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feat:merge main branch
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@@ -77,3 +77,4 @@ By analyzing this information, we can identify performance bottlenecks in model
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./vms/standalone.md
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./vms/index.md
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./openai.md
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51
docs/getting_started/install/cluster/openai.md
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51
docs/getting_started/install/cluster/openai.md
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@@ -0,0 +1,51 @@
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OpenAI-Compatible RESTful APIs
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==================================
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(openai-apis-index)=
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### Install Prepare
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You must [deploy DB-GPT cluster](https://db-gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html) first.
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### Launch Model API Server
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```bash
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dbgpt start apiserver --controller_addr http://127.0.0.1:8000 --api_keys EMPTY
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```
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By default, the Model API Server starts on port 8100.
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### Validate with cURL
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#### List models
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```bash
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curl http://127.0.0.1:8100/api/v1/models \
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-H "Authorization: Bearer EMPTY" \
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-H "Content-Type: application/json"
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```
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#### Chat completions
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```bash
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curl http://127.0.0.1:8100/api/v1/chat/completions \
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-H "Authorization: Bearer EMPTY" \
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-H "Content-Type: application/json" \
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-d '{"model": "vicuna-13b-v1.5", "messages": [{"role": "user", "content": "hello"}]}'
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```
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### Validate with OpenAI Official SDK
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#### Chat completions
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```python
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import openai
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openai.api_key = "EMPTY"
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openai.api_base = "http://127.0.0.1:8100/api/v1"
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model = "vicuna-13b-v1.5"
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completion = openai.ChatCompletion.create(
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model=model,
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messages=[{"role": "user", "content": "hello"}]
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)
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# print the completion
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print(completion.choices[0].message.content)
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```
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@@ -24,9 +24,12 @@ PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions
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#Azure
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LLM_MODEL=chatgpt_proxyllm
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OPENAI_API_TYPE=azure
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PROXY_API_KEY={your-openai-sk}
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PROXY_SERVER_URL=https://xx.openai.azure.com/v1/chat/completions
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PROXY_API_KEY={your-azure-sk}
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PROXY_API_BASE=https://{your domain}.openai.azure.com/
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PROXY_API_TYPE=azure
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PROXY_SERVER_URL=xxxx
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PROXY_API_VERSION=2023-05-15
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PROXYLLM_BACKEND=gpt-35-turbo
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#Aliyun tongyi
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LLM_MODEL=tongyi_proxyllm
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