import numpy as np
 from numpy.typing import NDArray
 from pydantic import BaseModel
 
 from llama_stack.apis.common.content_types import (
+    URL,
     InterleavedContent,
 )
 from llama_stack.apis.inference import OpenAIEmbeddingsRequestWithExtraBody
+from llama_stack.apis.tools import RAGDocument
 from llama_stack.apis.vector_io import Chunk, ChunkMetadata, QueryChunksResponse
 from llama_stack.apis.vector_stores import VectorStore
 from llama_stack.log import get_logger
@@ -126,6 +129,31 @@ def content_from_data_and_mime_type(data: bytes | str, mime_type: str | None, en
         return ""
 
 
+async def content_from_doc(doc: RAGDocument) -> str:
+    if isinstance(doc.content, URL):
+        if doc.content.uri.startswith("data:"):
+            return content_from_data(doc.content.uri)
+        async with httpx.AsyncClient() as client:
+            r = await client.get(doc.content.uri)
+        if doc.mime_type == "application/pdf":
+            return parse_pdf(r.content)
+        return r.text
+    elif isinstance(doc.content, str):
+        pattern = re.compile("^(https?://|file://|data:)")
+        if pattern.match(doc.content):
+            if doc.content.startswith("data:"):
+                return content_from_data(doc.content)
+            async with httpx.AsyncClient() as client:
+                r = await client.get(doc.content)
+            if doc.mime_type == "application/pdf":
+                return parse_pdf(r.content)
+            return r.text
+        return doc.content
+    else:
+        # will raise ValueError if the content is not List[InterleavedContent] or InterleavedContent
+        return interleaved_content_as_str(doc.content)
+
+
 def make_overlapped_chunks(
     document_id: str, text: str, window_len: int, overlap_len: int, metadata: dict[str, Any]
 ) -> list[Chunk]: