Merge branch 'main' into version-check-step

Author: michaelfeil

.tool-versions

@@ -1 +1,2 @@
 poetry 1.8.2
+python 3.11.9

libs/client_infinity/infinity_client/infinity_client/vision_client.py

@@ -0,0 +1,119 @@
+from io import BytesIO
+import base64
+import threading
+from concurrent.futures import ThreadPoolExecutor, Future
+import numpy as np  # Import numpy
+from typing import Union
+HAS_IMPORTS = True
+try:
+    from PIL import Image
+    import numpy as np
+    
+except ImportError:
+    HAS_IMPORTS = False
+    
+try:
+    import requests
+    from requests.adapters import HTTPAdapter, Retry
+except ImportError:
+    HAS_IMPORTS = False
+
+class InfinityVisionAPI:
+    def __init__(self, url: str = "https://infinity-multimodal.modal.michaelfeil.eu", format="base64") -> None:        
+        req = requests.post(
+            url + "/embeddings",
+            json={ # get shape of output
+                "model": "michaelfeil/colqwen2-v0.1",
+                "input": ["test"],
+                "encoding_format": "float",
+                "modality": "text"
+            }
+        )
+        req.raise_for_status()
+        self.url = url
+        self.hidden_dim = np.array(req.json()["data"][0]["embedding"]).shape[-1]
+        self.format = format
+        self.tp = ThreadPoolExecutor()
+        self.tp.__enter__()
+        
+        self.sem = threading.Semaphore(64)
+        self.session = requests.Session()
+        adapter = HTTPAdapter(max_retries=Retry(total=10, backoff_factor=0.5))
+        self.session.mount("https://", adapter)
+        self.session.mount("http://", adapter)
+    
+    def _image_payload(self, images: list["Image.Image"]) -> list[str]:
+        if not HAS_IMPORTS:
+            raise ImportError("PIL is required to use this class")
+        b64_strs = []
+        for image in images:
+            buffered = BytesIO()
+            if not hasattr(image, "save"):
+                raise ValueError("Image must be a PIL Image")
+            image.save(buffered, format="JPEG")
+            img_str = base64.b64encode(buffered.getvalue()).decode("utf-8")
+            b64_strs.append(f"data:image/jpeg;base64,{img_str}")
+        return b64_strs
+    
+    def _text_payload(self, texts: list[str]) -> list[str]:
+        return texts
+    
+    def health(self) -> bool:
+        req = self.session.get(f"{self.url}/health")
+        req.raise_for_status()
+        return req.status_code == 200
+    
+    def _request(self, model: str, images_or_text: list[Union["Image.Image", str]]) -> dict:
+        if all(hasattr(item, "save") for item in images_or_text):
+            payload = self._image_payload(images_or_text)
+            modality = "image"
+        elif all(isinstance(item, str) for item in images_or_text):
+            payload = self._text_payload(images_or_text)
+            modality = "text"
+        else:
+            raise ValueError("Images and text cannot be mixed in a single request")
+
+        embeddings_req = self.session.post(
+            f"{self.url}/embeddings",
+            json={
+                "model": model,
+                "input": payload,
+                "encoding_format": self.format,
+                "modality": modality
+            }
+        )
+        embeddings_req.raise_for_status()
+        embeddings = embeddings_req.json()
+
+        if self.format == "base64":
+            embeddings_decoded = [
+                np.frombuffer(
+                    base64.b64decode(e["embedding"]), dtype=np.float32
+                ).reshape(-1, self.hidden_dim)
+                for e in embeddings["data"]
+            ]
+        else:
+            embeddings_decoded = [
+                np.array(e["embedding"])
+                for e in embeddings["data"]
+            ]
+        return embeddings_decoded, embeddings["usage"]["total_tokens"]
+    
+    def embed(self, model: str, sentences: list[str]) -> Future[list]:
+        self.health()
+        with self.sem:
+            return self.tp.submit(self._request, model=model, images_or_text=sentences)
+    
+    def image_embed(self, model: str, images: list["Image.Image"]) -> Future[list]:
+        self.health()  # Call once instead of per image
+        with self.sem:
+            return self.tp.submit(self._request, model=model, images_or_text=images)
+        
+def test_colpali():
+    colpali = InfinityVisionAPI()
+    future = colpali.embed("michaelfeil/colqwen2-v0.1", ["test"])
+    embeddings, total_tokens = future.result()
+    print(embeddings, total_tokens)
+    
+if __name__ == "__main__":
+    test_colpali()  

libs/client_infinity/run_generate_with_hook.sh

@@ -38,5 +38,6 @@ pip install openapi-python-client==0.21.1
 	   --custom-template-path=./template
 
 # copy the readme to docs
+cp ./template/vision_client.py ./infinity_client/infinity_client/vision_client.py
 cp ./infinity_client/README.md ./../../docs/docs/client_infinity.md
 # Cleanup will be called due to the trap