docs: add vllm llama_cpp docs and standardize configs (#2386)

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yyhhyy 2025-03-03 16:59:07 +08:00 committed by GitHub
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9 changed files with 136 additions and 51 deletions

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@ -53,5 +53,3 @@ name = "${env:EMBEDDING_MODEL_NAME:-text-embedding-3-small}"
provider = "${env:EMBEDDING_MODEL_PROVIDER:-proxy/openai}"
api_url = "${env:EMBEDDING_MODEL_API_URL:-https://api.openai.com/v1/embeddings}"
api_key = "${env:OPENAI_API_KEY}"

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@ -26,6 +26,7 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/THUDM/glm-4-9b-chat-hf"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
@ -33,4 +34,4 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "models/BAAI/glm-4-9b-chat-hf"

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@ -21,20 +21,13 @@ persist_path = "pilot/data"
# Model Configurations
[models]
[[models.llms]]
name = "Qwen2.5-Coder-0.5B-Instruct"
# Please make sure install DB-GPT with '--extra llama_cpp_server' extra
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "llama.cpp.server"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/qwen2.5-coder-0.5b-instruct-q4_k_m.gguf"
[[models.llms]]
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "llama.cpp.server"
# https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-1.5B-GGUF
path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf"
# path = "the-model-path-in-the-local-file-system"
path = "models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
@ -42,5 +35,4 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/bge-large-zh-v1.5"
path = "models/BAAI/bge-large-zh-v1.5"

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@ -21,19 +21,13 @@ persist_path = "pilot/data"
# Model Configurations
[models]
[[models.llms]]
name = "Qwen2.5-Coder-0.5B-Instruct"
# Please make sure install DB-GPT with '--extra llama_cpp' extra
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "llama.cpp"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/qwen2.5-coder-0.5b-instruct-q4_k_m.gguf"
[[models.llms]]
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "llama.cpp"
# https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-1.5B-GGUF
path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf"
# path = "the-model-path-in-the-local-file-system"
path = "models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf"
[[models.embeddings]]
@ -42,5 +36,5 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/bge-large-zh-v1.5"
path = "models/BAAI/bge-large-zh-v1.5"

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@ -26,7 +26,7 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/Qwen2.5-Coder-0.5B-Instruct"
path = "models/Qwen2.5-Coder-0.5B-Instruct"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
@ -34,5 +34,5 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/bge-large-zh-v1.5"
path = "models/BAAI/bge-large-zh-v1.5"

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@ -21,21 +21,13 @@ persist_path = "pilot/data"
# Model Configurations
[models]
[[models.llms]]
name = "Qwen2.5-Coder-0.5B-Instruct"
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "vllm"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/Qwen2.5-Coder-0.5B-Instruct"
# dtype="float32"
# [[models.llms]]
# name = "DeepSeek-R1-Distill-Qwen-1.5B"
# provider = "vllm"
# # If not provided, the model will be downloaded from the Hugging Face model hub
# # uncomment the following line to specify the model path in the local file system
# # path = "the-model-path-in-the-local-file-system"
# path = "/data/models/DeepSeek-R1-Distill-Qwen-1.5B"
path = "models/DeepSeek-R1-Distill-Qwen-1.5B"
# dtype = "float32"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"

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@ -24,13 +24,9 @@ persist_path = "pilot/data"
[models]
[[models.llms]]
name = "deepseek-reasoner"
# name = "deepseek-chat"
provider = "proxy/deepseek"
api_key = "${env:DEEPSEEK_API_KEY}"
[[models.llms]]
name = "deepseek-chat"
provider = "proxy/deepseek"
api_key = "${env:DEEPSEEK_API_KEY}"
api_key = "your_deepseek_api_key"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
@ -38,4 +34,4 @@ provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
path = "/data/models/bge-large-zh-v1.5"
path = "models/bge-large-zh-v1.5"

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@ -7,7 +7,7 @@
- Install docusaurus dependencies, generate node_modules folder.
```
sudo yarn install
yarn install
```
### launch

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@ -74,6 +74,19 @@ uv --version
```
## Deploy DB-GPT
:::tip
If you are in the China region, you can add --index-url=https://pypi.tuna.tsinghua.edu.cn/simple at the end of the command.Like this:
```bash
uv sync --all-packages \
--extra "base" \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "dbgpts" \
--index-url=https://pypi.tuna.tsinghua.edu.cn/simple
```
This tutorial assumes that you can establish network communication with the dependency download sources.
:::
### Install Dependencies
@ -83,13 +96,15 @@ uv --version
{label: 'OpenAI (proxy)', value: 'openai'},
{label: 'DeepSeek (proxy)', value: 'deepseek'},
{label: 'GLM4 (local)', value: 'glm-4'},
{label: 'VLLM (local)', value: 'vllm'},
{label: 'LLAMA_CPP (local)', value: 'llama_cpp'},
]}>
<TabItem value="openai" label="OpenAI(proxy)">
```bash
# Use uv to install dependencies needed for OpenAI proxy
uv sync --all-packages --frozen \
uv sync --all-packages \
--extra "base" \
--extra "proxy_openai" \
--extra "rag" \
@ -129,7 +144,7 @@ uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/
```bash
# Use uv to install dependencies needed for OpenAI proxy
uv sync --all-packages --frozen \
uv sync --all-packages \
--extra "base" \
--extra "proxy_openai" \
--extra "rag" \
@ -141,8 +156,17 @@ uv sync --all-packages --frozen \
To run DB-GPT with DeepSeek proxy, you must provide the DeepSeek API key in the `configs/dbgpt-proxy-deepseek.toml`.
And you can specify your embedding model in the `configs/dbgpt-proxy-deepseek.toml` configuration file, the default embedding model is `BAAI/bge-large-zh-v1.5`. If you want to use other embedding models, you can modify the `configs/dbgpt-proxy-deepseek.toml` configuration file and specify the `name` and `provider` of the embedding model in the `[[models.embeddings]]` section. The provider can be `hf`.
And you can specify your embedding model in the `configs/dbgpt-proxy-deepseek.toml` configuration file, the default embedding model is `BAAI/bge-large-zh-v1.5`. If you want to use other embedding models, you can modify the `configs/dbgpt-proxy-deepseek.toml` configuration file and specify the `name` and `provider` of the embedding model in the `[[models.embeddings]]` section. The provider can be `hf`.Finally, you need to append `--extra "hf"` at the end of the dependency installation command. Here's the updated command:
```bash
uv sync --all-packages \
--extra "base" \
--extra "proxy_openai" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "dbgpts" \
--extra "hf"
```
**Model Configurations**:
```toml
# Model Configurations
[models]
@ -178,7 +202,7 @@ uv run python packages/dbgpt-app/src/dbgpt_app/dbgpt_server.py --config configs/
```bash
# Use uv to install dependencies needed for GLM4
# Install core dependencies and select desired extensions
uv sync --all-packages --frozen \
uv sync --all-packages \
--extra "base" \
--extra "hf" \
--extra "rag" \
@ -214,6 +238,94 @@ Then run the following command to start the webserver:
```bash
uv run dbgpt start webserver --config configs/dbgpt-local-glm.toml
```
</TabItem>
<TabItem value="vllm" label="VLLM(local)">
```bash
# Use uv to install dependencies needed for vllm
# Install core dependencies and select desired extensions
uv sync --all-packages \
--extra "base" \
--extra "vllm" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "quant_bnb" \
--extra "dbgpts"
```
### Run Webserver
To run DB-GPT with the local model. You can modify the `configs/dbgpt-local-vllm.toml` configuration file to specify the model path and other parameters.
```toml
# Model Configurations
[models]
[[models.llms]]
name = "THUDM/glm-4-9b-chat-hf"
provider = "vllm"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
```
In the above configuration file, `[[models.llms]]` specifies the LLM model, and `[[models.embeddings]]` specifies the embedding model. If you not provide the `path` parameter, the model will be downloaded from the Hugging Face model hub according to the `name` parameter.
Then run the following command to start the webserver:
```bash
uv run dbgpt start webserver --config configs/dbgpt-local-vllm.toml
```
</TabItem>
<TabItem value="llama_cpp" label="LLAMA_CPP(local)">
```bash
# Use uv to install dependencies needed for llama-cpp
# Install core dependencies and select desired extensions
uv sync --all-packages \
--extra "base" \
--extra "llama_cpp" \
--extra "rag" \
--extra "storage_chromadb" \
--extra "quant_bnb" \
--extra "dbgpts"
```
### Run Webserver
To run DB-GPT with the local model. You can modify the `configs/dbgpt-local-llama-cpp.toml` configuration file to specify the model path and other parameters.
```toml
# Model Configurations
[models]
[[models.llms]]
name = "DeepSeek-R1-Distill-Qwen-1.5B"
provider = "llama.cpp"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
[[models.embeddings]]
name = "BAAI/bge-large-zh-v1.5"
provider = "hf"
# If not provided, the model will be downloaded from the Hugging Face model hub
# uncomment the following line to specify the model path in the local file system
# path = "the-model-path-in-the-local-file-system"
```
In the above configuration file, `[[models.llms]]` specifies the LLM model, and `[[models.embeddings]]` specifies the embedding model. If you not provide the `path` parameter, the model will be downloaded from the Hugging Face model hub according to the `name` parameter.
Then run the following command to start the webserver:
```bash
uv run dbgpt start webserver --config configs/dbgpt-local-llama-cpp.toml
```
</TabItem>