DB-GPT V0.7.0版本更新：支持MCP协议、集成DeepSeek R1模型、GraphRAG检索链路增强、架构全面升级等

[rag.storage.graph]type = "TuGraph"host="127.0.0.1"port=7687username="admin"password="73@TuGraph"
enable_summary="True"triplet_graph_enabled="True"document_graph_enabled="True"
# 向量图检索配置项enable_similarity_search="True"knowledge_graph_embedding_batch_size=20similarity_search_topk=5extract_score_threshold=0.7

[rag.storage.graph]type = "TuGraph"host="127.0.0.1"port=7687username="admin"password="73@TuGraph"
enable_summary="True"triplet_graph_enabled="True"document_graph_enabled="True"
# 意图识别+Text2GQL图检索配置项enable_text_search="True"
# 使用Ollama部署独立text2gql模型，开启以下配置项# text2gql_model_enabled="True"# text2gql_model_name="tugraph/CodeLlama-7b-Cypher-hf:latest"

[system]# Load language from environment variable(It is set by the hook)language = "${env:DBGPT_LANG:-zh}"api_keys = []encrypt_key = "your_secret_key"# Server Configurations[service.web]host = "0.0.0.0"port = 5670[service.web.database]type = "sqlite"path = "pilot/meta_data/dbgpt.db"[service.model.worker]host = "127.0.0.1"[rag.storage][rag.storage.vector]type = "chroma"persist_path = "pilot/data"# Model Configurations[models][[models.llms]]name = "deepseek-reasoner"# name = "deepseek-chat"provider = "proxy/deepseek"api_key = "your_deepseek_api_key"

[[serves]]type = "file"# Default backend for file serverdefault_backend = "s3"
[[serves.backends]]type = "oss"endpoint = "https://oss-cn-beijing.aliyuncs.com"region = "oss-cn-beijing"access_key_id = "${env:OSS_ACCESS_KEY_ID}"access_key_secret = "${env:OSS_ACCESS_KEY_SECRET}"fixed_bucket = "{your_bucket_name}"
[[serves.backends]]# Use Tencent COS s3 compatible API as the file servertype = "s3"endpoint = "https://cos.ap-beijing.myqcloud.com"region = "ap-beijing"access_key_id = "${env:COS_SECRETID}"access_key_secret = "${env:COS_SECRETKEY}"fixed_bucket = "{your_bucket_name}

# Model Configurations[models][[models.llms]]name = "DeepSeek-R1-Distill-Qwen-1.5B"provider = "llama.cpp.server"# If not provided, the model will be downloaded from the Hugging Face model hub# uncomment the following line to specify the model path in the local file system# https://huggingface.co/bartowski/DeepSeek-R1-Distill-Qwen-1.5B-GGUF# path = "the-model-path-in-the-local-file-system"path = "models/DeepSeek-R1-Distill-Qwen-1.5B-Q4_K_M.gguf

[app]# 统一配置所有场景的温度temperature = 0.6
[[app.configs]]name = "chat_excel"# 使用自定义的温度配置temperature = 0.1duckdb_extensions_dir = []force_install = true
[[app.configs]]name = "chat_normal"memory = {type="token", max_token_limit=20000}
[[app.configs]]name = "chat_with_db_qa"schema_retrieve_top_k = 50memory = {type="window", keep_start_rounds=0, keep_end_rounds=10}

docker run -it --rm -e SILICONFLOW_API_KEY=${SILICONFLOW_API_KEY} \ -p 5670:5670 --name dbgpt eosphorosai/dbgpt-openai

bash docker/base/build_image.sh --install-mode openai

from openai import OpenAI
DBGPT_API_KEY = "dbgpt"
client = OpenAI(    api_key=DBGPT_API_KEY,    base_url="http://localhost:5670/api/v2",)
messages = [    {        "role": "user",        "content": "Hello, how are you?",    },]
has_thinking = Falsereasoning_content = ""for chunk in client.chat.completions.create(    model="deepseek-chat",    messages=messages,    extra_body={        "chat_mode": "chat_normal",    },    stream=True,    max_tokens=4096,):    delta_content = chunk.choices[0].delta.content    if hasattr(chunk.choices[0].delta, "reasoning_content"):        reasoning_content = chunk.choices[0].delta.reasoning_content    if reasoning_content:        if not has_thinking:            print("<thinking>", flush=True)        print(reasoning_content, end="", flush=True)        has_thinking = True    if delta_content:        if has_thinking:            print("</thinking>", flush=True)        print(delta_content, end="", flush=True)        has_thinking = False

mysql -h127.0.0.1 -uroot -p{your_password} < ./assets/schema/upgrade/v0_7_0/upgrade_to_v0.7.0.sql