mirror of
https://github.com/csunny/DB-GPT.git
synced 2025-07-30 23:28:35 +00:00
Merge remote-tracking branch 'origin/main' into feat_rag_graph
This commit is contained in:
aries_ckt
commit
f223460c30
@ -34,6 +34,7 @@ CREATE TABLE `knowledge_document` (
|
||||
`content` LONGTEXT NOT NULL COMMENT 'knowledge embedding sync result',
|
||||
`result` TEXT NULL COMMENT 'knowledge content',
|
||||
`vector_ids` LONGTEXT NULL COMMENT 'vector_ids',
|
||||
`summary` LONGTEXT NULL COMMENT 'knowledge summary',
|
||||
`gmt_created` TIMESTAMP DEFAULT CURRENT_TIMESTAMP COMMENT 'created time',
|
||||
`gmt_modified` TIMESTAMP DEFAULT CURRENT_TIMESTAMP ON UPDATE CURRENT_TIMESTAMP COMMENT 'update time',
|
||||
PRIMARY KEY (`id`),
|
||||
|
||||
@ -28,7 +28,9 @@ WORKDIR /app
|
||||
# RUN pip3 install -i $PIP_INDEX_URL ".[all]"
|
||||
|
||||
RUN pip3 install --upgrade pip -i $PIP_INDEX_URL \
|
||||
&& pip3 install -i $PIP_INDEX_URL ".[$DB_GPT_INSTALL_MODEL]"
|
||||
&& pip3 install -i $PIP_INDEX_URL ".[$DB_GPT_INSTALL_MODEL]" \
|
||||
# install openai for proxyllm
|
||||
&& pip3 install -i $PIP_INDEX_URL ".[openai]"
|
||||
|
||||
RUN (if [ "${LANGUAGE}" = "zh" ]; \
|
||||
# language is zh, download zh_core_web_sm from github
|
||||
|
||||
@ -57,6 +57,12 @@ If you are low hardware requirements you can install DB-GPT by Using third-part
|
||||
|
||||
.. tab:: OpenAI
|
||||
|
||||
Installing Dependencies
|
||||
|
||||
.. code-block::
|
||||
|
||||
pip install -e ".[openai]"
|
||||
|
||||
Download embedding model
|
||||
|
||||
.. code-block:: shell
|
||||
@ -69,7 +75,7 @@ If you are low hardware requirements you can install DB-GPT by Using third-part
|
||||
or
|
||||
git clone https://huggingface.co/moka-ai/m3e-large
|
||||
|
||||
Configure LLM_MODEL and PROXY_API_URL and API_KEY in `.env` file
|
||||
Configure LLM_MODEL, PROXY_API_URL and API_KEY in `.env` file
|
||||
|
||||
.. code-block:: shell
|
||||
|
||||
@ -291,7 +297,7 @@ If you are low hardware requirements you can install DB-GPT by Using third-part
|
||||
|
||||
**1. Download a pre-converted model file.**
|
||||
|
||||
Suppose you want to use [Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-13b-v1.5), you can download the file already converted from [TheBloke/vicuna-13B-v1.5-GGUF](https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF), only one file is needed. Download it to the `models` directory and rename it to `ggml-model-q4_0.gguf`.
|
||||
Suppose you want to use `Vicuna 13B v1.5 <https://huggingface.co/lmsys/vicuna-13b-v1.5>`_ , you can download the file already converted from `TheBloke/vicuna-13B-v1.5-GGUF <https://huggingface.co/TheBloke/vicuna-13B-v1.5-GGUF>`_ , only one file is needed. Download it to the `models` directory and rename it to `ggml-model-q4_0.gguf`.
|
||||
|
||||
.. code-block::
|
||||
|
||||
@ -299,7 +305,7 @@ If you are low hardware requirements you can install DB-GPT by Using third-part
|
||||
|
||||
**2. Convert It Yourself**
|
||||
|
||||
You can convert the model file yourself according to the instructions in [llama.cpp#prepare-data--run](https://github.com/ggerganov/llama.cpp#prepare-data--run), and put the converted file in the models directory and rename it to `ggml-model-q4_0.gguf`.
|
||||
You can convert the model file yourself according to the instructions in `llama.cpp#prepare-data--run <https://github.com/ggerganov/llama.cpp#prepare-data--run>`_ , and put the converted file in the models directory and rename it to `ggml-model-q4_0.gguf`.
|
||||
|
||||
**Installing Dependencies**
|
||||
|
||||
|
||||
@ -6,7 +6,9 @@ LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG
|
||||
* LLM_MODEL=vicuna-13b
|
||||
|
||||
MODEL_SERVER_ADDRESS
|
||||
|
||||
* MODEL_SERVER=http://127.0.0.1:8000
|
||||
|
||||
LIMIT_MODEL_CONCURRENCY
|
||||
|
||||
* LIMIT_MODEL_CONCURRENCY=5
|
||||
@ -84,21 +86,6 @@ embedding recall max token ,2000
|
||||
* WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network
|
||||
```
|
||||
|
||||
```{admonition} Vector Store SETTINGS
|
||||
#### Chroma
|
||||
* VECTOR_STORE_TYPE=Chroma
|
||||
#### MILVUS
|
||||
* VECTOR_STORE_TYPE=Milvus
|
||||
* MILVUS_URL=127.0.0.1
|
||||
* MILVUS_PORT=19530
|
||||
* MILVUS_USERNAME
|
||||
* MILVUS_PASSWORD
|
||||
* MILVUS_SECURE=
|
||||
|
||||
#### WEAVIATE
|
||||
* WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network
|
||||
```
|
||||
|
||||
```{admonition} Multi-GPU Setting
|
||||
See https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-visibility-cuda_visible_devices/
|
||||
If CUDA_VISIBLE_DEVICES is not configured, all available gpus will be used
|
||||
|
||||
@ -8,7 +8,7 @@ msgid ""
|
||||
msgstr ""
|
||||
"Project-Id-Version: DB-GPT 👏👏 0.4.0\n"
|
||||
"Report-Msgid-Bugs-To: \n"
|
||||
"POT-Creation-Date: 2023-11-04 15:08+0800\n"
|
||||
"POT-Creation-Date: 2023-11-06 19:38+0800\n"
|
||||
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
|
||||
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
|
||||
"Language: zh_CN\n"
|
||||
@ -19,27 +19,27 @@ msgstr ""
|
||||
"Content-Transfer-Encoding: 8bit\n"
|
||||
"Generated-By: Babel 2.12.1\n"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:4 bb7e1a7b70624a3d91804c5ce2d5216b
|
||||
#: ../../getting_started/install/deploy.rst:4 f3ea3305f122460aaa11999edc4b5de6
|
||||
msgid "Installation From Source"
|
||||
msgstr "源码安装"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:6 7524477e8b8a456b8e2b942621a6b225
|
||||
#: ../../getting_started/install/deploy.rst:6 bb941f2bd56d4eb48f7c4f75ebd74176
|
||||
msgid "To get started, install DB-GPT with the following steps."
|
||||
msgstr "按照以下步骤进行安装"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:10 871cdb0a7e004668b518168461d76945
|
||||
#: ../../getting_started/install/deploy.rst:10 27a1e092c1f945ceb9946ebdaf89b600
|
||||
msgid "1.Preparation"
|
||||
msgstr "1.准备"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:11 d845f48ec05a4bb7a5b0f95a0644029d
|
||||
#: ../../getting_started/install/deploy.rst:11 5c5bfbdc74a14c3b9b1f1ed66617cac8
|
||||
msgid "**Download DB-GPT**"
|
||||
msgstr "**下载DB-GPT项目**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:17 85df7bb2b73141058ba0ca5a73e46701
|
||||
#: ../../getting_started/install/deploy.rst:17 3065ee2f34f9417598a37fd699a4863e
|
||||
msgid "**Install Miniconda**"
|
||||
msgstr "**安装Miniconda**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:19 b7b3e120ded344d09fb2d7a3a7903497
|
||||
#: ../../getting_started/install/deploy.rst:19 f9f3a653ffb8447284686aa37a7bb79a
|
||||
msgid ""
|
||||
"We use Sqlite as default database, so there is no need for database "
|
||||
"installation. If you choose to connect to other databases, you can "
|
||||
@ -50,386 +50,397 @@ msgid ""
|
||||
"<https://docs.conda.io/en/latest/miniconda.html>`_"
|
||||
msgstr ""
|
||||
"目前使用Sqlite作为默认数据库，因此DB-"
|
||||
"GPT快速部署不需要部署相关数据库服务。如果你想使用其他数据库，需要先部署相关数据库服务。我们目前使用Miniconda进行python环境和包依赖管理[安装"
|
||||
" Miniconda](https://docs.conda.io/en/latest/miniconda.html)"
|
||||
"GPT快速部署不需要部署相关数据库服务。如果你想使用其他数据库，需要先部署相关数据库服务。我们目前使用Miniconda进行python环境和包依赖管理。`如何安装"
|
||||
" Miniconda <https://docs.conda.io/en/latest/miniconda.html>`_ 。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:36 a5e34db6a6b8450c8a1f35377a1dc93b
|
||||
#: ../../getting_started/install/deploy.rst:36 a2cd2fdd1d16421f9cbe341040b153b6
|
||||
msgid "2.Deploy LLM Service"
|
||||
msgstr "2.部署LLM服务"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:37 5c49829b086e4d5fbcb5a971a34fa3b5
|
||||
#: ../../getting_started/install/deploy.rst:37 180a121e3c994a92a917ace80bf12386
|
||||
msgid ""
|
||||
"DB-GPT can be deployed on servers with low hardware requirements or on "
|
||||
"servers with high hardware requirements."
|
||||
msgstr "DB-GPT可以部署在对硬件要求不高的服务器，也可以部署在对硬件要求高的服务器"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:39 7c2f7e2b49054d17850942bef9570c45
|
||||
#: ../../getting_started/install/deploy.rst:39 395608515c0348d5849030b58da7b659
|
||||
msgid ""
|
||||
"If you are low hardware requirements you can install DB-GPT by Using "
|
||||
"third-part LLM REST API Service OpenAI, Azure, tongyi."
|
||||
msgstr "Low hardware requirements模式适用于对接第三方模型服务的api,比如OpenAI, 通义千问, 文心.cpp。"
|
||||
msgstr "低硬件要求模式适用于对接第三方模型服务的 API，比如 OpenAI、通义千问、 文心一言等。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:43 8c392e63073a47698e38456c5994ff25
|
||||
#: ../../getting_started/install/deploy.rst:43 e29297e61e2e4d05ba88f0e1c2b1f365
|
||||
msgid "As our project has the ability to achieve OpenAI performance of over 85%,"
|
||||
msgstr "使用OpenAI服务可以让DB-GPT准确率达到85%"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:48 148f8d85c02345219ac535003fe05fca
|
||||
#: ../../getting_started/install/deploy.rst:48 d0d70d51e8684c2891c58a6da4941a52
|
||||
msgid "Notice make sure you have install git-lfs"
|
||||
msgstr "确认是否已经安装git-lfs"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:50 7f719273d1ff4d888376030531a560c4
|
||||
#: ../../getting_started/install/deploy.rst:50 0d2781fd38eb467ebad2a3c310a344e6
|
||||
msgid "centos:yum install git-lfs"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:52 f5c2035bca214e3fbef2b70be0e76247
|
||||
#: ../../getting_started/install/deploy.rst:52 1574ea24ad6443409070aa3a1f7abe87
|
||||
msgid "ubuntu:apt-get install git-lfs"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:54 cb1b52b9323e4e9fbccfd3c42067a568
|
||||
#: ../../getting_started/install/deploy.rst:54 ad86473d5c87447091c713f45cbfed0e
|
||||
msgid "macos:brew install git-lfs"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:58
|
||||
#: ../../getting_started/install/deploy.rst:223
|
||||
#: 9c601ee0c06646d58cbd2c2fff342cb5 d3fe6c712b4145d0856487c65057c2a0
|
||||
#: ../../getting_started/install/deploy.rst:229
|
||||
#: 3dd1e40f33924faab63634907a7f6511 dce32420face4ab2b99caf7f3900ede9
|
||||
msgid "OpenAI"
|
||||
msgstr "OpenAI"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:60
|
||||
#: ../../getting_started/install/deploy.rst:207
|
||||
#: 5e0e84bd11084d158e18b702022aaa96 6a9c9830e5184eac8b6b7425780f4a3e
|
||||
#: ../../getting_started/install/deploy.rst:60 1f66400540114de2820761ef80137805
|
||||
msgid "Installing Dependencies"
|
||||
msgstr "安装依赖"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:66
|
||||
#: ../../getting_started/install/deploy.rst:213
|
||||
#: 31b856a6fc094334a37914c046cb1bb1 42b2f6d36ca4487f8e31d59bba123fca
|
||||
msgid "Download embedding model"
|
||||
msgstr "下载embedding model"
|
||||
msgstr "下载 embedding 模型"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:72
|
||||
#: ../../getting_started/install/deploy.rst:231
|
||||
#: 953d6931627641b985588abf8b6ad9fd d52e287164ca44f79709f4645a1bb774
|
||||
msgid "Configure LLM_MODEL and PROXY_API_URL and API_KEY in `.env` file"
|
||||
msgstr "在`.env`文件设置LLM_MODEL and PROXY_API_URL and API_KEY"
|
||||
#: ../../getting_started/install/deploy.rst:78 f970fb69e47c40d7bda381ec6f045829
|
||||
msgid "Configure LLM_MODEL, PROXY_API_URL and API_KEY in `.env` file"
|
||||
msgstr "在 `.env` 文件中设置 LLM_MODEL、PROXY_API_URL 和 API_KEY"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:82
|
||||
#: ../../getting_started/install/deploy.rst:282
|
||||
#: 6dc89616c0bf46a898a49606d1349039 e30b7458fc0d46e8bcf0ec61a2be2e19
|
||||
#: ../../getting_started/install/deploy.rst:88
|
||||
#: ../../getting_started/install/deploy.rst:288
|
||||
#: 6ca04c88fc60480db2ebdc9b234a0bbb 709cfe74c45c4eff83a7d77bb30b4a2b
|
||||
msgid "Make sure your .env configuration is not overwritten"
|
||||
msgstr "认.env文件不会被覆盖\""
|
||||
msgstr "确保你的 .env 文件不会被覆盖"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:85 7adce7ac878d4b1ab9249d840d302520
|
||||
#: ../../getting_started/install/deploy.rst:91 147aea0d753f44588f4a0c56002334ab
|
||||
msgid "Vicuna"
|
||||
msgstr "Vicuna"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:86 44a32c4cd0b140838b49f739d68ff42d
|
||||
#: ../../getting_started/install/deploy.rst:92 6a0bd60c4ca2478cb0f3d85aff70cd3b
|
||||
msgid ""
|
||||
"`Vicuna-v1.5 <https://huggingface.co/lmsys/vicuna-13b-v1.5>`_ based on "
|
||||
"llama-2 has been released, we recommend you set `LLM_MODEL=vicuna-"
|
||||
"13b-v1.5` to try this model)"
|
||||
msgstr ""
|
||||
"基于 llama-2 的模型 `Vicuna-v1.5 <https://huggingface.co/lmsys/vicuna-"
|
||||
"13b-v1.5>`_ 已经发布，我们推荐你通过配置 `LLM_MODEL=vicuna-13b-v1.5` 来尝试这个模型"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:88 f4991de79c434961b0ee777cf42dc491
|
||||
#: ../../getting_started/install/deploy.rst:94 6a111c2ef31f41d4b737cf8b6f36fb16
|
||||
msgid "vicuna-v1.5 hardware requirements"
|
||||
msgstr ""
|
||||
msgstr "vicuna-v1.5 的硬件要求"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:92
|
||||
#: ../../getting_started/install/deploy.rst:137
|
||||
#: 596405ba21d046c993c18d0bcaf95048 ca993a5f1b194707bbe3d5d6e9d670d8
|
||||
#: ../../getting_started/install/deploy.rst:98
|
||||
#: ../../getting_started/install/deploy.rst:143
|
||||
#: dc24c0238ce141df8bdce26cc0e2ddbb e04f1ea4b36940f3a28b66cdff7b702e
|
||||
msgid "Model"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:93
|
||||
#: ../../getting_started/install/deploy.rst:138
|
||||
#: 8437bb47daa94ca8a43b0eadc1286842 d040d964198d4c95afa6dd0d86586cc2
|
||||
msgid "Quantize"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:94
|
||||
#: ../../getting_started/install/deploy.rst:139
|
||||
#: 453bc4416d89421699d90d940498b9ac 5de996bb955f4c6ca8d5651985ced255
|
||||
msgid "VRAM Size"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:95
|
||||
#: ../../getting_started/install/deploy.rst:98 077d52b8588e4ddd956acb7ff86bc458
|
||||
#: fcf0ca75422343d19b12502fb7746f08
|
||||
msgid "vicuna-7b-v1.5"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:96
|
||||
#: ../../getting_started/install/deploy.rst:102
|
||||
#: ../../getting_started/install/deploy.rst:141
|
||||
#: ../../getting_started/install/deploy.rst:147
|
||||
#: 799b310b76454f79a261300253a9bb75 bd98df81ec8941b69c7d1de107154b1b
|
||||
#: ca71731754f545d589e506cb53e6f042 d4023553e4384cc4955ef5bf4dc4d05b
|
||||
msgid "4-bit"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:97
|
||||
#: ../../getting_started/install/deploy.rst:142
|
||||
#: adfc22d9c6394046bb632831a4a3b066 e4e7762c714a49828da630a5f9d641dc
|
||||
msgid "8 GB"
|
||||
msgstr ""
|
||||
msgstr "模型"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:99
|
||||
#: ../../getting_started/install/deploy.rst:105
|
||||
#: ../../getting_started/install/deploy.rst:144
|
||||
#: ../../getting_started/install/deploy.rst:150
|
||||
#: 0d6bd1c4a6464df38723de978c09e8bc 106e20f7727f43d280b7560dc0f19bbc
|
||||
#: 5a95691d1d134638bffa478daf2577a0 f8add03506244306b1ac6e10c5a2bc1f
|
||||
msgid "8-bit"
|
||||
msgstr ""
|
||||
#: b6473e65ca1a437a84226531be4da26d e0a2f7580685480aa13ca462418764d3
|
||||
msgid "Quantize"
|
||||
msgstr "量化"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:100
|
||||
#: ../../getting_started/install/deploy.rst:103
|
||||
#: ../../getting_started/install/deploy.rst:145
|
||||
#: ../../getting_started/install/deploy.rst:148
|
||||
#: 03222e6a682e406bb56c21025412fbe5 2eb88af3efb74fb79899d7818a79d177
|
||||
#: 9705b9d7d2084f0b8867f6aeec66251b bca6e5151e244a318f4c2078c3f82118
|
||||
msgid "12 GB"
|
||||
msgstr ""
|
||||
#: 56471c3b174d4adf9e8cb5bebaa300a6 d82297b8b9c148c3906d8ee4ed10d8a0
|
||||
msgid "VRAM Size"
|
||||
msgstr "显存"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:101
|
||||
#: ../../getting_started/install/deploy.rst:104
|
||||
#: 1596cbb77c954d1db00e2fa283d66a9a c6e8dc1600d14ee5a47f45f75ec709f4
|
||||
msgid "vicuna-13b-v1.5"
|
||||
#: 1214432602fe47a28479ce3e21a7d88b 51838e72e42248f199653f1bf08c8155
|
||||
msgid "vicuna-7b-v1.5"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:102
|
||||
#: ../../getting_started/install/deploy.rst:108
|
||||
#: ../../getting_started/install/deploy.rst:147
|
||||
#: ../../getting_started/install/deploy.rst:153
|
||||
#: a64439f4e6f64c42bb76fbb819556784 ed95f498641e4a0f976318df608a1d67
|
||||
#: fc400814509048b4a1cbe1e07c539285 ff7a8cb2cce8438cb6cb0d80dabfc2b5
|
||||
msgid "4-bit"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:103
|
||||
#: ../../getting_started/install/deploy.rst:148
|
||||
#: 2726e8a278c34e6db59147e9f66f2436 5feab5755a41403c9d641da697de4651
|
||||
msgid "8 GB"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:105
|
||||
#: ../../getting_started/install/deploy.rst:111
|
||||
#: ../../getting_started/install/deploy.rst:150
|
||||
#: ../../getting_started/install/deploy.rst:156
|
||||
#: 1984406682da4da3ad7b275e44085d07 2f027d838d0c46409e54c066d7983aae
|
||||
#: 5c5878fe64944872b6769f075fedca05 e2507408a9c5423988e17b7029b487e4
|
||||
msgid "8-bit"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:106
|
||||
#: ../../getting_started/install/deploy.rst:109
|
||||
#: ../../getting_started/install/deploy.rst:151
|
||||
#: 00ee98593b7f4b29b5d97fa5a323bfa1 0169214dcf8d46c791b3482c81283fa0
|
||||
#: ../../getting_started/install/deploy.rst:154
|
||||
#: 332f50702c7b46e79ea0af5cbf86c6d5 381d23253cfd40109bacefca6a179f91
|
||||
#: aafe2423c25546e789e4804e3fd91d1d cc56990a58e941d6ba023cbd4dca0357
|
||||
msgid "12 GB"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:107
|
||||
#: ../../getting_started/install/deploy.rst:110
|
||||
#: 1f14e2fa6d41493cb208f55eddff9773 6457f6307d8546beb5f2fb69c30922d8
|
||||
msgid "vicuna-13b-v1.5"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:112
|
||||
#: ../../getting_started/install/deploy.rst:157
|
||||
#: e24d3a36b5ce4cfe861dce2d1c4db592 f2e66b2da7954aaab0ee526b25a371f5
|
||||
msgid "20 GB"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:122
|
||||
#: ../../getting_started/install/deploy.rst:169
|
||||
#: ../../getting_started/install/deploy.rst:195
|
||||
#: 852f800e380c44d3b1b5bdaa881406b7 89ddd6c4a4874f4f8b7f051d797e364a
|
||||
#: a2b4436f8fcb44ada3b5bf5c1099e646
|
||||
#: ../../getting_started/install/deploy.rst:128
|
||||
#: ../../getting_started/install/deploy.rst:175
|
||||
#: ../../getting_started/install/deploy.rst:201
|
||||
#: 1719c11f92874c47a87c00c634b9fad8 4596fcbe415d42fdbb29b92964fae070
|
||||
#: e639ae6076a64b7b9de08527966e4550
|
||||
msgid "The model files are large and will take a long time to download."
|
||||
msgstr ""
|
||||
msgstr "这个模型权重文件比较大，需要花费较长时间来下载。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:124
|
||||
#: ../../getting_started/install/deploy.rst:171
|
||||
#: ../../getting_started/install/deploy.rst:197
|
||||
#: 18ccf7acdf3045588a19866c0161f44b 3d3d93649ec943c8b2b725807e969cbf
|
||||
#: 6205975466f54d8fbf8e30b58df6b9c2
|
||||
#: ../../getting_started/install/deploy.rst:130
|
||||
#: ../../getting_started/install/deploy.rst:177
|
||||
#: ../../getting_started/install/deploy.rst:203
|
||||
#: 4ec1492d389f403ebd9dd805fcaac68e ac6c68e2bf9b47c694ea8e0506014b10
|
||||
#: e39be72282e64760903aaba45f8effb8
|
||||
msgid "**Configure LLM_MODEL in `.env` file**"
|
||||
msgstr ""
|
||||
msgstr "**在 `.env` 文件中配置 LLM_MODEL**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:131
|
||||
#: ../../getting_started/install/deploy.rst:228
|
||||
#: 022addaea4ea432584b8a27ea83a9dc4 7a95d25c58d2491e855dff42f6d3a686
|
||||
#: ../../getting_started/install/deploy.rst:137
|
||||
#: ../../getting_started/install/deploy.rst:234
|
||||
#: 7ce4e2253ef24a7ea890ade04ce36682 b9d5bf4fa09649c4a098503132ce7c0c
|
||||
msgid "Baichuan"
|
||||
msgstr ""
|
||||
msgstr "百川"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:133
|
||||
#: b5374f6d0ca44ee9bd984c0198515861
|
||||
#: ../../getting_started/install/deploy.rst:139
|
||||
#: ffdad6a70558457fa825bad4d811100d
|
||||
msgid "Baichuan hardware requirements"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:140
|
||||
#: ../../getting_started/install/deploy.rst:143
|
||||
#: 6a5e6034c185455d81e8ba5349dbc191 9d0109817c524747909417924bb0bd31
|
||||
msgid "baichuan-7b"
|
||||
msgstr ""
|
||||
msgstr "百川 的硬件要求"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:146
|
||||
#: ../../getting_started/install/deploy.rst:149
|
||||
#: b3300ed57f0c4ed98816652ea4a5c3c8 d57e98b46fb14f649d6ac8df27edcfd8
|
||||
#: 59d9b64f54d34971a68e93e3101def06 a66ce354d8f143ce920303241cd8947e
|
||||
msgid "baichuan-7b"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:152
|
||||
#: ../../getting_started/install/deploy.rst:155
|
||||
#: c530662259ca4ec5b03a18e4b690e17a fa3af65ecca54daab961f55729bbc40e
|
||||
msgid "baichuan-13b"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:173
|
||||
#: ac684ccbea7d4f5380c43ad02a1b7231
|
||||
#: ../../getting_started/install/deploy.rst:179
|
||||
#: efd73637994a4b7c97ef3557e1f3161c
|
||||
msgid "please rename Baichuan path to \"baichuan2-13b\" or \"baichuan2-7b\""
|
||||
msgstr "将Baichuan模型目录修改为\"baichuan2-13b\" 或 \"baichuan2-7b\""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:179
|
||||
#: d2e283112ed1440b93510c331922b37c
|
||||
#: ../../getting_started/install/deploy.rst:185
|
||||
#: 435a3f0d0fe84b49a7305e2c0f51a5df
|
||||
msgid "ChatGLM"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:199
|
||||
#: ae30d065461e446e86b02d3903108a02
|
||||
msgid "please rename chatglm model path to \"chatglm2-6b\""
|
||||
msgstr "将chatglm模型目录修改为\"chatglm2-6b\""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:205
|
||||
#: 81a8983e89c4400abc20080c86865c02
|
||||
#: 165e23d3d40d4756b5a6a2580d015213
|
||||
msgid "please rename chatglm model path to \"chatglm2-6b\""
|
||||
msgstr "将 chatglm 模型目录修改为\"chatglm2-6b\""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:211
|
||||
#: b651ebb5e0424b8992bc8b49d2280bee
|
||||
msgid "Other LLM API"
|
||||
msgstr ""
|
||||
msgstr "其它模型 API"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:219
|
||||
#: 3df3d5e0e4f64813a7ca2adccd056c4a
|
||||
#: ../../getting_started/install/deploy.rst:225
|
||||
#: 4eabdc25f4a34676b3ece620c88d866f
|
||||
msgid "Now DB-GPT support LLM REST API TYPE:"
|
||||
msgstr "目前DB-GPT支持的大模型REST API类型:"
|
||||
msgstr "目前DB-GPT支持的大模型 REST API 类型:"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:224
|
||||
#: 4f7cb7a3f324410d8a34ba96d7c9536a
|
||||
#: ../../getting_started/install/deploy.rst:230
|
||||
#: d361963cc3404e5ca55a823f1f1f545c
|
||||
msgid "Azure"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:225
|
||||
#: ee6cbe48a8094637b6b8a049446042c6
|
||||
#: ../../getting_started/install/deploy.rst:231
|
||||
#: 3b0f17c74aaa4bbd9db935973fa1c36b
|
||||
msgid "Aliyun tongyi"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:226
|
||||
#: a3d9c061b0f440258be88cc3143b1887
|
||||
#: ../../getting_started/install/deploy.rst:232
|
||||
#: 7c4c457a499943b8804e31046551006d
|
||||
msgid "Baidu wenxin"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:227
|
||||
#: 176001fb6eaf448f9eca1abd256b3ef4
|
||||
#: ../../getting_started/install/deploy.rst:233
|
||||
#: ac1880a995184295acf07fff987d7c56
|
||||
msgid "Zhipu"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:229
|
||||
#: 17fecc140be2474ea1566d44641a27c0
|
||||
#: ../../getting_started/install/deploy.rst:235
|
||||
#: 6927500d7d3445b7b1981da1df4e1666
|
||||
msgid "Bard"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:284
|
||||
#: bc34832cec754984a40c4d01526f0f83
|
||||
#: ../../getting_started/install/deploy.rst:237
|
||||
#: 419d564de18c485780d9336b852735b6
|
||||
msgid "Configure LLM_MODEL and PROXY_API_URL and API_KEY in `.env` file"
|
||||
msgstr "在`.env`文件设置 LLM_MODEL、PROXY_API_URL和 API_KEY"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:290
|
||||
#: 71d5203682e24e2e896e4b9913471f78
|
||||
msgid "llama.cpp"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:286
|
||||
#: aa4b14e726dc447f92dec48f38a3d770
|
||||
#: ../../getting_started/install/deploy.rst:292
|
||||
#: 36a2b82f711a4c0f9491aca9c84d3c91
|
||||
msgid ""
|
||||
"DB-GPT already supports `llama.cpp "
|
||||
"<https://github.com/ggerganov/llama.cpp>`_ via `llama-cpp-python "
|
||||
"<https://github.com/abetlen/llama-cpp-python>`_ ."
|
||||
msgstr ""
|
||||
"DB-GPT 已经支持了 `llama.cpp <https://github.com/ggerganov/llama.cpp>`_ via "
|
||||
"`llama-cpp-python <https://github.com/abetlen/llama-cpp-python>`_ ."
|
||||
"DB-GPT 已经通过 `llama-cpp-python <https://github.com/abetlen/llama-cpp-"
|
||||
"python>`_ 支持了 `llama.cpp <https://github.com/ggerganov/llama.cpp>`_ 。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:288
|
||||
#: e97a868ad05b48ee8d25017457c1b7ee
|
||||
#: ../../getting_started/install/deploy.rst:294
|
||||
#: 439064115dca4ae08d8e60041f2ffe17
|
||||
msgid "**Preparing Model Files**"
|
||||
msgstr "**准备Model文件**"
|
||||
msgstr "**准备模型文件**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:290
|
||||
#: 8efc7824082545a4a98ebbd6b12fa00f
|
||||
#: ../../getting_started/install/deploy.rst:296
|
||||
#: 7291d6fa20b34942926e7765c01f25c9
|
||||
msgid ""
|
||||
"To use llama.cpp, you need to prepare a gguf format model file, and there"
|
||||
" are two common ways to obtain it, you can choose either:"
|
||||
msgstr "使用 llama.cpp，你需要准备 gguf 格式的文件，你可以通过以下两种方法获取"
|
||||
msgstr "为了使用 llama.cpp，你需要准备 gguf 格式的文件，你可以通过以下两种方法获取"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:292
|
||||
#: 5b9f3a35779c4f89a4b9051904c5f7d8
|
||||
#: ../../getting_started/install/deploy.rst:298
|
||||
#: 45752f3f5dd847469da0c5edddc530fa
|
||||
msgid "**1. Download a pre-converted model file.**"
|
||||
msgstr "**1.下载已转换的模型文件.**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:294
|
||||
#: e15f33a2e5fc4caaa9ad2e10cabf9d32
|
||||
#: ../../getting_started/install/deploy.rst:300
|
||||
#: c451db2157ff49b2b4992aed9907ddfa
|
||||
msgid ""
|
||||
"Suppose you want to use [Vicuna 13B v1.5](https://huggingface.co/lmsys"
|
||||
"/vicuna-13b-v1.5), you can download the file already converted from "
|
||||
"[TheBloke/vicuna-13B-v1.5-GGUF](https://huggingface.co/TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF), only one file is needed. Download it to the `models` "
|
||||
"Suppose you want to use `Vicuna 13B v1.5 <https://huggingface.co/lmsys"
|
||||
"/vicuna-13b-v1.5>`_ , you can download the file already converted from "
|
||||
"`TheBloke/vicuna-13B-v1.5-GGUF <https://huggingface.co/TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF>`_ , only one file is needed. Download it to the `models` "
|
||||
"directory and rename it to `ggml-model-q4_0.gguf`."
|
||||
msgstr ""
|
||||
"假设您想使用[Vicuna 13B v1.5](https://huggingface.co/lmsys/vicuna-"
|
||||
"13b-v1.5)您可以从[TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF](https://huggingface.co/TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF)下载已转换的文件，只需要一个文件。将其下载到models目录并将其重命名为 `ggml-"
|
||||
"假设您想使用 `Vicuna 13B v1.5 <https://huggingface.co/lmsys/vicuna-"
|
||||
"13b-v1.5>`_ 您可以从 `TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF <https://huggingface.co/TheBloke/vicuna-"
|
||||
"13B-v1.5-GGUF>`_ 下载已转换的文件，只需要一个文件。将其下载到models目录并将其重命名为 `ggml-"
|
||||
"model-q4_0.gguf`。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:300
|
||||
#: 81ab603cd9b94cabb48add9541590b8b
|
||||
#: ../../getting_started/install/deploy.rst:306
|
||||
#: f5b92b51622b43d398b3dc13a5892c29
|
||||
msgid "**2. Convert It Yourself**"
|
||||
msgstr "**2. 自行转换**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:302
|
||||
#: f9b46aa70a0842d6acc7e837c8b27778
|
||||
#: ../../getting_started/install/deploy.rst:308
|
||||
#: 8838ae6dcecf44ecad3fd963980c8eb3
|
||||
msgid ""
|
||||
"You can convert the model file yourself according to the instructions in "
|
||||
"[llama.cpp#prepare-data--run](https://github.com/ggerganov/llama.cpp"
|
||||
"#prepare-data--run), and put the converted file in the models directory "
|
||||
"and rename it to `ggml-model-q4_0.gguf`."
|
||||
"`llama.cpp#prepare-data--run <https://github.com/ggerganov/llama.cpp"
|
||||
"#prepare-data--run>`_ , and put the converted file in the models "
|
||||
"directory and rename it to `ggml-model-q4_0.gguf`."
|
||||
msgstr ""
|
||||
"您可以根据[llama.cpp#prepare-data--run](https://github.com/ggerganov/llama.cpp"
|
||||
"#prepare-data--run)中的说明自行转换模型文件，并把转换后的文件放在models目录中，并重命名为`ggml-"
|
||||
"您可以根据 `llama.cpp#prepare-data--run <https://github.com/ggerganov/llama.cpp"
|
||||
"#prepare-data--run>`_ 中的说明自行转换模型文件，并把转换后的文件放在models目录中，并重命名为`ggml-"
|
||||
"model-q4_0.gguf`。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:304
|
||||
#: 5278c05568dd4ad688bcc181e45caa18
|
||||
#: ../../getting_started/install/deploy.rst:310
|
||||
#: 3fe28d6e5eaa4bdf9c5c44a914c3577c
|
||||
msgid "**Installing Dependencies**"
|
||||
msgstr "**安装依赖**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:306
|
||||
#: bd67bf651a9a4096a37e73851b5fad98
|
||||
#: ../../getting_started/install/deploy.rst:312
|
||||
#: bdc10d2e88cc4c3f84a8c4a8dc2037a9
|
||||
msgid ""
|
||||
"llama.cpp is an optional dependency in DB-GPT, and you can manually "
|
||||
"install it using the following command:"
|
||||
msgstr "llama.cpp在DB-GPT中是可选安装项, 你可以通过以下命令进行安装"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:313
|
||||
#: 71ed6ae4d0aa4d3ca27ebfffc42a5095
|
||||
#: ../../getting_started/install/deploy.rst:319
|
||||
#: 9c136493448b43b5b27f66af74ff721e
|
||||
msgid "**3.Modifying the Configuration File**"
|
||||
msgstr "**3.修改配置文件**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:315
|
||||
#: a0a2fdb799524ef4938df2c306536e2a
|
||||
#: ../../getting_started/install/deploy.rst:321
|
||||
#: c835a7dee1dd409fb861e7b886c6dc5b
|
||||
msgid "Next, you can directly modify your `.env` file to enable llama.cpp."
|
||||
msgstr "修改`.env`文件使用llama.cpp"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:322
|
||||
#: ../../getting_started/install/deploy.rst:390
|
||||
#: 03694cbb7dfa432cb32116925357f008
|
||||
#: ../../getting_started/install/deploy.rst:328
|
||||
#: ../../getting_started/install/deploy.rst:396
|
||||
#: 296e6d08409544918fee0c31b1bf195c a81e5d882faf4722b0e10d53f635f53c
|
||||
msgid ""
|
||||
"Then you can run it according to `Run <https://db-"
|
||||
"gpt.readthedocs.io/en/latest/getting_started/install/deploy/deploy.html#run>`_"
|
||||
msgstr ""
|
||||
"然后你可以根据[运行](https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-"
|
||||
"cn/zh_CN/latest/getting_started/install/deploy/deploy.html#run)来运行"
|
||||
"然后你可以根据 `运行 <https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-"
|
||||
"cn/zh_CN/latest/getting_started/install/deploy/deploy.html#run>`_ 来运行。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:325
|
||||
#: c9bbc15ea2fc4562bdf87a4e099600a0
|
||||
#: ../../getting_started/install/deploy.rst:331
|
||||
#: 0f7f487ee11a4e01a95f7c504f0469ba
|
||||
msgid "**More Configurations**"
|
||||
msgstr "**更多配置文件**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:327
|
||||
#: 9beff887d8554d1dac169de93625c1b2
|
||||
#: ../../getting_started/install/deploy.rst:333
|
||||
#: b0f9964497f64fb5b3740099232cd72b
|
||||
msgid ""
|
||||
"In DB-GPT, the model configuration can be done through `{model "
|
||||
"name}_{config key}`."
|
||||
msgstr "在DB-GPT中，模型配置可以通过`{模型名称}_{配置名}` 来配置。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:329
|
||||
#: 6cad64bcc9f141de85479203b21f7f9e
|
||||
#: ../../getting_started/install/deploy.rst:335
|
||||
#: 7c225de4fe9d4dd3a3c2b2a33802e656
|
||||
msgid "More Configurations"
|
||||
msgstr "**更多配置文件**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:333
|
||||
#: de97b0d5b59c4bfabbd47b7e1766cc70
|
||||
#: ../../getting_started/install/deploy.rst:339
|
||||
#: 5cc1671910314796a9ce0b5107d3c9fe
|
||||
msgid "Environment Variable Key"
|
||||
msgstr "环境变量Key"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:334
|
||||
#: 76afa3655f644576b54b4422bad3fcad
|
||||
#: ../../getting_started/install/deploy.rst:340
|
||||
#: 4359ed4e11bb47ad89a605cbf9016cd5
|
||||
msgid "Default"
|
||||
msgstr "默认值"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:335
|
||||
#: 0dcabfeeb4874e08ba53d4b4aa6a1d73
|
||||
#: ../../getting_started/install/deploy.rst:341
|
||||
#: 5cf0efc6d1014665bb9dbdae96bf2726
|
||||
msgid "Description"
|
||||
msgstr "描述"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:336
|
||||
#: c1670aec27cf4c068d825456817d7667
|
||||
#: ../../getting_started/install/deploy.rst:342
|
||||
#: e7c291f80a9a40fa90d642901eca02c6
|
||||
msgid "llama_cpp_prompt_template"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:337
|
||||
#: ../../getting_started/install/deploy.rst:340
|
||||
#: ../../getting_started/install/deploy.rst:343
|
||||
#: ../../getting_started/install/deploy.rst:346
|
||||
#: ../../getting_started/install/deploy.rst:352
|
||||
#: ../../getting_started/install/deploy.rst:358
|
||||
#: 49c8f875066f49c6aac195805f88e4a9
|
||||
#: ../../getting_started/install/deploy.rst:364
|
||||
#: 07dc7fc4e51e4d9faf8e5221bcf03ee0 549f3c57a2e9427880e457e653ce1182
|
||||
#: 7ad961957f7b49d08e4aff347749b78d c1eab368175c4fa88fe0b471919523b2
|
||||
#: e2e0bf9903484972b6d20e6837010029
|
||||
msgid "None"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:338
|
||||
#: bb31e19533e5434b832d5975bf579dc3
|
||||
#: ../../getting_started/install/deploy.rst:344
|
||||
#: 6b5044a2009f432c92fcd65db42506d8
|
||||
msgid ""
|
||||
"Prompt template name, now support: zero_shot, vicuna_v1.1,alpaca,llama-2"
|
||||
",baichuan-chat,internlm-chat, If None, the prompt template is "
|
||||
@ -438,183 +449,183 @@ msgstr ""
|
||||
"Prompt template 现在可以支持`zero_shot, vicuna_v1.1,alpaca,llama-2,baichuan-"
|
||||
"chat,internlm-chat`, 如果是None, 可以根据模型路径来自动获取模型 Prompt template"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:339
|
||||
#: 38ed23e19e2a47cf8fbacb035cfe1292
|
||||
#: ../../getting_started/install/deploy.rst:345
|
||||
#: e01c860441ad43b88c0a8d012f97d2d8
|
||||
msgid "llama_cpp_model_path"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:341
|
||||
#: eb069fdfdfeb4b17b723ee6733ba50c2
|
||||
#: ../../getting_started/install/deploy.rst:347
|
||||
#: 1cb68d772e454812a1a0c6de4950b8ce
|
||||
msgid "Model path"
|
||||
msgstr "模型路径"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:342
|
||||
#: 348d3ff3e4f44ceb99aadead11f5cca5
|
||||
#: ../../getting_started/install/deploy.rst:348
|
||||
#: 6dac03820edb4fbd8a0856405e84c5bc
|
||||
msgid "llama_cpp_n_gpu_layers"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:343
|
||||
#: 007deecf593b4553b6ca8df3e9240a28
|
||||
#: ../../getting_started/install/deploy.rst:349
|
||||
#: 8cd5607b7941427f9a342ca7a00e5778
|
||||
msgid "1000000000"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:344
|
||||
#: 4da435c417f0482581895bf4d052a6a1
|
||||
#: ../../getting_started/install/deploy.rst:350
|
||||
#: 61c9297656da434aa7ac2b49cf61ea9d
|
||||
msgid ""
|
||||
"Number of layers to offload to the GPU, Set this to 1000000000 to offload"
|
||||
" all layers to the GPU. If your GPU VRAM is not enough, you can set a low"
|
||||
" number, eg: 10"
|
||||
msgstr "要将多少网络层转移到GPU上，将其设置为1000000000以将所有层转移到GPU上。如果您的 GPU 内存不足，可以设置较低的数字，例如：10。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:345
|
||||
#: 4f186505ea81467590cf817d116e6879
|
||||
#: ../../getting_started/install/deploy.rst:351
|
||||
#: 8c2d2182557a483aa2fda590c24faaf3
|
||||
msgid "llama_cpp_n_threads"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:347
|
||||
#: b94dc2fb2cd14a4a8f38ba95b05fbb5b
|
||||
#: ../../getting_started/install/deploy.rst:353
|
||||
#: cc442f61ffc442ecbd98c1e7f5598e1a
|
||||
msgid ""
|
||||
"Number of threads to use. If None, the number of threads is automatically"
|
||||
" determined"
|
||||
msgstr "要使用的线程数量。如果为None，则线程数量将自动确定。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:348
|
||||
#: 867f2357430440eba4e749a8a39bff18
|
||||
#: ../../getting_started/install/deploy.rst:354
|
||||
#: 8d5e917d86f048348106e6923638a0c2
|
||||
msgid "llama_cpp_n_batch"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:349
|
||||
#: 78516f7b23264147bae11e13426097eb
|
||||
#: ../../getting_started/install/deploy.rst:355
|
||||
#: ee2719a0a8cd4a77846cffd8e675638f
|
||||
msgid "512"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:350
|
||||
#: c2ec97ffef3e4a70afef6634e78801a2
|
||||
#: ../../getting_started/install/deploy.rst:356
|
||||
#: 845b354315384762a611ad2daa539d57
|
||||
msgid "Maximum number of prompt tokens to batch together when calling llama_eval"
|
||||
msgstr "在调用llama_eval时，批处理在一起的prompt tokens的最大数量"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:351
|
||||
#: 29ad3c5ab7134da49d6fca3b42d734d6
|
||||
#: ../../getting_started/install/deploy.rst:357
|
||||
#: a95e788bfa5f46f3bcd6356dfd9f87eb
|
||||
msgid "llama_cpp_n_gqa"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:353
|
||||
#: e5bc322b0c824465b1adbd162838a3b7
|
||||
#: ../../getting_started/install/deploy.rst:359
|
||||
#: 23ad9b5f34b5440bb90b2b21bab25763
|
||||
msgid "Grouped-query attention. Must be 8 for llama-2 70b."
|
||||
msgstr "对于 llama-2 70B 模型，Grouped-query attention 必须为8。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:354
|
||||
#: 43e7d412238a4f0ba8227938d9fa4172
|
||||
#: ../../getting_started/install/deploy.rst:360
|
||||
#: 9ce25b7966fc40ec8be47ecfaf5f9994
|
||||
msgid "llama_cpp_rms_norm_eps"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:355
|
||||
#: 6328db04645b4b089593291e2ca13f79
|
||||
#: ../../getting_started/install/deploy.rst:361
|
||||
#: 58365f0d36af447ba976213646018431
|
||||
msgid "5e-06"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:356
|
||||
#: 6c8bf631cece42fa86954f5cf2d75503
|
||||
#: ../../getting_started/install/deploy.rst:362
|
||||
#: d00b742a759140b795ba5949f1ce9a36
|
||||
msgid "5e-6 is a good value for llama-2 models."
|
||||
msgstr "对于llama-2模型来说，5e-6是一个不错的值。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:357
|
||||
#: 22ebee39fe8b4cc18378447cac67e631
|
||||
#: ../../getting_started/install/deploy.rst:363
|
||||
#: b9972e9b19354f55a5e6d9c50513a620
|
||||
msgid "llama_cpp_cache_capacity"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:359
|
||||
#: 4dc98bede90f4237829f65513e4adf61
|
||||
#: ../../getting_started/install/deploy.rst:365
|
||||
#: 3c98c5396dd74db8b6d70fc50fa0754f
|
||||
msgid "Maximum cache capacity. Examples: 2000MiB, 2GiB"
|
||||
msgstr "模型缓存最大值. 例如: 2000MiB, 2GiB"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:360
|
||||
#: bead617b5af943dab0bc1209823d3c22
|
||||
#: ../../getting_started/install/deploy.rst:366
|
||||
#: 4277e155992c4442b69d665d6269bed6
|
||||
msgid "llama_cpp_prefer_cpu"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:361
|
||||
#: 8efb44f1cfc54b1a8b3ca8ea138113ee
|
||||
#: ../../getting_started/install/deploy.rst:367
|
||||
#: 049169c1210a4ecabb25702ed813ea0a
|
||||
msgid "False"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:362
|
||||
#: b854e6a44c8348ceb72ac382b73ceec5
|
||||
#: ../../getting_started/install/deploy.rst:368
|
||||
#: 60a39e93e7874491a93893de78b7d37e
|
||||
msgid ""
|
||||
"If a GPU is available, it will be preferred by default, unless "
|
||||
"prefer_cpu=False is configured."
|
||||
msgstr "如果有可用的GPU，默认情况下会优先使用GPU，除非配置了 prefer_cpu=False。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:365
|
||||
#: 41b0440eb0074e5fa8c87dd404efe0ce
|
||||
#: ../../getting_started/install/deploy.rst:371
|
||||
#: 7c86780fbf634de8873afd439389cf89
|
||||
msgid "vllm"
|
||||
msgstr ""
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:367
|
||||
#: 6fc445675bf74b2ea2fe0c7f2a64db69
|
||||
#: ../../getting_started/install/deploy.rst:373
|
||||
#: e2827892e43d420c85b8b83c4855d197
|
||||
msgid "vLLM is a fast and easy-to-use library for LLM inference and serving."
|
||||
msgstr "\"vLLM 是一个快速且易于使用的 LLM 推理和服务的库。"
|
||||
msgstr "vLLM 是一个快速且易于使用的 LLM 推理和服务的库。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:369
|
||||
#: aab5f913d29445c4b345180b8793ebc7
|
||||
#: ../../getting_started/install/deploy.rst:375
|
||||
#: 81bbfa3876a74244acc82d295803fdd4
|
||||
msgid "**Running vLLM**"
|
||||
msgstr "**运行vLLM**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:371
|
||||
#: a9560d697821404cb0abdabf9f479645
|
||||
#: ../../getting_started/install/deploy.rst:377
|
||||
#: 75bc518b444c417ba4d9c15246549327
|
||||
msgid "**1.Installing Dependencies**"
|
||||
msgstr "**1.安装依赖**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:373
|
||||
#: b008224d4f314e5c988335262c95a42e
|
||||
#: ../../getting_started/install/deploy.rst:379
|
||||
#: 725c620b0a5045c1a64a3b2a2e9b48f3
|
||||
msgid ""
|
||||
"vLLM is an optional dependency in DB-GPT, and you can manually install it"
|
||||
" using the following command:"
|
||||
msgstr "vLLM 在 DB-GPT 是一个可选依赖, 你可以使用下面的命令手动安装它："
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:379
|
||||
#: 7dc0c8e996124177935a4e0d9ef19837
|
||||
#: ../../getting_started/install/deploy.rst:385
|
||||
#: 6f4b540107764f3592cc07cf170e4911
|
||||
msgid "**2.Modifying the Configuration File**"
|
||||
msgstr "**2.修改配置文件**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:381
|
||||
#: 49667576b44c46bf87d5bf4d207dd63a
|
||||
#: ../../getting_started/install/deploy.rst:387
|
||||
#: b8576a1572674c4890e09b73e02cf0e8
|
||||
msgid "Next, you can directly modify your .env file to enable vllm."
|
||||
msgstr "你可以直接修改你的 `.env` 文件"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:388
|
||||
#: f16a78f0ee6545babab0d66f12654c0a
|
||||
#: ../../getting_started/install/deploy.rst:394
|
||||
#: b006745f3aee4651aaa0cf79081b5d7f
|
||||
msgid ""
|
||||
"You can view the models supported by vLLM `here "
|
||||
"<https://vllm.readthedocs.io/en/latest/models/supported_models.html"
|
||||
"#supported-models>`_"
|
||||
msgstr ""
|
||||
"你可以在 "
|
||||
"[这里](https://vllm.readthedocs.io/en/latest/models/supported_models.html"
|
||||
"#supported-models) 查看 vLLM 支持的模型。"
|
||||
"你可以在 `这里 "
|
||||
"<https://vllm.readthedocs.io/en/latest/models/supported_models.html"
|
||||
"#supported-models>`_ 查看 vLLM 支持的模型。"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:397
|
||||
#: f7ae366723e9494d8177eeb963ba0ed9
|
||||
#: ../../getting_started/install/deploy.rst:403
|
||||
#: bc8057ee75e14737bf8fca3ceb555dac
|
||||
msgid "3.Prepare sql example(Optional)"
|
||||
msgstr "3.准备 sql example(可选)"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:398
|
||||
#: d302aac8ddb346598fc9d73e0f6c2cbc
|
||||
#: ../../getting_started/install/deploy.rst:404
|
||||
#: 9b0b9112237c4b3aaa1dd5d704ea32e6
|
||||
msgid "**(Optional) load examples into SQLite**"
|
||||
msgstr "**(可选) load examples into SQLite**"
|
||||
msgstr "**(可选) 加载样例数据到 SQLite 数据库中**"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:405
|
||||
#: ae2a315e40854f27a074f2c0f2506014
|
||||
#: ../../getting_started/install/deploy.rst:411
|
||||
#: 0815e13b96264ffcba1526c82ba2e7c8
|
||||
msgid "On windows platform:"
|
||||
msgstr ""
|
||||
msgstr "在 Windows 平台："
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:412
|
||||
#: 36b4b7a813844af3a4ec8062b39059a3
|
||||
#: ../../getting_started/install/deploy.rst:418
|
||||
#: 577a4167ecac4fa88586961f225f0487
|
||||
msgid "4.Run db-gpt server"
|
||||
msgstr "4.运行db-gpt server"
|
||||
|
||||
#: ../../getting_started/install/deploy.rst:418
|
||||
#: e56f15e563484027b7efb2147c9b71a7
|
||||
#: ../../getting_started/install/deploy.rst:424
|
||||
#: a9f96b064b674f80824257b4b0a18e2a
|
||||
msgid "**Open http://localhost:5000 with your browser to see the product.**"
|
||||
msgstr "打开浏览器访问http://localhost:5000"
|
||||
|
||||
@ -633,3 +644,9 @@ msgstr "打开浏览器访问http://localhost:5000"
|
||||
#~ " LLM model."
|
||||
#~ msgstr ""
|
||||
|
||||
#~ msgid "百川"
|
||||
#~ msgstr ""
|
||||
|
||||
#~ msgid "百川 硬件要求"
|
||||
#~ msgstr ""
|
||||
|
||||
|
||||
@ -8,7 +8,7 @@ msgid ""
|
||||
msgstr ""
|
||||
"Project-Id-Version: DB-GPT 0.3.0\n"
|
||||
"Report-Msgid-Bugs-To: \n"
|
||||
"POT-Creation-Date: 2023-10-25 23:56+0800\n"
|
||||
"POT-Creation-Date: 2023-11-06 19:00+0800\n"
|
||||
"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
|
||||
"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
|
||||
"Language: zh_CN\n"
|
||||
@ -19,31 +19,27 @@ msgstr ""
|
||||
"Content-Transfer-Encoding: 8bit\n"
|
||||
"Generated-By: Babel 2.12.1\n"
|
||||
|
||||
#: ../../index.rst:34 ../../index.rst:45 71dd3acc56354242aad5a920c2805328
|
||||
#: ../../index.rst:34 ../../index.rst:45 8bc3a47457a34995816985436034e233
|
||||
msgid "Getting Started"
|
||||
msgstr "开始"
|
||||
|
||||
#: ../../index.rst:60 ../../index.rst:81 0f2fc16a44b043019556f5f3e0d0e2c0
|
||||
#: ../../index.rst:60 ../../index.rst:81 1a4e8a5dc7754967a0af9fb3d2e53017
|
||||
msgid "Modules"
|
||||
msgstr "模块"
|
||||
|
||||
#: ../../index.rst:95 ../../index.rst:111 2624521b920a4b3b9eac3fec76635ab8
|
||||
msgid "Use Cases"
|
||||
msgstr "示例"
|
||||
|
||||
#: ../../index.rst:125 ../../index.rst:128 accec2bb9c5149f184a87e03955d6b22
|
||||
#: ../../index.rst:96 ../../index.rst:99 c815772ae8514f0c9b26911b0dd73f54
|
||||
msgid "Reference"
|
||||
msgstr "参考"
|
||||
|
||||
#: ../../index.rst:138 ../../index.rst:144 26278dabd4944d1a9f14330e83935162
|
||||
#: ../../index.rst:109 ../../index.rst:115 dabe4c3409df489f84e4ec588f2b34a5
|
||||
msgid "Resources"
|
||||
msgstr "资源"
|
||||
|
||||
#: ../../index.rst:7 9277d505dda74ae0862cd09d05cf5e63
|
||||
#: ../../index.rst:7 7626b01b253546ac83ca0cf130dfa091
|
||||
msgid "Welcome to DB-GPT!"
|
||||
msgstr "欢迎来到DB-GPT中文文档"
|
||||
|
||||
#: ../../index.rst:8 9fa76a01965746978a00ac411fca13a8
|
||||
#: ../../index.rst:8 6037e5e0d7f7428ba92315a91ccfd53f
|
||||
msgid ""
|
||||
"As large models are released and iterated upon, they are becoming "
|
||||
"increasingly intelligent. However, in the process of using large models, "
|
||||
@ -61,7 +57,7 @@ msgstr ""
|
||||
"，我们启动了DB-"
|
||||
"GPT项目，为所有基于数据库的场景构建一个完整的私有大模型解决方案。该方案“”支持本地部署，既可应用于“独立私有环境”，又可根据业务模块进行“独立部署”和“隔离”，确保“大模型”的能力绝对私有、安全、可控。"
|
||||
|
||||
#: ../../index.rst:10 b12b6f91c5664f61aa9e4d7cd500b922
|
||||
#: ../../index.rst:10 ab2a181d517047e6992171786c83f8e3
|
||||
msgid ""
|
||||
"**DB-GPT** is an experimental open-source project that uses localized GPT"
|
||||
" large models to interact with your data and environment. With this "
|
||||
@ -71,39 +67,39 @@ msgstr ""
|
||||
"DB-GPT 是一个开源的以数据库为基础的GPT实验项目，使用本地化的GPT大模型与您的数据和环境进行交互，无数据泄露风险100% 私密，100%"
|
||||
" 安全。"
|
||||
|
||||
#: ../../index.rst:12 7032e17191394f7090141927644fb512
|
||||
#: ../../index.rst:12 9cfb7515430d49af8a1ca47f60264a58
|
||||
msgid "**Features**"
|
||||
msgstr "特性"
|
||||
|
||||
#: ../../index.rst:13 5a7a8e5eace34d5f9a4f779bf5122928
|
||||
#: ../../index.rst:13 2a1f84e455c84d9ca66c65f92e5b0d78
|
||||
msgid ""
|
||||
"Currently, we have released multiple key features, which are listed below"
|
||||
" to demonstrate our current capabilities:"
|
||||
msgstr "目前我们已经发布了多种关键的特性，这里一一列举展示一下当前发布的能力。"
|
||||
|
||||
#: ../../index.rst:15 5d0f67aacb8b4bc893a306ccbd6a3778
|
||||
#: ../../index.rst:15 43de30ce92da4c3cbe43ae4e4c9f1869
|
||||
msgid "SQL language capabilities - SQL generation - SQL diagnosis"
|
||||
msgstr "SQL语言能力 - SQL生成 - SQL诊断"
|
||||
|
||||
#: ../../index.rst:19 556eaf756fec431ca5c453208292ab4f
|
||||
#: ../../index.rst:19 edfeef5284e7426a9e551e782bc5702c
|
||||
msgid ""
|
||||
"Private domain Q&A and data processing - Database knowledge Q&A - Data "
|
||||
"processing"
|
||||
msgstr "私有领域问答与数据处理 - 数据库知识问答 - 数据处理"
|
||||
|
||||
#: ../../index.rst:23 5148ad898ec041858eddbeaa646d3f1b
|
||||
#: ../../index.rst:23 7a42f17049b943f88dd8f17baa440144
|
||||
msgid ""
|
||||
"Plugins - Support custom plugin execution tasks and natively support the "
|
||||
"Auto-GPT plugin, such as:"
|
||||
msgstr "插件模型 - 支持自定义插件执行任务，并原生支持Auto-GPT插件，例如:* SQL自动执行，获取查询结果 * 自动爬取学习知识"
|
||||
|
||||
#: ../../index.rst:26 34c7ff33bc1c401480603a5197ecb1c4
|
||||
#: ../../index.rst:26 8b48d7b60bbc439da50a624c4048e6f6
|
||||
msgid ""
|
||||
"Unified vector storage/indexing of knowledge base - Support for "
|
||||
"unstructured data such as PDF, Markdown, CSV, and WebURL"
|
||||
msgstr "知识库统一向量存储/索引 - 非结构化数据支持包括PDF、MarkDown、CSV、WebURL"
|
||||
|
||||
#: ../../index.rst:29 9d7095e5b08249e6bb5c724929537e6c
|
||||
#: ../../index.rst:29 97df482893924bd18e9a101922e7c374
|
||||
#, fuzzy
|
||||
msgid ""
|
||||
"Multi LLMs Support - Supports multiple large language models, currently "
|
||||
@ -111,63 +107,63 @@ msgid ""
|
||||
"codet5p"
|
||||
msgstr "多模型支持 - 支持多种大语言模型, 当前已支持Vicuna(7b,13b), ChatGLM-6b(int4, int8)"
|
||||
|
||||
#: ../../index.rst:35 caa368eab40e4efb953865740a3c9018
|
||||
#: ../../index.rst:35 1ef26ead30ed4b7fb966c8a17307cdc5
|
||||
msgid ""
|
||||
"How to get started using DB-GPT to interact with your data and "
|
||||
"environment."
|
||||
msgstr "开始使用DB-GPT与您的数据环境进行交互。"
|
||||
|
||||
#: ../../index.rst:36 34cecad11f8b4a3e96bfa0a31814e3d2
|
||||
#: ../../index.rst:36 3b44ab3576944bf6aa221f35bc051f4e
|
||||
#, fuzzy
|
||||
msgid "`Quickstart Guide <./getting_started/getting_started.html>`_"
|
||||
msgstr "`使用指南 <./getting_started/getting_started.html>`_"
|
||||
|
||||
#: ../../index.rst:38 892598cdc16d45c68383033b08b7233f
|
||||
#: ../../index.rst:38 430cb239cdce42a0b62db46aba3f3bdb
|
||||
msgid "Concepts and terminology"
|
||||
msgstr "相关概念"
|
||||
|
||||
#: ../../index.rst:40 887cc43a3a134aba96eb7ca11e5ca86f
|
||||
#: ../../index.rst:40 ded4d9f80066498e90ba6214520013f7
|
||||
#, fuzzy
|
||||
msgid "`Concepts and Terminology <./getting_started/concepts.html>`_"
|
||||
msgstr "`相关概念 <./getting_started/concepts.html>`_"
|
||||
|
||||
#: ../../index.rst:42 133e25c7dce046b1ab262489ecb60b4a
|
||||
#: ../../index.rst:42 cd662e53621e474d901146813c750044
|
||||
msgid "Coming soon..."
|
||||
msgstr ""
|
||||
|
||||
#: ../../index.rst:44 a9e0812d32714a6f81ed75aa70f0c20e
|
||||
#: ../../index.rst:44 15edba57f1de44af8aff76735a2593de
|
||||
msgid "`Tutorials <.getting_started/tutorials.html>`_"
|
||||
msgstr "`教程 <.getting_started/tutorials.html>`_"
|
||||
|
||||
#: ../../index.rst:62 4fccfd3082174f58926a9811f39e4d96
|
||||
#: ../../index.rst:62 779454b29d8e4e6eb21497025922d1b8
|
||||
msgid ""
|
||||
"These modules are the core abstractions with which we can interact with "
|
||||
"data and environment smoothly."
|
||||
msgstr "这些模块是我们可以与数据和环境顺利地进行交互的核心组成。"
|
||||
|
||||
#: ../../index.rst:63 ecac40207ada454e9a68356f575dbca9
|
||||
#: ../../index.rst:63 bcd0e8c88c7b4807a91dd442416bec19
|
||||
msgid ""
|
||||
"It's very important for DB-GPT, DB-GPT also provide standard, extendable "
|
||||
"interfaces."
|
||||
msgstr "DB-GPT还提供了标准的、可扩展的接口。"
|
||||
|
||||
#: ../../index.rst:65 9d852bed582449e89dc13312ddf29eed
|
||||
#: ../../index.rst:65 1e785dc6925045e8ba106cf4a3b17cac
|
||||
msgid ""
|
||||
"The docs for each module contain quickstart examples, how to guides, "
|
||||
"reference docs, and conceptual guides."
|
||||
msgstr "每个模块的文档都包含快速入门的例子、操作指南、参考文档和相关概念等内容。"
|
||||
|
||||
#: ../../index.rst:67 3167446539de449aba2de694fe901bcf
|
||||
#: ../../index.rst:67 9c9fddd14bfd40339889f5d1f0b04163
|
||||
msgid "The modules are as follows"
|
||||
msgstr "组成模块如下："
|
||||
|
||||
#: ../../index.rst:69 442683a5f154429da87f452e49bcbb5c
|
||||
#: ../../index.rst:69 4a19083cadd04b8e8b649a622e0ceccd
|
||||
msgid ""
|
||||
"`LLMs <./modules/llms.html>`_: Supported multi models management and "
|
||||
"integrations."
|
||||
msgstr "`LLMs <./modules/llms.html>`_:基于FastChat提供大模型的运行环境。支持多模型管理和集成。 "
|
||||
|
||||
#: ../../index.rst:71 3cf320b6199e4ce78235bce8b1be60a2
|
||||
#: ../../index.rst:71 436a139225574aa5b066a1835d38238d
|
||||
msgid ""
|
||||
"`Prompts <./modules/prompts.html>`_: Prompt management, optimization, and"
|
||||
" serialization for multi database."
|
||||
@ -175,86 +171,41 @@ msgstr ""
|
||||
"`Prompt自动生成与优化 <./modules/prompts.html>`_: 自动化生成高质量的Prompt "
|
||||
"，并进行优化，提高系统的响应效率"
|
||||
|
||||
#: ../../index.rst:73 4f29ed67ea2a4a3ca824ac8b8b33cae6
|
||||
#: ../../index.rst:73 6c53edfb2e494c5fba6efb5ade48c310
|
||||
msgid "`Plugins <./modules/plugins.html>`_: Plugins management, scheduler."
|
||||
msgstr "`Agent与插件： <./modules/plugins.html>`_:提供Agent和插件机制，使得用户可以自定义并增强系统的行为。"
|
||||
|
||||
#: ../../index.rst:75 d651f9d93bb54b898ef97407501cc6cf
|
||||
#: ../../index.rst:75 6328760e8faf4e8296f3e1edd486316c
|
||||
#, fuzzy
|
||||
msgid ""
|
||||
"`Knowledge <./modules/knowledge.html>`_: Knowledge management, embedding,"
|
||||
" and search."
|
||||
msgstr "`知识库能力： <./modules/knowledge.html>`_: 支持私域知识库问答能力, "
|
||||
|
||||
#: ../../index.rst:77 fd6dd2adcd844baa84602b650d89e507
|
||||
#: ../../index.rst:77 da272ccf56e3498d92009ac7101b0c45
|
||||
msgid ""
|
||||
"`Connections <./modules/connections.html>`_: Supported multi databases "
|
||||
"connection. management connections and interact with this."
|
||||
msgstr "`连接模块 <./modules/connections.html>`_: 用于连接不同的模块和数据源，实现数据的流转和交互 "
|
||||
|
||||
#: ../../index.rst:79 7e388a9d8c044169923508ccdeb2d9a5
|
||||
#: ../../index.rst:79 1a0551f62d9d418a9dec267fbcb49af0
|
||||
#, fuzzy
|
||||
msgid "`Vector <./modules/vector.html>`_: Supported multi vector database."
|
||||
msgstr "`LLMs <./modules/llms.html>`_:基于FastChat提供大模型的运行环境。支持多模型管理和集成。 "
|
||||
|
||||
#: ../../index.rst:97 9d37bf061a784d5ca92d1de33b0834f3
|
||||
msgid "Best Practices and built-in implementations for common DB-GPT use cases:"
|
||||
msgstr "DB-GPT用例的最佳实践和内置方法:"
|
||||
|
||||
#: ../../index.rst:99 ba264bbe31d24c7887a30cbd5442e157
|
||||
msgid ""
|
||||
"`Sql generation and diagnosis "
|
||||
"<./use_cases/sql_generation_and_diagnosis.html>`_: SQL generation and "
|
||||
"diagnosis."
|
||||
msgstr "`Sql生成和诊断 <./use_cases/sql_generation_and_diagnosis.html>`_: Sql生成和诊断。"
|
||||
|
||||
#: ../../index.rst:101 1166f6aeba064a3990d4b0caa87db274
|
||||
msgid ""
|
||||
"`knownledge Based QA <./use_cases/knownledge_based_qa.html>`_: A "
|
||||
"important scene for user to chat with database documents, codes, bugs and"
|
||||
" schemas."
|
||||
msgstr "`知识库问答 <./use_cases/knownledge_based_qa.html>`_: 用户与数据库文档、代码和bug聊天的重要场景\""
|
||||
|
||||
#: ../../index.rst:103 b32610ada3a0440e9b029b8dffe7c79e
|
||||
msgid ""
|
||||
"`Chatbots <./use_cases/chatbots.html>`_: Language model love to chat, use"
|
||||
" multi models to chat."
|
||||
msgstr "`聊天机器人 <./use_cases/chatbots.html>`_: 使用多模型进行对话"
|
||||
|
||||
#: ../../index.rst:105 d9348b8112df4839ab14a74a42b63715
|
||||
msgid ""
|
||||
"`Querying Database Data <./use_cases/query_database_data.html>`_: Query "
|
||||
"and Analysis data from databases and give charts."
|
||||
msgstr "`查询数据库数据 <./use_cases/query_database_data.html>`_:从数据库中查询和分析数据并给出图表。"
|
||||
|
||||
#: ../../index.rst:107 ef978dc1f4254e5eb4ca487c31c03f7c
|
||||
msgid ""
|
||||
"`Interacting with apis <./use_cases/interacting_with_api.html>`_: "
|
||||
"Interact with apis, such as create a table, deploy a database cluster, "
|
||||
"create a database and so on."
|
||||
msgstr ""
|
||||
"`API交互 <./use_cases/interacting_with_api.html>`_: "
|
||||
"与API交互，例如创建表、部署数据库集群、创建数据库等。"
|
||||
|
||||
#: ../../index.rst:109 49a549d7d38f493ba48e162785b4ac5d
|
||||
msgid ""
|
||||
"`Tool use with plugins <./use_cases/tool_use_with_plugin>`_: According to"
|
||||
" Plugin use tools to manage databases autonomoly."
|
||||
msgstr "`插件工具 <./use_cases/tool_use_with_plugin>`_: 根据插件使用工具自主管理数据库。"
|
||||
|
||||
#: ../../index.rst:126 db86500484fa4f14918b0ad4e5a7326d
|
||||
#: ../../index.rst:97 9aceee0dbe1e4f7da499ac6aab23aea2
|
||||
msgid ""
|
||||
"Full documentation on all methods, classes, installation methods, and "
|
||||
"integration setups for DB-GPT."
|
||||
msgstr "关于DB-GPT的所有方法、类、安装方法和集成设置的完整文档。"
|
||||
|
||||
#: ../../index.rst:140 a44abbb370a841658801bb2729fa62c9
|
||||
#: ../../index.rst:111 c9a729f4e1964894bae215793647ab75
|
||||
msgid ""
|
||||
"Additional resources we think may be useful as you develop your "
|
||||
"application!"
|
||||
msgstr "“我们认为在您开发应用程序时可能有用的其他资源!”"
|
||||
|
||||
#: ../../index.rst:142 4d0da8471db240dba842949b6796be7a
|
||||
#: ../../index.rst:113 06e6e4b7776c405fa94ae7b59253162d
|
||||
msgid ""
|
||||
"`Discord <https://discord.gg/eZHE94MN>`_: if your have some problem or "
|
||||
"ideas, you can talk from discord."
|
||||
@ -272,3 +223,58 @@ msgstr "`Discord <https://discord.gg/eZHE94MN>`_:如果您有任何问题，可
|
||||
#~ msgid "Guides for how other companies/products can be used with DB-GPT"
|
||||
#~ msgstr "其他公司/产品如何与DB-GPT一起使用的方法指南"
|
||||
|
||||
#~ msgid "Use Cases"
|
||||
#~ msgstr "示例"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "Best Practices and built-in "
|
||||
#~ "implementations for common DB-GPT use"
|
||||
#~ " cases:"
|
||||
#~ msgstr "DB-GPT用例的最佳实践和内置方法:"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`Sql generation and diagnosis "
|
||||
#~ "<./use_cases/sql_generation_and_diagnosis.html>`_: SQL "
|
||||
#~ "generation and diagnosis."
|
||||
#~ msgstr "`Sql生成和诊断 <./use_cases/sql_generation_and_diagnosis.html>`_: Sql生成和诊断。"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`knownledge Based QA "
|
||||
#~ "<./use_cases/knownledge_based_qa.html>`_: A important "
|
||||
#~ "scene for user to chat with "
|
||||
#~ "database documents, codes, bugs and "
|
||||
#~ "schemas."
|
||||
#~ msgstr ""
|
||||
#~ "`知识库问答 <./use_cases/knownledge_based_qa.html>`_: "
|
||||
#~ "用户与数据库文档、代码和bug聊天的重要场景\""
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`Chatbots <./use_cases/chatbots.html>`_: Language "
|
||||
#~ "model love to chat, use multi "
|
||||
#~ "models to chat."
|
||||
#~ msgstr "`聊天机器人 <./use_cases/chatbots.html>`_: 使用多模型进行对话"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`Querying Database Data "
|
||||
#~ "<./use_cases/query_database_data.html>`_: Query and "
|
||||
#~ "Analysis data from databases and give"
|
||||
#~ " charts."
|
||||
#~ msgstr "`查询数据库数据 <./use_cases/query_database_data.html>`_:从数据库中查询和分析数据并给出图表。"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`Interacting with apis "
|
||||
#~ "<./use_cases/interacting_with_api.html>`_: Interact with"
|
||||
#~ " apis, such as create a table, "
|
||||
#~ "deploy a database cluster, create a "
|
||||
#~ "database and so on."
|
||||
#~ msgstr ""
|
||||
#~ "`API交互 <./use_cases/interacting_with_api.html>`_: "
|
||||
#~ "与API交互，例如创建表、部署数据库集群、创建数据库等。"
|
||||
|
||||
#~ msgid ""
|
||||
#~ "`Tool use with plugins "
|
||||
#~ "<./use_cases/tool_use_with_plugin>`_: According to "
|
||||
#~ "Plugin use tools to manage databases "
|
||||
#~ "autonomoly."
|
||||
#~ msgstr "`插件工具 <./use_cases/tool_use_with_plugin>`_: 根据插件使用工具自主管理数据库。"
|
||||
|
||||
|
||||
@ -111,7 +111,7 @@ def load_native_plugins(cfg: Config):
|
||||
print("save file")
|
||||
cfg.set_plugins(scan_plugins(cfg.debug_mode))
|
||||
else:
|
||||
print("get file faild，response code：", response.status_code)
|
||||
print("get file failed，response code：", response.status_code)
|
||||
except Exception as e:
|
||||
print("load plugin from git exception!" + str(e))
|
||||
|
||||
|
||||
@ -106,3 +106,13 @@ class ClickhouseConnect(RDBMSDatabase):
|
||||
return [
|
||||
(table_comment[0], table_comment[1]) for table_comment in table_comments
|
||||
]
|
||||
|
||||
def table_simple_info(self):
|
||||
# group_concat() not supported in clickhouse, use arrayStringConcat+groupArray instead; and quotes need to be escaped
|
||||
_sql = f"""
|
||||
select concat(TABLE_NAME, \'(\' , arrayStringConcat(groupArray(column_name),\'-\'), \')\') as schema_info
|
||||
from information_schema.COLUMNS where table_schema=\'{self.get_current_db_name()}\' group by TABLE_NAME; """
|
||||
|
||||
cursor = self.session.execute(text(_sql))
|
||||
results = cursor.fetchall()
|
||||
return results
|
||||
|
||||
@ -1021,6 +1021,7 @@ def run_worker_manager(
|
||||
system_app,
|
||||
os.path.join(LOGDIR, worker_params.tracer_file),
|
||||
root_operation_name="DB-GPT-WorkerManager-Entry",
|
||||
tracer_storage_cls=worker_params.tracer_storage_cls,
|
||||
)
|
||||
|
||||
_start_local_worker(worker_manager, worker_params)
|
||||
|
||||
@ -88,6 +88,12 @@ class ModelControllerParameters(BaseParameters):
|
||||
"help": "The filename to store tracer span records",
|
||||
},
|
||||
)
|
||||
tracer_storage_cls: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The storage class to storage tracer span records",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -138,6 +144,12 @@ class ModelAPIServerParameters(BaseParameters):
|
||||
"help": "The filename to store tracer span records",
|
||||
},
|
||||
)
|
||||
tracer_storage_cls: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The storage class to storage tracer span records",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
@ -226,6 +238,12 @@ class ModelWorkerParameters(BaseModelParameters):
|
||||
"help": "The filename to store tracer span records",
|
||||
},
|
||||
)
|
||||
tracer_storage_cls: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The storage class to storage tracer span records",
|
||||
},
|
||||
)
|
||||
|
||||
|
||||
@dataclass
|
||||
|
||||
@ -36,7 +36,10 @@ def tongyi_generate_stream(
|
||||
if message.role == ModelMessageRoleType.HUMAN:
|
||||
history.append({"role": "user", "content": message.content})
|
||||
for message in messages:
|
||||
if message.role == ModelMessageRoleType.SYSTEM:
|
||||
if (
|
||||
message.role == ModelMessageRoleType.SYSTEM
|
||||
or message.role == ModelMessageRoleType.HUMAN
|
||||
):
|
||||
history.append({"role": "user", "content": message.content})
|
||||
# elif message.role == ModelMessageRoleType.HUMAN:
|
||||
# history.append({"role": "user", "content": message.content})
|
||||
@ -45,17 +48,24 @@ def tongyi_generate_stream(
|
||||
else:
|
||||
pass
|
||||
|
||||
# temp_his = history[::-1]
|
||||
temp_his = history
|
||||
temp_his = history[::-1]
|
||||
last_user_input = None
|
||||
for m in temp_his:
|
||||
if m["role"] == "user":
|
||||
last_user_input = m
|
||||
break
|
||||
|
||||
if last_user_input:
|
||||
temp_his = history
|
||||
prompt_input = None
|
||||
for m in temp_his:
|
||||
if m["role"] == "user":
|
||||
prompt_input = m
|
||||
break
|
||||
|
||||
if last_user_input and prompt_input and last_user_input != prompt_input:
|
||||
history.remove(last_user_input)
|
||||
history.append(last_user_input)
|
||||
history.remove(prompt_input)
|
||||
history.append(prompt_input)
|
||||
|
||||
gen = Generation()
|
||||
res = gen.call(
|
||||
|
||||
@ -172,7 +172,7 @@ async def test_connect(db_config: DBConfig = Body()):
|
||||
CFG.LOCAL_DB_MANAGE.test_connect(db_config)
|
||||
return Result.succ(True)
|
||||
except Exception as e:
|
||||
return Result.faild(code="E1001", msg=str(e))
|
||||
return Result.failed(code="E1001", msg=str(e))
|
||||
|
||||
|
||||
@router.post("/v1/chat/db/summary", response_model=Result[bool])
|
||||
@ -305,7 +305,7 @@ async def params_load(
|
||||
return Result.succ(get_hist_messages(conv_uid))
|
||||
except Exception as e:
|
||||
logger.error("excel load error!", e)
|
||||
return Result.faild(code="E000X", msg=f"File Load Error {e}")
|
||||
return Result.failed(code="E000X", msg=f"File Load Error {e}")
|
||||
|
||||
|
||||
@router.post("/v1/chat/dialogue/delete")
|
||||
@ -352,7 +352,7 @@ async def get_chat_instance(dialogue: ConversationVo = Body()) -> BaseChat:
|
||||
|
||||
if not ChatScene.is_valid_mode(dialogue.chat_mode):
|
||||
raise StopAsyncIteration(
|
||||
Result.faild("Unsupported Chat Mode," + dialogue.chat_mode + "!")
|
||||
Result.failed("Unsupported Chat Mode," + dialogue.chat_mode + "!")
|
||||
)
|
||||
|
||||
chat_param = {
|
||||
@ -430,7 +430,7 @@ async def model_types(controller: BaseModelController = Depends(get_model_contro
|
||||
return Result.succ(list(types))
|
||||
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"controller model types error {e}")
|
||||
return Result.failed(code="E000X", msg=f"controller model types error {e}")
|
||||
|
||||
|
||||
@router.get("/v1/model/supports")
|
||||
@ -440,7 +440,7 @@ async def model_supports(worker_manager: WorkerManager = Depends(get_worker_mana
|
||||
models = await worker_manager.supported_models()
|
||||
return Result.succ(FlatSupportedModel.from_supports(models))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"Fetch supportd models error {e}")
|
||||
return Result.failed(code="E000X", msg=f"Fetch supportd models error {e}")
|
||||
|
||||
|
||||
async def no_stream_generator(chat):
|
||||
|
||||
@ -107,7 +107,7 @@ async def get_editor_sql(con_uid: str, round: int):
|
||||
.replace("\n", " ")
|
||||
)
|
||||
return Result.succ(json.loads(context))
|
||||
return Result.faild(msg="not have sql!")
|
||||
return Result.failed(msg="not have sql!")
|
||||
|
||||
|
||||
@router.post("/v1/editor/sql/run", response_model=Result[SqlRunData])
|
||||
@ -116,7 +116,7 @@ async def editor_sql_run(run_param: dict = Body()):
|
||||
db_name = run_param["db_name"]
|
||||
sql = run_param["sql"]
|
||||
if not db_name and not sql:
|
||||
return Result.faild("SQL run param error！")
|
||||
return Result.failed("SQL run param error！")
|
||||
conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name)
|
||||
|
||||
try:
|
||||
@ -169,7 +169,7 @@ async def sql_editor_submit(sql_edit_context: ChatSqlEditContext = Body()):
|
||||
)
|
||||
history_mem.update(history_messages)
|
||||
return Result.succ(None)
|
||||
return Result.faild(msg="Edit Faild!")
|
||||
return Result.failed(msg="Edit Failed!")
|
||||
|
||||
|
||||
@router.get("/v1/editor/chart/list", response_model=Result[ChartList])
|
||||
@ -191,7 +191,7 @@ async def get_editor_chart_list(con_uid: str):
|
||||
charts=json.loads(element["data"]["content"]),
|
||||
)
|
||||
return Result.succ(chart_list)
|
||||
return Result.faild(msg="Not have charts!")
|
||||
return Result.failed(msg="Not have charts!")
|
||||
|
||||
|
||||
@router.post("/v1/editor/chart/info", response_model=Result[ChartDetail])
|
||||
@ -210,7 +210,7 @@ async def get_editor_chart_info(param: dict = Body()):
|
||||
logger.error(
|
||||
"this dashboard dialogue version too old, can't support editor!"
|
||||
)
|
||||
return Result.faild(
|
||||
return Result.failed(
|
||||
msg="this dashboard dialogue version too old, can't support editor!"
|
||||
)
|
||||
for element in last_round["messages"]:
|
||||
@ -234,7 +234,7 @@ async def get_editor_chart_info(param: dict = Body()):
|
||||
)
|
||||
|
||||
return Result.succ(detail)
|
||||
return Result.faild(msg="Can't Find Chart Detail Info!")
|
||||
return Result.failed(msg="Can't Find Chart Detail Info!")
|
||||
|
||||
|
||||
@router.post("/v1/editor/chart/run", response_model=Result[ChartRunData])
|
||||
@ -244,7 +244,7 @@ async def editor_chart_run(run_param: dict = Body()):
|
||||
sql = run_param["sql"]
|
||||
chart_type = run_param["chart_type"]
|
||||
if not db_name and not sql:
|
||||
return Result.faild("SQL run param error！")
|
||||
return Result.failed("SQL run param error！")
|
||||
try:
|
||||
dashboard_data_loader: DashboardDataLoader = DashboardDataLoader()
|
||||
db_conn = CFG.LOCAL_DB_MANAGE.get_connect(db_name)
|
||||
@ -334,7 +334,7 @@ async def chart_editor_submit(chart_edit_context: ChatChartEditContext = Body())
|
||||
)
|
||||
except Exception as e:
|
||||
logger.error(f"edit chart exception!{str(e)}", e)
|
||||
return Result.faild(msg=f"Edit chart exception!{str(e)}")
|
||||
return Result.failed(msg=f"Edit chart exception!{str(e)}")
|
||||
history_mem.update(history_messages)
|
||||
return Result.succ(None)
|
||||
return Result.faild(msg="Edit Faild!")
|
||||
return Result.failed(msg="Edit Failed!")
|
||||
|
||||
@ -17,11 +17,11 @@ class Result(Generic[T], BaseModel):
|
||||
return Result(success=True, err_code=None, err_msg=None, data=data)
|
||||
|
||||
@classmethod
|
||||
def faild(cls, msg):
|
||||
def failed(cls, msg):
|
||||
return Result(success=False, err_code="E000X", err_msg=msg, data=None)
|
||||
|
||||
@classmethod
|
||||
def faild(cls, code, msg):
|
||||
def failed(cls, code, msg):
|
||||
return Result(success=False, err_code=code, err_msg=msg, data=None)
|
||||
|
||||
|
||||
|
||||
@ -7,4 +7,4 @@ async def validation_exception_handler(request: Request, exc: RequestValidationE
|
||||
message = ""
|
||||
for error in exc.errors():
|
||||
message += ".".join(error.get("loc")) + ":" + error.get("msg") + ";"
|
||||
return Result.faild(code="E0001", msg=message)
|
||||
return Result.failed(code="E0001", msg=message)
|
||||
|
||||
@ -13,6 +13,7 @@ from pilot.scene.base_message import ModelMessage, ModelMessageRoleType
|
||||
from pilot.scene.message import OnceConversation
|
||||
from pilot.utils import get_or_create_event_loop
|
||||
from pilot.utils.executor_utils import ExecutorFactory, blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
from pydantic import Extra
|
||||
from pilot.memory.chat_history.chat_hisotry_factory import ChatHistory
|
||||
|
||||
@ -38,6 +39,7 @@ class BaseChat(ABC):
|
||||
|
||||
arbitrary_types_allowed = True
|
||||
|
||||
@trace("BaseChat.__init__")
|
||||
def __init__(self, chat_param: Dict):
|
||||
"""Chat Module Initialization
|
||||
Args:
|
||||
@ -143,7 +145,14 @@ class BaseChat(ABC):
|
||||
)
|
||||
self.current_message.tokens = 0
|
||||
if self.prompt_template.template:
|
||||
current_prompt = self.prompt_template.format(**input_values)
|
||||
metadata = {
|
||||
"template_scene": self.prompt_template.template_scene,
|
||||
"input_values": input_values,
|
||||
}
|
||||
with root_tracer.start_span(
|
||||
"BaseChat.__call_base.prompt_template.format", metadata=metadata
|
||||
):
|
||||
current_prompt = self.prompt_template.format(**input_values)
|
||||
self.current_message.add_system_message(current_prompt)
|
||||
|
||||
llm_messages = self.generate_llm_messages()
|
||||
@ -175,6 +184,14 @@ class BaseChat(ABC):
|
||||
except StopAsyncIteration:
|
||||
return True # 迭代器已经执行结束
|
||||
|
||||
def _get_span_metadata(self, payload: Dict) -> Dict:
|
||||
metadata = {k: v for k, v in payload.items()}
|
||||
del metadata["prompt"]
|
||||
metadata["messages"] = list(
|
||||
map(lambda m: m if isinstance(m, dict) else m.dict(), metadata["messages"])
|
||||
)
|
||||
return metadata
|
||||
|
||||
async def stream_call(self):
|
||||
# TODO Retry when server connection error
|
||||
payload = await self.__call_base()
|
||||
@ -182,6 +199,10 @@ class BaseChat(ABC):
|
||||
self.skip_echo_len = len(payload.get("prompt").replace("</s>", " ")) + 11
|
||||
logger.info(f"Requert: \n{payload}")
|
||||
ai_response_text = ""
|
||||
span = root_tracer.start_span(
|
||||
"BaseChat.stream_call", metadata=self._get_span_metadata(payload)
|
||||
)
|
||||
payload["span_id"] = span.span_id
|
||||
try:
|
||||
from pilot.model.cluster import WorkerManagerFactory
|
||||
|
||||
@ -199,19 +220,25 @@ class BaseChat(ABC):
|
||||
self.current_message.add_ai_message(msg)
|
||||
view_msg = self.knowledge_reference_call(msg)
|
||||
self.current_message.add_view_message(view_msg)
|
||||
span.end()
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
logger.error("model response parase faild！" + str(e))
|
||||
logger.error("model response parase failed！" + str(e))
|
||||
self.current_message.add_view_message(
|
||||
f"""<span style=\"color:red\">ERROR!</span>{str(e)}\n {ai_response_text} """
|
||||
)
|
||||
### store current conversation
|
||||
span.end(metadata={"error": str(e)})
|
||||
self.memory.append(self.current_message)
|
||||
|
||||
async def nostream_call(self):
|
||||
payload = await self.__call_base()
|
||||
logger.info(f"Request: \n{payload}")
|
||||
ai_response_text = ""
|
||||
span = root_tracer.start_span(
|
||||
"BaseChat.nostream_call", metadata=self._get_span_metadata(payload)
|
||||
)
|
||||
payload["span_id"] = span.span_id
|
||||
try:
|
||||
from pilot.model.cluster import WorkerManagerFactory
|
||||
|
||||
@ -219,7 +246,8 @@ class BaseChat(ABC):
|
||||
ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
|
||||
).create()
|
||||
|
||||
model_output = await worker_manager.generate(payload)
|
||||
with root_tracer.start_span("BaseChat.invoke_worker_manager.generate"):
|
||||
model_output = await worker_manager.generate(payload)
|
||||
|
||||
### output parse
|
||||
ai_response_text = (
|
||||
@ -234,11 +262,18 @@ class BaseChat(ABC):
|
||||
ai_response_text
|
||||
)
|
||||
)
|
||||
### run
|
||||
# result = self.do_action(prompt_define_response)
|
||||
result = await blocking_func_to_async(
|
||||
self._executor, self.do_action, prompt_define_response
|
||||
)
|
||||
metadata = {
|
||||
"model_output": model_output.to_dict(),
|
||||
"ai_response_text": ai_response_text,
|
||||
"prompt_define_response": self._parse_prompt_define_response(
|
||||
prompt_define_response
|
||||
),
|
||||
}
|
||||
with root_tracer.start_span("BaseChat.do_action", metadata=metadata):
|
||||
### run
|
||||
result = await blocking_func_to_async(
|
||||
self._executor, self.do_action, prompt_define_response
|
||||
)
|
||||
|
||||
### llm speaker
|
||||
speak_to_user = self.get_llm_speak(prompt_define_response)
|
||||
@ -255,12 +290,14 @@ class BaseChat(ABC):
|
||||
|
||||
view_message = view_message.replace("\n", "\\n")
|
||||
self.current_message.add_view_message(view_message)
|
||||
span.end()
|
||||
except Exception as e:
|
||||
print(traceback.format_exc())
|
||||
logger.error("model response parase faild！" + str(e))
|
||||
self.current_message.add_view_message(
|
||||
f"""<span style=\"color:red\">ERROR!</span>{str(e)}\n {ai_response_text} """
|
||||
)
|
||||
span.end(metadata={"error": str(e)})
|
||||
### store dialogue
|
||||
self.memory.append(self.current_message)
|
||||
return self.current_ai_response()
|
||||
@ -345,7 +382,7 @@ class BaseChat(ABC):
|
||||
text += self.__load_example_messages()
|
||||
|
||||
### Load History
|
||||
text += self.__load_histroy_messages()
|
||||
text += self.__load_history_messages()
|
||||
|
||||
### Load User Input
|
||||
text += self.__load_user_message()
|
||||
@ -371,7 +408,7 @@ class BaseChat(ABC):
|
||||
messages += self.__load_example_messages(str_message=False)
|
||||
|
||||
### Load History
|
||||
messages += self.__load_histroy_messages(str_message=False)
|
||||
messages += self.__load_history_messages(str_message=False)
|
||||
|
||||
### Load User Input
|
||||
messages += self.__load_user_message(str_message=False)
|
||||
@ -427,7 +464,7 @@ class BaseChat(ABC):
|
||||
)
|
||||
return example_text if str_message else example_messages
|
||||
|
||||
def __load_histroy_messages(self, str_message: bool = True):
|
||||
def __load_history_messages(self, str_message: bool = True):
|
||||
history_text = ""
|
||||
history_messages = []
|
||||
if self.prompt_template.need_historical_messages:
|
||||
@ -513,3 +550,21 @@ class BaseChat(ABC):
|
||||
|
||||
"""
|
||||
pass
|
||||
|
||||
def _parse_prompt_define_response(self, prompt_define_response: Any) -> Any:
|
||||
if not prompt_define_response:
|
||||
return ""
|
||||
if isinstance(prompt_define_response, str) or isinstance(
|
||||
prompt_define_response, dict
|
||||
):
|
||||
return prompt_define_response
|
||||
if isinstance(prompt_define_response, tuple):
|
||||
if hasattr(prompt_define_response, "_asdict"):
|
||||
# namedtuple
|
||||
return prompt_define_response._asdict()
|
||||
else:
|
||||
return dict(
|
||||
zip(range(len(prompt_define_response)), prompt_define_response)
|
||||
)
|
||||
else:
|
||||
return prompt_define_response
|
||||
|
||||
@ -11,6 +11,7 @@ from pilot.common.string_utils import extract_content
|
||||
from .prompt import prompt
|
||||
from pilot.component import ComponentType
|
||||
from pilot.base_modules.agent.controller import ModuleAgent
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -51,6 +52,7 @@ class ChatAgent(BaseChat):
|
||||
|
||||
self.api_call = ApiCall(plugin_generator=self.plugins_prompt_generator)
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict[str, str]:
|
||||
input_values = {
|
||||
"user_goal": self.current_user_input,
|
||||
@ -63,7 +65,10 @@ class ChatAgent(BaseChat):
|
||||
|
||||
def stream_plugin_call(self, text):
|
||||
text = text.replace("\n", " ")
|
||||
return self.api_call.run(text)
|
||||
with root_tracer.start_span(
|
||||
"ChatAgent.stream_plugin_call.api_call", metadata={"text": text}
|
||||
):
|
||||
return self.api_call.run(text)
|
||||
|
||||
def __list_to_prompt_str(self, list: List) -> str:
|
||||
return "\n".join(f"{i + 1 + 1}. {item}" for i, item in enumerate(list))
|
||||
|
||||
@ -13,6 +13,7 @@ from pilot.scene.chat_dashboard.data_preparation.report_schma import (
|
||||
from pilot.scene.chat_dashboard.prompt import prompt
|
||||
from pilot.scene.chat_dashboard.data_loader import DashboardDataLoader
|
||||
from pilot.utils.executor_utils import blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -53,6 +54,7 @@ class ChatDashboard(BaseChat):
|
||||
data = f.read()
|
||||
return json.loads(data)
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
try:
|
||||
from pilot.summary.db_summary_client import DBSummaryClient
|
||||
|
||||
@ -52,8 +52,8 @@ class DashboardDataLoader:
|
||||
values.append(value_item)
|
||||
return field_names, values
|
||||
except Exception as e:
|
||||
logger.debug("Prepare Chart Data Faild!" + str(e))
|
||||
raise ValueError("Prepare Chart Data Faild!")
|
||||
logger.debug("Prepare Chart Data Failed!" + str(e))
|
||||
raise ValueError("Prepare Chart Data Failed!")
|
||||
|
||||
def get_chart_values_by_db(self, db_name: str, chart_sql: str):
|
||||
logger.info(f"get_chart_values_by_db:{db_name},{chart_sql}")
|
||||
|
||||
@ -42,7 +42,7 @@ RESPONSE_FORMAT = [
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
PROMPT_NEED_STREAM_OUT = False
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatDashboard.value(),
|
||||
@ -50,9 +50,9 @@ prompt = PromptTemplate(
|
||||
response_format=json.dumps(RESPONSE_FORMAT, indent=4),
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=ChatDashboardOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
CFG.prompt_template_registry.register(prompt, is_default=True)
|
||||
|
||||
@ -14,6 +14,7 @@ from pilot.scene.chat_data.chat_excel.excel_learning.chat import ExcelLearning
|
||||
from pilot.common.path_utils import has_path
|
||||
from pilot.configs.model_config import LLM_MODEL_CONFIG, KNOWLEDGE_UPLOAD_ROOT_PATH
|
||||
from pilot.base_modules.agent.common.schema import Status
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -62,6 +63,7 @@ class ChatExcel(BaseChat):
|
||||
# ]
|
||||
return "\n".join(f"{i+1}. {item}" for i, item in enumerate(command_strings))
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
input_values = {
|
||||
"user_input": self.current_user_input,
|
||||
@ -88,4 +90,9 @@ class ChatExcel(BaseChat):
|
||||
|
||||
def stream_plugin_call(self, text):
|
||||
text = text.replace("\n", " ")
|
||||
return self.api_call.run_display_sql(text, self.excel_reader.get_df_by_sql_ex)
|
||||
with root_tracer.start_span(
|
||||
"ChatExcel.stream_plugin_call.run_display_sql", metadata={"text": text}
|
||||
):
|
||||
return self.api_call.run_display_sql(
|
||||
text, self.excel_reader.get_df_by_sql_ex
|
||||
)
|
||||
|
||||
@ -51,7 +51,7 @@ _PROMPT_SCENE_DEFINE = (
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_STREAM_OUT = True
|
||||
|
||||
# Temperature is a configuration hyperparameter that controls the randomness of language model output.
|
||||
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
|
||||
@ -63,9 +63,9 @@ prompt = PromptTemplate(
|
||||
input_variables=["user_input", "table_name", "disply_type"],
|
||||
template_define=_PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=ChatExcelOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
need_historical_messages=True,
|
||||
# example_selector=sql_data_example,
|
||||
|
||||
@ -13,6 +13,7 @@ from pilot.scene.chat_data.chat_excel.excel_learning.prompt import prompt
|
||||
from pilot.scene.chat_data.chat_excel.excel_reader import ExcelReader
|
||||
from pilot.json_utils.utilities import DateTimeEncoder
|
||||
from pilot.utils.executor_utils import blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -44,6 +45,7 @@ class ExcelLearning(BaseChat):
|
||||
if parent_mode:
|
||||
self.current_message.chat_mode = parent_mode.value()
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
# colunms, datas = self.excel_reader.get_sample_data()
|
||||
colunms, datas = await blocking_func_to_async(
|
||||
|
||||
@ -67,7 +67,7 @@ PROMPT_SCENE_DEFINE = (
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
PROMPT_NEED_STREAM_OUT = False
|
||||
|
||||
# Temperature is a configuration hyperparameter that controls the randomness of language model output.
|
||||
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
|
||||
@ -80,9 +80,9 @@ prompt = PromptTemplate(
|
||||
response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=LearningExcelOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
# example_selector=sql_data_example,
|
||||
temperature=PROMPT_TEMPERATURE,
|
||||
|
||||
@ -6,6 +6,7 @@ from pilot.common.sql_database import Database
|
||||
from pilot.configs.config import Config
|
||||
from pilot.scene.chat_db.auto_execute.prompt import prompt
|
||||
from pilot.utils.executor_utils import blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -35,10 +36,13 @@ class ChatWithDbAutoExecute(BaseChat):
|
||||
raise ValueError(
|
||||
f"{ChatScene.ChatWithDbExecute.value} mode should chose db!"
|
||||
)
|
||||
|
||||
self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name)
|
||||
with root_tracer.start_span(
|
||||
"ChatWithDbAutoExecute.get_connect", metadata={"db_name": self.db_name}
|
||||
):
|
||||
self.database = CFG.LOCAL_DB_MANAGE.get_connect(self.db_name)
|
||||
self.top_k: int = 200
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
"""
|
||||
generate input values
|
||||
@ -55,13 +59,14 @@ class ChatWithDbAutoExecute(BaseChat):
|
||||
# query=self.current_user_input,
|
||||
# topk=CFG.KNOWLEDGE_SEARCH_TOP_SIZE,
|
||||
# )
|
||||
table_infos = await blocking_func_to_async(
|
||||
self._executor,
|
||||
client.get_db_summary,
|
||||
self.db_name,
|
||||
self.current_user_input,
|
||||
CFG.KNOWLEDGE_SEARCH_TOP_SIZE,
|
||||
)
|
||||
with root_tracer.start_span("ChatWithDbAutoExecute.get_db_summary"):
|
||||
table_infos = await blocking_func_to_async(
|
||||
self._executor,
|
||||
client.get_db_summary,
|
||||
self.db_name,
|
||||
self.current_user_input,
|
||||
CFG.KNOWLEDGE_SEARCH_TOP_SIZE,
|
||||
)
|
||||
except Exception as e:
|
||||
print("db summary find error!" + str(e))
|
||||
if not table_infos:
|
||||
@ -80,4 +85,8 @@ class ChatWithDbAutoExecute(BaseChat):
|
||||
|
||||
def do_action(self, prompt_response):
|
||||
print(f"do_action:{prompt_response}")
|
||||
return self.database.run(prompt_response.sql)
|
||||
with root_tracer.start_span(
|
||||
"ChatWithDbAutoExecute.do_action.run_sql",
|
||||
metadata=prompt_response.to_dict(),
|
||||
):
|
||||
return self.database.run(prompt_response.sql)
|
||||
|
||||
@ -12,6 +12,9 @@ class SqlAction(NamedTuple):
|
||||
sql: str
|
||||
thoughts: Dict
|
||||
|
||||
def to_dict(self) -> Dict[str, Dict]:
|
||||
return {"sql": self.sql, "thoughts": self.thoughts}
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -33,7 +33,7 @@ RESPONSE_FORMAT_SIMPLE = {
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
PROMPT_NEED_STREAM_OUT = False
|
||||
|
||||
# Temperature is a configuration hyperparameter that controls the randomness of language model output.
|
||||
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
|
||||
@ -46,9 +46,9 @@ prompt = PromptTemplate(
|
||||
response_format=json.dumps(RESPONSE_FORMAT_SIMPLE, ensure_ascii=False, indent=4),
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=DbChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
# example_selector=sql_data_example,
|
||||
temperature=PROMPT_TEMPERATURE,
|
||||
|
||||
@ -36,7 +36,7 @@ RESPONSE_FORMAT_SIMPLE = {
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
PROMPT_NEED_STREAM_OUT = False
|
||||
|
||||
# Temperature is a configuration hyperparameter that controls the randomness of language model output.
|
||||
# A high temperature produces more unpredictable and creative results, while a low temperature produces more common and conservative output.
|
||||
@ -50,9 +50,9 @@ prompt = PromptTemplate(
|
||||
template_is_strict=False,
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=DbChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
# example_selector=sql_data_example,
|
||||
temperature=PROMPT_TEMPERATURE,
|
||||
|
||||
@ -6,6 +6,7 @@ from pilot.common.sql_database import Database
|
||||
from pilot.configs.config import Config
|
||||
from pilot.scene.chat_db.professional_qa.prompt import prompt
|
||||
from pilot.utils.executor_utils import blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -39,6 +40,7 @@ class ChatWithDbQA(BaseChat):
|
||||
else len(self.tables)
|
||||
)
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
table_info = ""
|
||||
dialect = "mysql"
|
||||
|
||||
@ -54,7 +54,7 @@ _DEFAULT_TEMPLATE = (
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_STREAM_OUT = True
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatWithDbQA.value(),
|
||||
@ -62,9 +62,9 @@ prompt = PromptTemplate(
|
||||
response_format=None,
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=NormalChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@ -6,6 +6,7 @@ from pilot.configs.config import Config
|
||||
from pilot.base_modules.agent.commands.command import execute_command
|
||||
from pilot.base_modules.agent import PluginPromptGenerator
|
||||
from .prompt import prompt
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -50,6 +51,7 @@ class ChatWithPlugin(BaseChat):
|
||||
self.plugins_prompt_generator
|
||||
)
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
input_values = {
|
||||
"input": self.current_user_input,
|
||||
|
||||
@ -4,6 +4,7 @@ from pilot.scene.base import ChatScene
|
||||
from pilot.configs.config import Config
|
||||
|
||||
from pilot.scene.chat_knowledge.inner_db_summary.prompt import prompt
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -31,6 +32,7 @@ class InnerChatDBSummary(BaseChat):
|
||||
self.db_input = db_select
|
||||
self.db_summary = db_summary
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
input_values = {
|
||||
"db_input": self.db_input,
|
||||
|
||||
@ -33,7 +33,7 @@ RESPONSE_FORMAT = {"table": ["orders", "products"]}
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = False
|
||||
PROMPT_NEED_STREAM_OUT = False
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.InnerChatDBSummary.value(),
|
||||
@ -41,9 +41,9 @@ prompt = PromptTemplate(
|
||||
response_format=json.dumps(RESPONSE_FORMAT, indent=4),
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE + PROMPT_RESPONSE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=NormalChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@ -15,6 +15,7 @@ from pilot.configs.model_config import (
|
||||
from pilot.scene.chat_knowledge.v1.prompt import prompt
|
||||
from pilot.server.knowledge.service import KnowledgeService
|
||||
from pilot.utils.executor_utils import blocking_func_to_async
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -92,6 +93,7 @@ class ChatKnowledge(BaseChat):
|
||||
"""return reference"""
|
||||
return text + f"\n\n{self.parse_source_view(self.sources)}"
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
if self.space_context:
|
||||
self.prompt_template.template_define = self.space_context["prompt"]["scene"]
|
||||
|
||||
@ -33,7 +33,7 @@ _DEFAULT_TEMPLATE = (
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_STREAM_OUT = True
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatKnowledge.value(),
|
||||
@ -41,9 +41,9 @@ prompt = PromptTemplate(
|
||||
response_format=None,
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=NormalChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@ -33,7 +33,7 @@ _DEFAULT_TEMPLATE = (
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_STREAM_OUT = True
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatKnowledge.value(),
|
||||
@ -41,9 +41,9 @@ prompt = PromptTemplate(
|
||||
response_format=None,
|
||||
template_define=None,
|
||||
template=_DEFAULT_TEMPLATE,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=NormalChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@ -5,6 +5,7 @@ from pilot.scene.base import ChatScene
|
||||
from pilot.configs.config import Config
|
||||
|
||||
from pilot.scene.chat_normal.prompt import prompt
|
||||
from pilot.utils.tracer import root_tracer, trace
|
||||
|
||||
CFG = Config()
|
||||
|
||||
@ -21,6 +22,7 @@ class ChatNormal(BaseChat):
|
||||
chat_param=chat_param,
|
||||
)
|
||||
|
||||
@trace()
|
||||
async def generate_input_values(self) -> Dict:
|
||||
input_values = {"input": self.current_user_input}
|
||||
return input_values
|
||||
|
||||
@ -11,7 +11,7 @@ CFG = Config()
|
||||
|
||||
PROMPT_SEP = SeparatorStyle.SINGLE.value
|
||||
|
||||
PROMPT_NEED_NEED_STREAM_OUT = True
|
||||
PROMPT_NEED_STREAM_OUT = True
|
||||
|
||||
prompt = PromptTemplate(
|
||||
template_scene=ChatScene.ChatNormal.value(),
|
||||
@ -19,9 +19,9 @@ prompt = PromptTemplate(
|
||||
response_format=None,
|
||||
template_define=PROMPT_SCENE_DEFINE,
|
||||
template=None,
|
||||
stream_out=PROMPT_NEED_NEED_STREAM_OUT,
|
||||
stream_out=PROMPT_NEED_STREAM_OUT,
|
||||
output_parser=NormalChatOutputParser(
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_NEED_STREAM_OUT
|
||||
sep=PROMPT_SEP, is_stream_out=PROMPT_NEED_STREAM_OUT
|
||||
),
|
||||
)
|
||||
|
||||
|
||||
@ -147,6 +147,12 @@ class WebWerverParameters(BaseParameters):
|
||||
"help": "The filename to store tracer span records",
|
||||
},
|
||||
)
|
||||
tracer_storage_cls: Optional[str] = field(
|
||||
default=None,
|
||||
metadata={
|
||||
"help": "The storage class to storage tracer span records",
|
||||
},
|
||||
)
|
||||
disable_alembic_upgrade: Optional[bool] = field(
|
||||
default=False,
|
||||
metadata={
|
||||
|
||||
@ -195,7 +195,11 @@ def run_uvicorn(param: WebWerverParameters):
|
||||
def run_webserver(param: WebWerverParameters = None):
|
||||
if not param:
|
||||
param = _get_webserver_params()
|
||||
initialize_tracer(system_app, os.path.join(LOGDIR, param.tracer_file))
|
||||
initialize_tracer(
|
||||
system_app,
|
||||
os.path.join(LOGDIR, param.tracer_file),
|
||||
tracer_storage_cls=param.tracer_storage_cls,
|
||||
)
|
||||
|
||||
with root_tracer.start_span(
|
||||
"run_webserver",
|
||||
|
||||
@ -48,7 +48,7 @@ def space_add(request: KnowledgeSpaceRequest):
|
||||
knowledge_space_service.create_knowledge_space(request)
|
||||
return Result.succ([])
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"space add error {e}")
|
||||
return Result.failed(code="E000X", msg=f"space add error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/space/list")
|
||||
@ -57,7 +57,7 @@ def space_list(request: KnowledgeSpaceRequest):
|
||||
try:
|
||||
return Result.succ(knowledge_space_service.get_knowledge_space(request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"space list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"space list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/space/delete")
|
||||
@ -66,7 +66,7 @@ def space_delete(request: KnowledgeSpaceRequest):
|
||||
try:
|
||||
return Result.succ(knowledge_space_service.delete_space(request.name))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"space list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"space list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/arguments")
|
||||
@ -75,7 +75,7 @@ def arguments(space_name: str):
|
||||
try:
|
||||
return Result.succ(knowledge_space_service.arguments(space_name))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"space list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"space list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/argument/save")
|
||||
@ -86,7 +86,7 @@ def arguments_save(space_name: str, argument_request: SpaceArgumentRequest):
|
||||
knowledge_space_service.argument_save(space_name, argument_request)
|
||||
)
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"space list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"space list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/document/add")
|
||||
@ -100,7 +100,7 @@ def document_add(space_name: str, request: KnowledgeDocumentRequest):
|
||||
)
|
||||
# return Result.succ([])
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document add error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document add error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/document/list")
|
||||
@ -111,7 +111,7 @@ def document_list(space_name: str, query_request: DocumentQueryRequest):
|
||||
knowledge_space_service.get_knowledge_documents(space_name, query_request)
|
||||
)
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/document/delete")
|
||||
@ -122,7 +122,7 @@ def document_delete(space_name: str, query_request: DocumentQueryRequest):
|
||||
knowledge_space_service.delete_document(space_name, query_request.doc_name)
|
||||
)
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/document/upload")
|
||||
@ -159,9 +159,9 @@ async def document_upload(
|
||||
)
|
||||
)
|
||||
# return Result.succ([])
|
||||
return Result.faild(code="E000X", msg=f"doc_file is None")
|
||||
return Result.failed(code="E000X", msg=f"doc_file is None")
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document add error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document add error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/document/sync")
|
||||
@ -173,7 +173,7 @@ def document_sync(space_name: str, request: DocumentSyncRequest):
|
||||
)
|
||||
return Result.succ([])
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document sync error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document sync error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{space_name}/chunk/list")
|
||||
@ -182,7 +182,7 @@ def document_list(space_name: str, query_request: ChunkQueryRequest):
|
||||
try:
|
||||
return Result.succ(knowledge_space_service.get_document_chunks(query_request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"document chunk list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"document chunk list error {e}")
|
||||
|
||||
|
||||
@router.post("/knowledge/{vector_name}/query")
|
||||
|
||||
@ -33,9 +33,9 @@ async def model_params():
|
||||
params.append(model_dict)
|
||||
return Result.succ(params)
|
||||
if not worker_instance:
|
||||
return Result.faild(code="E000X", msg=f"can not find worker manager")
|
||||
return Result.failed(code="E000X", msg=f"can not find worker manager")
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"model stop failed {e}")
|
||||
return Result.failed(code="E000X", msg=f"model stop failed {e}")
|
||||
|
||||
|
||||
@router.get("/v1/worker/model/list")
|
||||
@ -78,7 +78,7 @@ async def model_list():
|
||||
return Result.succ(responses)
|
||||
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"model list error {e}")
|
||||
return Result.failed(code="E000X", msg=f"model list error {e}")
|
||||
|
||||
|
||||
@router.post("/v1/worker/model/stop")
|
||||
@ -91,11 +91,11 @@ async def model_stop(request: WorkerStartupRequest):
|
||||
ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
|
||||
).create()
|
||||
if not worker_manager:
|
||||
return Result.faild(code="E000X", msg=f"can not find worker manager")
|
||||
return Result.failed(code="E000X", msg=f"can not find worker manager")
|
||||
request.params = {}
|
||||
return Result.succ(await worker_manager.model_shutdown(request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"model stop failed {e}")
|
||||
return Result.failed(code="E000X", msg=f"model stop failed {e}")
|
||||
|
||||
|
||||
@router.post("/v1/worker/model/start")
|
||||
@ -106,7 +106,7 @@ async def model_start(request: WorkerStartupRequest):
|
||||
ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
|
||||
).create()
|
||||
if not worker_manager:
|
||||
return Result.faild(code="E000X", msg=f"can not find worker manager")
|
||||
return Result.failed(code="E000X", msg=f"can not find worker manager")
|
||||
return Result.succ(await worker_manager.model_startup(request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E000X", msg=f"model start failed {e}")
|
||||
return Result.failed(code="E000X", msg=f"model start failed {e}")
|
||||
|
||||
@ -11,12 +11,12 @@ prompt_manage_service = PromptManageService()
|
||||
|
||||
@router.post("/prompt/add")
|
||||
def prompt_add(request: PromptManageRequest):
|
||||
print(f"/space/add params: {request}")
|
||||
print(f"/prompt/add params: {request}")
|
||||
try:
|
||||
prompt_manage_service.create_prompt(request)
|
||||
return Result.succ([])
|
||||
except Exception as e:
|
||||
return Result.faild(code="E010X", msg=f"prompt add error {e}")
|
||||
return Result.failed(code="E010X", msg=f"prompt add error {e}")
|
||||
|
||||
|
||||
@router.post("/prompt/list")
|
||||
@ -25,7 +25,7 @@ def prompt_list(request: PromptManageRequest):
|
||||
try:
|
||||
return Result.succ(prompt_manage_service.get_prompts(request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E010X", msg=f"prompt list error {e}")
|
||||
return Result.failed(code="E010X", msg=f"prompt list error {e}")
|
||||
|
||||
|
||||
@router.post("/prompt/update")
|
||||
@ -34,7 +34,7 @@ def prompt_update(request: PromptManageRequest):
|
||||
try:
|
||||
return Result.succ(prompt_manage_service.update_prompt(request))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E010X", msg=f"prompt update error {e}")
|
||||
return Result.failed(code="E010X", msg=f"prompt update error {e}")
|
||||
|
||||
|
||||
@router.post("/prompt/delete")
|
||||
@ -43,4 +43,4 @@ def prompt_delete(request: PromptManageRequest):
|
||||
try:
|
||||
return Result.succ(prompt_manage_service.delete_prompt(request.prompt_name))
|
||||
except Exception as e:
|
||||
return Result.faild(code="E010X", msg=f"prompt delete error {e}")
|
||||
return Result.failed(code="E010X", msg=f"prompt delete error {e}")
|
||||
|
||||
@ -1,5 +1,6 @@
|
||||
from typing import Callable, Awaitable, Any
|
||||
import asyncio
|
||||
import contextvars
|
||||
from abc import ABC, abstractmethod
|
||||
from concurrent.futures import Executor, ThreadPoolExecutor
|
||||
from functools import partial
|
||||
@ -55,6 +56,12 @@ async def blocking_func_to_async(
|
||||
"""
|
||||
if asyncio.iscoroutinefunction(func):
|
||||
raise ValueError(f"The function {func} is not blocking function")
|
||||
|
||||
# This function will be called within the new thread, capturing the current context
|
||||
ctx = contextvars.copy_context()
|
||||
|
||||
def run_with_context():
|
||||
return ctx.run(partial(func, *args, **kwargs))
|
||||
|
||||
loop = asyncio.get_event_loop()
|
||||
sync_function_noargs = partial(func, *args, **kwargs)
|
||||
return await loop.run_in_executor(executor, sync_function_noargs)
|
||||
return await loop.run_in_executor(executor, run_with_context)
|
||||
|
||||
@ -7,9 +7,14 @@ from pilot.utils.tracer.base import (
|
||||
SpanStorageType,
|
||||
TracerContext,
|
||||
)
|
||||
from pilot.utils.tracer.span_storage import MemorySpanStorage, FileSpanStorage
|
||||
from pilot.utils.tracer.span_storage import (
|
||||
MemorySpanStorage,
|
||||
FileSpanStorage,
|
||||
SpanStorageContainer,
|
||||
)
|
||||
from pilot.utils.tracer.tracer_impl import (
|
||||
root_tracer,
|
||||
trace,
|
||||
initialize_tracer,
|
||||
DefaultTracer,
|
||||
TracerManager,
|
||||
@ -25,7 +30,9 @@ __all__ = [
|
||||
"TracerContext",
|
||||
"MemorySpanStorage",
|
||||
"FileSpanStorage",
|
||||
"SpanStorageContainer",
|
||||
"root_tracer",
|
||||
"trace",
|
||||
"initialize_tracer",
|
||||
"DefaultTracer",
|
||||
"TracerManager",
|
||||
|
||||
@ -1,6 +1,6 @@
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Dict, Callable, Optional
|
||||
from typing import Dict, Callable, Optional, List
|
||||
from dataclasses import dataclass
|
||||
from abc import ABC, abstractmethod
|
||||
from enum import Enum
|
||||
@ -121,6 +121,11 @@ class SpanStorage(BaseComponent, ABC):
|
||||
def append_span(self, span: Span):
|
||||
"""Store the given span. This needs to be implemented by subclasses."""
|
||||
|
||||
def append_span_batch(self, spans: List[Span]):
|
||||
"""Store the span batch"""
|
||||
for span in spans:
|
||||
self.append_span(span)
|
||||
|
||||
|
||||
class Tracer(BaseComponent, ABC):
|
||||
"""Abstract base class for tracing operations.
|
||||
|
||||
@ -5,11 +5,12 @@ import datetime
|
||||
import threading
|
||||
import queue
|
||||
import logging
|
||||
from typing import Optional, List
|
||||
from concurrent.futures import Executor, ThreadPoolExecutor
|
||||
|
||||
from pilot.component import SystemApp
|
||||
from pilot.utils.tracer.base import Span, SpanStorage
|
||||
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
@ -24,8 +25,81 @@ class MemorySpanStorage(SpanStorage):
|
||||
self.spans.append(span)
|
||||
|
||||
|
||||
class SpanStorageContainer(SpanStorage):
|
||||
def __init__(
|
||||
self,
|
||||
system_app: SystemApp | None = None,
|
||||
batch_size=10,
|
||||
flush_interval=10,
|
||||
executor: Executor = None,
|
||||
):
|
||||
super().__init__(system_app)
|
||||
if not executor:
|
||||
executor = ThreadPoolExecutor(thread_name_prefix="trace_storage_sync_")
|
||||
self.executor = executor
|
||||
self.storages: List[SpanStorage] = []
|
||||
self.last_date = (
|
||||
datetime.datetime.now().date()
|
||||
) # Store the current date for checking date changes
|
||||
self.queue = queue.Queue()
|
||||
self.batch_size = batch_size
|
||||
self.flush_interval = flush_interval
|
||||
self.last_flush_time = time.time()
|
||||
self.flush_signal_queue = queue.Queue()
|
||||
self.flush_thread = threading.Thread(
|
||||
target=self._flush_to_storages, daemon=True
|
||||
)
|
||||
self.flush_thread.start()
|
||||
|
||||
def append_storage(self, storage: SpanStorage):
|
||||
"""Append sotrage to container
|
||||
|
||||
Args:
|
||||
storage ([`SpanStorage`]): The storage to be append to current container
|
||||
"""
|
||||
self.storages.append(storage)
|
||||
|
||||
def append_span(self, span: Span):
|
||||
self.queue.put(span)
|
||||
if self.queue.qsize() >= self.batch_size:
|
||||
try:
|
||||
self.flush_signal_queue.put_nowait(True)
|
||||
except queue.Full:
|
||||
pass # If the signal queue is full, it's okay. The flush thread will handle it.
|
||||
|
||||
def _flush_to_storages(self):
|
||||
while True:
|
||||
interval = time.time() - self.last_flush_time
|
||||
if interval < self.flush_interval:
|
||||
try:
|
||||
self.flush_signal_queue.get(
|
||||
block=True, timeout=self.flush_interval - interval
|
||||
)
|
||||
except Exception:
|
||||
# Timeout
|
||||
pass
|
||||
|
||||
spans_to_write = []
|
||||
while not self.queue.empty():
|
||||
spans_to_write.append(self.queue.get())
|
||||
for s in self.storages:
|
||||
|
||||
def append_and_ignore_error(
|
||||
storage: SpanStorage, spans_to_write: List[SpanStorage]
|
||||
):
|
||||
try:
|
||||
storage.append_span_batch(spans_to_write)
|
||||
except Exception as e:
|
||||
logger.warn(
|
||||
f"Append spans to storage {str(storage)} failed: {str(e)}, span_data: {spans_to_write}"
|
||||
)
|
||||
|
||||
self.executor.submit(append_and_ignore_error, s, spans_to_write)
|
||||
self.last_flush_time = time.time()
|
||||
|
||||
|
||||
class FileSpanStorage(SpanStorage):
|
||||
def __init__(self, filename: str, batch_size=10, flush_interval=10):
|
||||
def __init__(self, filename: str):
|
||||
super().__init__()
|
||||
self.filename = filename
|
||||
# Split filename into prefix and suffix
|
||||
@ -36,29 +110,18 @@ class FileSpanStorage(SpanStorage):
|
||||
datetime.datetime.now().date()
|
||||
) # Store the current date for checking date changes
|
||||
self.queue = queue.Queue()
|
||||
self.batch_size = batch_size
|
||||
self.flush_interval = flush_interval
|
||||
self.last_flush_time = time.time()
|
||||
self.flush_signal_queue = queue.Queue()
|
||||
|
||||
if not os.path.exists(filename):
|
||||
# New file if not exist
|
||||
os.makedirs(os.path.dirname(filename), exist_ok=True)
|
||||
with open(filename, "a"):
|
||||
pass
|
||||
self.flush_thread = threading.Thread(target=self._flush_to_file, daemon=True)
|
||||
self.flush_thread.start()
|
||||
|
||||
def append_span(self, span: Span):
|
||||
span_data = span.to_dict()
|
||||
logger.debug(f"append span: {span_data}")
|
||||
self.queue.put(span_data)
|
||||
self._write_to_file([span])
|
||||
|
||||
if self.queue.qsize() >= self.batch_size:
|
||||
try:
|
||||
self.flush_signal_queue.put_nowait(True)
|
||||
except queue.Full:
|
||||
pass # If the signal queue is full, it's okay. The flush thread will handle it.
|
||||
def append_span_batch(self, spans: List[Span]):
|
||||
self._write_to_file(spans)
|
||||
|
||||
def _get_dated_filename(self, date: datetime.date) -> str:
|
||||
"""Return the filename based on a specific date."""
|
||||
@ -73,31 +136,15 @@ class FileSpanStorage(SpanStorage):
|
||||
os.rename(self.filename, self._get_dated_filename(self.last_date))
|
||||
self.last_date = current_date
|
||||
|
||||
def _write_to_file(self):
|
||||
def _write_to_file(self, spans: List[Span]):
|
||||
self._roll_over_if_needed()
|
||||
spans_to_write = []
|
||||
while not self.queue.empty():
|
||||
spans_to_write.append(self.queue.get())
|
||||
|
||||
with open(self.filename, "a") as file:
|
||||
for span_data in spans_to_write:
|
||||
for span in spans:
|
||||
span_data = span.to_dict()
|
||||
try:
|
||||
file.write(json.dumps(span_data, ensure_ascii=False) + "\n")
|
||||
except Exception as e:
|
||||
logger.warning(
|
||||
f"Write span to file failed: {str(e)}, span_data: {span_data}"
|
||||
)
|
||||
|
||||
def _flush_to_file(self):
|
||||
while True:
|
||||
interval = time.time() - self.last_flush_time
|
||||
if interval < self.flush_interval:
|
||||
try:
|
||||
self.flush_signal_queue.get(
|
||||
block=True, timeout=self.flush_interval - interval
|
||||
)
|
||||
except Exception:
|
||||
# Timeout
|
||||
pass
|
||||
self._write_to_file()
|
||||
self.last_flush_time = time.time()
|
||||
|
||||
@ -7,44 +7,53 @@ import time
|
||||
from unittest.mock import patch
|
||||
from datetime import datetime, timedelta
|
||||
|
||||
from pilot.utils.tracer import SpanStorage, FileSpanStorage, Span, SpanType
|
||||
from pilot.utils.tracer import (
|
||||
SpanStorage,
|
||||
FileSpanStorage,
|
||||
Span,
|
||||
SpanType,
|
||||
SpanStorageContainer,
|
||||
)
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def storage(request):
|
||||
if not request or not hasattr(request, "param"):
|
||||
batch_size = 10
|
||||
flush_interval = 10
|
||||
file_does_not_exist = False
|
||||
else:
|
||||
batch_size = request.param.get("batch_size", 10)
|
||||
flush_interval = request.param.get("flush_interval", 10)
|
||||
file_does_not_exist = request.param.get("file_does_not_exist", False)
|
||||
|
||||
if file_does_not_exist:
|
||||
with tempfile.TemporaryDirectory() as tmp_dir:
|
||||
filename = os.path.join(tmp_dir, "non_existent_file.jsonl")
|
||||
storage_instance = FileSpanStorage(
|
||||
filename, batch_size=batch_size, flush_interval=flush_interval
|
||||
)
|
||||
storage_instance = FileSpanStorage(filename)
|
||||
yield storage_instance
|
||||
else:
|
||||
with tempfile.NamedTemporaryFile(delete=True) as tmp_file:
|
||||
filename = tmp_file.name
|
||||
storage_instance = FileSpanStorage(
|
||||
filename, batch_size=batch_size, flush_interval=flush_interval
|
||||
)
|
||||
storage_instance = FileSpanStorage(filename)
|
||||
yield storage_instance
|
||||
|
||||
|
||||
@pytest.fixture
|
||||
def storage_container(request):
|
||||
if not request or not hasattr(request, "param"):
|
||||
batch_size = 10
|
||||
flush_interval = 10
|
||||
else:
|
||||
batch_size = request.param.get("batch_size", 10)
|
||||
flush_interval = request.param.get("flush_interval", 10)
|
||||
storage_container = SpanStorageContainer(
|
||||
batch_size=batch_size, flush_interval=flush_interval
|
||||
)
|
||||
yield storage_container
|
||||
|
||||
|
||||
def read_spans_from_file(filename):
|
||||
with open(filename, "r") as f:
|
||||
return [json.loads(line) for line in f.readlines()]
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"storage", [{"batch_size": 1, "flush_interval": 5}], indirect=True
|
||||
)
|
||||
def test_write_span(storage: SpanStorage):
|
||||
span = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
storage.append_span(span)
|
||||
@ -55,9 +64,6 @@ def test_write_span(storage: SpanStorage):
|
||||
assert spans_in_file[0]["trace_id"] == "1"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"storage", [{"batch_size": 1, "flush_interval": 5}], indirect=True
|
||||
)
|
||||
def test_incremental_write(storage: SpanStorage):
|
||||
span1 = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
span2 = Span("2", "c", SpanType.BASE, "d", "op2")
|
||||
@ -70,9 +76,6 @@ def test_incremental_write(storage: SpanStorage):
|
||||
assert len(spans_in_file) == 2
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"storage", [{"batch_size": 2, "flush_interval": 5}], indirect=True
|
||||
)
|
||||
def test_sync_and_async_append(storage: SpanStorage):
|
||||
span = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
|
||||
@ -88,27 +91,7 @@ def test_sync_and_async_append(storage: SpanStorage):
|
||||
assert len(spans_in_file) == 2
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_flush_policy(storage: SpanStorage):
|
||||
span = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
|
||||
for _ in range(storage.batch_size - 1):
|
||||
storage.append_span(span)
|
||||
|
||||
spans_in_file = read_spans_from_file(storage.filename)
|
||||
assert len(spans_in_file) == 0
|
||||
|
||||
# Trigger batch write
|
||||
storage.append_span(span)
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
spans_in_file = read_spans_from_file(storage.filename)
|
||||
assert len(spans_in_file) == storage.batch_size
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"storage", [{"batch_size": 2, "file_does_not_exist": True}], indirect=True
|
||||
)
|
||||
@pytest.mark.parametrize("storage", [{"file_does_not_exist": True}], indirect=True)
|
||||
def test_non_existent_file(storage: SpanStorage):
|
||||
span = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
span2 = Span("2", "c", SpanType.BASE, "d", "op2")
|
||||
@ -116,7 +99,7 @@ def test_non_existent_file(storage: SpanStorage):
|
||||
time.sleep(0.1)
|
||||
|
||||
spans_in_file = read_spans_from_file(storage.filename)
|
||||
assert len(spans_in_file) == 0
|
||||
assert len(spans_in_file) == 1
|
||||
|
||||
storage.append_span(span2)
|
||||
time.sleep(0.1)
|
||||
@ -126,9 +109,7 @@ def test_non_existent_file(storage: SpanStorage):
|
||||
assert spans_in_file[1]["trace_id"] == "2"
|
||||
|
||||
|
||||
@pytest.mark.parametrize(
|
||||
"storage", [{"batch_size": 1, "file_does_not_exist": True}], indirect=True
|
||||
)
|
||||
@pytest.mark.parametrize("storage", [{"file_does_not_exist": True}], indirect=True)
|
||||
def test_log_rollover(storage: SpanStorage):
|
||||
# mock start date
|
||||
mock_start_date = datetime(2023, 10, 18, 23, 59)
|
||||
@ -167,3 +148,27 @@ def test_log_rollover(storage: SpanStorage):
|
||||
spans_in_dated_file = read_spans_from_file(dated_filename)
|
||||
assert len(spans_in_dated_file) == 1
|
||||
assert spans_in_dated_file[0]["trace_id"] == "1"
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
@pytest.mark.parametrize("storage_container", [{"batch_size": 5}], indirect=True)
|
||||
async def test_container_flush_policy(
|
||||
storage_container: SpanStorageContainer, storage: FileSpanStorage
|
||||
):
|
||||
storage_container.append_storage(storage)
|
||||
span = Span("1", "a", SpanType.BASE, "b", "op1")
|
||||
|
||||
filename = storage.filename
|
||||
|
||||
for _ in range(storage_container.batch_size - 1):
|
||||
storage_container.append_span(span)
|
||||
|
||||
spans_in_file = read_spans_from_file(filename)
|
||||
assert len(spans_in_file) == 0
|
||||
|
||||
# Trigger batch write
|
||||
storage_container.append_span(span)
|
||||
await asyncio.sleep(0.1)
|
||||
|
||||
spans_in_file = read_spans_from_file(filename)
|
||||
assert len(spans_in_file) == storage_container.batch_size
|
||||
|
||||
@ -303,8 +303,6 @@ def chat(
|
||||
print(table.get_formatted_string(out_format=output, **out_kwargs))
|
||||
if sys_table:
|
||||
print(sys_table.get_formatted_string(out_format=output, **out_kwargs))
|
||||
if hide_conv:
|
||||
return
|
||||
|
||||
if not found_trace_id:
|
||||
print(f"Can't found conversation with trace_id: {trace_id}")
|
||||
@ -315,9 +313,12 @@ def chat(
|
||||
trace_spans = [s for s in reversed(trace_spans)]
|
||||
hierarchy = _build_trace_hierarchy(trace_spans)
|
||||
if tree:
|
||||
print("\nInvoke Trace Tree:\n")
|
||||
print(f"\nInvoke Trace Tree(trace_id: {trace_id}):\n")
|
||||
_print_trace_hierarchy(hierarchy)
|
||||
|
||||
if hide_conv:
|
||||
return
|
||||
|
||||
trace_spans = _get_ordered_trace_from(hierarchy)
|
||||
table = PrettyTable(["Key", "Value Value"], title="Chat Trace Details")
|
||||
split_long_text = output == "text"
|
||||
@ -340,36 +341,43 @@ def chat(
|
||||
table.add_row(["echo", metadata.get("echo")])
|
||||
elif "error" in metadata:
|
||||
table.add_row(["BaseChat Error", metadata.get("error")])
|
||||
if op == "BaseChat.nostream_call" and not sp["end_time"]:
|
||||
if "model_output" in metadata:
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat model_output",
|
||||
split_string_by_terminal_width(
|
||||
metadata.get("model_output").get("text"),
|
||||
split=split_long_text,
|
||||
),
|
||||
]
|
||||
)
|
||||
if "ai_response_text" in metadata:
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat ai_response_text",
|
||||
split_string_by_terminal_width(
|
||||
metadata.get("ai_response_text"), split=split_long_text
|
||||
),
|
||||
]
|
||||
)
|
||||
if "prompt_define_response" in metadata:
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat prompt_define_response",
|
||||
split_string_by_terminal_width(
|
||||
metadata.get("prompt_define_response"),
|
||||
split=split_long_text,
|
||||
),
|
||||
]
|
||||
if op == "BaseChat.do_action" and not sp["end_time"]:
|
||||
if "model_output" in metadata:
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat model_output",
|
||||
split_string_by_terminal_width(
|
||||
metadata.get("model_output").get("text"),
|
||||
split=split_long_text,
|
||||
),
|
||||
]
|
||||
)
|
||||
if "ai_response_text" in metadata:
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat ai_response_text",
|
||||
split_string_by_terminal_width(
|
||||
metadata.get("ai_response_text"), split=split_long_text
|
||||
),
|
||||
]
|
||||
)
|
||||
if "prompt_define_response" in metadata:
|
||||
prompt_define_response = metadata.get("prompt_define_response") or ""
|
||||
if isinstance(prompt_define_response, dict) or isinstance(
|
||||
prompt_define_response, type([])
|
||||
):
|
||||
prompt_define_response = json.dumps(
|
||||
prompt_define_response, ensure_ascii=False
|
||||
)
|
||||
table.add_row(
|
||||
[
|
||||
"BaseChat prompt_define_response",
|
||||
split_string_by_terminal_width(
|
||||
prompt_define_response,
|
||||
split=split_long_text,
|
||||
),
|
||||
]
|
||||
)
|
||||
if op == "DefaultModelWorker_call.generate_stream_func":
|
||||
if not sp["end_time"]:
|
||||
table.add_row(["llm_adapter", metadata.get("llm_adapter")])
|
||||
|
||||
@ -1,6 +1,10 @@
|
||||
from typing import Dict, Optional
|
||||
from contextvars import ContextVar
|
||||
from functools import wraps
|
||||
import asyncio
|
||||
import inspect
|
||||
import logging
|
||||
|
||||
|
||||
from pilot.component import SystemApp, ComponentType
|
||||
from pilot.utils.tracer.base import (
|
||||
@ -12,6 +16,9 @@ from pilot.utils.tracer.base import (
|
||||
TracerContext,
|
||||
)
|
||||
from pilot.utils.tracer.span_storage import MemorySpanStorage
|
||||
from pilot.utils.module_utils import import_from_checked_string
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
|
||||
class DefaultTracer(Tracer):
|
||||
@ -154,26 +161,51 @@ class TracerManager:
|
||||
root_tracer: TracerManager = TracerManager()
|
||||
|
||||
|
||||
def trace(operation_name: str, **trace_kwargs):
|
||||
def trace(operation_name: Optional[str] = None, **trace_kwargs):
|
||||
def decorator(func):
|
||||
@wraps(func)
|
||||
async def wrapper(*args, **kwargs):
|
||||
with root_tracer.start_span(operation_name, **trace_kwargs):
|
||||
def sync_wrapper(*args, **kwargs):
|
||||
name = (
|
||||
operation_name if operation_name else _parse_operation_name(func, *args)
|
||||
)
|
||||
with root_tracer.start_span(name, **trace_kwargs):
|
||||
return func(*args, **kwargs)
|
||||
|
||||
@wraps(func)
|
||||
async def async_wrapper(*args, **kwargs):
|
||||
name = (
|
||||
operation_name if operation_name else _parse_operation_name(func, *args)
|
||||
)
|
||||
with root_tracer.start_span(name, **trace_kwargs):
|
||||
return await func(*args, **kwargs)
|
||||
|
||||
return wrapper
|
||||
if asyncio.iscoroutinefunction(func):
|
||||
return async_wrapper
|
||||
else:
|
||||
return sync_wrapper
|
||||
|
||||
return decorator
|
||||
|
||||
|
||||
def _parse_operation_name(func, *args):
|
||||
self_name = None
|
||||
if inspect.signature(func).parameters.get("self"):
|
||||
self_name = args[0].__class__.__name__
|
||||
func_name = func.__name__
|
||||
if self_name:
|
||||
return f"{self_name}.{func_name}"
|
||||
return func_name
|
||||
|
||||
|
||||
def initialize_tracer(
|
||||
system_app: SystemApp,
|
||||
tracer_filename: str,
|
||||
root_operation_name: str = "DB-GPT-Web-Entry",
|
||||
tracer_storage_cls: str = None,
|
||||
):
|
||||
if not system_app:
|
||||
return
|
||||
from pilot.utils.tracer.span_storage import FileSpanStorage
|
||||
from pilot.utils.tracer.span_storage import FileSpanStorage, SpanStorageContainer
|
||||
|
||||
trace_context_var = ContextVar(
|
||||
"trace_context",
|
||||
@ -181,7 +213,15 @@ def initialize_tracer(
|
||||
)
|
||||
tracer = DefaultTracer(system_app)
|
||||
|
||||
system_app.register_instance(FileSpanStorage(tracer_filename))
|
||||
storage_container = SpanStorageContainer(system_app)
|
||||
storage_container.append_storage(FileSpanStorage(tracer_filename))
|
||||
|
||||
if tracer_storage_cls:
|
||||
logger.info(f"Begin parse storage class {tracer_storage_cls}")
|
||||
storage = import_from_checked_string(tracer_storage_cls, SpanStorage)
|
||||
storage_container.append_storage(storage())
|
||||
|
||||
system_app.register_instance(storage_container)
|
||||
system_app.register_instance(tracer)
|
||||
root_tracer.initialize(system_app, trace_context_var)
|
||||
if system_app.app:
|
||||
|
||||
Loading…
Reference in New Issue
Block a user