假设您在部署后已设置以下环境变量:
export OPENAI_API_KEY=<API key>
export OPENAI_BASE_URL=<API base url>
重要: 在使用此代理 API 之前,请仔细阅读以下几点:
- 如果您之前使用 OpenAI Embedding模型来创建向量,请注意切换到新模型可能没有那么直接。不同模型具有不同的维度(例如,embed-multilingual-v3.0 有 1024 个维度),即使对于相同的文本,它们也可能产生不同的结果。
- 如果您使用 OpenAI Embedding模型传入的是整数编码(例如与 LangChain 一起使用),此方案将尝试使用
tiktoken
进行解码以检索原始文本。但是,无法保证解码后的文本准确无误。 - 如果您对长文本使用 OpenAI Embedding,您应该验证 Bedrock 模型支持的最大Token数,例如为获得最佳性能,Bedrock 建议将文本长度限制在少于 512 个Token。
Request 示例
curl $OPENAI_BASE_URL/embeddings \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"input": "The food was delicious and the waiter...",
"model": "text-embedding-ada-002",
"encoding_format": "float"
}'
Response 示例
{
"object": "list",
"data": [
{
"object": "embedding",
"embedding": [
-0.02279663,
-0.024612427,
0.012863159,
...
0.01612854,
0.0038928986
],
"index": 0
}
],
"model": "cohere.embed-multilingual-v3",
"usage": {
"prompt_tokens": 0,
"total_tokens": 0
}
}
或者你可以使用OpenAI 的SDK
from openai import OpenAI
client = OpenAI()
def get_embedding(text, model="text-embedding-3-small"):
text = text.replace("\n", " ")
return client.embeddings.create(input=[text], model=model).data[0].embedding
text = "hello"
# will output like [0.003578186, 0.028717041, 0.031021118, -0.0014066696,...]
print(get_embedding(text))
或者 LangChain
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings(
model="text-embedding-3-large",
)
text = "This is a test document."
query_result = embeddings.embed_query(text)
print(query_result[:5])
doc_result = embeddings.embed_documents([text])
print(doc_result[0][:5])
重要:在使用此代理API进行多模态处理之前,请仔细阅读以下几点:
- 此API 仅支持Claude 3模型。
Request 示例
curl $OPENAI_BASE_URL/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "please identify and count all the objects in this images, list all the names"
},
{
"type": "image_url",
"image_url": {
"url": "https://github.com/aws-samples/bedrock-access-gateway/blob/main/assets/obj-detect.png?raw=true"
}
}
]
}
]
}'
如果您需要使用此API处理非公开图像,您可以先对图像进行base64编码,然后传递编码后的字符串。 将"image/jpeg"替换为实际的内容类型(content type)。目前仅支持"image/jpeg"、"image/png"、"image/gif"或"image/webp"。
curl $OPENAI_BASE_URL/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": [
{
"type": "text",
"text": "please identify and count all the objects in this images, list all the names"
},
{
"type": "image_url",
"image_url": {
"url": "data:image/jpeg;base64,<your image data>"
}
}
]
}
]
}'
Response 示例
{
"id": "msg_01BY3wcz41x7XrKhxY3VzWke",
"created": 1712543069,
"model": "anthropic.claude-3-sonnet-20240229-v1:0",
"system_fingerprint": "fp",
"choices": [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"content": "The image contains the following objects:\n\n1. A peach-colored short-sleeve button-up shirt\n2. An olive green plaid long coat/jacket\n3. A pair of white sneakers or canvas shoes\n4. A brown shoulder bag or purse\n5. A makeup brush or cosmetic applicator\n6. A tube or container (possibly lipstick or lip balm)\n7. A pair of sunglasses\n8. A thought bubble icon\n9. A footprint icon\n10. A leaf or plant icon\n11. A flower icon\n12. A cloud icon\n\nIn total, there are 12 distinct objects depicted in the illustrated scene."
}
}
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 197,
"completion_tokens": 147,
"total_tokens": 344
}
}
重要:在使用此代理API进行Tool Call之前,请仔细阅读以下几点:
- OpenAI 已经废弃使用Function Call,而推荐使用Tool Call,因此Function Call在此处不受支持,您应该改为Tool Call。
- 此API 仅支持Claude 3模型。
Request 示例
curl $OPENAI_BASE_URL/chat/completions \
-H "Content-Type: application/json" \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-d '{
"model": "gpt-3.5-turbo",
"messages": [
{
"role": "user",
"content": "What is the weather like in Shanghai today?"
}
],
"tools": [
{
"type": "function",
"function": {
"name": "get_current_weather",
"description": "Get the current weather in a given location",
"parameters": {
"type": "object",
"properties": {
"location": {
"type": "string",
"description": "The city or state which is required."
},
"unit": {
"type": "string",
"enum": [
"celsius",
"fahrenheit"
]
}
},
"required": [
"location"
]
}
}
},
{
"type": "function",
"function": {
"name": "get_current_location",
"description": "Use this tool to get the current location if user does not provide a location",
"parameters": {
"type": "object",
"properties": {}
}
}
}
],
"tool_choice": "auto"
}'
Response 示例
{
"id": "msg_01PjrKDWhYGsrTNdeqzWd6D9",
"created": 1712543689,
"model": "anthropic.claude-3-sonnet-20240229-v1:0",
"system_fingerprint": "fp",
"choices": [
{
"index": 0,
"finish_reason": "stop",
"message": {
"role": "assistant",
"tool_calls": [
{
"id": "0",
"type": "function",
"function": {
"name": "get_current_weather",
"arguments": "{\"location\": \"Shanghai\", \"unit\": \"celsius\"}"
}
}
]
}
}
],
"object": "chat.completion",
"usage": {
"prompt_tokens": 256,
"completion_tokens": 64,
"total_tokens": 320
}
}
You can try it with different questions, such as:
- Hello, who are you? (No tools are needed)
- What is the weather like today? (Should use get_current_location tool first)