revert: old approach changes

vector-serve/app/routes/transform.py

@@ -3,11 +3,9 @@
 from typing import TYPE_CHECKING, Any, List
 
 from app.models import model_org_name, get_model, parse_header
-from app.utils.chunking import recursive_text_chunk
 from fastapi import APIRouter, Header, HTTPException, Request
 from pydantic import BaseModel, conlist
 
-
 router = APIRouter(tags=["transform"])
 
 logging.basicConfig(level=logging.DEBUG)
@@ -42,13 +40,6 @@ def batch_transform(
     request: Request, payload: Batch, authorization: str = Header(None)
 ) -> ResponseModel:
     logging.info({"batch-predict-len": len(payload.input)})
-
-    chunked_input = []
-    for doc in payload.input:
-        chunked_input.extend(
-            recursive_text_chunk(doc, chunk_size=1000, chunk_overlap=200)
-        )
-
     batches = chunk_list(payload.input, BATCH_SIZE)
     num_batches = len(batches)
     responses: list[list[float]] = []

vector-serve/tests/test_endpoints.py

@@ -1,81 +1,22 @@
 from fastapi.testclient import TestClient
 from fastapi import FastAPI
 
-
 def test_ready_endpoint(test_client):
     response = test_client.get("/ready")
     assert response.status_code == 200
     assert response.json() == {"ready": True}
 
-
 def test_alive_endpoint(test_client):
     response = test_client.get("/alive")
     assert response.status_code == 200
     assert response.json() == {"alive": True}
 
-
 def test_model_info(test_client):
-    response = test_client.get(
-        "/v1/info", params={"model_name": "sentence-transformers/all-MiniLM-L6-v2"}
-    )
+    response = test_client.get("/v1/info", params={"model_name": "sentence-transformers/all-MiniLM-L6-v2"})
     assert response.status_code == 200
 
 
 def test_metrics_endpoint(test_client):
     response = test_client.get("/metrics")
     assert response.status_code == 200
     assert "all-MiniLM-L6-v2" in response.text
-
-
-# Simulate a large document
-long_text = "This is a very long document. " * 1000
-
-
-def test_chunking_basic(test_client):
-    payload = {"input": [long_text], "model": "all-MiniLM-L6-v2", "normalize": False}
-    response = test_client.post("/v1/embeddings", json=payload)
-
-    assert response.status_code == 200
-    response_data = response.json()
-
-    assert len(response_data["data"]) > 0
-    assert "embedding" in response_data["data"][0]
-    assert len(response_data["data"]) > 1
-
-
-def test_chunking_small_input(test_client):
-    small_text = "Short text."
-    payload = {"input": [small_text], "model": "all-MiniLM-L6-v2", "normalize": False}
-    response = test_client.post("/v1/embeddings", json=payload)
-
-    assert response.status_code == 200
-    response_data = response.json()
-
-    assert len(response_data["data"]) == 1
-    assert "embedding" in response_data["data"][0]
-
-
-def test_chunk_overlap(test_client):
-    payload = {"input": [long_text], "model": "all-MiniLM-L6-v2", "normalize": False}
-    response = test_client.post("/v1/embeddings", json=payload)
-
-    assert response.status_code == 200
-    response_data = response.json()
-
-    chunk_size = 1000
-    overlap_size = 200
-    num_chunks = len(long_text) // (chunk_size - overlap_size)
-
-    assert len(response_data["data"]) == num_chunks
-
-
-def test_large_input(test_client):
-    large_text = "Lorem ipsum " * 5000
-    payload = {"input": [large_text], "model": "all-MiniLM-L6-v2", "normalize": False}
-    response = test_client.post("/v1/embeddings", json=payload)
-
-    assert response.status_code == 200
-    response_data = response.json()
-
-    assert len(response_data["data"]) > 1
-    assert "embedding" in response_data["data"][0]