Upload test.py

test.py

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+import json
+import requests
+from typing import List
+from tqdm import tqdm
+from langchain.embeddings.base import Embeddings  # adjust if using another base class
+
+class CustomAPIEmbeddings(Embeddings):
+    def __init__(self, api_url: str, show_progress: bool = True, batch_size: int = 32):
+        self.api_url = api_url
+        self.show_progress = show_progress
+        self.batch_size = batch_size
+
+    def embed_documents(self, texts: List[str]) -> List[List[float]]:
+        lst_embedding = []
+        iterator = range(0, len(texts), self.batch_size)
+        iterator = tqdm(iterator) if self.show_progress else iterator
+
+        for i in iterator:
+            batch = texts[i: i + self.batch_size]
+            payload = json.dumps({"inputs": batch})
+            headers = {'Content-Type': 'application/json'}
+
+            try:
+                response = requests.post(self.api_url, headers=headers, data=payload)
+                embeddings = json.loads(response.text)
+                lst_embedding.extend(embeddings)  # assumes response is a list of embeddings
+            except Exception as e:
+                print(f"Error on batch {i // self.batch_size}: {e}")
+                print(response.text if response else "No response")
+
+        return lst_embedding
+
+    def embed_query(self, text: str) -> List[float]:
+        return self.embed_documents([text])[0]