Merge pull request #124 from justinh-rahb/anthropic-0.1.2

Update Anthropic Manifold Pipeline to v0.1.2

examples/pipelines/providers/anthropic_manifold_pipeline.py

@@ -2,9 +2,9 @@
 title: Anthropic Manifold Pipeline
 author: justinh-rahb
 date: 2024-06-20
-version: 1.1
+version: 1.2
 license: MIT
-description: A pipeline for generating text using the Anthropic API.
+description: A pipeline for generating text and processing images using the Anthropic API.
 requirements: requests, anthropic
 environment_variables: ANTHROPIC_API_KEY
 """
@@ -35,13 +35,11 @@ class Pipeline:
         self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
 
     def get_anthropic_models(self):
-        # In the future, this could fetch models dynamically from Anthropic
         return [
             {"id": "claude-3-haiku-20240307", "name": "claude-3-haiku"},
             {"id": "claude-3-opus-20240229", "name": "claude-3-opus"},
             {"id": "claude-3-sonnet-20240229", "name": "claude-3-sonnet"},
             {"id": "claude-3-5-sonnet-20240620", "name": "claude-3.5-sonnet"},
-            # Add other Anthropic models here as they become available
         ]
 
     async def on_startup(self):
@@ -53,63 +51,94 @@ class Pipeline:
         pass
 
     async def on_valves_updated(self):
-        # This function is called when the valves are updated.
         self.client = Anthropic(api_key=self.valves.ANTHROPIC_API_KEY)
         pass
 
-    # Pipelines are the models that are available in the manifold.
-    # It can be a list or a function that returns a list.
     def pipelines(self) -> List[dict]:
         return self.get_anthropic_models()
 
+    def process_image(self, image_data):
+        if image_data["image_url"]["url"].startswith("data:image"):
+            mime_type, base64_data = image_data["image_url"]["url"].split(",", 1)
+            media_type = mime_type.split(":")[1].split(";")[0]
+            return {
+                "type": "image",
+                "source": {
+                    "type": "base64",
+                    "media_type": media_type,
+                    "data": base64_data,
+                },
+            }
+        else:
+            return {
+                "type": "image",
+                "source": {"type": "url", "url": image_data["image_url"]["url"]},
+            }
+
     def pipe(
         self, user_message: str, model_id: str, messages: List[dict], body: dict
     ) -> Union[str, Generator, Iterator]:
         try:
-            if "user" in body:
+            # Remove unnecessary keys
-                del body["user"]
+            for key in ['user', 'chat_id', 'title']:
-            if "chat_id" in body:
+                body.pop(key, None)
-                del body["chat_id"]
+
-            if "title" in body:
+            system_message, messages = pop_system_message(messages)
-                del body["title"]
+
+            processed_messages = []
+            image_count = 0
+            total_image_size = 0
+
+            for message in messages:
+                processed_content = []
+                if isinstance(message.get("content"), list):
+                    for item in message["content"]:
+                        if item["type"] == "text":
+                            processed_content.append({"type": "text", "text": item["text"]})
+                        elif item["type"] == "image_url":
+                            if image_count >= 5:
+                                raise ValueError("Maximum of 5 images per API call exceeded")
+
+                            processed_image = self.process_image(item)
+                            processed_content.append(processed_image)
+
+                            if processed_image["source"]["type"] == "base64":
+                                image_size = len(processed_image["source"]["data"]) * 3 / 4
+                            else:
+                                image_size = 0
+
+                            total_image_size += image_size
+                            if total_image_size > 100 * 1024 * 1024:
+                                raise ValueError("Total size of images exceeds 100 MB limit")
+
+                            image_count += 1
+                else:
+                    processed_content = [{"type": "text", "text": message.get("content", "")}]
+
+                processed_messages.append({"role": message["role"], "content": processed_content})
+
+            # Prepare the payload
+            payload = {
+                "model": model_id,
+                "messages": processed_messages,
+                "max_tokens": body.get("max_tokens", 4096),
+                "temperature": body.get("temperature", 0.8),
+                "top_k": body.get("top_k", 40),
+                "top_p": body.get("top_p", 0.9),
+                "stop_sequences": body.get("stop", []),
+                **({"system": str(system_message)} if system_message else {}),
+                "stream": body.get("stream", False),
+            }
 
             if body.get("stream", False):
-                return self.stream_response(model_id, messages, body)
+                return self.stream_response(model_id, payload)
             else:
-                return self.get_completion(model_id, messages, body)
+                return self.get_completion(model_id, payload)
         except (RateLimitError, APIStatusError, APIConnectionError) as e:
             return f"Error: {e}"
 
-    def stream_response(
+    def stream_response(self, model_id: str, payload: dict) -> Generator:
-        self, model_id: str, messages: List[dict], body: dict
+        stream = self.client.messages.create(**payload)
-    ) -> Generator:
-        system_message, messages = pop_system_message(messages)
-
-        max_tokens = (
-            body.get("max_tokens") if body.get("max_tokens") is not None else 4096
-        )
-        temperature = (
-            body.get("temperature") if body.get("temperature") is not None else 0.8
-        )
-        top_k = body.get("top_k") if body.get("top_k") is not None else 40
-        top_p = body.get("top_p") if body.get("top_p") is not None else 0.9
-        stop_sequences = body.get("stop") if body.get("stop") is not None else []
-
-        stream = self.client.messages.create(
-            **{
-                "model": model_id,
-                **(
-                    {"system": system_message} if system_message else {}
-                ),  # Add system message if it exists (optional
-                "messages": messages,
-                "max_tokens": max_tokens,
-                "temperature": temperature,
-                "top_k": top_k,
-                "top_p": top_p,
-                "stop_sequences": stop_sequences,
-                "stream": True,
-            }
-        )
 
         for chunk in stream:
             if chunk.type == "content_block_start":
@@ -117,31 +146,6 @@ class Pipeline:
             elif chunk.type == "content_block_delta":
                 yield chunk.delta.text
 
-    def get_completion(self, model_id: str, messages: List[dict], body: dict) -> str:
+    def get_completion(self, model_id: str, payload: dict) -> str:
-        system_message, messages = pop_system_message(messages)
+        response = self.client.messages.create(**payload)
-
-        max_tokens = (
-            body.get("max_tokens") if body.get("max_tokens") is not None else 4096
-        )
-        temperature = (
-            body.get("temperature") if body.get("temperature") is not None else 0.8
-        )
-        top_k = body.get("top_k") if body.get("top_k") is not None else 40
-        top_p = body.get("top_p") if body.get("top_p") is not None else 0.9
-        stop_sequences = body.get("stop") if body.get("stop") is not None else []
-
-        response = self.client.messages.create(
-            **{
-                "model": model_id,
-                **(
-                    {"system": system_message} if system_message else {}
-                ),  # Add system message if it exists (optional
-                "messages": messages,
-                "max_tokens": max_tokens,
-                "temperature": temperature,
-                "top_k": top_k,
-                "top_p": top_p,
-                "stop_sequences": stop_sequences,
-            }
-        )
         return response.content[0].text