openai[patch]: image token counting (#23147)

Resolves #23000

---------

Co-authored-by: isaac hershenson <ihershenson@hmc.edu>
Co-authored-by: ccurme <chester.curme@gmail.com>

libs/partners/openai/langchain_openai/chat_models/base.py

@@ -2,10 +2,13 @@
 
 from __future__ import annotations
 
+import base64
 import json
 import logging
 import os
 import sys
+from io import BytesIO
+from math import ceil
 from operator import itemgetter
 from typing import (
     Any,
@@ -26,6 +29,7 @@ from typing import (
     cast,
     overload,
 )
+from urllib.parse import urlparse
 
 import openai
 import tiktoken
@@ -736,7 +740,13 @@ class BaseChatOpenAI(BaseChatModel):
     def get_num_tokens_from_messages(self, messages: List[BaseMessage]) -> int:
         """Calculate num tokens for gpt-3.5-turbo and gpt-4 with tiktoken package.
 
-        Official documentation: https://github.com/openai/openai-cookbook/blob/
+        **Requirements**: You must have the ``pillow`` installed if you want to count
+        image tokens if you are specifying the image as a base64 string, and you must
+        have both ``pillow`` and ``httpx`` installed if you are specifying the image
+        as a URL. If these aren't installed image inputs will be ignored in token
+        counting.
+
+        OpenAI reference: https://github.com/openai/openai-cookbook/blob/
         main/examples/How_to_format_inputs_to_ChatGPT_models.ipynb"""
         if sys.version_info[1] <= 7:
             return super().get_num_tokens_from_messages(messages)
@@ -753,7 +763,7 @@ class BaseChatOpenAI(BaseChatModel):
             raise NotImplementedError(
                 f"get_num_tokens_from_messages() is not presently implemented "
                 f"for model {model}. See "
-                "https://platform.openai.com/docs/guides/text-generation/managing-tokens"
+                "https://platform.openai.com/docs/guides/text-generation/managing-tokens"  # noqa: E501
                 " for information on how messages are converted to tokens."
             )
         num_tokens = 0
@@ -761,9 +771,27 @@ class BaseChatOpenAI(BaseChatModel):
         for message in messages_dict:
             num_tokens += tokens_per_message
             for key, value in message.items():
-                # Cast str(value) in case the message value is not a string
-                # This occurs with function messages
-                num_tokens += len(encoding.encode(str(value)))
+                if isinstance(value, list):
+                    for val in value:
+                        if isinstance(val, str) or val["type"] == "text":
+                            text = val["text"] if isinstance(val, dict) else val
+                            num_tokens += len(encoding.encode(text))
+                        elif val["type"] == "image_url":
+                            if val["image_url"].get("detail") == "low":
+                                num_tokens += 85
+                            else:
+                                image_size = _url_to_size(val["image_url"]["url"])
+                                if not image_size:
+                                    continue
+                                num_tokens += _count_image_tokens(*image_size)
+                        else:
+                            raise ValueError(
+                                f"Unrecognized content block type\n\n{val}"
+                            )
+                else:
+                    # Cast str(value) in case the message value is not a string
+                    # This occurs with function messages
+                    num_tokens += len(encoding.encode(value))
                 if key == "name":
                     num_tokens += tokens_per_name
         # every reply is primed with <im_start>assistant
@@ -1541,3 +1569,75 @@ def _lc_invalid_tool_call_to_openai_tool_call(
             "arguments": invalid_tool_call["args"],
         },
     }
+
+
+def _url_to_size(image_source: str) -> Optional[Tuple[int, int]]:
+    try:
+        from PIL import Image  # type: ignore[import]
+    except ImportError:
+        logger.info(
+            "Unable to count image tokens. To count image tokens please install "
+            "`pip install -U pillow httpx`."
+        )
+        return None
+    if _is_url(image_source):
+        try:
+            import httpx
+        except ImportError:
+            logger.info(
+                "Unable to count image tokens. To count image tokens please install "
+                "`pip install -U httpx`."
+            )
+            return None
+        response = httpx.get(image_source)
+        response.raise_for_status()
+        width, height = Image.open(BytesIO(response.content)).size
+        return width, height
+    elif _is_b64(image_source):
+        _, encoded = image_source.split(",", 1)
+        data = base64.b64decode(encoded)
+        width, height = Image.open(BytesIO(data)).size
+        return width, height
+    else:
+        return None
+
+
+def _count_image_tokens(width: int, height: int) -> int:
+    # Reference: https://platform.openai.com/docs/guides/vision/calculating-costs
+    width, height = _resize(width, height)
+    h = ceil(height / 512)
+    w = ceil(width / 512)
+    return (170 * h * w) + 85
+
+
+def _is_url(s: str) -> bool:
+    try:
+        result = urlparse(s)
+        return all([result.scheme, result.netloc])
+    except Exception as e:
+        logger.debug(f"Unable to parse URL: {e}")
+        return False
+
+
+def _is_b64(s: str) -> bool:
+    return s.startswith("data:image")
+
+
+def _resize(width: int, height: int) -> Tuple[int, int]:
+    # larger side must be <= 2048
+    if width > 2048 or height > 2048:
+        if width > height:
+            height = (height * 2048) // width
+            width = 2048
+        else:
+            width = (width * 2048) // height
+            height = 2048
+    # smaller side must be <= 768
+    if width > 768 and height > 768:
+        if width > height:
+            width = (width * 768) // height
+            height = 768
+        else:
+            height = (width * 768) // height
+            width = 768
+    return width, height