Merge pull request #169 from g453030291/aws_bedrock_example

Adding Examples:AWS Bedrock claude

examples/pipelines/providers/aws_bedrock_claude_pipeline.py

@@ -0,0 +1,181 @@
+"""
+title: AWS Bedrock Claude Pipeline
+author: G-mario
+date: 2024-08-18
+version: 1.0
+license: MIT
+description: A pipeline for generating text and processing images using the AWS Bedrock API(By Anthropic claude).
+requirements: requests, boto3
+environment_variables: AWS_ACCESS_KEY, AWS_SECRET_KEY, AWS_REGION_NAME
+"""
+import base64
+import json
+import logging
+from io import BytesIO
+from typing import List, Union, Generator, Iterator
+
+import boto3
+
+from pydantic import BaseModel
+
+import os
+import requests
+
+from utils.pipelines.main import pop_system_message
+
+
+class Pipeline:
+    class Valves(BaseModel):
+        AWS_ACCESS_KEY: str = ""
+        AWS_SECRET_KEY: str = ""
+        AWS_REGION_NAME: str = ""
+
+    def __init__(self):
+        self.type = "manifold"
+        # Optionally, you can set the id and name of the pipeline.
+        # Best practice is to not specify the id so that it can be automatically inferred from the filename, so that users can install multiple versions of the same pipeline.
+        # The identifier must be unique across all pipelines.
+        # The identifier must be an alphanumeric string that can include underscores or hyphens. It cannot contain spaces, special characters, slashes, or backslashes.
+        # self.id = "openai_pipeline"
+        self.name = "Bedrock: "
+
+        self.valves = self.Valves(
+            **{
+                "AWS_ACCESS_KEY": os.getenv("AWS_ACCESS_KEY", "your-aws-access-key-here"),
+                "AWS_SECRET_KEY": os.getenv("AWS_SECRET_KEY", "your-aws-secret-key-here"),
+                "AWS_REGION_NAME": os.getenv("AWS_REGION_NAME", "your-aws-region-name-here"),
+            }
+        )
+
+        self.bedrock = boto3.client(aws_access_key_id=self.valves.AWS_ACCESS_KEY,
+                                    aws_secret_access_key=self.valves.AWS_SECRET_KEY,
+                                    service_name="bedrock",
+                                    region_name=self.valves.AWS_REGION_NAME)
+        self.bedrock_runtime = boto3.client(aws_access_key_id=self.valves.AWS_ACCESS_KEY,
+                                            aws_secret_access_key=self.valves.AWS_SECRET_KEY,
+                                            service_name="bedrock-runtime",
+                                            region_name=self.valves.AWS_REGION_NAME)
+
+        self.pipelines = self.get_models()
+
+
+    async def on_startup(self):
+        # This function is called when the server is started.
+        print(f"on_startup:{__name__}")
+        pass
+
+    async def on_shutdown(self):
+        # This function is called when the server is stopped.
+        print(f"on_shutdown:{__name__}")
+        pass
+
+    async def on_valves_updated(self):
+        # This function is called when the valves are updated.
+        print(f"on_valves_updated:{__name__}")
+        self.bedrock = boto3.client(aws_access_key_id=self.valves.AWS_ACCESS_KEY,
+                                    aws_secret_access_key=self.valves.AWS_SECRET_KEY,
+                                    service_name="bedrock",
+                                    region_name=self.valves.AWS_REGION_NAME)
+        self.bedrock_runtime = boto3.client(aws_access_key_id=self.valves.AWS_ACCESS_KEY,
+                                            aws_secret_access_key=self.valves.AWS_SECRET_KEY,
+                                            service_name="bedrock-runtime",
+                                            region_name=self.valves.AWS_REGION_NAME)
+        self.pipelines = self.get_models()
+
+    def pipelines(self) -> List[dict]:
+        return self.get_models()
+
+    def get_models(self):
+        if self.valves.AWS_ACCESS_KEY and self.valves.AWS_SECRET_KEY:
+            try:
+                response = self.bedrock.list_foundation_models(byProvider='Anthropic', byInferenceType='ON_DEMAND')
+                return [
+                    {
+                        "id": model["modelId"],
+                        "name": model["modelName"],
+                    }
+                    for model in response["modelSummaries"]
+                ]
+            except Exception as e:
+                print(f"Error: {e}")
+                return [
+                    {
+                        "id": "error",
+                        "name": "Could not fetch models from Bedrock, please update the Access/Secret Key in the valves.",
+                    },
+                ]
+        else:
+            return []
+
+    def pipe(
+        self, user_message: str, model_id: str, messages: List[dict], body: dict
+    ) -> Union[str, Generator, Iterator]:
+        # This is where you can add your custom pipelines like RAG.
+        print(f"pipe:{__name__}")
+
+        system_message, messages = pop_system_message(messages)
+
+        logging.info(f"pop_system_message: {json.dumps(messages)}")
+
+        try:
+            processed_messages = []
+            image_count = 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({"text": item["text"]})
+                        elif item["type"] == "image_url":
+                            if image_count >= 20:
+                                raise ValueError("Maximum of 20 images per API call exceeded")
+                            processed_image = self.process_image(item["image_url"])
+                            processed_content.append(processed_image)
+                            image_count += 1
+                else:
+                    processed_content = [{"text": message.get("content", "")}]
+
+                processed_messages.append({"role": message["role"], "content": processed_content})
+
+            payload = {"modelId": model_id,
+                       "messages": processed_messages,
+                       "system": [{'text': system_message if system_message else 'you are an intelligent ai assistant'}],
+                       "inferenceConfig": {"temperature": body.get("temperature", 0.5)},
+                       "additionalModelRequestFields": {"top_k": body.get("top_k", 200), "top_p": body.get("top_p", 0.9)}
+                       }
+            if body.get("stream", False):
+                return self.stream_response(model_id, payload)
+            else:
+                return self.get_completion(model_id, payload)
+        except Exception as e:
+            return f"Error: {e}"
+
+    def process_image(self, image: str):
+        img_stream = None
+        if image["url"].startswith("data:image"):
+            if ',' in image["url"]:
+                base64_string = image["url"].split(',')[1]
+            image_data = base64.b64decode(base64_string)
+
+            img_stream = BytesIO(image_data)
+        else:
+            img_stream = requests.get(image["url"]).content
+        return {
+            "image": {"format": "png" if image["url"].endswith(".png") else "jpeg",
+                      "source": {"bytes": img_stream.read()}}
+        }
+
+    def stream_response(self, model_id: str, payload: dict) -> Generator:
+        if "system" in payload:
+            del payload["system"]
+        if "additionalModelRequestFields" in payload:
+            del payload["additionalModelRequestFields"]
+        streaming_response = self.bedrock_runtime.converse_stream(**payload)
+        for chunk in streaming_response["stream"]:
+            if "contentBlockDelta" in chunk:
+                yield chunk["contentBlockDelta"]["delta"]["text"]
+
+    def get_completion(self, model_id: str, payload: dict) -> str:
+        response = self.bedrock_runtime.converse(**payload)
+        return response['output']['message']['content'][0]['text']
+