316 lines
8.8 KiB
Markdown
316 lines
8.8 KiB
Markdown
[English](./Usage.md)
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# Usage Guide
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假设您在部署后已设置以下环境变量:
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```bash
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export OPENAI_API_KEY=<API key>
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export OPENAI_BASE_URL=<API base url>
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```
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## Models API
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你可以通过这个API 获取支持的models 列表。 另外，如果Amazon Bedrock有新模型加入后，你也可以用它来更新刷新模型列表。
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**Request 示例**
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```bash
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curl -s $OPENAI_BASE_URL/models -H "Authorization: Bearer $OPENAI_API_KEY" | jq .data
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```
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**Response 示例**
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```bash
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[
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...
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{
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"id": "anthropic.claude-3-5-sonnet-20240620-v1:0",
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"created": 1734416893,
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"object": "model",
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"owned_by": "bedrock"
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},
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{
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"id": "us.anthropic.claude-3-5-sonnet-20240620-v1:0",
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"created": 1734416893,
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"object": "model",
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"owned_by": "bedrock"
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},
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...
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]
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```
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## Embedding API
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**重要**: 在使用此代理 API 之前,请仔细阅读以下几点:
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1. 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。
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2. 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。
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3. 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。
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**Request 示例**
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```bash
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curl $OPENAI_BASE_URL/embeddings \
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-H "Authorization: Bearer $OPENAI_API_KEY" \
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-H "Content-Type: application/json" \
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-d '{
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"input": "The food was delicious and the waiter...",
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"model": "text-embedding-ada-002",
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"encoding_format": "float"
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}'
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```
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**Response 示例**
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```json
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{
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"object": "list",
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"data": [
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{
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"object": "embedding",
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"embedding": [
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-0.02279663,
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-0.024612427,
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0.012863159,
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...
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0.01612854,
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0.0038928986
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],
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"index": 0
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}
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],
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"model": "cohere.embed-multilingual-v3",
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"usage": {
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"prompt_tokens": 0,
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"total_tokens": 0
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}
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}
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```
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或者你可以使用OpenAI 的SDK
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```python
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from openai import OpenAI
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client = OpenAI()
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def get_embedding(text, model="text-embedding-3-small"):
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text = text.replace("\n", " ")
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return client.embeddings.create(input=[text], model=model).data[0].embedding
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text = "hello"
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# will output like [0.003578186, 0.028717041, 0.031021118, -0.0014066696,...]
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print(get_embedding(text))
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```
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或者 LangChain
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```python
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from langchain_openai import OpenAIEmbeddings
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embeddings = OpenAIEmbeddings(
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model="text-embedding-3-large",
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)
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text = "This is a test document."
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query_result = embeddings.embed_query(text)
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print(query_result[:5])
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doc_result = embeddings.embed_documents([text])
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print(doc_result[0][:5])
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```
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## Multimodal API
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**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:
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1. 此API 仅支持Claude 3模型。
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**Request 示例**
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```bash
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curl $OPENAI_BASE_URL/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $OPENAI_API_KEY" \
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-d '{
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "please identify and count all the objects in this images, list all the names"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "https://github.com/aws-samples/bedrock-access-gateway/blob/main/assets/obj-detect.png?raw=true"
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}
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}
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]
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}
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]
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}'
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```
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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。
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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"。
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```bash
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curl $OPENAI_BASE_URL/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $OPENAI_API_KEY" \
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-d '{
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": [
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{
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"type": "text",
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"text": "please identify and count all the objects in this images, list all the names"
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},
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{
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"type": "image_url",
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"image_url": {
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"url": "data:image/jpeg;base64,<your image data>"
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}
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}
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]
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}
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]
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}'
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```
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**Response 示例**
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```json
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{
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"id": "msg_01BY3wcz41x7XrKhxY3VzWke",
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"created": 1712543069,
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"model": "anthropic.claude-3-sonnet-20240229-v1:0",
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"system_fingerprint": "fp",
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"choices": [
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{
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"index": 0,
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"finish_reason": "stop",
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"message": {
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"role": "assistant",
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"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."
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}
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}
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],
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"object": "chat.completion",
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"usage": {
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"prompt_tokens": 197,
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"completion_tokens": 147,
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"total_tokens": 344
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}
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}
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```
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## Tool Call
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**重要**:在使用此代理API进行Tool Call之前,请仔细阅读以下几点:
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1. 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。
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1. 此API 仅支持Claude 3模型。
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**Request 示例**
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```bash
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curl $OPENAI_BASE_URL/chat/completions \
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-H "Content-Type: application/json" \
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-H "Authorization: Bearer $OPENAI_API_KEY" \
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-d '{
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"model": "gpt-3.5-turbo",
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"messages": [
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{
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"role": "user",
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"content": "What is the weather like in Shanghai today?"
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}
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],
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"tools": [
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{
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"description": "Get the current weather in a given location",
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"parameters": {
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"type": "object",
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"properties": {
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"location": {
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"type": "string",
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"description": "The city or state which is required."
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},
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"unit": {
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"type": "string",
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"enum": [
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"celsius",
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"fahrenheit"
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]
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}
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},
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"required": [
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"location"
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]
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}
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}
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},
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{
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"type": "function",
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"function": {
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"name": "get_current_location",
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"description": "Use this tool to get the current location if user does not provide a location",
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"parameters": {
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"type": "object",
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"properties": {}
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}
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}
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}
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],
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"tool_choice": "auto"
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}'
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```
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**Response 示例**
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```json
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{
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"id": "msg_01PjrKDWhYGsrTNdeqzWd6D9",
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"created": 1712543689,
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"model": "anthropic.claude-3-sonnet-20240229-v1:0",
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"system_fingerprint": "fp",
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"choices": [
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{
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"index": 0,
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"finish_reason": "stop",
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"message": {
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"role": "assistant",
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"tool_calls": [
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{
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"id": "0",
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"type": "function",
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"function": {
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"name": "get_current_weather",
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"arguments": "{\"location\": \"Shanghai\", \"unit\": \"celsius\"}"
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}
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}
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]
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}
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}
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],
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"object": "chat.completion",
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"usage": {
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"prompt_tokens": 256,
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"completion_tokens": 64,
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"total_tokens": 320
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}
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}
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```
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You can try it with different questions, such as:
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1. Hello, who are you? (No tools are needed)
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2. What is the weather like today? (Should use get_current_location tool first) |