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aws-samples · Mar 7, 2024 · ca39adf · ca39adf
2 parents 987ec07 + 82b799f
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diff --git a/.github/workflows/dependabot-ci.yml b/.github/workflows/dependabot-ci.yml
@@ -0,0 +1,21 @@
+name: Dependabot CI
+
+on:
+  pull_request:
+    branches: [main]
+    paths-ignore:
+      - "**.md"
+    types: [opened, synchronize, reopened]
+
+jobs:
+  deploy:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Use Node.js
+        uses: actions/setup-node@v4
+        with:
+          node-version: "18.x"
+      - name: Build Check
+        run: |
+          cd ./frontend && npm ci && npm run build
diff --git a/README.md b/README.md
@@ -8,12 +8,17 @@
 > [!Warning]
 > The current version (`v0.4.x`) has no compatibility with ex version (~`v0.3.0`) due to the change of DynamoDB table schema. **Please note that UPDATE (i.e. `cdk deploy`) FROM EX VERSION TO `v0.4.x` WILL DESTROY ALL OF EXISTING CONVERSATIONS.**
 
-This repository is a sample chatbot using the Anthropic company's LLM [Claude 2](https://www.anthropic.com/index/claude-2), one of the foundational models provided by [Amazon Bedrock](https://aws.amazon.com/bedrock/) for generative AI.
+This repository is a sample chatbot using the Anthropic company's LLM [Claude](https://www.anthropic.com/), one of the foundational models provided by [Amazon Bedrock](https://aws.amazon.com/bedrock/) for generative AI.
 
 ### Basic Conversation
 
+Not only text but also images are available with [Anthropic's Claude 3 Sonnet](https://www.anthropic.com/news/claude-3-family).
+
 ![](./docs/imgs/demo.gif)
 
+> [!Note]
+> Currently the image will be compressed into 800px jpeg due to DynamoDB [item size limitation](https://docs.aws.amazon.com/amazondynamodb/latest/developerguide/ServiceQuotas.html#limits-items). [Issue](https://github.com/aws-samples/bedrock-claude-chat/issues/131)
+
 ### Bot Personalization
 
 Add your own instruction and give external knowledge as URL or files (a.k.a [RAG](./docs/RAG.md)). The bot can be shared among application users.
@@ -39,7 +44,7 @@ TODO
 
 ## 🚀 Super-easy Deployment
 
-- On us-east-1 region, open [Bedrock Model access](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess) > `Manage model access` > Check `Anthropic / Claude`, `Anthropic / Claude Instant` and `Cohere / Embed Multilingual` then `Save changes`.
+- On us-east-1 region, open [Bedrock Model access](https://us-east-1.console.aws.amazon.com/bedrock/home?region=us-east-1#/modelaccess) > `Manage model access` > Check `Anthropic / Claude`, `Anthropic / Claude Instant`, `Anthropic / Claude 3 Sonnet` and `Cohere / Embed Multilingual` then `Save changes`.
 
 <details>
 <summary>Screenshot</summary>
@@ -77,7 +82,6 @@ It's an architecture built on AWS managed services, eliminating the need for inf
 
 - [Amazon DynamoDB](https://aws.amazon.com/dynamodb/): NoSQL database for conversation history storage
 - [Amazon API Gateway](https://aws.amazon.com/api-gateway/) + [AWS Lambda](https://aws.amazon.com/lambda/): Backend API endpoint ([AWS Lambda Web Adapter](https://github.com/awslabs/aws-lambda-web-adapter), [FastAPI](https://fastapi.tiangolo.com/))
-- [Amazon SNS](https://aws.amazon.com/sns/): Used to decouple streaming calls between API Gateway and Bedrock because streaming responses can take over 30 seconds in total, exceeding the limitations of HTTP integration (See [quota](https://docs.aws.amazon.com/apigateway/latest/developerguide/limits.html)).
 - [Amazon CloudFront](https://aws.amazon.com/cloudfront/) + [S3](https://aws.amazon.com/s3/): Frontend application delivery ([React](https://react.dev/), [Tailwind CSS](https://tailwindcss.com/))
 - [AWS WAF](https://aws.amazon.com/waf/): IP address restriction
 - [Amazon Cognito](https://aws.amazon.com/cognito/): User authentication
@@ -189,18 +193,19 @@ BedrockChatStack.FrontendURL = https://xxxxx.cloudfront.net
 Edit [config.py](./backend/app/config.py) and run `cdk deploy`.
 
 ```py
+# See: https://docs.anthropic.com/claude/reference/complete_post
 GENERATION_CONFIG = {
-    "max_tokens_to_sample": 500,
-    "temperature": 0.6,
+    "max_tokens": 2000,
     "top_k": 250,
     "top_p": 0.999,
+    "temperature": 0.6,
     "stop_sequences": ["Human: ", "Assistant: "],
 }
 
 EMBEDDING_CONFIG = {
-    "model_id": "amazon.titan-embed-text-v1",
+    "model_id": "cohere.embed-multilingual-v3",
     "chunk_size": 1000,
-    "chunk_overlap": 100,
+    "chunk_overlap": 200,
 }
 ```
 

diff --git a/backend/app/bedrock.py b/backend/app/bedrock.py
@@ -2,8 +2,9 @@
 import logging
 import os
 
-from anthropic import Anthropic
+from anthropic import AnthropicBedrock
 from app.config import ANTHROPIC_PRICING, EMBEDDING_CONFIG, GENERATION_CONFIG
+from app.repositories.models.conversation import MessageModel
 from app.utils import get_bedrock_client
 
 logger = logging.getLogger(__name__)
@@ -12,32 +13,53 @@
 
 
 client = get_bedrock_client()
-anthropic_client = Anthropic()
-
-
-def _create_body(model: str, prompt: str):
-    if model in ("claude-instant-v1", "claude-v2"):
-        parameter = GENERATION_CONFIG
-        parameter["prompt"] = prompt
-        return json.dumps(parameter)
-    else:
-        raise NotImplementedError()
-
-
-def _extract_output_text(model: str, response) -> str:
-    if model in ("claude-instant-v1", "claude-v2"):
-        output = json.loads(response.get("body").read())
-        output_txt = output["completion"]
-        if output_txt[0] == " ":
-            # claude outputs a space at the beginning of the text
-            output_txt = output_txt[1:]
-        return output_txt
-    else:
-        raise NotImplementedError()
-
-
-def count_tokens(text: str) -> int:
-    return anthropic_client.count_tokens(text)
+anthropic_client = AnthropicBedrock()
+
+
+def compose_args_for_anthropic_client(
+    messages: list[MessageModel],
+    model: str,
+    instruction: str | None = None,
+    stream: bool = False,
+) -> dict:
+    """Compose arguments for Anthropic client.
+    Ref: https://docs.anthropic.com/claude/reference/messages_post
+    """
+    arg_messages = []
+    for message in messages:
+        if message.role not in ["system", "instruction"]:
+            content = []
+            for c in message.content:
+                if c.content_type == "text":
+                    content.append(
+                        {
+                            "type": "text",
+                            "text": c.body,
+                        }
+                    )
+                elif c.content_type == "image":
+                    content.append(
+                        {
+                            "type": "image",
+                            "source": {
+                                "type": "base64",
+                                "media_type": c.media_type,
+                                "data": c.body,
+                            },
+                        }
+                    )
+            m = {"role": message.role, "content": content}
+            arg_messages.append(m)
+
+    args = {
+        **GENERATION_CONFIG,
+        "model": get_model_id(model),
+        "messages": arg_messages,
+        "stream": stream,
+    }
+    if instruction:
+        args["system"] = instruction
+    return args
 
 
 def calculate_price(
@@ -61,26 +83,12 @@ def get_model_id(model: str) -> str:
         return "anthropic.claude-v2:1"
     elif model == "claude-instant-v1":
         return "anthropic.claude-instant-v1"
+    elif model == "claude-v3-sonnet":
+        return "anthropic.claude-3-sonnet-20240229-v1:0"
     else:
         raise NotImplementedError()
 
 
-def invoke(prompt: str, model: str) -> str:
-    payload = _create_body(model, prompt)
-
-    model_id = get_model_id(model)
-    accept = "application/json"
-    content_type = "application/json"
-
-    response = client.invoke_model(
-        body=payload, modelId=model_id, accept=accept, contentType=content_type
-    )
-
-    output_txt = _extract_output_text(model, response)
-
-    return output_txt
-
-
 def calculate_query_embedding(question: str) -> list[float]:
     model_id = EMBEDDING_CONFIG["model_id"]
 

diff --git a/backend/app/config.py b/backend/app/config.py
@@ -2,10 +2,10 @@
 # Adjust the values according to your application.
 # See: https://docs.anthropic.com/claude/reference/complete_post
 GENERATION_CONFIG = {
-    "max_tokens_to_sample": 2000,
-    "temperature": 0.6,
+    "max_tokens": 2000,
     "top_k": 250,
     "top_p": 0.999,
+    "temperature": 0.6,
     "stop_sequences": ["Human: ", "Assistant: "],
 }
 
@@ -24,8 +24,8 @@
 }
 
 # Used for price estimation.
-# NOTE: The following is based on 2024-01-29.
-# See: https://aws.amazon.com/jp/bedrock/pricing/
+# NOTE: The following is based on 2024-03-07
+# See: https://aws.amazon.com/bedrock/pricing/
 ANTHROPIC_PRICING = {
     "us-east-1": {
         "claude-instant-v1": {
@@ -36,6 +36,7 @@
             "input": 0.00080,
             "output": 0.00240,
         },
+        "claude-v3-sonnet": {"input": 0.00300, "output": 0.01500},
     },
     "us-west-2": {
         "claude-instant-v1": {
@@ -46,6 +47,7 @@
             "input": 0.00080,
             "output": 0.00240,
         },
+        "claude-v3-sonnet": {"input": 0.00300, "output": 0.01500},
     },
     "ap-northeast-1": {
         "claude-instant-v1": {
@@ -66,5 +68,6 @@
             "input": 0.00080,
             "output": 0.00240,
         },
+        "claude-v3-sonnet": {"input": 0.00300, "output": 0.01500},
     },
 }
diff --git a/backend/app/repositories/conversation.py b/backend/app/repositories/conversation.py
@@ -131,9 +131,24 @@ def find_conversation_by_id(user_id: str, conversation_id: str) -> ConversationM
         message_map={
             k: MessageModel(
                 role=v["role"],
-                content=ContentModel(
-                    content_type=v["content"]["content_type"],
-                    body=v["content"]["body"],
+                content=(
+                    [
+                        ContentModel(
+                            content_type=c["content_type"],
+                            body=c["body"],
+                            media_type=c["media_type"],
+                        )
+                        for c in v["content"]
+                    ]
+                    if type(v["content"]) == list
+                    else [
+                        # For backward compatibility
+                        ContentModel(
+                            content_type=v["content"]["content_type"],
+                            body=v["content"]["body"],
+                            media_type=None,
+                        )
+                    ]
                 ),
                 model=v["model"],
                 children=v["children"],

diff --git a/backend/app/repositories/models/conversation.py b/backend/app/repositories/models/conversation.py
@@ -1,17 +1,18 @@
-from typing import Literal
+from typing import Literal, Optional
 
 from pydantic import BaseModel
 
 
 class ContentModel(BaseModel):
-    content_type: Literal["text"]
+    content_type: Literal["text", "image"]
+    media_type: Optional[str]
     body: str
 
 
 class MessageModel(BaseModel):
     role: str
-    content: ContentModel
-    model: Literal["claude-instant-v1", "claude-v2"]
+    content: list[ContentModel]
+    model: Literal["claude-instant-v1", "claude-v2", "claude-v3-sonnet"]
     children: list[str]
     parent: str | None
     create_time: float

diff --git a/backend/app/repositories/models/custom_bot.py b/backend/app/repositories/models/custom_bot.py
@@ -1,4 +1,4 @@
-from typing import Literal
+from typing import Literal, Optional
 
 from app.routes.schemas.bot import type_sync_status
 from pydantic import BaseModel

diff --git a/backend/app/routes/schemas/conversation.py b/backend/app/routes/schemas/conversation.py
@@ -5,14 +5,18 @@
 
 
 class Content(BaseSchema):
-    content_type: Literal["text"]
-    body: str
+    content_type: Literal["text", "image"]
+    media_type: Optional[str] = Field(
+        None,
+        description="MIME type of the image. Must be specified if `content_type` is `image`.",
+    )
+    body: str = Field(..., description="Content body. Text or base64 encoded image.")
 
 
 class MessageInput(BaseSchema):
     role: str
-    content: Content
-    model: Literal["claude-instant-v1", "claude-v2"]
+    content: list[Content]
+    model: Literal["claude-instant-v1", "claude-v2", "claude-v3-sonnet"]
     parent_message_id: str | None
     message_id: str | None = Field(
         ..., description="Unique message id. If not provided, it will be generated."
@@ -21,9 +25,9 @@ class MessageInput(BaseSchema):
 
 class MessageOutput(BaseSchema):
     role: str
-    content: Content
+    content: list[Content]
     # NOTE: "claude" will be deprecated (same as "claude-v2")
-    model: Literal["claude-instant-v1", "claude-v2", "claude"]
+    model: Literal["claude-instant-v1", "claude-v2", "claude", "claude-v3-sonnet"]
     children: list[str]
     parent: str | None