feat:merge main branch

2025-08-15 23:13:15 +00:00 · 2023-11-03 10:27:48 +08:00 · 2023-11-03 10:27:48 +08:00 · 6fe7bfd63d
commit 6fe7bfd63d
parent 9cc6386301 7f5bd8d1c2
37 changed files with 1550 additions and 166 deletions
--- a/126
+++ b/126
@ -0,0 +1,126 @@
+# Contributor Covenant Code of Conduct
+
+## Our Pledge
+
+We as members, contributors, and leaders pledge to make participation in our
+community a harassment-free experience for everyone, regardless of age, body
+size, visible or invisible disability, ethnicity, sex characteristics, gender
+identity and expression, level of experience, education, socio-economic status,
+nationality, personal appearance, race, caste, color, religion, or sexual
+identity and orientation.
+
+We pledge to act and interact in ways that contribute to an open, welcoming,
+diverse, inclusive, and healthy community.
+
+## Our Standards
+
+Examples of behavior that contributes to a positive environment for our
+community include:
+
+* Demonstrating empathy and kindness toward other people
+* Being respectful of differing opinions, viewpoints, and experiences
+* Giving and gracefully accepting constructive feedback
+* Accepting responsibility and apologizing to those affected by our mistakes,
+  and learning from the experience
+* Focusing on what is best not just for us as individuals, but for the overall
+  community
+
+Examples of unacceptable behavior include:
+
+* The use of sexualized language or imagery, and sexual attention or advances of
+  any kind
+* Trolling, insulting or derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or email address,
+  without their explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+  professional setting
+
+## Enforcement Responsibilities
+
+Community leaders are responsible for clarifying and enforcing our standards of
+acceptable behavior and will take appropriate and fair corrective action in
+response to any behavior that they deem inappropriate, threatening, offensive,
+or harmful.
+
+Community leaders have the right and responsibility to remove, edit, or reject
+comments, commits, code, wiki edits, issues, and other contributions that are
+not aligned to this Code of Conduct, and will communicate reasons for moderation
+decisions when appropriate.
+
+## Scope
+
+This Code of Conduct applies within all community spaces, and also applies when
+an individual is officially representing the community in public spaces.
+Examples of representing our community include using an official e-mail address,
+posting via an official social media account, or acting as an appointed
+representative at an online or offline event.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported to the community leaders responsible for enforcement at
+[INSERT CONTACT METHOD].
+All complaints will be reviewed and investigated promptly and fairly.
+
+All community leaders are obligated to respect the privacy and security of the
+reporter of any incident.
+
+## Enforcement Guidelines
+
+Community leaders will follow these Community Impact Guidelines in determining
+the consequences for any action they deem in violation of this Code of Conduct:
+
+### 1. Correction
+
+*Community Impact*: Use of inappropriate language or other behavior deemed
+unprofessional or unwelcome in the community.
+
+*Consequence*: A private, written warning from community leaders, providing
+clarity around the nature of the violation and an explanation of why the
+behavior was inappropriate. A public apology may be requested.
+
+### 2. Warning
+
+*Community Impact*: A violation through a single incident or series of
+actions.
+
+*Consequence*: A warning with consequences for continued behavior. No
+interaction with the people involved, including unsolicited interaction with
+those enforcing the Code of Conduct, for a specified period of time. This
+includes avoiding interactions in community spaces as well as external channels
+like social media. Violating these terms may lead to a temporary or permanent
+ban.
+
+### 3. Temporary Ban
+
+*Community Impact*: A serious violation of community standards, including
+sustained inappropriate behavior.
+
+*Consequence*: A temporary ban from any sort of interaction or public
+communication with the community for a specified period of time. No public or
+private interaction with the people involved, including unsolicited interaction
+with those enforcing the Code of Conduct, is allowed during this period.
+Violating these terms may lead to a permanent ban.
+
+### 4. Permanent Ban
+
+*Community Impact*: Demonstrating a pattern of violation of community
+standards, including sustained inappropriate behavior, harassment of an
+individual, or aggression toward or disparagement of classes of individuals.
+
+*Consequence*: A permanent ban from any sort of public interaction within the
+community.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage],
+version 2.1, available at
+[https://www.contributor-covenant.org/version/2/1/code_of_conduct.html][v2.1].
+
+Community Impact Guidelines were inspired by
+[Mozilla's code of conduct enforcement ladder][Mozilla CoC].
+
+For answers to common questions about this code of conduct, see the FAQ at
+[https://www.contributor-covenant.org/faq][FAQ]. Translations are available at
+[https://www.contributor-covenant.org/translations][translations].
--- a/README.md
+++ b/README.md
@ -43,8 +43,8 @@ DB-GPT is an experimental open-source project that uses localized GPT large mode


 ## Contents
- [install](#install)
- [demo](#demo)
+- [Install](#install)
+- [Demo](#demo)
 - [introduction](#introduction)
 - [features](#features)
 - [contribution](#contribution)
@ -177,7 +177,7 @@ Currently, we have released multiple key features, which are listed below to dem
 | [StarRocks](https://github.com/StarRocks/starrocks)                             | No          |           TODO                              |

 ## Introduction 
-Is the architecture of the entire DB-GPT shown in the following figure:
+The architecture of the entire DB-GPT is shown.

 <p align="center">
  <img src="./assets/DB-GPT.png" width="800" />
@ -213,7 +213,7 @@ The core capabilities mainly consist of the following parts:

 ## Contribution

- Please run `black .` before submitting the code. Contributing guidelines, [how to contribution](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)
+- Please run `black .` before submitting the code. Contributing guidelines, [how to contribute](https://github.com/csunny/DB-GPT/blob/main/CONTRIBUTING.md)

 ## RoadMap

@ -330,7 +330,7 @@ As of October 10, 2023, by fine-tuning an open-source model of 13 billion parame
 The MIT License (MIT)

 ## Contact Information
-We are working on building a community, if you have any ideas about building the community, feel free to contact us.
+We are working on building a community, if you have any ideas for building the community, feel free to contact us.
 [![](https://dcbadge.vercel.app/api/server/nASQyBjvY?compact=true&style=flat)](https://discord.gg/nASQyBjvY)

 <p align="center">
--- a/assets/wechat.jpg
+++ b/assets/wechat.jpg
--- a/docker/compose_examples/cluster-docker-compose.yml
+++ b/docker/compose_examples/cluster-docker-compose.yml
@ -7,6 +7,16 @@ services:
    restart: unless-stopped
    networks:
      - dbgptnet
+  api-server:
+    image: eosphorosai/dbgpt:latest
+    command: dbgpt start apiserver --controller_addr http://controller:8000
+    restart: unless-stopped
+    depends_on:
+      - controller
+    networks:
+      - dbgptnet
+    ports:
+      - 8100:8100/tcp
  llm-worker:
    image: eosphorosai/dbgpt:latest
    command: dbgpt start worker --model_name vicuna-13b-v1.5 --model_path /app/models/vicuna-13b-v1.5 --port 8001 --controller_addr http://controller:8000
--- a/docs/conf.py
+++ b/docs/conf.py
@ -32,7 +32,7 @@ extensions = [
    "sphinx_panels",
    "sphinx_tabs.tabs",
    "IPython.sphinxext.ipython_console_highlighting",
-    'sphinx.ext.autosectionlabel'
+    "sphinx.ext.autosectionlabel",
 ]
 source_suffix = [".ipynb", ".html", ".md", ".rst"]

--- a/docs/getting_started/install/cluster/cluster.rst
+++ b/docs/getting_started/install/cluster/cluster.rst
@ -77,3 +77,4 @@ By analyzing this information, we can identify performance bottlenecks in model

   ./vms/standalone.md
   ./vms/index.md
+   ./openai.md
--- a/docs/getting_started/install/cluster/openai.md
+++ b/docs/getting_started/install/cluster/openai.md
@ -0,0 +1,51 @@
+OpenAI-Compatible RESTful APIs
+==================================
+(openai-apis-index)=
+
+### Install Prepare
+
+You must [deploy DB-GPT cluster](https://db-gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html) first.
+
+### Launch Model API Server
+
+```bash
+dbgpt start apiserver --controller_addr http://127.0.0.1:8000 --api_keys EMPTY
+```
+By default, the Model API Server starts on port 8100.
+
+### Validate with cURL
+
+#### List models
+
+```bash
+curl http://127.0.0.1:8100/api/v1/models \
+-H "Authorization: Bearer EMPTY" \
+-H "Content-Type: application/json"
+```
+
+#### Chat completions
+
+```bash
+curl http://127.0.0.1:8100/api/v1/chat/completions \
+-H "Authorization: Bearer EMPTY" \
+-H "Content-Type: application/json" \
+-d '{"model": "vicuna-13b-v1.5", "messages": [{"role": "user", "content": "hello"}]}'
+```
+
+### Validate with OpenAI Official SDK
+
+#### Chat completions
+
+```python
+import openai
+openai.api_key = "EMPTY"
+openai.api_base = "http://127.0.0.1:8100/api/v1"
+model = "vicuna-13b-v1.5"
+
+completion = openai.ChatCompletion.create(
+  model=model,
+  messages=[{"role": "user", "content": "hello"}]
+)
+# print the completion
+print(completion.choices[0].message.content)
+```
--- a/docs/getting_started/install/llm/proxyllm/proxyllm.md
+++ b/docs/getting_started/install/llm/proxyllm/proxyllm.md
@ -24,9 +24,12 @@ PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions

 #Azure
 LLM_MODEL=chatgpt_proxyllm
-OPENAI_API_TYPE=azure
-PROXY_API_KEY={your-openai-sk}
-PROXY_SERVER_URL=https://xx.openai.azure.com/v1/chat/completions
+PROXY_API_KEY={your-azure-sk}
+PROXY_API_BASE=https://{your domain}.openai.azure.com/
+PROXY_API_TYPE=azure
+PROXY_SERVER_URL=xxxx
+PROXY_API_VERSION=2023-05-15
+PROXYLLM_BACKEND=gpt-35-turbo

 #Aliyun tongyi
 LLM_MODEL=tongyi_proxyllm
--- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/cluster/openai.po
+++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/cluster/openai.po
@ -0,0 +1,71 @@
+# SOME DESCRIPTIVE TITLE.
+# Copyright (C) 2023, csunny
+# This file is distributed under the same license as the DB-GPT package.
+# FIRST AUTHOR <EMAIL@ADDRESS>, 2023.
+#
+#, fuzzy
+msgid ""
+msgstr ""
+"Project-Id-Version: DB-GPT 👏👏 0.4.0\n"
+"Report-Msgid-Bugs-To: \n"
+"POT-Creation-Date: 2023-11-02 21:09+0800\n"
+"PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
+"Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
+"Language: zh_CN\n"
+"Language-Team: zh_CN <LL@li.org>\n"
+"Plural-Forms: nplurals=1; plural=0;\n"
+"MIME-Version: 1.0\n"
+"Content-Type: text/plain; charset=utf-8\n"
+"Content-Transfer-Encoding: 8bit\n"
+"Generated-By: Babel 2.12.1\n"
+
+#: ../../getting_started/install/cluster/openai.md:1
+#: 01f4e2bf853341198633b367efec1522
+msgid "OpenAI-Compatible RESTful APIs"
+msgstr "OpenAI RESTful 兼容接口"
+
+#: ../../getting_started/install/cluster/openai.md:5
+#: d8717e42335e4027bf4e76b3d28768ee
+msgid "Install Prepare"
+msgstr "安装准备"
+
+#: ../../getting_started/install/cluster/openai.md:7
+#: 9a48d8ee116942468de4c6faf9a64758
+msgid ""
+"You must [deploy DB-GPT cluster](https://db-"
+"gpt.readthedocs.io/en/latest/getting_started/install/cluster/vms/index.html)"
+" first."
+msgstr "你必须先部署 [DB-GPT 集群]"
+"(https://db-gpt.readthedocs.io/projects/db-gpt-docs-zh-cn/zh-cn/latest/getting_started/install/cluster/vms/index.html)。"
+
+#: ../../getting_started/install/cluster/openai.md:9
+#: 7673a7121f004f7ca6b1a94a7e238fa3
+msgid "Launch Model API Server"
+msgstr "启动模型 API Server"
+
+#: ../../getting_started/install/cluster/openai.md:14
+#: 84a925c2cbcd4e4895a1d2d2fe8f720f
+msgid "By default, the Model API Server starts on port 8100."
+msgstr "默认情况下，模型 API Server 使用 8100 端口启动。"
+
+#: ../../getting_started/install/cluster/openai.md:16
+#: e53ed41977cd4721becd51eba05c6609
+msgid "Validate with cURL"
+msgstr "通过 cURL 验证"
+
+#: ../../getting_started/install/cluster/openai.md:18
+#: 7c883b410b5c4e53a256bf17c1ded80d
+msgid "List models"
+msgstr "列出模型"
+
+#: ../../getting_started/install/cluster/openai.md:26
+#: ../../getting_started/install/cluster/openai.md:37
+#: 7cf0ed13f0754f149ec085cd6cf7a45a 990d5d5ed5d64ab49550e68495b9e7a0
+msgid "Chat completions"
+msgstr ""
+
+#: ../../getting_started/install/cluster/openai.md:35
+#: 81583edd22df44e091d18a0832278131
+msgid "Validate with OpenAI Official SDK"
+msgstr "通过 OpenAI 官方 SDK 验证"
+
--- a/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po
+++ b/docs/locales/zh_CN/LC_MESSAGES/getting_started/install/environment/environment.po
@ -8,7 +8,7 @@ msgid ""
 msgstr ""
 "Project-Id-Version: DB-GPT 👏👏 0.3.5\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2023-11-02 10:10+0800\n"
+"POT-Creation-Date: 2023-11-02 21:04+0800\n"
 "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 "Language: zh_CN\n"
@ -20,292 +20,292 @@ msgstr ""
 "Generated-By: Babel 2.12.1\n"

 #: ../../getting_started/install/environment/environment.md:1
-#: 28d2f84fc8884e78afad8118cd59c654
+#: a17719d2f4374285a7beb4d1db470146
 #, fuzzy
 msgid "Environment Parameter"
 msgstr "环境变量说明"

 #: ../../getting_started/install/environment/environment.md:4
-#: c83fbb5e1aa643cdb09fffe7f3d1a3c5
+#: 9a62e6fff7914eeaa2d195ddef4fcb61
 msgid "LLM MODEL Config"
 msgstr "模型配置"

 #: ../../getting_started/install/environment/environment.md:5
-#: eb675965ae57407e8d8bf90fed8e9e2a
+#: 90e3991538324ecfac8cac7ef2103ac2
 msgid "LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG"
 msgstr "LLM Model Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG"

 #: ../../getting_started/install/environment/environment.md:6
-#: 5d28d35126d849ea9b0d963fd1ba8699
+#: 1f45af01100c4586acbc05469e3006bc
 msgid "LLM_MODEL=vicuna-13b"
 msgstr "LLM_MODEL=vicuna-13b"

 #: ../../getting_started/install/environment/environment.md:8
-#: 01955b2d0fbe4d94939ebf2cbb380bdd
+#: bed14b704f154c2db525f7fafd3aa5a4
 msgid "MODEL_SERVER_ADDRESS"
 msgstr "MODEL_SERVER_ADDRESS"

 #: ../../getting_started/install/environment/environment.md:9
-#: 4eaaa9ab59854c0386b28b3111c82784
+#: ea42946cfe4f4ad996bf82c1996e7344
 msgid "MODEL_SERVER=http://127.0.0.1:8000  LIMIT_MODEL_CONCURRENCY"
 msgstr "MODEL_SERVER=http://127.0.0.1:8000  LIMIT_MODEL_CONCURRENCY"

 #: ../../getting_started/install/environment/environment.md:12
-#: 5c2dd05e16834443b7451c2541b59757
+#: 021c261231f342fdba34098b1baa06fd
 msgid "LIMIT_MODEL_CONCURRENCY=5"
 msgstr "LIMIT_MODEL_CONCURRENCY=5"

 #: ../../getting_started/install/environment/environment.md:14
-#: 7707836c2fb04e7da13d2d59b5f9566f
+#: afaf0ba7fd09463d8ff74b514ed7264c
 msgid "MAX_POSITION_EMBEDDINGS"
 msgstr "MAX_POSITION_EMBEDDINGS"

 #: ../../getting_started/install/environment/environment.md:16
-#: ee24a7d3d8384e61b715ef3bd362b965
+#: e4517a942bca4361a64a00408f993f5b
 msgid "MAX_POSITION_EMBEDDINGS=4096"
 msgstr "MAX_POSITION_EMBEDDINGS=4096"

 #: ../../getting_started/install/environment/environment.md:18
-#: 90b51aa4e46b4d1298c672e0052c2f68
+#: 78d2ef04ed4548b9b7b0fb8ae35c9d5c
 msgid "QUANTIZE_QLORA"
 msgstr "QUANTIZE_QLORA"

 #: ../../getting_started/install/environment/environment.md:20
-#: 7de7a8eb431e4973ae00f68ca0686281
+#: bfa65db03c6d46bba293331f03ab15ac
 msgid "QUANTIZE_QLORA=True"
 msgstr "QUANTIZE_QLORA=True"

 #: ../../getting_started/install/environment/environment.md:22
-#: e331ca016a474f4aa4e9182165a2693a
+#: 1947d45a7f184821910b4834ad5f1897
 msgid "QUANTIZE_8bit"
 msgstr "QUANTIZE_8bit"

 #: ../../getting_started/install/environment/environment.md:24
-#: 519ccce5a0884778be2719c437a17bd4
+#: 4a2ee2919d0e4bdaa13c9d92eefd2aac
 msgid "QUANTIZE_8bit=True"
 msgstr "QUANTIZE_8bit=True"

 #: ../../getting_started/install/environment/environment.md:27
-#: 1c0586d070f046de8d0f9f94a6b508b4
+#: 348dc1e411b54ab09414f40a20e934e4
 msgid "LLM PROXY Settings"
 msgstr "LLM PROXY Settings"

 #: ../../getting_started/install/environment/environment.md:28
-#: c208c3f4b13f4b39962de814e5be6ab9
+#: a692e78425a040f5828ab54ff9a33f77
 msgid "OPENAI Key"
 msgstr "OPENAI Key"

 #: ../../getting_started/install/environment/environment.md:30
-#: 9228bbee2faa4467b1d24f1125faaac8
+#: 940d00e25a424acf92951a314a64e5ea
 msgid "PROXY_API_KEY={your-openai-sk}"
 msgstr "PROXY_API_KEY={your-openai-sk}"

 #: ../../getting_started/install/environment/environment.md:31
-#: 759ae581883348019c1ba79e8954728a
+#: 4bd27547ae6041679e91f2a363cd1deb
 msgid "PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions"
 msgstr "PROXY_SERVER_URL=https://api.openai.com/v1/chat/completions"

 #: ../../getting_started/install/environment/environment.md:33
-#: 83f3952917d34aab80bd34119f7d1e20
+#: cfa3071afb0b47baad6bd729d4a02cb9
 msgid "from https://bard.google.com/     f12-> application-> __Secure-1PSID"
 msgstr "from https://bard.google.com/     f12-> application-> __Secure-1PSID"

 #: ../../getting_started/install/environment/environment.md:35
-#: 1d70707ca82749bb90b2bed1aee44d62
+#: a17efa03b10f47f68afac9e865982a75
 msgid "BARD_PROXY_API_KEY={your-bard-token}"
 msgstr "BARD_PROXY_API_KEY={your-bard-token}"

 #: ../../getting_started/install/environment/environment.md:38
-#: 38a2091fa223493ea23cb9bbb33cf58e
+#: 6bcfe90574da4d82a459e8e11bf73cba
 msgid "DATABASE SETTINGS"
 msgstr "DATABASE SETTINGS"

 #: ../../getting_started/install/environment/environment.md:39
-#: 5134180d7a5945b48b072a1eb92b27ba
+#: 2b1e62d9bf5d4af5a22f68c8248eaafb
 msgid "SQLite database (Current default database)"
 msgstr "SQLite database (Current default database)"

 #: ../../getting_started/install/environment/environment.md:40
-#: 6875e2300e094668a45fa4f2551e0d30
+#: 8a909ac3b3c943da8dbc4e8dd596c80c
 msgid "LOCAL_DB_PATH=data/default_sqlite.db"
 msgstr "LOCAL_DB_PATH=data/default_sqlite.db"

 #: ../../getting_started/install/environment/environment.md:41
-#: 034e8f06f24f44af9d8184563f99b4b3
+#: 90ae6507932f4815b6e180051738bb93
 msgid "LOCAL_DB_TYPE=sqlite # Database Type default:sqlite"
 msgstr "LOCAL_DB_TYPE=sqlite # Database Type default:sqlite"

 #: ../../getting_started/install/environment/environment.md:43
-#: f688149a97f740269f80b79775236ce9
+#: d2ce34e0dcf44ccf9e8007d548ba7b0a
 msgid "MYSQL database"
 msgstr "MYSQL database"

 #: ../../getting_started/install/environment/environment.md:44
-#: 6db0b305137d45a3aa036e4f2262f460
+#: c07159d63c334f6cbb95fcc30bfb7ea5
 msgid "LOCAL_DB_TYPE=mysql"
 msgstr "LOCAL_DB_TYPE=mysql"

 #: ../../getting_started/install/environment/environment.md:45
-#: b6d662ce8d5f44f0b54a7f6e7c66f5a5
+#: e16700b2ea8d411e91d010c1cde7aecc
 msgid "LOCAL_DB_USER=root"
 msgstr "LOCAL_DB_USER=root"

 #: ../../getting_started/install/environment/environment.md:46
-#: cd7493d61ac9415283640dc6c018d2f4
+#: bfc2dce1bf374121b6861e677b4e1ffa
 msgid "LOCAL_DB_PASSWORD=aa12345678"
 msgstr "LOCAL_DB_PASSWORD=aa12345678"

 #: ../../getting_started/install/environment/environment.md:47
-#: 4ea2a622b23f4342a4c2ab7f8d9c4e8d
+#: bc384739f5b04e21a34d0d2b78e7906c
 msgid "LOCAL_DB_HOST=127.0.0.1"
 msgstr "LOCAL_DB_HOST=127.0.0.1"

 #: ../../getting_started/install/environment/environment.md:48
-#: 936db95a0ab246098028f4dbb596cd17
+#: e5253d452e0d42b7ac308fe6fbfb5017
 msgid "LOCAL_DB_PORT=3306"
 msgstr "LOCAL_DB_PORT=3306"

 #: ../../getting_started/install/environment/environment.md:51
-#: d9255f25989840ea9c9e7b34f3947c87
+#: 9ca8f6fe06ed4cbab390f94be252e165
 msgid "EMBEDDING SETTINGS"
 msgstr "EMBEDDING SETTINGS"

 #: ../../getting_started/install/environment/environment.md:52
-#: b09291d32aca43928a981e873476a985
+#: 76c7c260293c4b49bae057143fd48377
 msgid "EMBEDDING MODEL Name, see /pilot/configs/model_config.LLM_MODEL_CONFIG"
 msgstr "EMBEDDING模型, 参考see /pilot/configs/model_config.LLM_MODEL_CONFIG"

 #: ../../getting_started/install/environment/environment.md:53
-#: 63de573b03a54413b997f18a1ccee279
+#: f1d63a0128ce493cae37d34f1976bcca
 msgid "EMBEDDING_MODEL=text2vec"
 msgstr "EMBEDDING_MODEL=text2vec"

 #: ../../getting_started/install/environment/environment.md:55
-#: 0ef8defbab544bd0b9475a036f278489
+#: b8fbb99109d04781b2dd5bc5d6efa5bd
 msgid "Embedding Chunk size, default 500"
 msgstr "Embedding 切片大小, 默认500"

 #: ../../getting_started/install/environment/environment.md:57
-#: 33dbc7941d054baa8c6ecfc0bf1ce271
+#: bf8256576ea34f6a9c5f261ab9aab676
 msgid "KNOWLEDGE_CHUNK_SIZE=500"
 msgstr "KNOWLEDGE_CHUNK_SIZE=500"

 #: ../../getting_started/install/environment/environment.md:59
-#: e6ee9f2620ab45ecbc8e9c0642f5ca42
+#: 9b156c6b599b4c02a58ce023b4ff25f2
 msgid "Embedding Chunk Overlap, default 100"
 msgstr "Embedding chunk Overlap, 文本块之间的最大重叠量。保留一些重叠可以保持文本块之间的连续性（例如使用滑动窗口）,默认100"

 #: ../../getting_started/install/environment/environment.md:60
-#: fcddf64340a04df4ab95176fc2fc67a6
+#: dcafd903c36041ac85ac99a14dbee512
 msgid "KNOWLEDGE_CHUNK_OVERLAP=100"
 msgstr "KNOWLEDGE_CHUNK_OVERLAP=100"

 #: ../../getting_started/install/environment/environment.md:62
-#: 61272200194b4461a921581feb1273da
+#: 6c3244b7e5e24b0188c7af4bb52e9134
 #, fuzzy
 msgid "embedding recall top k,5"
 msgstr "embedding 召回topk, 默认5"

 #: ../../getting_started/install/environment/environment.md:64
-#: b433091f055542b1b89ff2d525ac99e4
+#: f4a2f30551cf4fe1a7ff3c7c74ec77be
 msgid "KNOWLEDGE_SEARCH_TOP_SIZE=5"
 msgstr "KNOWLEDGE_SEARCH_TOP_SIZE=5"

 #: ../../getting_started/install/environment/environment.md:66
-#: 1db0de41aebd4caa8cc2eaecb4cacd6a
+#: 593f2512362f467e92fdaa60dd5903a0
 #, fuzzy
 msgid "embedding recall max token ,2000"
 msgstr "embedding向量召回最大token, 默认2000"

 #: ../../getting_started/install/environment/environment.md:68
-#: 81b9d862e58941a4b09680a7520cdabe
+#: 83d6d28914be4d6282d457272e508ddc
 msgid "KNOWLEDGE_SEARCH_MAX_TOKEN=5"
 msgstr "KNOWLEDGE_SEARCH_MAX_TOKEN=5"

 #: ../../getting_started/install/environment/environment.md:71
 #: ../../getting_started/install/environment/environment.md:87
-#: cac73575d54544778bdee09b18532fd9 f78a509949a64f03aa330f31901e2e7a
+#: 6bc1b9d995e74294a1c78e783c550db7 d33c77ded834438e9f4a2df06e7e041a
 msgid "Vector Store SETTINGS"
 msgstr "Vector Store SETTINGS"

 #: ../../getting_started/install/environment/environment.md:72
 #: ../../getting_started/install/environment/environment.md:88
-#: 5ebba1cb047b4b09849000244237dfbb 7e9285e91bcb4b2d9413909c0d0a06a7
+#: 9cafa06e2d584f70afd848184e0fa52a f01057251b8b4ffea806192dfe1048ed
 msgid "Chroma"
 msgstr "Chroma"

 #: ../../getting_started/install/environment/environment.md:73
 #: ../../getting_started/install/environment/environment.md:89
-#: 05625cfcc23c4745ae1fa0d94ce5450c 3a8615f1507f4fc49d1adda5100a4edf
+#: e6c16fab37484769b819aeecbc13e6db faad299722e5400e95ec6ac3c1e018b8
 msgid "VECTOR_STORE_TYPE=Chroma"
 msgstr "VECTOR_STORE_TYPE=Chroma"

 #: ../../getting_started/install/environment/environment.md:74
 #: ../../getting_started/install/environment/environment.md:90
-#: 5b559376aea44f159262e6d4b75c7ec1 e954782861404b10b4e893e01cf74452
+#: 4eca3a51716d406f8ffd49c06550e871 581ee9dd38064b119660c44bdd00cbaa
 msgid "MILVUS"
 msgstr "MILVUS"

 #: ../../getting_started/install/environment/environment.md:75
 #: ../../getting_started/install/environment/environment.md:91
-#: 55ee8199c97a4929aeefd32370f2b92d 8f40c02543ea4a2ca9632dd9e8a08c2e
+#: 814c93048bed46589358a854d6c99683 b72b1269a2224f5f961214e41c019f21
 msgid "VECTOR_STORE_TYPE=Milvus"
 msgstr "VECTOR_STORE_TYPE=Milvus"

 #: ../../getting_started/install/environment/environment.md:76
 #: ../../getting_started/install/environment/environment.md:92
-#: 528a01d25720491c8e086bf43a62ad92 ba1386d551d7494a85681a2803081a6f
+#: 73ae665f1db9402883662734588fd02c c4da20319c994e83ba5a7706db967178
 msgid "MILVUS_URL=127.0.0.1"
 msgstr "MILVUS_URL=127.0.0.1"

 #: ../../getting_started/install/environment/environment.md:77
 #: ../../getting_started/install/environment/environment.md:93
-#: b031950dafcd4d4783c120dc933c4178 c2e9c8cdd41741e3aba01e59a6ef245d
+#: e30c5288516d42aa858a485db50490c1 f843b2e58bcb4e4594e3c28499c341d0
 msgid "MILVUS_PORT=19530"
 msgstr "MILVUS_PORT=19530"

 #: ../../getting_started/install/environment/environment.md:78
 #: ../../getting_started/install/environment/environment.md:94
-#: 27b0a64af6434cb2840373e2b38c9bd5 d0e4d79af7954b129ffff7303a1ec3ce
+#: 158669efcc7d4bcaac1c8dd01b499029 24e88ffd32f242f281c56c0ec3ad2639
 msgid "MILVUS_USERNAME"
 msgstr "MILVUS_USERNAME"

 #: ../../getting_started/install/environment/environment.md:79
 #: ../../getting_started/install/environment/environment.md:95
-#: 27aa1a5b61e64dd6bfe29124e274809e 5c58892498ce4f46a59f54b2887822d4
+#: 111a985297184c8aa5a0dd8e14a58445 6602093a6bb24d6792548e2392105c82
 msgid "MILVUS_PASSWORD"
 msgstr "MILVUS_PASSWORD"

 #: ../../getting_started/install/environment/environment.md:80
 #: ../../getting_started/install/environment/environment.md:96
-#: 009e57d4acc5434da2146f0545911c85 bac8888dcbff47fbb0ea8ae685445aac
+#: 47bdfcd78fbe4ccdb5f49b717a6d01a6 b96c0545b2044926a8a8190caf94ad25
 msgid "MILVUS_SECURE="
 msgstr "MILVUS_SECURE="

 #: ../../getting_started/install/environment/environment.md:82
 #: ../../getting_started/install/environment/environment.md:98
-#: a6eeb16ab5274045bee88ecc3d93e09e eb341774403d47658b9b7e94c4c16d5c
+#: 755c32b5d6c54607907a138b5474c0ec ff4f2a7ddaa14f089dda7a14e1062c36
 msgid "WEAVIATE"
 msgstr "WEAVIATE"

 #: ../../getting_started/install/environment/environment.md:83
-#: fbd97522d8da4824b41b99298fd41069
+#: 23b2ce83385d40a589a004709f9864be
 msgid "VECTOR_STORE_TYPE=Weaviate"
 msgstr "VECTOR_STORE_TYPE=Weaviate"

 #: ../../getting_started/install/environment/environment.md:84
 #: ../../getting_started/install/environment/environment.md:99
-#: 341785b4abfe42b5af1c2e04497261f4 a81cc2aabc8240f3ac1f674d9350bff4
+#: 9acef304d89a448a9e734346705ba872 cf5151b6c1594ccd8beb1c3f77769acb
 msgid "WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network"
 msgstr "WEAVIATE_URL=https://kt-region-m8hcy0wc.weaviate.network"

 #: ../../getting_started/install/environment/environment.md:102
-#: 5bb9e5daa36241d499089c1b1910f729
+#: c3003516b2364051bf34f8c3086e348a
 msgid "Multi-GPU Setting"
 msgstr "Multi-GPU Setting"

 #: ../../getting_started/install/environment/environment.md:103
-#: 30df45b7f1f7423c9f18c6360f0b7600
+#: ade8fc381c5e438aa29d159c10041713
 msgid ""
 "See https://developer.nvidia.com/blog/cuda-pro-tip-control-gpu-"
 "visibility-cuda_visible_devices/ If CUDA_VISIBLE_DEVICES is not "
@ -315,49 +315,49 @@ msgstr ""
 "cuda_visible_devices/ 如果 CUDA_VISIBLE_DEVICES没有设置, 会使用所有可用的gpu"

 #: ../../getting_started/install/environment/environment.md:106
-#: 8631ea968dfb4d90a7ae6bdb2acdfdce
+#: e137bd19be5e410ba6709027dbf2923a
 msgid "CUDA_VISIBLE_DEVICES=0"
 msgstr "CUDA_VISIBLE_DEVICES=0"

 #: ../../getting_started/install/environment/environment.md:108
-#: 0010422280dd4fe79326ebceb2a66f0e
+#: 7669947acbdc4b1d92bcc029a8353a5d
 msgid ""
 "Optionally, you can also specify the gpu ID to use before the starting "
 "command"
 msgstr "你也可以通过启动命令设置gpu ID"

 #: ../../getting_started/install/environment/environment.md:110
-#: 00106f7341304fbd9425721ea8e6a261
+#: 751743d1753b4051beea46371278d793
 msgid "CUDA_VISIBLE_DEVICES=3,4,5,6"
 msgstr "CUDA_VISIBLE_DEVICES=3,4,5,6"

 #: ../../getting_started/install/environment/environment.md:112
-#: 720aa8b3478744d78e4b10dfeccb50b4
+#: 3acc3de0af0d4df2bb575e161e377f85
 msgid "You can configure the maximum memory used by each GPU."
 msgstr "可以设置GPU的最大内存"

 #: ../../getting_started/install/environment/environment.md:114
-#: f9639ac96a244296832c75bbcbdae2af
+#: 67f1d9b172b84294a44ecace5436e6e0
 msgid "MAX_GPU_MEMORY=16Gib"
 msgstr "MAX_GPU_MEMORY=16Gib"

 #: ../../getting_started/install/environment/environment.md:117
-#: fc4d955fdb3e4256af5c8f29b042dcd6
+#: 3c69dfe48bcf46b89b76cac1e7849a66
 msgid "Other Setting"
 msgstr "Other Setting"

 #: ../../getting_started/install/environment/environment.md:118
-#: 66b14a834e884339be2d48392e884933
+#: d5015b70f4fe4d20a63de9d87f86957a
 msgid "Language Settings(influence prompt language)"
 msgstr "Language Settings(涉及prompt语言以及知识切片方式)"

 #: ../../getting_started/install/environment/environment.md:119
-#: 5c9f05174eb84edd9e1316cc0721a840
+#: 5543c28bb8e34c9fb3bb6b063c2b1750
 msgid "LANGUAGE=en"
 msgstr "LANGUAGE=en"

 #: ../../getting_started/install/environment/environment.md:120
-#: 7f6d62117d024c51bba9255fa4fcf151
+#: cb4ed5b892ee41068c1ca76cb29aa400
 msgid "LANGUAGE=zh"
 msgstr "LANGUAGE=zh"

--- a/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po
+++ b/docs/locales/zh_CN/LC_MESSAGES/modules/knowledge.po
@ -8,7 +8,7 @@ msgid ""
 msgstr ""
 "Project-Id-Version: DB-GPT 0.3.0\n"
 "Report-Msgid-Bugs-To: \n"
-"POT-Creation-Date: 2023-11-02 10:10+0800\n"
+"POT-Creation-Date: 2023-11-02 21:04+0800\n"
 "PO-Revision-Date: YEAR-MO-DA HO:MI+ZONE\n"
 "Last-Translator: FULL NAME <EMAIL@ADDRESS>\n"
 "Language: zh_CN\n"
@ -19,11 +19,11 @@ msgstr ""
 "Content-Transfer-Encoding: 8bit\n"
 "Generated-By: Babel 2.12.1\n"

-#: ../../modules/knowledge.md:1 436b94d3a8374ed18feb5c14893a84e6
+#: ../../modules/knowledge.md:1 b94b3b15cb2441ed9d78abd222a717b7
 msgid "Knowledge"
 msgstr "知识"

-#: ../../modules/knowledge.md:3 918a3747cbed42d18b8c9c4547e67b14
+#: ../../modules/knowledge.md:3 c6d6e308a6ce42948d29e928136ef561
 #, fuzzy
 msgid ""
 "As the knowledge base is currently the most significant user demand "
@ -34,15 +34,15 @@ msgstr ""
 "由于知识库是当前用户需求最显著的场景，我们原生支持知识库的构建和处理。同时，我们还在本项目中提供了多种知识库管理策略，如:pdf,md , "
 "txt, word, ppt"

-#: ../../modules/knowledge.md:4 d4d4b5d57918485aafa457bb9fdcf626
+#: ../../modules/knowledge.md:4 268abc408d40410ba90cf5f121dc5270
 msgid "Default built-in knowledge base"
 msgstr ""

-#: ../../modules/knowledge.md:5 d4d4b5d57918485aafa457bb9fdcf626
+#: ../../modules/knowledge.md:5 558c3364c38b458a8ebf81030efc2a48
 msgid "Custom addition of knowledge bases"
 msgstr ""

-#: ../../modules/knowledge.md:6 984361ce835c4c3492e29e1fb897348a
+#: ../../modules/knowledge.md:6 9cb3ce62da1440579c095848c7aef88c
 msgid ""
 "Various usage scenarios such as constructing knowledge bases through "
 "plugin capabilities and web crawling. Users only need to organize the "
@ -50,53 +50,53 @@ msgid ""
 "the knowledge base required for the large model."
 msgstr ""

-#: ../../modules/knowledge.md:9 746e4fbd3212460198be51b90caee2c8
+#: ../../modules/knowledge.md:9 b8ca6bc4dd9845baa56e36eea7fac2a2
 #, fuzzy
 msgid "Create your own knowledge repository"
 msgstr "创建你自己的知识库"

-#: ../../modules/knowledge.md:11 1c46b33b0532417c824efbaa3e687c3f
+#: ../../modules/knowledge.md:11 17d7178a67924f43aa5b6293707ef041
 msgid ""
 "1.Place personal knowledge files or folders in the pilot/datasets "
 "directory."
 msgstr ""

-#: ../../modules/knowledge.md:13 3b16f387b5354947a89d6df77bd65bdb
+#: ../../modules/knowledge.md:13 31c31f14bf444981939689f9a9fb038a
 #, fuzzy
 msgid ""
 "We currently support many document formats: txt, pdf, md, html, doc, ppt,"
 " and url."
 msgstr "当前支持txt, pdf, md, html, doc, ppt, url文档格式"

-#: ../../modules/knowledge.md:15 09ec337d7da4418db854e58afb6c0980
+#: ../../modules/knowledge.md:15 9ad2f2e05f8842a9b9d8469a3704df23
 msgid "before execution:"
 msgstr "开始前"

-#: ../../modules/knowledge.md:22 c09b3decb018485f8e56830ddc156194
+#: ../../modules/knowledge.md:22 6fd2775914b641c4b8e486417b558ea6
 msgid ""
 "2.Update your .env, set your vector store type, VECTOR_STORE_TYPE=Chroma "
 "(now only support Chroma and Milvus, if you set Milvus, please set "
 "MILVUS_URL and MILVUS_PORT)"
 msgstr ""

-#: ../../modules/knowledge.md:25 74460ec7709441d5945ce9f745a26d20
+#: ../../modules/knowledge.md:25 131c5f58898a4682940910980edb2043
 msgid "2.Run the knowledge repository initialization command"
 msgstr ""

-#: ../../modules/knowledge.md:31 4498ec4e46ff4e24b45dd855e829bd32
+#: ../../modules/knowledge.md:31 2cf550f17881497bb881b19efcc18c23
 msgid ""
 "Optionally, you can run `dbgpt knowledge load --help` command to see more"
 " usage."
 msgstr ""

-#: ../../modules/knowledge.md:33 5048ac3289e540f2a2b5fd0e5ed043f5
+#: ../../modules/knowledge.md:33 c8a2ea571b944bdfbcad48fa8b54fcc9
 msgid ""
 "3.Add the knowledge repository in the interface by entering the name of "
 "your knowledge repository (if not specified, enter \"default\") so you "
 "can use it for Q&A based on your knowledge base."
 msgstr ""

-#: ../../modules/knowledge.md:35 deeccff20f7f453dad0881b63dae2a18
+#: ../../modules/knowledge.md:35 b701170ad75e49dea7d7734c15681e0f
 msgid ""
 "Note that the default vector model used is text2vec-large-chinese (which "
 "is a large model, so if your personal computer configuration is not "
--- a/pilot/base_modules/agent/db/init.py
+++ b/pilot/base_modules/agent/db/init.py
--- a/pilot/component.py
+++ b/pilot/component.py
@ -46,6 +46,8 @@ class ComponentType(str, Enum):
    WORKER_MANAGER = "dbgpt_worker_manager"
    WORKER_MANAGER_FACTORY = "dbgpt_worker_manager_factory"
    MODEL_CONTROLLER = "dbgpt_model_controller"
+    MODEL_REGISTRY = "dbgpt_model_registry"
+    MODEL_API_SERVER = "dbgpt_model_api_server"
    AGENT_HUB = "dbgpt_agent_hub"
    EXECUTOR_DEFAULT = "dbgpt_thread_pool_default"
    TRACER = "dbgpt_tracer"
@ -69,7 +71,6 @@ class BaseComponent(LifeCycle, ABC):
        This method needs to be implemented by every component to define how it integrates
        with the main system app.
        """
-        pass


 T = TypeVar("T", bound=BaseComponent)
@ -91,13 +92,28 @@ class SystemApp(LifeCycle):
        """Returns the internal ASGI app."""
        return self._asgi_app

-    def register(self, component: Type[BaseComponent], *args, **kwargs):
-        """Register a new component by its type."""
+    def register(self, component: Type[BaseComponent], *args, **kwargs) -> T:
+        """Register a new component by its type.
+
+        Args:
+            component (Type[BaseComponent]): The component class to register
+
+        Returns:
+            T: The instance of registered component
+        """
        instance = component(self, *args, **kwargs)
        self.register_instance(instance)
+        return instance

-    def register_instance(self, instance: T):
-        """Register an already initialized component."""
+    def register_instance(self, instance: T) -> T:
+        """Register an already initialized component.
+
+        Args:
+            instance (T): The component instance to register
+
+        Returns:
+            T: The instance of registered component
+        """
        name = instance.name
        if isinstance(name, ComponentType):
            name = name.value
@ -108,18 +124,34 @@ class SystemApp(LifeCycle):
        logger.info(f"Register component with name {name} and instance: {instance}")
        self.components[name] = instance
        instance.init_app(self)
+        return instance

    def get_component(
        self,
        name: Union[str, ComponentType],
        component_type: Type[T],
        default_component=_EMPTY_DEFAULT_COMPONENT,
+        or_register_component: Type[BaseComponent] = None,
+        *args,
+        **kwargs,
    ) -> T:
-        """Retrieve a registered component by its name and type."""
+        """Retrieve a registered component by its name and type.
+
+        Args:
+            name (Union[str, ComponentType]): Component name
+            component_type (Type[T]): The type of current retrieve component
+            default_component : The default component instance if not retrieve by name
+            or_register_component (Type[BaseComponent]): The new component to register if not retrieve by name
+
+        Returns:
+            T: The instance retrieved by component name
+        """
        if isinstance(name, ComponentType):
            name = name.value
        component = self.components.get(name)
        if not component:
+            if or_register_component:
+                return self.register(or_register_component, *args, **kwargs)
            if default_component != _EMPTY_DEFAULT_COMPONENT:
                return default_component
            raise ValueError(f"No component found with name {name}")
--- a/pilot/configs/model_config.py
+++ b/pilot/configs/model_config.py
@ -78,6 +78,10 @@ LLM_MODEL_CONFIG = {
    "internlm-7b": os.path.join(MODEL_PATH, "internlm-chat-7b"),
    "internlm-7b-8k": os.path.join(MODEL_PATH, "internlm-chat-7b-8k"),
    "internlm-20b": os.path.join(MODEL_PATH, "internlm-chat-20b"),
+    "codellama-7b": os.path.join(MODEL_PATH, "CodeLlama-7b-Instruct-hf"),
+    "codellama-7b-sql-sft": os.path.join(MODEL_PATH, "codellama-7b-sql-sft"),
+    "codellama-13b": os.path.join(MODEL_PATH, "CodeLlama-13b-Instruct-hf"),
+    "codellama-13b-sql-sft": os.path.join(MODEL_PATH, "codellama-13b-sql-sft"),
    # For test now
    "opt-125m": os.path.join(MODEL_PATH, "opt-125m"),
 }
--- a/pilot/model/adapter.py
+++ b/pilot/model/adapter.py
@ -320,6 +320,19 @@ class Llama2Adapter(BaseLLMAdaper):
        return model, tokenizer


+class CodeLlamaAdapter(BaseLLMAdaper):
+    """The model adapter for codellama"""
+
+    def match(self, model_path: str):
+        return "codellama" in model_path.lower()
+
+    def loader(self, model_path: str, from_pretrained_kwargs: dict):
+        model, tokenizer = super().loader(model_path, from_pretrained_kwargs)
+        model.config.eos_token_id = tokenizer.eos_token_id
+        model.config.pad_token_id = tokenizer.pad_token_id
+        return model, tokenizer
+
+
 class BaichuanAdapter(BaseLLMAdaper):
    """The model adapter for Baichuan models (e.g., baichuan-inc/Baichuan-13B-Chat)"""

@ -420,6 +433,7 @@ register_llm_model_adapters(FalconAdapater)
 register_llm_model_adapters(GorillaAdapter)
 register_llm_model_adapters(GPT4AllAdapter)
 register_llm_model_adapters(Llama2Adapter)
+register_llm_model_adapters(CodeLlamaAdapter)
 register_llm_model_adapters(BaichuanAdapter)
 register_llm_model_adapters(WizardLMAdapter)
 register_llm_model_adapters(LlamaCppAdapater)
--- a/pilot/model/base.py
+++ b/pilot/model/base.py
@ -2,7 +2,7 @@
 # -*- coding: utf-8 -*-

 from enum import Enum
-from typing import TypedDict, Optional, Dict, List
+from typing import TypedDict, Optional, Dict, List, Any
 from dataclasses import dataclass, asdict
 from datetime import datetime
 from pilot.utils.parameter_utils import ParameterDescription
@ -52,6 +52,8 @@ class ModelOutput:
    text: str
    error_code: int
    model_context: Dict = None
+    finish_reason: str = None
+    usage: Dict[str, Any] = None

    def to_dict(self) -> Dict:
        return asdict(self)
--- a/pilot/model/cli.py
+++ b/pilot/model/cli.py
@ -8,6 +8,7 @@ from pilot.configs.model_config import LOGDIR
 from pilot.model.base import WorkerApplyType
 from pilot.model.parameter import (
    ModelControllerParameters,
+    ModelAPIServerParameters,
    ModelWorkerParameters,
    ModelParameters,
    BaseParameters,
@ -441,15 +442,27 @@ def stop_model_worker(port: int):


@click.command(name="apiserver")
+@EnvArgumentParser.create_click_option(ModelAPIServerParameters)
 def start_apiserver(**kwargs):
-    """Start apiserver(TODO)"""
-    raise NotImplementedError
+    """Start apiserver"""
+
+    if kwargs["daemon"]:
+        log_file = os.path.join(LOGDIR, "model_apiserver_uvicorn.log")
+        _run_current_with_daemon("ModelAPIServer", log_file)
+    else:
+        from pilot.model.cluster import run_apiserver
+
+        run_apiserver()


@click.command(name="apiserver")
-def stop_apiserver(**kwargs):
-    """Start apiserver(TODO)"""
-    raise NotImplementedError
+@add_stop_server_options
+def stop_apiserver(port: int):
+    """Stop apiserver"""
+    name = "ModelAPIServer"
+    if port:
+        name = f"{name}-{port}"
+    _stop_service("apiserver", name, port=port)


 def _stop_all_model_server(**kwargs):
--- a/pilot/model/cluster/init.py
+++ b/pilot/model/cluster/init.py
@ -21,6 +21,7 @@ from pilot.model.cluster.controller.controller import (
    run_model_controller,
    BaseModelController,
 )
+from pilot.model.cluster.apiserver.api import run_apiserver

 from pilot.model.cluster.worker.remote_manager import RemoteWorkerManager

@ -40,4 +41,5 @@ __all__ = [
    "ModelRegistryClient",
    "RemoteWorkerManager",
    "run_model_controller",
+    "run_apiserver",
 ]
--- a/pilot/model/cluster/apiserver/init.py
+++ b/pilot/model/cluster/apiserver/init.py
--- a/pilot/model/cluster/apiserver/api.py
+++ b/pilot/model/cluster/apiserver/api.py
@ -0,0 +1,443 @@
+"""A server that provides OpenAI-compatible RESTful APIs. It supports:
+- Chat Completions. (Reference: https://platform.openai.com/docs/api-reference/chat)
+
+Adapted from https://github.com/lm-sys/FastChat/blob/main/fastchat/serve/openai_api_server.py
+"""
+from typing import Optional, List, Dict, Any, Generator
+
+import logging
+import asyncio
+import shortuuid
+import json
+from fastapi import APIRouter, FastAPI
+from fastapi import Depends, HTTPException
+from fastapi.exceptions import RequestValidationError
+from fastapi.middleware.cors import CORSMiddleware
+from fastapi.responses import StreamingResponse
+from fastapi.security.http import HTTPAuthorizationCredentials, HTTPBearer
+
+from pydantic import BaseSettings
+
+from fastchat.protocol.openai_api_protocol import (
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse,
+    ChatMessage,
+    ChatCompletionResponseChoice,
+    DeltaMessage,
+    EmbeddingsRequest,
+    EmbeddingsResponse,
+    ErrorResponse,
+    ModelCard,
+    ModelList,
+    ModelPermission,
+    UsageInfo,
+)
+from fastchat.protocol.api_protocol import (
+    APIChatCompletionRequest,
+    APITokenCheckRequest,
+    APITokenCheckResponse,
+    APITokenCheckResponseItem,
+)
+from fastchat.serve.openai_api_server import create_error_response, check_requests
+from fastchat.constants import ErrorCode
+
+from pilot.component import BaseComponent, ComponentType, SystemApp
+from pilot.utils.parameter_utils import EnvArgumentParser
+from pilot.scene.base_message import ModelMessage, ModelMessageRoleType
+from pilot.model.base import ModelInstance, ModelOutput
+from pilot.model.parameter import ModelAPIServerParameters, WorkerType
+from pilot.model.cluster import ModelRegistry, ModelRegistryClient
+from pilot.model.cluster.manager_base import WorkerManager, WorkerManagerFactory
+from pilot.utils.utils import setup_logging
+
+logger = logging.getLogger(__name__)
+
+
+class APIServerException(Exception):
+    def __init__(self, code: int, message: str):
+        self.code = code
+        self.message = message
+
+
+class APISettings(BaseSettings):
+    api_keys: Optional[List[str]] = None
+
+
+api_settings = APISettings()
+get_bearer_token = HTTPBearer(auto_error=False)
+
+
+async def check_api_key(
+    auth: Optional[HTTPAuthorizationCredentials] = Depends(get_bearer_token),
+) -> str:
+    if api_settings.api_keys:
+        if auth is None or (token := auth.credentials) not in api_settings.api_keys:
+            raise HTTPException(
+                status_code=401,
+                detail={
+                    "error": {
+                        "message": "",
+                        "type": "invalid_request_error",
+                        "param": None,
+                        "code": "invalid_api_key",
+                    }
+                },
+            )
+        return token
+    else:
+        # api_keys not set; allow all
+        return None
+
+
+class APIServer(BaseComponent):
+    name = ComponentType.MODEL_API_SERVER
+
+    def init_app(self, system_app: SystemApp):
+        self.system_app = system_app
+
+    def get_worker_manager(self) -> WorkerManager:
+        """Get the worker manager component instance
+
+        Raises:
+            APIServerException: If can't get worker manager component instance
+        """
+        worker_manager = self.system_app.get_component(
+            ComponentType.WORKER_MANAGER_FACTORY, WorkerManagerFactory
+        ).create()
+        if not worker_manager:
+            raise APIServerException(
+                ErrorCode.INTERNAL_ERROR,
+                f"Could not get component {ComponentType.WORKER_MANAGER_FACTORY} from system_app",
+            )
+        return worker_manager
+
+    def get_model_registry(self) -> ModelRegistry:
+        """Get the model registry component instance
+
+        Raises:
+            APIServerException: If can't get model registry component instance
+        """
+
+        controller = self.system_app.get_component(
+            ComponentType.MODEL_REGISTRY, ModelRegistry
+        )
+        if not controller:
+            raise APIServerException(
+                ErrorCode.INTERNAL_ERROR,
+                f"Could not get component {ComponentType.MODEL_REGISTRY} from system_app",
+            )
+        return controller
+
+    async def get_model_instances_or_raise(
+        self, model_name: str
+    ) -> List[ModelInstance]:
+        """Get healthy model instances with request model name
+
+        Args:
+            model_name (str): Model name
+
+        Raises:
+            APIServerException: If can't get healthy model instances with request model name
+        """
+        registry = self.get_model_registry()
+        registry_model_name = f"{model_name}@llm"
+        model_instances = await registry.get_all_instances(
+            registry_model_name, healthy_only=True
+        )
+        if not model_instances:
+            all_instances = await registry.get_all_model_instances(healthy_only=True)
+            models = [
+                ins.model_name.split("@llm")[0]
+                for ins in all_instances
+                if ins.model_name.endswith("@llm")
+            ]
+            if models:
+                models = "&&".join(models)
+                message = f"Only {models} allowed now, your model {model_name}"
+            else:
+                message = f"No models allowed now, your model {model_name}"
+            raise APIServerException(ErrorCode.INVALID_MODEL, message)
+        return model_instances
+
+    async def get_available_models(self) -> ModelList:
+        """Return available models
+
+        Just include LLM and embedding models.
+
+        Returns:
+            List[ModelList]: The list of models.
+        """
+        registry = self.get_model_registry()
+        model_instances = await registry.get_all_model_instances(healthy_only=True)
+        model_name_set = set()
+        for inst in model_instances:
+            name, worker_type = WorkerType.parse_worker_key(inst.model_name)
+            if worker_type == WorkerType.LLM or worker_type == WorkerType.TEXT2VEC:
+                model_name_set.add(name)
+        models = list(model_name_set)
+        models.sort()
+        # TODO: return real model permission details
+        model_cards = []
+        for m in models:
+            model_cards.append(
+                ModelCard(
+                    id=m, root=m, owned_by="DB-GPT", permission=[ModelPermission()]
+                )
+            )
+        return ModelList(data=model_cards)
+
+    async def chat_completion_stream_generator(
+        self, model_name: str, params: Dict[str, Any], n: int
+    ) -> Generator[str, Any, None]:
+        """Chat stream completion generator
+
+        Args:
+            model_name (str): Model name
+            params (Dict[str, Any]): The parameters pass to model worker
+            n (int): How many completions to generate for each prompt.
+        """
+        worker_manager = self.get_worker_manager()
+        id = f"chatcmpl-{shortuuid.random()}"
+        finish_stream_events = []
+        for i in range(n):
+            # First chunk with role
+            choice_data = ChatCompletionResponseStreamChoice(
+                index=i,
+                delta=DeltaMessage(role="assistant"),
+                finish_reason=None,
+            )
+            chunk = ChatCompletionStreamResponse(
+                id=id, choices=[choice_data], model=model_name
+            )
+            yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+
+            previous_text = ""
+            async for model_output in worker_manager.generate_stream(params):
+                model_output: ModelOutput = model_output
+                if model_output.error_code != 0:
+                    yield f"data: {json.dumps(model_output.to_dict(), ensure_ascii=False)}\n\n"
+                    yield "data: [DONE]\n\n"
+                    return
+                decoded_unicode = model_output.text.replace("\ufffd", "")
+                delta_text = decoded_unicode[len(previous_text) :]
+                previous_text = (
+                    decoded_unicode
+                    if len(decoded_unicode) > len(previous_text)
+                    else previous_text
+                )
+
+                if len(delta_text) == 0:
+                    delta_text = None
+                choice_data = ChatCompletionResponseStreamChoice(
+                    index=i,
+                    delta=DeltaMessage(content=delta_text),
+                    finish_reason=model_output.finish_reason,
+                )
+                chunk = ChatCompletionStreamResponse(
+                    id=id, choices=[choice_data], model=model_name
+                )
+                if delta_text is None:
+                    if model_output.finish_reason is not None:
+                        finish_stream_events.append(chunk)
+                    continue
+                yield f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+        # There is not "content" field in the last delta message, so exclude_none to exclude field "content".
+        for finish_chunk in finish_stream_events:
+            yield f"data: {finish_chunk.json(exclude_none=True, ensure_ascii=False)}\n\n"
+        yield "data: [DONE]\n\n"
+
+    async def chat_completion_generate(
+        self, model_name: str, params: Dict[str, Any], n: int
+    ) -> ChatCompletionResponse:
+        """Generate completion
+        Args:
+            model_name (str): Model name
+            params (Dict[str, Any]): The parameters pass to model worker
+            n (int): How many completions to generate for each prompt.
+        """
+        worker_manager: WorkerManager = self.get_worker_manager()
+        choices = []
+        chat_completions = []
+        for i in range(n):
+            model_output = asyncio.create_task(worker_manager.generate(params))
+            chat_completions.append(model_output)
+        try:
+            all_tasks = await asyncio.gather(*chat_completions)
+        except Exception as e:
+            return create_error_response(ErrorCode.INTERNAL_ERROR, str(e))
+        usage = UsageInfo()
+        for i, model_output in enumerate(all_tasks):
+            model_output: ModelOutput = model_output
+            if model_output.error_code != 0:
+                return create_error_response(model_output.error_code, model_output.text)
+            choices.append(
+                ChatCompletionResponseChoice(
+                    index=i,
+                    message=ChatMessage(role="assistant", content=model_output.text),
+                    finish_reason=model_output.finish_reason or "stop",
+                )
+            )
+            if model_output.usage:
+                task_usage = UsageInfo.parse_obj(model_output.usage)
+                for usage_key, usage_value in task_usage.dict().items():
+                    setattr(usage, usage_key, getattr(usage, usage_key) + usage_value)
+
+        return ChatCompletionResponse(model=model_name, choices=choices, usage=usage)
+
+
+def get_api_server() -> APIServer:
+    api_server = global_system_app.get_component(
+        ComponentType.MODEL_API_SERVER, APIServer, default_component=None
+    )
+    if not api_server:
+        global_system_app.register(APIServer)
+    return global_system_app.get_component(ComponentType.MODEL_API_SERVER, APIServer)
+
+
+router = APIRouter()
+
+
+@router.get("/v1/models", dependencies=[Depends(check_api_key)])
+async def get_available_models(api_server: APIServer = Depends(get_api_server)):
+    return await api_server.get_available_models()
+
+
+@router.post("/v1/chat/completions", dependencies=[Depends(check_api_key)])
+async def create_chat_completion(
+    request: APIChatCompletionRequest, api_server: APIServer = Depends(get_api_server)
+):
+    await api_server.get_model_instances_or_raise(request.model)
+    error_check_ret = check_requests(request)
+    if error_check_ret is not None:
+        return error_check_ret
+    params = {
+        "model": request.model,
+        "messages": ModelMessage.to_dict_list(
+            ModelMessage.from_openai_messages(request.messages)
+        ),
+        "echo": False,
+    }
+    if request.temperature:
+        params["temperature"] = request.temperature
+    if request.top_p:
+        params["top_p"] = request.top_p
+    if request.max_tokens:
+        params["max_new_tokens"] = request.max_tokens
+    if request.stop:
+        params["stop"] = request.stop
+    if request.user:
+        params["user"] = request.user
+
+    # TODO check token length
+    if request.stream:
+        generator = api_server.chat_completion_stream_generator(
+            request.model, params, request.n
+        )
+        return StreamingResponse(generator, media_type="text/event-stream")
+    return await api_server.chat_completion_generate(request.model, params, request.n)
+
+
+def _initialize_all(controller_addr: str, system_app: SystemApp):
+    from pilot.model.cluster import RemoteWorkerManager, ModelRegistryClient
+    from pilot.model.cluster.worker.manager import _DefaultWorkerManagerFactory
+
+    if not system_app.get_component(
+        ComponentType.MODEL_REGISTRY, ModelRegistry, default_component=None
+    ):
+        # Register model registry if not exist
+        registry = ModelRegistryClient(controller_addr)
+        registry.name = ComponentType.MODEL_REGISTRY.value
+        system_app.register_instance(registry)
+
+    registry = system_app.get_component(
+        ComponentType.MODEL_REGISTRY, ModelRegistry, default_component=None
+    )
+    worker_manager = RemoteWorkerManager(registry)
+
+    # Register worker manager component if not exist
+    system_app.get_component(
+        ComponentType.WORKER_MANAGER_FACTORY,
+        WorkerManagerFactory,
+        or_register_component=_DefaultWorkerManagerFactory,
+        worker_manager=worker_manager,
+    )
+    # Register api server component if not exist
+    system_app.get_component(
+        ComponentType.MODEL_API_SERVER, APIServer, or_register_component=APIServer
+    )
+
+
+def initialize_apiserver(
+    controller_addr: str,
+    app=None,
+    system_app: SystemApp = None,
+    host: str = None,
+    port: int = None,
+    api_keys: List[str] = None,
+):
+    global global_system_app
+    global api_settings
+    embedded_mod = True
+    if not app:
+        embedded_mod = False
+        app = FastAPI()
+        app.add_middleware(
+            CORSMiddleware,
+            allow_origins=["*"],
+            allow_credentials=True,
+            allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"],
+            allow_headers=["*"],
+        )
+
+    if not system_app:
+        system_app = SystemApp(app)
+    global_system_app = system_app
+
+    if api_keys:
+        api_settings.api_keys = api_keys
+
+    app.include_router(router, prefix="/api", tags=["APIServer"])
+
+    @app.exception_handler(APIServerException)
+    async def validation_apiserver_exception_handler(request, exc: APIServerException):
+        return create_error_response(exc.code, exc.message)
+
+    @app.exception_handler(RequestValidationError)
+    async def validation_exception_handler(request, exc):
+        return create_error_response(ErrorCode.VALIDATION_TYPE_ERROR, str(exc))
+
+    _initialize_all(controller_addr, system_app)
+
+    if not embedded_mod:
+        import uvicorn
+
+        uvicorn.run(app, host=host, port=port, log_level="info")
+
+
+def run_apiserver():
+    parser = EnvArgumentParser()
+    env_prefix = "apiserver_"
+    apiserver_params: ModelAPIServerParameters = parser.parse_args_into_dataclass(
+        ModelAPIServerParameters,
+        env_prefixes=[env_prefix],
+    )
+    setup_logging(
+        "pilot",
+        logging_level=apiserver_params.log_level,
+        logger_filename=apiserver_params.log_file,
+    )
+    api_keys = None
+    if apiserver_params.api_keys:
+        api_keys = apiserver_params.api_keys.strip().split(",")
+
+    initialize_apiserver(
+        apiserver_params.controller_addr,
+        host=apiserver_params.host,
+        port=apiserver_params.port,
+        api_keys=api_keys,
+    )
+
+
+if __name__ == "__main__":
+    run_apiserver()
--- a/pilot/model/cluster/apiserver/tests/init.py
+++ b/pilot/model/cluster/apiserver/tests/init.py
--- a/pilot/model/cluster/apiserver/tests/test_api.py
+++ b/pilot/model/cluster/apiserver/tests/test_api.py
@ -0,0 +1,248 @@
+import pytest
+import pytest_asyncio
+from aioresponses import aioresponses
+from fastapi import FastAPI
+from fastapi.middleware.cors import CORSMiddleware
+from httpx import AsyncClient, HTTPError
+
+from pilot.component import SystemApp
+from pilot.utils.openai_utils import chat_completion_stream, chat_completion
+
+from pilot.model.cluster.apiserver.api import (
+    api_settings,
+    initialize_apiserver,
+    ModelList,
+    UsageInfo,
+    ChatCompletionResponse,
+    ChatCompletionResponseStreamChoice,
+    ChatCompletionStreamResponse,
+    ChatMessage,
+    ChatCompletionResponseChoice,
+    DeltaMessage,
+)
+from pilot.model.cluster.tests.conftest import _new_cluster
+
+from pilot.model.cluster.worker.manager import _DefaultWorkerManagerFactory
+
+app = FastAPI()
+app.add_middleware(
+    CORSMiddleware,
+    allow_origins=["*"],
+    allow_credentials=True,
+    allow_methods=["GET", "POST", "PUT", "PATCH", "DELETE", "OPTIONS"],
+    allow_headers=["*"],
+)
+
+
+@pytest_asyncio.fixture
+async def system_app():
+    return SystemApp(app)
+
+
+@pytest_asyncio.fixture
+async def client(request, system_app: SystemApp):
+    param = getattr(request, "param", {})
+    api_keys = param.get("api_keys", [])
+    client_api_key = param.get("client_api_key")
+    if "num_workers" not in param:
+        param["num_workers"] = 2
+    if "api_keys" in param:
+        del param["api_keys"]
+    headers = {}
+    if client_api_key:
+        headers["Authorization"] = "Bearer " + client_api_key
+    print(f"param: {param}")
+    if api_settings:
+        # Clear global api keys
+        api_settings.api_keys = []
+    async with AsyncClient(app=app, base_url="http://test", headers=headers) as client:
+        async with _new_cluster(**param) as cluster:
+            worker_manager, model_registry = cluster
+            system_app.register(_DefaultWorkerManagerFactory, worker_manager)
+            system_app.register_instance(model_registry)
+            # print(f"Instances {model_registry.registry}")
+            initialize_apiserver(None, app, system_app, api_keys=api_keys)
+            yield client
+
+
+@pytest.mark.asyncio
+async def test_get_all_models(client: AsyncClient):
+    res = await client.get("/api/v1/models")
+    res.status_code == 200
+    model_lists = ModelList.parse_obj(res.json())
+    print(f"model list json: {res.json()}")
+    assert model_lists.object == "list"
+    assert len(model_lists.data) == 2
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "client, expected_messages",
+    [
+        ({"stream_messags": ["Hello", " world."]}, "Hello world."),
+        ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。"),
+    ],
+    indirect=["client"],
+)
+async def test_chat_completions(client: AsyncClient, expected_messages):
+    chat_data = {
+        "model": "test-model-name-0",
+        "messages": [{"role": "user", "content": "Hello"}],
+        "stream": True,
+    }
+    full_text = ""
+    async for text in chat_completion_stream(
+        "/api/v1/chat/completions", chat_data, client
+    ):
+        full_text += text
+    assert full_text == expected_messages
+
+    assert (
+        await chat_completion("/api/v1/chat/completions", chat_data, client)
+        == expected_messages
+    )
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "client, expected_messages, client_api_key",
+    [
+        (
+            {"stream_messags": ["Hello", " world."], "api_keys": ["abc"]},
+            "Hello world.",
+            "abc",
+        ),
+        ({"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc"]}, "你好，我是张三。", "abc"),
+    ],
+    indirect=["client"],
+)
+async def test_chat_completions_with_openai_lib_async_no_stream(
+    client: AsyncClient, expected_messages: str, client_api_key: str
+):
+    import openai
+
+    openai.api_key = client_api_key
+    openai.api_base = "http://test/api/v1"
+
+    model_name = "test-model-name-0"
+
+    with aioresponses() as mocked:
+        mock_message = {"text": expected_messages}
+        one_res = ChatCompletionResponseChoice(
+            index=0,
+            message=ChatMessage(role="assistant", content=expected_messages),
+            finish_reason="stop",
+        )
+        data = ChatCompletionResponse(
+            model=model_name, choices=[one_res], usage=UsageInfo()
+        )
+        mock_message = f"{data.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+        # Mock http request
+        mocked.post(
+            "http://test/api/v1/chat/completions", status=200, body=mock_message
+        )
+        completion = await openai.ChatCompletion.acreate(
+            model=model_name,
+            messages=[{"role": "user", "content": "Hello! What is your name?"}],
+        )
+        assert completion.choices[0].message.content == expected_messages
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "client, expected_messages, client_api_key",
+    [
+        (
+            {"stream_messags": ["Hello", " world."], "api_keys": ["abc"]},
+            "Hello world.",
+            "abc",
+        ),
+        ({"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc"]}, "你好，我是张三。", "abc"),
+    ],
+    indirect=["client"],
+)
+async def test_chat_completions_with_openai_lib_async_stream(
+    client: AsyncClient, expected_messages: str, client_api_key: str
+):
+    import openai
+
+    openai.api_key = client_api_key
+    openai.api_base = "http://test/api/v1"
+
+    model_name = "test-model-name-0"
+
+    with aioresponses() as mocked:
+        mock_message = {"text": expected_messages}
+        choice_data = ChatCompletionResponseStreamChoice(
+            index=0,
+            delta=DeltaMessage(content=expected_messages),
+            finish_reason="stop",
+        )
+        chunk = ChatCompletionStreamResponse(
+            id=0, choices=[choice_data], model=model_name
+        )
+        mock_message = f"data: {chunk.json(exclude_unset=True, ensure_ascii=False)}\n\n"
+        mocked.post(
+            "http://test/api/v1/chat/completions",
+            status=200,
+            body=mock_message,
+            content_type="text/event-stream",
+        )
+
+        stream_stream_resp = ""
+        async for stream_resp in await openai.ChatCompletion.acreate(
+            model=model_name,
+            messages=[{"role": "user", "content": "Hello! What is your name?"}],
+            stream=True,
+        ):
+            stream_stream_resp = stream_resp.choices[0]["delta"].get("content", "")
+        assert stream_stream_resp == expected_messages
+
+
+@pytest.mark.asyncio
+@pytest.mark.parametrize(
+    "client, expected_messages, api_key_is_error",
+    [
+        (
+            {
+                "stream_messags": ["Hello", " world."],
+                "api_keys": ["abc", "xx"],
+                "client_api_key": "abc",
+            },
+            "Hello world.",
+            False,
+        ),
+        ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。", False),
+        (
+            {"stream_messags": ["你好，我是", "张三。"], "api_keys": ["abc", "xx"]},
+            "你好，我是张三。",
+            True,
+        ),
+        (
+            {
+                "stream_messags": ["你好，我是", "张三。"],
+                "api_keys": ["abc", "xx"],
+                "client_api_key": "error_api_key",
+            },
+            "你好，我是张三。",
+            True,
+        ),
+    ],
+    indirect=["client"],
+)
+async def test_chat_completions_with_api_keys(
+    client: AsyncClient, expected_messages: str, api_key_is_error: bool
+):
+    chat_data = {
+        "model": "test-model-name-0",
+        "messages": [{"role": "user", "content": "Hello"}],
+        "stream": True,
+    }
+    if api_key_is_error:
+        with pytest.raises(HTTPError):
+            await chat_completion("/api/v1/chat/completions", chat_data, client)
+    else:
+        assert (
+            await chat_completion("/api/v1/chat/completions", chat_data, client)
+            == expected_messages
+        )
--- a/pilot/model/cluster/controller/controller.py
+++ b/pilot/model/cluster/controller/controller.py
@ -66,7 +66,9 @@ class LocalModelController(BaseModelController):
            f"Get all instances with {model_name}, healthy_only: {healthy_only}"
        )
        if not model_name:
-            return await self.registry.get_all_model_instances()
+            return await self.registry.get_all_model_instances(
+                healthy_only=healthy_only
+            )
        else:
            return await self.registry.get_all_instances(model_name, healthy_only)

@ -98,8 +100,10 @@ class _RemoteModelController(BaseModelController):


 class ModelRegistryClient(_RemoteModelController, ModelRegistry):
-    async def get_all_model_instances(self) -> List[ModelInstance]:
-        return await self.get_all_instances()
+    async def get_all_model_instances(
+        self, healthy_only: bool = False
+    ) -> List[ModelInstance]:
+        return await self.get_all_instances(healthy_only=healthy_only)

    @sync_api_remote(path="/api/controller/models")
    def sync_get_all_instances(
--- a/pilot/model/cluster/registry.py
+++ b/pilot/model/cluster/registry.py
@ -1,22 +1,37 @@
 import random
 import threading
 import time
+import logging
 from abc import ABC, abstractmethod
 from collections import defaultdict
 from datetime import datetime, timedelta
-from typing import Dict, List, Tuple
+from typing import Dict, List, Optional, Tuple
 import itertools

+from pilot.component import BaseComponent, ComponentType, SystemApp
 from pilot.model.base import ModelInstance


-class ModelRegistry(ABC):
+logger = logging.getLogger(__name__)
+
+
+class ModelRegistry(BaseComponent, ABC):
    """
    Abstract base class for a model registry. It provides an interface
    for registering, deregistering, fetching instances, and sending heartbeats
    for instances.
    """

+    name = ComponentType.MODEL_REGISTRY
+
+    def __init__(self, system_app: SystemApp | None = None):
+        self.system_app = system_app
+        super().__init__(system_app)
+
+    def init_app(self, system_app: SystemApp):
+        """Initialize the component with the main application."""
+        self.system_app = system_app
+
    @abstractmethod
    async def register_instance(self, instance: ModelInstance) -> bool:
        """
@ -65,9 +80,11 @@ class ModelRegistry(ABC):
        """Fetch all instances of a given model. Optionally, fetch only the healthy instances."""

    @abstractmethod
-    async def get_all_model_instances(self) -> List[ModelInstance]:
+    async def get_all_model_instances(
+        self, healthy_only: bool = False
+    ) -> List[ModelInstance]:
        """
-        Fetch all instances of all models
+        Fetch all instances of all models, Optionally, fetch only the healthy instances.

        Returns:
        - List[ModelInstance]: A list of instances for the all models.
@ -105,8 +122,12 @@ class ModelRegistry(ABC):

 class EmbeddedModelRegistry(ModelRegistry):
    def __init__(
-        self, heartbeat_interval_secs: int = 60, heartbeat_timeout_secs: int = 120
+        self,
+        system_app: SystemApp | None = None,
+        heartbeat_interval_secs: int = 60,
+        heartbeat_timeout_secs: int = 120,
    ):
+        super().__init__(system_app)
        self.registry: Dict[str, List[ModelInstance]] = defaultdict(list)
        self.heartbeat_interval_secs = heartbeat_interval_secs
        self.heartbeat_timeout_secs = heartbeat_timeout_secs
@ -180,9 +201,14 @@ class EmbeddedModelRegistry(ModelRegistry):
            instances = [ins for ins in instances if ins.healthy == True]
        return instances

-    async def get_all_model_instances(self) -> List[ModelInstance]:
-        print(self.registry)
-        return list(itertools.chain(*self.registry.values()))
+    async def get_all_model_instances(
+        self, healthy_only: bool = False
+    ) -> List[ModelInstance]:
+        logger.debug("Current registry metadata:\n{self.registry}")
+        instances = list(itertools.chain(*self.registry.values()))
+        if healthy_only:
+            instances = [ins for ins in instances if ins.healthy == True]
+        return instances

    async def send_heartbeat(self, instance: ModelInstance) -> bool:
        _, exist_ins = self._get_instances(
--- a/pilot/model/cluster/tests/init.py
+++ b/pilot/model/cluster/tests/init.py
--- a/pilot/model/cluster/worker/tests/base_tests.py
+++ b/pilot/model/cluster/worker/tests/base_tests.py
@ -6,6 +6,7 @@ from pilot.model.parameter import ModelParameters, ModelWorkerParameters, Worker
 from pilot.model.base import ModelOutput
 from pilot.model.cluster.worker_base import ModelWorker
 from pilot.model.cluster.worker.manager import (
+    WorkerManager,
    LocalWorkerManager,
    RegisterFunc,
    DeregisterFunc,
@ -13,6 +14,23 @@ from pilot.model.cluster.worker.manager import (
    ApplyFunction,
 )

+from pilot.model.base import ModelInstance
+from pilot.model.cluster.registry import ModelRegistry, EmbeddedModelRegistry
+
+
+@pytest.fixture
+def model_registry(request):
+    return EmbeddedModelRegistry()
+
+
+@pytest.fixture
+def model_instance():
+    return ModelInstance(
+        model_name="test_model",
+        host="192.168.1.1",
+        port=5000,
+    )
+

 class MockModelWorker(ModelWorker):
    def __init__(
@ -51,8 +69,10 @@ class MockModelWorker(ModelWorker):
            raise Exception("Stop worker error for mock")

    def generate_stream(self, params: Dict) -> Iterator[ModelOutput]:
+        full_text = ""
        for msg in self.stream_messags:
-            yield ModelOutput(text=msg, error_code=0)
+            full_text += msg
+            yield ModelOutput(text=full_text, error_code=0)

    def generate(self, params: Dict) -> ModelOutput:
        output = None
@ -67,6 +87,8 @@ class MockModelWorker(ModelWorker):
 _TEST_MODEL_NAME = "vicuna-13b-v1.5"
 _TEST_MODEL_PATH = "/app/models/vicuna-13b-v1.5"

+ClusterType = Tuple[WorkerManager, ModelRegistry]
+

 def _new_worker_params(
    model_name: str = _TEST_MODEL_NAME,
@ -85,7 +107,9 @@ def _create_workers(
    worker_type: str = WorkerType.LLM.value,
    stream_messags: List[str] = None,
    embeddings: List[List[float]] = None,
-) -> List[Tuple[ModelWorker, ModelWorkerParameters]]:
+    host: str = "127.0.0.1",
+    start_port=8001,
+) -> List[Tuple[ModelWorker, ModelWorkerParameters, ModelInstance]]:
    workers = []
    for i in range(num_workers):
        model_name = f"test-model-name-{i}"
@ -98,10 +122,16 @@ def _create_workers(
            stream_messags=stream_messags,
            embeddings=embeddings,
        )
+        model_instance = ModelInstance(
+            model_name=WorkerType.to_worker_key(model_name, worker_type),
+            host=host,
+            port=start_port + i,
+            healthy=True,
+        )
        worker_params = _new_worker_params(
            model_name, model_path, worker_type=worker_type
        )
-        workers.append((worker, worker_params))
+        workers.append((worker, worker_params, model_instance))
    return workers


@ -127,12 +157,12 @@ async def _start_worker_manager(**kwargs):
        model_registry=model_registry,
    )

-    for worker, worker_params in _create_workers(
+    for worker, worker_params, model_instance in _create_workers(
        num_workers, error_worker, stop_error, stream_messags, embeddings
    ):
        worker_manager.add_worker(worker, worker_params)
    if workers:
-        for worker, worker_params in workers:
+        for worker, worker_params, model_instance in workers:
            worker_manager.add_worker(worker, worker_params)

    if start:
@ -143,6 +173,15 @@ async def _start_worker_manager(**kwargs):
        await worker_manager.stop()


+async def _create_model_registry(
+    workers: List[Tuple[ModelWorker, ModelWorkerParameters, ModelInstance]]
+) -> ModelRegistry:
+    registry = EmbeddedModelRegistry()
+    for _, _, inst in workers:
+        assert await registry.register_instance(inst) == True
+    return registry
+
+
@pytest_asyncio.fixture
 async def manager_2_workers(request):
    param = getattr(request, "param", {})
@ -166,3 +205,27 @@ async def manager_2_embedding_workers(request):
    )
    async with _start_worker_manager(workers=workers, **param) as worker_manager:
        yield (worker_manager, workers)
+
+
+@asynccontextmanager
+async def _new_cluster(**kwargs) -> ClusterType:
+    num_workers = kwargs.get("num_workers", 0)
+    workers = _create_workers(
+        num_workers, stream_messags=kwargs.get("stream_messags", [])
+    )
+    if "num_workers" in kwargs:
+        del kwargs["num_workers"]
+    registry = await _create_model_registry(
+        workers,
+    )
+    async with _start_worker_manager(workers=workers, **kwargs) as worker_manager:
+        yield (worker_manager, registry)
+
+
+@pytest_asyncio.fixture
+async def cluster_2_workers(request):
+    param = getattr(request, "param", {})
+    workers = _create_workers(2)
+    registry = await _create_model_registry(workers)
+    async with _start_worker_manager(workers=workers, **param) as worker_manager:
+        yield (worker_manager, registry)
--- a/pilot/model/cluster/worker/default_worker.py
+++ b/pilot/model/cluster/worker/default_worker.py
@ -256,15 +256,22 @@ class DefaultModelWorker(ModelWorker):
        return params, model_context, generate_stream_func, model_span

    def _handle_output(self, output, previous_response, model_context):
+        finish_reason = None
+        usage = None
        if isinstance(output, dict):
            finish_reason = output.get("finish_reason")
+            usage = output.get("usage")
            output = output["text"]
            if finish_reason is not None:
                logger.info(f"finish_reason: {finish_reason}")
        incremental_output = output[len(previous_response) :]
        print(incremental_output, end="", flush=True)
        model_output = ModelOutput(
-            text=output, error_code=0, model_context=model_context
+            text=output,
+            error_code=0,
+            model_context=model_context,
+            finish_reason=finish_reason,
+            usage=usage,
        )
        return model_output, incremental_output, output

--- a/pilot/model/cluster/worker/manager.py
+++ b/pilot/model/cluster/worker/manager.py
@ -99,9 +99,7 @@ class LocalWorkerManager(WorkerManager):
        )

    def _worker_key(self, worker_type: str, model_name: str) -> str:
-        if isinstance(worker_type, WorkerType):
-            worker_type = worker_type.value
-        return f"{model_name}@{worker_type}"
+        return WorkerType.to_worker_key(model_name, worker_type)

    async def run_blocking_func(self, func, *args):
        if asyncio.iscoroutinefunction(func):
--- a/pilot/model/cluster/worker/tests/test_manager.py
+++ b/pilot/model/cluster/worker/tests/test_manager.py
@ -3,7 +3,7 @@ import pytest
 from typing import List, Iterator, Dict, Tuple
 from dataclasses import asdict
 from pilot.model.parameter import ModelParameters, ModelWorkerParameters, WorkerType
-from pilot.model.base import ModelOutput, WorkerApplyType
+from pilot.model.base import ModelOutput, WorkerApplyType, ModelInstance
 from pilot.model.cluster.base import WorkerApplyRequest, WorkerStartupRequest
 from pilot.model.cluster.worker_base import ModelWorker
 from pilot.model.cluster.manager_base import WorkerRunData
@ -14,7 +14,7 @@ from pilot.model.cluster.worker.manager import (
    SendHeartbeatFunc,
    ApplyFunction,
 )
-from pilot.model.cluster.worker.tests.base_tests import (
+from pilot.model.cluster.tests.conftest import (
    MockModelWorker,
    manager_2_workers,
    manager_with_2_workers,
@ -216,7 +216,7 @@ async def test__remove_worker():
    workers = _create_workers(3)
    async with _start_worker_manager(workers=workers, stop=False) as manager:
        assert len(manager.workers) == 3
-        for _, worker_params in workers:
+        for _, worker_params, _ in workers:
            manager._remove_worker(worker_params)
        not_exist_parmas = _new_worker_params(
            model_name="this is a not exist worker params"
@ -229,7 +229,7 @@ async def test__remove_worker():
 async def test_model_startup(mock_build_worker):
    async with _start_worker_manager() as manager:
        workers = _create_workers(1)
-        worker, worker_params = workers[0]
+        worker, worker_params, model_instance = workers[0]
        mock_build_worker.return_value = worker

        req = WorkerStartupRequest(
@ -245,7 +245,7 @@ async def test_model_startup(mock_build_worker):

    async with _start_worker_manager() as manager:
        workers = _create_workers(1, error_worker=True)
-        worker, worker_params = workers[0]
+        worker, worker_params, model_instance = workers[0]
        mock_build_worker.return_value = worker
        req = WorkerStartupRequest(
            host="127.0.0.1",
@ -263,7 +263,7 @@ async def test_model_startup(mock_build_worker):
 async def test_model_shutdown(mock_build_worker):
    async with _start_worker_manager(start=False, stop=False) as manager:
        workers = _create_workers(1)
-        worker, worker_params = workers[0]
+        worker, worker_params, model_instance = workers[0]
        mock_build_worker.return_value = worker

        req = WorkerStartupRequest(
@ -298,7 +298,7 @@ async def test_get_model_instances(is_async):
    workers = _create_workers(3)
    async with _start_worker_manager(workers=workers, stop=False) as manager:
        assert len(manager.workers) == 3
-        for _, worker_params in workers:
+        for _, worker_params, _ in workers:
            model_name = worker_params.model_name
            worker_type = worker_params.worker_type
            if is_async:
@ -326,7 +326,7 @@ async def test__simple_select(
    ]
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        instances = await manager.get_model_instances(worker_type, model_name)
@ -351,7 +351,7 @@ async def test_select_one_instance(
    ],
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        if is_async:
@ -376,7 +376,7 @@ async def test__get_model(
    ],
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        params = {"model": model_name}
@ -403,13 +403,13 @@ async def test_generate_stream(
    expected_messages: str,
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        params = {"model": model_name}
        text = ""
        async for out in manager.generate_stream(params):
-            text += out.text
+            text = out.text
        assert text == expected_messages


@ -417,8 +417,8 @@ async def test_generate_stream(
@pytest.mark.parametrize(
    "manager_with_2_workers, expected_messages",
    [
-        ({"stream_messags": ["Hello", " world."]}, " world."),
-        ({"stream_messags": ["你好，我是", "张三。"]}, "张三。"),
+        ({"stream_messags": ["Hello", " world."]}, "Hello world."),
+        ({"stream_messags": ["你好，我是", "张三。"]}, "你好，我是张三。"),
    ],
    indirect=["manager_with_2_workers"],
 )
@ -429,7 +429,7 @@ async def test_generate(
    expected_messages: str,
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        params = {"model": model_name}
@ -454,7 +454,7 @@ async def test_embeddings(
    is_async: bool,
 ):
    manager, workers = manager_2_embedding_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        params = {"model": model_name, "input": ["hello", "world"]}
@ -472,7 +472,7 @@ async def test_parameter_descriptions(
    ]
 ):
    manager, workers = manager_with_2_workers
-    for _, worker_params in workers:
+    for _, worker_params, _ in workers:
        model_name = worker_params.model_name
        worker_type = worker_params.worker_type
        params = await manager.parameter_descriptions(worker_type, model_name)
--- a/pilot/model/conversation.py
+++ b/pilot/model/conversation.py
@ -339,6 +339,27 @@ register_conv_template(
    )
 )

+
+# codellama template
+# reference: https://github.com/facebookresearch/llama/blob/cfc3fc8c1968d390eb830e65c63865e980873a06/llama/generation.py#L212
+# reference2 : https://github.com/eosphoros-ai/DB-GPT-Hub/blob/main/README.zh.md
+register_conv_template(
+    Conversation(
+        name="codellama",
+        system="<s>[INST] <<SYS>>\nI want you to act as a SQL terminal in front of an example database, you need only to return the sql command to me.Below is an instruction that describes a task, Write a response that appropriately completes the request."
+        "If you don't know the answer to the request, please don't share false information.\n<</SYS>>\n\n",
+        roles=("[INST]", "[/INST]"),
+        messages=(),
+        offset=0,
+        sep_style=SeparatorStyle.LLAMA2,
+        sep=" ",
+        sep2=" </s><s>",
+        stop_token_ids=[2],
+        system_formatter=lambda msg: f"<s>[INST] <<SYS>>\n{msg}\n<</SYS>>\n\n",
+    )
+)
+
+
 # Alpaca default template
 register_conv_template(
    Conversation(
--- a/pilot/model/model_adapter.py
+++ b/pilot/model/model_adapter.py
@ -45,6 +45,10 @@ _OLD_MODELS = [
    "llama-cpp",
    "proxyllm",
    "gptj-6b",
+    "codellama-13b-sql-sft",
+    "codellama-7b",
+    "codellama-7b-sql-sft",
+    "codellama-13b",
 ]


@ -148,8 +152,12 @@ class LLMModelAdaper:
                conv.append_message(conv.roles[1], content)
            else:
                raise ValueError(f"Unknown role: {role}")
+
        if system_messages:
-            conv.set_system_message("".join(system_messages))
+            if isinstance(conv, Conversation):
+                conv.set_system_message("".join(system_messages))
+            else:
+                conv.update_system_message("".join(system_messages))

        # Add a blank message for the assistant.
        conv.append_message(conv.roles[1], None)
@ -459,7 +467,8 @@ register_conv_template(
        sep="\n",
        sep2="</s>",
        stop_str=["</s>", "[UNK]"],
-    )
+    ),
+    override=True,
 )
 # source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L227
 register_conv_template(
@ -474,7 +483,8 @@ register_conv_template(
        sep="###",
        sep2="</s>",
        stop_str=["</s>", "[UNK]"],
-    )
+    ),
+    override=True,
 )
 # source: https://huggingface.co/BAAI/AquilaChat2-34B/blob/4608b75855334b93329a771aee03869dbf7d88cc/predict.py#L242
 register_conv_template(
@ -487,5 +497,6 @@ register_conv_template(
        sep="",
        sep2="</s>",
        stop_str=["</s>", "<|endoftext|>"],
-    )
+    ),
+    override=True,
 )
--- a/pilot/model/parameter.py
+++ b/pilot/model/parameter.py
@ -1,9 +1,10 @@
 #!/usr/bin/env python3
 # -*- coding: utf-8 -*-
+
 import os
 from dataclasses import dataclass, field
 from enum import Enum
-from typing import Dict, Optional
+from typing import Dict, Optional, Union, Tuple

 from pilot.model.conversation import conv_templates
 from pilot.utils.parameter_utils import BaseParameters
@ -19,6 +20,35 @@ class WorkerType(str, Enum):
    def values():
        return [item.value for item in WorkerType]

+    @staticmethod
+    def to_worker_key(worker_name, worker_type: Union[str, "WorkerType"]) -> str:
+        """Generate worker key from worker name and worker type
+
+        Args:
+            worker_name (str): Worker name(eg., chatglm2-6b)
+            worker_type (Union[str, "WorkerType"]): Worker type(eg., 'llm', or [`WorkerType.LLM`])
+
+        Returns:
+            str: Generated worker key
+        """
+        if "@" in worker_name:
+            raise ValueError(f"Invaild symbol '@' in your worker name {worker_name}")
+        if isinstance(worker_type, WorkerType):
+            worker_type = worker_type.value
+        return f"{worker_name}@{worker_type}"
+
+    @staticmethod
+    def parse_worker_key(worker_key: str) -> Tuple[str, str]:
+        """Parse worker name and worker type from worker key
+
+        Args:
+            worker_key (str): Worker key generated by [`WorkerType.to_worker_key`]
+
+        Returns:
+            Tuple[str, str]: Worker name and worker type
+        """
+        return tuple(worker_key.split("@"))
+

@dataclass
 class ModelControllerParameters(BaseParameters):
@ -60,6 +90,56 @@ class ModelControllerParameters(BaseParameters):
    )


+@dataclass
+class ModelAPIServerParameters(BaseParameters):
+    host: Optional[str] = field(
+        default="0.0.0.0", metadata={"help": "Model API server deploy host"}
+    )
+    port: Optional[int] = field(
+        default=8100, metadata={"help": "Model API server deploy port"}
+    )
+    daemon: Optional[bool] = field(
+        default=False, metadata={"help": "Run Model API server in background"}
+    )
+    controller_addr: Optional[str] = field(
+        default="http://127.0.0.1:8000",
+        metadata={"help": "The Model controller address to connect"},
+    )
+
+    api_keys: Optional[str] = field(
+        default=None,
+        metadata={"help": "Optional list of comma separated API keys"},
+    )
+
+    log_level: Optional[str] = field(
+        default=None,
+        metadata={
+            "help": "Logging level",
+            "valid_values": [
+                "FATAL",
+                "ERROR",
+                "WARNING",
+                "WARNING",
+                "INFO",
+                "DEBUG",
+                "NOTSET",
+            ],
+        },
+    )
+    log_file: Optional[str] = field(
+        default="dbgpt_model_apiserver.log",
+        metadata={
+            "help": "The filename to store log",
+        },
+    )
+    tracer_file: Optional[str] = field(
+        default="dbgpt_model_apiserver_tracer.jsonl",
+        metadata={
+            "help": "The filename to store tracer span records",
+        },
+    )
+
+
@dataclass
 class BaseModelParameters(BaseParameters):
    model_name: str = field(metadata={"help": "Model name", "tags": "fixed"})
--- a/pilot/scene/base_message.py
+++ b/pilot/scene/base_message.py
@ -1,7 +1,7 @@
 from __future__ import annotations

 from abc import ABC, abstractmethod
-from typing import Any, Dict, List, Tuple, Optional
+from typing import Any, Dict, List, Tuple, Optional, Union

 from pydantic import BaseModel, Field, root_validator

@ -70,14 +70,6 @@ class SystemMessage(BaseMessage):
        return "system"


-class ModelMessage(BaseModel):
-    """Type of message that interaction between dbgpt-server and llm-server"""
-
-    """Similar to openai's message format"""
-    role: str
-    content: str
-
-
 class ModelMessageRoleType:
    """ "Type of ModelMessage role"""

@ -87,6 +79,45 @@ class ModelMessageRoleType:
    VIEW = "view"


+class ModelMessage(BaseModel):
+    """Type of message that interaction between dbgpt-server and llm-server"""
+
+    """Similar to openai's message format"""
+    role: str
+    content: str
+
+    @staticmethod
+    def from_openai_messages(
+        messages: Union[str, List[Dict[str, str]]]
+    ) -> List["ModelMessage"]:
+        """Openai message format to current ModelMessage format"""
+        if isinstance(messages, str):
+            return [ModelMessage(role=ModelMessageRoleType.HUMAN, content=messages)]
+        result = []
+        for message in messages:
+            msg_role = message["role"]
+            content = message["content"]
+            if msg_role == "system":
+                result.append(
+                    ModelMessage(role=ModelMessageRoleType.SYSTEM, content=content)
+                )
+            elif msg_role == "user":
+                result.append(
+                    ModelMessage(role=ModelMessageRoleType.HUMAN, content=content)
+                )
+            elif msg_role == "assistant":
+                result.append(
+                    ModelMessage(role=ModelMessageRoleType.AI, content=content)
+                )
+            else:
+                raise ValueError(f"Unknown role: {msg_role}")
+        return result
+
+    @staticmethod
+    def to_dict_list(messages: List["ModelMessage"]) -> List[Dict[str, str]]:
+        return list(map(lambda m: m.dict(), messages))
+
+
 class Generation(BaseModel):
    """Output of a single generation."""

--- a/pilot/scene/chat_knowledge/v1/chat.py
+++ b/pilot/scene/chat_knowledge/v1/chat.py
@ -1,3 +1,4 @@
+import json
 import os
 from typing import Dict, List

@ -110,12 +111,14 @@ class ChatKnowledge(BaseChat):
            list(map(lambda doc: doc.metadata, docs)), "source"
        )

-        self.current_message.knowledge_source = self.sources
-        if not docs:
-            raise ValueError(
-                "you have no knowledge space, please add your knowledge space"
-            )
-        context = [d.page_content for d in docs]
+        if not docs or len(docs) == 0:
+            print("no relevant docs to retrieve")
+            context = "no relevant docs to retrieve"
+            # raise ValueError(
+            #     "you have no knowledge space, please add your knowledge space"
+            # )
+        else:
+            context = [d.page_content for d in docs]
        context = context[: self.max_token]
        relations = list(
            set([os.path.basename(str(d.metadata.get("source", ""))) for d in docs])
@ -128,17 +131,26 @@ class ChatKnowledge(BaseChat):
        return input_values

    def parse_source_view(self, sources: List):
-        html_title = f"##### **References:**"
-        lines = ""
+        """
+        build knowledge reference view message to web
+        {
+            "title":"References",
+            "reference":{
+                "name":"aa.pdf",
+                "pages":["1","2","3"]
+            },
+        }
+        """
+        references = {"title": "References", "reference": {}}
        for item in sources:
            source = item["source"] if "source" in item else ""
-            pages = ",".join(item["pages"]) if "pages" in item else ""
-            lines += f"{source}"
+            references["reference"]["name"] = source
+            pages = item["pages"] if "pages" in item else []
            if len(pages) > 0:
-                lines += f", **pages**:{pages}\n\n"
-            else:
-                lines += "\n\n"
-        html = f"""{html_title}\n{lines}"""
+                references["reference"]["pages"] = pages
+        html = (
+            f"""<references>{json.dumps(references, ensure_ascii=False)}</references>"""
+        )
        return html

    def merge_by_key(self, data, key):
--- a/pilot/server/chat_adapter.py
+++ b/pilot/server/chat_adapter.py
@ -215,6 +215,16 @@ class Llama2ChatAdapter(BaseChatAdpter):
        return get_conv_template("llama-2")


+class CodeLlamaChatAdapter(BaseChatAdpter):
+    """The model ChatAdapter for codellama ."""
+
+    def match(self, model_path: str):
+        return "codellama" in model_path.lower()
+
+    def get_conv_template(self, model_path: str) -> Conversation:
+        return get_conv_template("codellama")
+
+
 class BaichuanChatAdapter(BaseChatAdpter):
    def match(self, model_path: str):
        return "baichuan" in model_path.lower()
@ -268,6 +278,7 @@ register_llm_model_chat_adapter(FalconChatAdapter)
 register_llm_model_chat_adapter(GorillaChatAdapter)
 register_llm_model_chat_adapter(GPT4AllChatAdapter)
 register_llm_model_chat_adapter(Llama2ChatAdapter)
+register_llm_model_chat_adapter(CodeLlamaChatAdapter)
 register_llm_model_chat_adapter(BaichuanChatAdapter)
 register_llm_model_chat_adapter(WizardLMChatAdapter)
 register_llm_model_chat_adapter(LlamaCppChatAdapter)
--- a/pilot/utils/openai_utils.py
+++ b/pilot/utils/openai_utils.py
@ -0,0 +1,99 @@
+from typing import Dict, Any, Awaitable, Callable, Optional, Iterator
+import httpx
+import asyncio
+import logging
+import json
+
+logger = logging.getLogger(__name__)
+MessageCaller = Callable[[str], Awaitable[None]]
+
+
+async def _do_chat_completion(
+    url: str,
+    chat_data: Dict[str, Any],
+    client: httpx.AsyncClient,
+    headers: Dict[str, Any] = {},
+    timeout: int = 60,
+    caller: Optional[MessageCaller] = None,
+) -> Iterator[str]:
+    async with client.stream(
+        "POST",
+        url,
+        headers=headers,
+        json=chat_data,
+        timeout=timeout,
+    ) as res:
+        if res.status_code != 200:
+            error_message = await res.aread()
+            if error_message:
+                error_message = error_message.decode("utf-8")
+            logger.error(
+                f"Request failed with status {res.status_code}. Error: {error_message}"
+            )
+            raise httpx.RequestError(
+                f"Request failed with status {res.status_code}",
+                request=res.request,
+            )
+        async for line in res.aiter_lines():
+            if line:
+                if not line.startswith("data: "):
+                    if caller:
+                        await caller(line)
+                    yield line
+                else:
+                    decoded_line = line.split("data: ", 1)[1]
+                    if decoded_line.lower().strip() != "[DONE]".lower():
+                        obj = json.loads(decoded_line)
+                        if obj["choices"][0]["delta"].get("content") is not None:
+                            text = obj["choices"][0]["delta"].get("content")
+                            if caller:
+                                await caller(text)
+                            yield text
+            await asyncio.sleep(0.02)
+
+
+async def chat_completion_stream(
+    url: str,
+    chat_data: Dict[str, Any],
+    client: Optional[httpx.AsyncClient] = None,
+    headers: Dict[str, Any] = {},
+    timeout: int = 60,
+    caller: Optional[MessageCaller] = None,
+) -> Iterator[str]:
+    if client:
+        async for text in _do_chat_completion(
+            url,
+            chat_data,
+            client=client,
+            headers=headers,
+            timeout=timeout,
+            caller=caller,
+        ):
+            yield text
+    else:
+        async with httpx.AsyncClient() as client:
+            async for text in _do_chat_completion(
+                url,
+                chat_data,
+                client=client,
+                headers=headers,
+                timeout=timeout,
+                caller=caller,
+            ):
+                yield text
+
+
+async def chat_completion(
+    url: str,
+    chat_data: Dict[str, Any],
+    client: Optional[httpx.AsyncClient] = None,
+    headers: Dict[str, Any] = {},
+    timeout: int = 60,
+    caller: Optional[MessageCaller] = None,
+) -> str:
+    full_text = ""
+    async for text in chat_completion_stream(
+        url, chat_data, client, headers=headers, timeout=timeout, caller=caller
+    ):
+        full_text += text
+    return full_text
--- a/requirements/dev-requirements.txt
+++ b/requirements/dev-requirements.txt
@ -8,6 +8,7 @@ pytest-integration
 pytest-mock
 pytest-recording
 pytesseract==0.3.10
+aioresponses
 # python code format
 black
 # for git hooks