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Add Tool call support

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This commit is contained in:
Aiden Dai
2024-04-03 11:10:19 +08:00
parent e49a579a41
commit f1440602ce
3 changed files with 264 additions and 116 deletions
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303
src/api/models/bedrock.py
View File

@@ -1,6 +1,7 @@
import base64
import json
import logging
from abc import ABC
from typing import AsyncIterable, Iterable
import boto3
@@ -13,17 +14,21 @@ from api.schema import (
# Chat
ChatResponse,
ChatRequest,
ChatRequestMessage,
Choice,
ChatResponseMessage,
Usage,
ChatStreamResponse,
ChoiceDelta,
ImageContent,
TextContent,
ResponseFunction,
ToolCall,
Tool,
# Embeddings
EmbeddingsRequest,
EmbeddingsResponse,
EmbeddingsUsage,
Embedding, TextContent,
Embedding,
)
from api.setting import DEBUG, AWS_REGION
@@ -58,7 +63,7 @@ ENCODER = tiktoken.get_encoding("cl100k_base")
# https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters.html
class BedrockModel(BaseChatModel):
class BedrockModel(BaseChatModel, ABC):
accept = "application/json"
content_type = "application/json"
@@ -86,9 +91,22 @@ class BedrockModel(BaseChatModel):
model: str,
message: str,
message_id: str,
tools_message: str | None = None,
input_tokens: int = 0,
output_tokens: int = 0,
) -> ChatResponse:
if tools_message:
# For tool response, the content is empty
tools = self._parse_tools_response(tools_message)
choice = Choice(
index=0,
message=ChatResponseMessage(
role="assistant",
tool_calls=tools,
),
finish_reason="stop",
)
else:
choice = Choice(
index=0,
message=ChatResponseMessage(
@@ -132,25 +150,31 @@ class BedrockModel(BaseChatModel):
return response
def get_model(model_id: str) -> BedrockModel:
model_name = SUPPORTED_BEDROCK_MODELS.get(model_id, "")
if DEBUG:
logger.info("model name is " + model_name)
if model_name in ["Claude Instant", "Claude", "Claude 3 Sonnet", "Claude 3 Haiku"]:
return ClaudeModel()
elif model_name in ["Llama 2 Chat 13B", "Llama 2 Chat 70B"]:
return Llama2Model()
elif model_name in ["Mistral 7B Instruct", "Mixtral 8x7B Instruct"]:
return MistralModel()
else:
logger.error("Unsupported model id " + model_id)
raise ValueError("Invalid model ID")
class ClaudeModel(BedrockModel):
anthropic_version = "bedrock-2023-05-31"
def _get_base64_image(self, image_url: str):
def _parse_tools_response(self, tools_messages: str) -> list[ToolCall]:
"""Parse the tools response
Example tool message like:
\n{\n "name": "get_current_weather",\n "arguments": {\n "location": "Shanghai"... }\n}\n
"""
function = json.loads(
tools_messages.replace("\n", " ").encode("unicode_escape")
)
args = json.dumps(function.get("arguments", {}))
function = ResponseFunction(
name=function["name"], arguments=args.replace("\\\\n", "\\n")
)
return [
ToolCall(
id="0",
function=function,
)
]
def _get_base64_image(self, image_url: str) -> str:
# Send a request to the image URL
response = requests.get(image_url)
# Check if the request was successful
@@ -159,34 +183,44 @@ class ClaudeModel(BedrockModel):
image_content = response.content
# Encode the image content as base64
base64_image = base64.b64encode(image_content)
return base64_image.decode('utf-8')
return base64_image.decode("utf-8")
else:
raise HTTPException(status_code=500, detail="Unable to access the image url")
def _parse_messages(self, messages: list[ChatRequestMessage]) -> list[dict]:
# Refer to: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
converted_messages = []
for msg in messages:
if isinstance(msg.content, str):
converted_messages.append({"role": msg.role, "content": msg.content})
continue
raise HTTPException(
status_code=500, detail="Unable to access the image url"
)
def _parse_content_parts(
self, content: list[TextContent | ImageContent]
) -> list[dict]:
# See: https://docs.aws.amazon.com/bedrock/latest/userguide/model-parameters-anthropic-claude-messages.html
content_parts = []
for part in msg.content:
for part in content:
if isinstance(part, TextContent):
content_parts.append(part.model_dump())
else:
content_parts.append({
content_parts.append(
{
"type": "image",
"source": {
"type": "base64",
"media_type": "image/jpeg",
"data": self._get_base64_image(part.image_url.url)
"data": self._get_base64_image(part.image_url.url),
},
}
})
)
return content_parts
converted_messages.append({"role": msg.role, "content": content_parts})
return converted_messages
def _create_tool_prompt(self, tools: list[Tool]) -> str:
tool_prompt = "\nYou have access to the following tools:\n"
tool_prompt += json.dumps(
[tool.function.model_dump() for tool in tools], indent=2
)
tool_prompt += (
"\nIf you need to use one of the above tools, "
"only respond with a JSON object matching the following schema inside a <tool></tool> xml tag: \n"
'{"name": $TOOL_NAME, "arguments": {"$PARAMETER_NAME": "$PARAMETER_VALUE", ...}\n'
)
return tool_prompt
def _parse_args(self, chat_request: ChatRequest) -> dict:
args = {
@@ -195,25 +229,73 @@ class ClaudeModel(BedrockModel):
"top_p": chat_request.top_p,
"temperature": chat_request.temperature,
}
start = 0
if chat_request.messages[0].role == "system":
args["system"] = chat_request.messages[0].content
start = 1
args["messages"] = self._parse_messages(chat_request.messages[start:])
system_prompt = ""
converted_messages = []
for message in chat_request.messages:
if message.role == "system":
system_prompt += message.content + "\n"
elif message.role == "user" and not isinstance(message.content, str):
converted_messages.append(
{
"role": message.role,
"content": self._parse_content_parts(message.content),
}
)
elif message.role == "assistant" and not message.content:
# if content is empty
# create the content using the tool call info.
tool_content = "Should use {} tool with args: {}".format(
message.tool_calls[0].function.name,
message.tool_calls[0].function.arguments,
)
converted_messages.append(
{"role": message.role, "content": tool_content}
)
elif message.role == "tool":
# Since bedrock does not support tool role
# Convert the tool message to a user message.
converted_messages.append(
{
"role": "user",
"content": "The result of the tool call is " + message.content,
}
)
else:
converted_messages.append(
{"role": message.role, "content": message.content}
)
if chat_request.tools:
system_prompt += self._create_tool_prompt(chat_request.tools)
args["messages"] = converted_messages
if system_prompt:
if DEBUG:
logger.info("System Prompt: " + system_prompt)
args["system"] = system_prompt.replace("\n", "")
return args
def chat(self, chat_request: ChatRequest) -> ChatResponse:
if DEBUG:
logger.info("Raw request: " + chat_request.model_dump_json())
response = self._invoke_model(
args=self._parse_args(chat_request), model_id=chat_request.model
)
response_body = json.loads(response.get("body").read())
if DEBUG:
logger.info("Bedrock response body: " + str(response_body))
message = response_body["content"][0]["text"]
tools_message = None
start = message.find("<tool>")
end = message.find("</tool>")
if start != -1 and end != -1:
tools_message = message[start + 6: end]
return self._create_response(
model=chat_request.model,
message=response_body["content"][0]["text"],
message_id=response_body["id"],
tools_message=tools_message,
input_tokens=response_body["usage"]["input_tokens"],
output_tokens=response_body["usage"]["output_tokens"],
)
@@ -256,7 +338,7 @@ class ClaudeModel(BedrockModel):
class Llama2Model(BedrockModel):
def _convert_prompt(self, messages: list[ChatRequestMessage]) -> str:
def _convert_prompt(self, chat_request: ChatRequest) -> str:
"""Create a prompt message follow below example:
<s>[INST] <<SYS>>\n{your_system_message}\n<</SYS>>\n\n{user_message_1} [/INST] {model_reply_1}</s>
@@ -264,21 +346,26 @@ class Llama2Model(BedrockModel):
"""
if DEBUG:
logger.info("Convert below messages to prompt for Llama 2: ")
for msg in messages:
for msg in chat_request.messages:
logger.info(msg.model_dump_json())
bos_token = "<s>"
eos_token = "</s>"
prompt = bos_token + "[INST] "
start = 0
prompt = ""
end_turn = False
if messages[0].role == "system":
prompt += "<<SYS>>\n" + messages[0].content + "\n<<SYS>>\n\n"
start = 1
# TODO: Add validation
for i in range(start, len(messages)):
msg = messages[i]
system_prompt = ""
for msg in chat_request.messages:
if msg.role == "system":
system_prompt += "\n" + msg.content + "\n"
continue
if msg.role == "tool":
raise HTTPException(
status_code=500,
detail="Tool prompt is not supported for Llama 2 model",
)
if not isinstance(msg.content, str):
raise HTTPException(status_code=400, detail="Content must be a string for Llama 2 model")
raise HTTPException(
status_code=400, detail="Content must be a string for Llama 2 model"
)
if msg.role == "user":
if end_turn:
prompt += bos_token + "[INST] "
@@ -287,12 +374,16 @@ class Llama2Model(BedrockModel):
else:
prompt += msg.content + eos_token
end_turn = True
if system_prompt:
system_prompt = "<<SYS>>" + system_prompt + "<</SYS>>"
prompt = bos_token + "[INST] " + system_prompt + prompt
if DEBUG:
logger.info("Converted prompt: " + prompt.replace("\n", "\\n"))
return prompt
def _parse_args(self, chat_request: ChatRequest) -> dict:
prompt = self._convert_prompt(chat_request.messages)
prompt = self._convert_prompt(chat_request)
return {
"prompt": prompt,
"max_gen_len": chat_request.max_tokens,
@@ -340,29 +431,36 @@ class Llama2Model(BedrockModel):
class MistralModel(BedrockModel):
def _convert_prompt(self, messages: list[ChatRequestMessage]) -> str:
def _convert_prompt(self, chat_request: ChatRequest) -> str:
"""Create a prompt message follow below example:
<s>[INST] {your_system_message}\n{user_message_1} [/INST] {model_reply_1}</s>
<s>[INST] {user_message_2} [/INST]
"""
# TODO: maybe reuse the Llama 2 one.
if DEBUG:
logger.info("Convert below messages to prompt for Llama 2: ")
for msg in messages:
logger.info("Convert below messages to prompt for Mistral/Mixtral model: ")
for msg in chat_request.messages:
logger.info(msg.model_dump_json())
bos_token = "<s>"
eos_token = "</s>"
prompt = bos_token + "[INST] "
start = 0
prompt = ""
end_turn = False
if messages[0].role == "system":
prompt += messages[0].content + "\n"
start = 1
# TODO: Add validation
for i in range(start, len(messages)):
msg = messages[i]
system_prompt = ""
for msg in chat_request.messages:
if msg.role == "system":
system_prompt += "\n" + msg.content + "\n"
continue
if msg.role == "tool":
raise HTTPException(
status_code=500,
detail="Tool prompt is not supported for Mistral/Mixtral model",
)
if not isinstance(msg.content, str):
raise HTTPException(status_code=400, detail="Content must be a string for Mistral/Mixtral model")
raise HTTPException(
status_code=400,
detail="Content must be a string for Mistral/Mixtral model",
)
if msg.role == "user":
if end_turn:
prompt += bos_token + "[INST] "
@@ -371,12 +469,14 @@ class MistralModel(BedrockModel):
else:
prompt += msg.content + eos_token
end_turn = True
prompt = bos_token + "[INST] " + system_prompt + prompt
if DEBUG:
logger.info("Converted prompt: " + prompt.replace("\n", "\\n"))
return prompt
def _parse_args(self, chat_request: ChatRequest) -> dict:
prompt = self._convert_prompt(chat_request.messages)
prompt = self._convert_prompt(chat_request)
return {
"prompt": prompt,
"max_tokens": chat_request.max_tokens,
@@ -422,7 +522,7 @@ class MistralModel(BedrockModel):
yield self._stream_response_to_bytes(response)
class BedrockEmbeddingsModel(BaseEmbeddingsModel):
class BedrockEmbeddingsModel(BaseEmbeddingsModel, ABC):
accept = "application/json"
content_type = "application/json"
@@ -446,10 +546,8 @@ class BedrockEmbeddingsModel(BaseEmbeddingsModel):
output_tokens: int = 0,
) -> EmbeddingsResponse:
data = [
Embedding(
index=i,
embedding=embedding
) for i, embedding in enumerate(embeddings)
Embedding(index=i, embedding=embedding)
for i, embedding in enumerate(embeddings)
]
response = EmbeddingsResponse(
data=data,
@@ -465,19 +563,6 @@ class BedrockEmbeddingsModel(BaseEmbeddingsModel):
return response
def get_embeddings_model(model_id: str) -> BedrockEmbeddingsModel:
model_name = SUPPORTED_BEDROCK_EMBEDDING_MODELS.get(model_id, "")
if DEBUG:
logger.info("model name is " + model_name)
if model_name in ["Cohere Embed Multilingual", "Cohere Embed English"]:
return CohereEmbeddingsModel()
elif model_name in ["Titan Embeddings G1 - Text", "Titan Multimodal Embeddings G1"]:
return TitanEmbeddingsModel()
else:
logger.error("Unsupported model id " + model_id)
raise ValueError("Invalid model ID")
class CohereEmbeddingsModel(BedrockEmbeddingsModel):
def _parse_args(self, embeddings_request: EmbeddingsRequest) -> dict:
@@ -528,17 +613,25 @@ class TitanEmbeddingsModel(BedrockEmbeddingsModel):
def _parse_args(self, embeddings_request: EmbeddingsRequest) -> dict:
if isinstance(embeddings_request.input, str):
input_text = embeddings_request.input
elif isinstance(embeddings_request.input, list) and len(embeddings_request.input) == 1:
elif (
isinstance(embeddings_request.input, list)
and len(embeddings_request.input) == 1
):
input_text = embeddings_request.input[0]
else:
raise ValueError("Amazon Titan Embeddings models support only single strings as input.")
raise ValueError(
"Amazon Titan Embeddings models support only single strings as input."
)
args = {
"inputText": input_text,
# Note: inputImage is not supported!
}
if embeddings_request.model == "amazon.titan-embed-image-v1":
args["embeddingConfig"] = embeddings_request.embedding_config if embeddings_request.embedding_config else {
"outputEmbeddingLength": 1024}
args["embeddingConfig"] = (
embeddings_request.embedding_config
if embeddings_request.embedding_config
else {"outputEmbeddingLength": 1024}
)
return args
def embed(self, embeddings_request: EmbeddingsRequest) -> EmbeddingsResponse:
@@ -552,5 +645,39 @@ class TitanEmbeddingsModel(BedrockEmbeddingsModel):
return self._create_response(
embeddings=[response_body["embedding"]],
model=embeddings_request.model,
input_tokens=response_body["inputTextTokenCount"]
input_tokens=response_body["inputTextTokenCount"],
)
def get_model(model_id: str) -> BedrockModel:
model_name = SUPPORTED_BEDROCK_MODELS.get(model_id, "")
if DEBUG:
logger.info("model name is " + model_name)
if model_name in ["Claude Instant", "Claude", "Claude 3 Sonnet", "Claude 3 Haiku"]:
return ClaudeModel()
elif model_name in ["Llama 2 Chat 13B", "Llama 2 Chat 70B"]:
return Llama2Model()
elif model_name in ["Mistral 7B Instruct", "Mixtral 8x7B Instruct"]:
return MistralModel()
else:
logger.error("Unsupported model id " + model_id)
raise HTTPException(
status_code=500,
detail="Unsupported model id " + model_id,
)
def get_embeddings_model(model_id: str) -> BedrockEmbeddingsModel:
model_name = SUPPORTED_BEDROCK_EMBEDDING_MODELS.get(model_id, "")
if DEBUG:
logger.info("model name is " + model_name)
if model_name in ["Cohere Embed Multilingual", "Cohere Embed English"]:
return CohereEmbeddingsModel()
elif model_name in ["Titan Embeddings G1 - Text", "Titan Multimodal Embeddings G1"]:
return TitanEmbeddingsModel()
else:
logger.error("Unsupported model id " + model_id)
raise HTTPException(
status_code=500,
detail="Unsupported model id " + model_id,
)

47
src/api/schema.py
View File

@@ -16,6 +16,17 @@ class Models(BaseModel):
data: list[Model] = []
class ResponseFunction(BaseModel):
name: str
arguments: str
class ToolCall(BaseModel):
id: str
type: Literal["function"] = "function"
function: ResponseFunction
class TextContent(BaseModel):
type: Literal["text"] = "text"
text: str
@@ -31,12 +42,31 @@ class ImageContent(BaseModel):
image_url: ImageUrl
class ChatRequestMessage(BaseModel):
class SystemMessage(BaseModel):
name: str | None = None
role: Literal["user", "assistant", "system"]
role: Literal["system"] = "system"
content: str
class UserMessage(BaseModel):
name: str | None = None
role: Literal["user"] = "user"
content: str | list[TextContent | ImageContent]
class AssistantMessage(BaseModel):
name: str | None = None
role: Literal["assistant"] = "assistant"
content: str | None
tool_calls: list[ToolCall] | None = None
class ToolMessage(BaseModel):
role: Literal["tool"] = "tool"
content: str
tool_call_id: str
class Function(BaseModel):
name: str
description: str | None = None
@@ -49,7 +79,7 @@ class Tool(BaseModel):
class ChatRequest(BaseModel):
messages: list[ChatRequestMessage]
messages: list[SystemMessage | UserMessage | AssistantMessage | ToolMessage]
model: str
frequency_penalty: float | None = Field(default=0.0, le=2.0, ge=-2.0) # Not used
presence_penalty: float | None = Field(default=0.0, le=2.0, ge=-2.0) # Not used
@@ -69,17 +99,6 @@ class Usage(BaseModel):
total_tokens: int
class ResponseFunction(BaseModel):
name: str
arguments: str
class ToolCall(BaseModel):
id: str
type: Literal["function"] = "function"
function: ResponseFunction
class ChatResponseMessage(BaseModel):
# tool_calls
role: Literal["assistant"] | None = None

2
src/requirements.txt
View File

@@ -2,3 +2,5 @@ fastapi==0.110.0
pydantic==2.6.3
uvicorn==0.27.0.post1
mangum==0.17.0
tiktoken==0.6.0
requests==2.31.0