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chore(model): Modify siliconflow name (#2194)

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Fangyin Cheng 2024-12-12 16:47:14 +08:00 committed by GitHub
parent 4da1809b31
commit dab55493f5
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10 changed files with 48 additions and 28 deletions
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2
.env.template
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@ -76,6 +76,8 @@ KNOWLEDGE_GRAPH_SEARCH_TOP_SIZE=200
## you can set this value to a higher value for better performance.
## if out of memory when load large document, you can set this value to a lower value.
# KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD=10
## Maximum number of threads to use when loading chunks, please make sure your vector db can support multi-threading.
# KNOWLEDGE_MAX_THREADS=1
#KNOWLEDGE_CHUNK_OVERLAP=50
# Control whether to display the source document of knowledge on the front end.
KNOWLEDGE_CHAT_SHOW_RELATIONS=False

17
dbgpt/_private/config.py
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@ -140,16 +140,16 @@ class Config(metaclass=Singleton):
os.environ["claude_proxyllm_api_base"] = os.getenv(
"ANTHROPIC_BASE_URL", "https://api.anthropic.com"
)
self.silicon_flow_proxy_api_key = os.getenv("SILICON_FLOW_API_KEY")
if self.silicon_flow_proxy_api_key:
self.siliconflow_proxy_api_key = os.getenv("SILICONFLOW_API_KEY")
if self.siliconflow_proxy_api_key:
os.environ[
"silicon_flow_proxyllm_proxy_api_key"
] = self.silicon_flow_proxy_api_key
os.environ["silicon_flow_proxyllm_proxyllm_backend"] = os.getenv(
"SILICON_FLOW_MODEL_VERSION", "Qwen/Qwen2.5-Coder-32B-Instruct"
"siliconflow_proxyllm_proxy_api_key"
] = self.siliconflow_proxy_api_key
os.environ["siliconflow_proxyllm_proxyllm_backend"] = os.getenv(
"SILICONFLOW_MODEL_VERSION", "Qwen/Qwen2.5-Coder-32B-Instruct"
)
os.environ["silicon_flow_proxyllm_api_base"] = os.getenv(
"SILICON_FLOW_API_BASE", "https://api.siliconflow.cn/v1"
os.environ["siliconflow_proxyllm_api_base"] = os.getenv(
"SILICONFLOW_API_BASE", "https://api.siliconflow.cn/v1"
)
self.proxy_server_url = os.getenv("PROXY_SERVER_URL")
@ -277,6 +277,7 @@ class Config(metaclass=Singleton):
self.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD = int(
os.getenv("KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD", 10)
)
self.KNOWLEDGE_MAX_THREADS = int(os.getenv("KNOWLEDGE_MAX_THREADS", 1))
# default recall similarity score, between 0 and 1
self.KNOWLEDGE_SEARCH_RECALL_SCORE = float(
os.getenv("KNOWLEDGE_SEARCH_RECALL_SCORE", 0.3)

4
dbgpt/configs/model_config.py
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@ -80,7 +80,7 @@ LLM_MODEL_CONFIG = {
# https://platform.deepseek.com/api-docs/
"deepseek_proxyllm": "deepseek_proxyllm",
# https://docs.siliconflow.cn/quickstart
"silicon_flow_proxyllm": "silicon_flow_proxyllm",
"siliconflow_proxyllm": "siliconflow_proxyllm",
"llama-2-7b": os.path.join(MODEL_PATH, "Llama-2-7b-chat-hf"),
"llama-2-13b": os.path.join(MODEL_PATH, "Llama-2-13b-chat-hf"),
"llama-2-70b": os.path.join(MODEL_PATH, "Llama-2-70b-chat-hf"),
@ -322,7 +322,7 @@ EMBEDDING_MODEL_CONFIG = {
"bge-reranker-large": os.path.join(MODEL_PATH, "bge-reranker-large"),
# Proxy rerank model
"rerank_proxy_http_openapi": "rerank_proxy_http_openapi",
"rerank_proxy_silicon_flow": "rerank_proxy_silicon_flow",
"rerank_proxy_siliconflow": "rerank_proxy_siliconflow",
}

2
dbgpt/model/adapter/embeddings_loader.py
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@ -109,7 +109,7 @@ class EmbeddingLoader:
if proxy_param.proxy_backend:
openapi_param["model_name"] = proxy_param.proxy_backend
return OpenAPIRerankEmbeddings(**openapi_param)
elif model_name in ["rerank_proxy_silicon_flow"]:
elif model_name in ["rerank_proxy_siliconflow"]:
from dbgpt.rag.embedding.rerank import SiliconFlowRerankEmbeddings
proxy_param = cast(ProxyEmbeddingParameters, param)

6
dbgpt/model/adapter/proxy_adapter.py
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@ -349,7 +349,7 @@ class SiliconFlowProxyLLMModelAdapter(ProxyLLMModelAdapter):
return True
def do_match(self, lower_model_name_or_path: Optional[str] = None):
return lower_model_name_or_path == "silicon_flow_proxyllm"
return lower_model_name_or_path == "siliconflow_proxyllm"
def get_llm_client_class(
self, params: ProxyModelParameters
@ -359,9 +359,9 @@ class SiliconFlowProxyLLMModelAdapter(ProxyLLMModelAdapter):
return SiliconFlowLLMClient
def get_async_generate_stream_function(self, model, model_path: str):
from dbgpt.model.proxy.llms.siliconflow import silicon_flow_generate_stream
from dbgpt.model.proxy.llms.siliconflow import siliconflow_generate_stream
return silicon_flow_generate_stream
return siliconflow_generate_stream
register_model_adapter(OpenAIProxyLLMModelAdapter)

2
dbgpt/model/parameter.py
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@ -621,7 +621,7 @@ _EMBEDDING_PARAMETER_CLASS_TO_NAME_CONFIG = {
"proxy_tongyi",
"proxy_qianfan",
"rerank_proxy_http_openapi",
"rerank_proxy_silicon_flow",
"rerank_proxy_siliconflow",
]
}

16
dbgpt/model/proxy/llms/siliconflow.py
View File

@ -12,10 +12,10 @@ if TYPE_CHECKING:
ClientType = Union[AsyncAzureOpenAI, AsyncOpenAI]
_SILICON_FLOW_DEFAULT_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
_SILICONFLOW_DEFAULT_MODEL = "Qwen/Qwen2.5-Coder-32B-Instruct"
async def silicon_flow_generate_stream(
async def siliconflow_generate_stream(
model: ProxyModel, tokenizer, params, device, context_len=2048
):
client: SiliconFlowLLMClient = model.proxy_llm_client
@ -39,7 +39,7 @@ class SiliconFlowLLMClient(OpenAILLMClient):
model: Optional[str] = None,
proxies: Optional["ProxiesTypes"] = None,
timeout: Optional[int] = 240,
model_alias: Optional[str] = "silicon_flow_proxyllm",
model_alias: Optional[str] = "siliconflow_proxyllm",
context_length: Optional[int] = None,
openai_client: Optional["ClientType"] = None,
openai_kwargs: Optional[Dict[str, Any]] = None,
@ -47,11 +47,11 @@ class SiliconFlowLLMClient(OpenAILLMClient):
):
api_base = (
api_base
or os.getenv("SILICON_FLOW_API_BASE")
or os.getenv("SILICONFLOW_API_BASE")
or "https://api.siliconflow.cn/v1"
)
api_key = api_key or os.getenv("SILICON_FLOW_API_KEY")
model = model or _SILICON_FLOW_DEFAULT_MODEL
api_key = api_key or os.getenv("SILICONFLOW_API_KEY")
model = model or _SILICONFLOW_DEFAULT_MODEL
if not context_length:
if "200k" in model:
context_length = 200 * 1024
@ -60,7 +60,7 @@ class SiliconFlowLLMClient(OpenAILLMClient):
if not api_key:
raise ValueError(
"SiliconFlow API key is required, please set 'SILICON_FLOW_API_KEY' in environment "
"SiliconFlow API key is required, please set 'SILICONFLOW_API_KEY' in environment "
"or pass it as an argument."
)
@ -83,5 +83,5 @@ class SiliconFlowLLMClient(OpenAILLMClient):
def default_model(self) -> str:
model = self._model
if not model:
model = _SILICON_FLOW_DEFAULT_MODEL
model = _SILICONFLOW_DEFAULT_MODEL
return model

13
dbgpt/rag/assembler/embedding.py
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@ -1,4 +1,5 @@
"""Embedding Assembler."""
from concurrent.futures import ThreadPoolExecutor
from typing import Any, List, Optional
@ -130,7 +131,11 @@ class EmbeddingAssembler(BaseAssembler):
Returns:
List[str]: List of chunk ids.
"""
return self._index_store.load_document(self._chunks)
max_chunks_once_load = kwargs.get("max_chunks_once_load", 10)
max_threads = kwargs.get("max_threads", 1)
return self._index_store.load_document_with_limit(
self._chunks, max_chunks_once_load, max_threads
)
async def apersist(self, **kwargs) -> List[str]:
"""Persist chunks into store.
@ -139,7 +144,11 @@ class EmbeddingAssembler(BaseAssembler):
List[str]: List of chunk ids.
"""
# persist chunks into vector store
return await self._index_store.aload_document(self._chunks)
max_chunks_once_load = kwargs.get("max_chunks_once_load", 10)
max_threads = kwargs.get("max_threads", 1)
return await self._index_store.aload_document_with_limit(
self._chunks, max_chunks_once_load, max_threads
)
def _extract_info(self, chunks) -> List[Chunk]:
"""Extract info from chunks."""

4
dbgpt/rag/embedding/rerank.py
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@ -175,10 +175,10 @@ class SiliconFlowRerankEmbeddings(OpenAPIRerankEmbeddings):
"""Initialize the SiliconFlowRerankEmbeddings."""
# If the API key is not provided, try to get it from the environment
if "api_key" not in kwargs:
kwargs["api_key"] = os.getenv("SILICON_FLOW_API_KEY")
kwargs["api_key"] = os.getenv("SILICONFLOW_API_KEY")
if "api_url" not in kwargs:
env_api_url = os.getenv("SILICON_FLOW_API_BASE")
env_api_url = os.getenv("SILICONFLOW_API_BASE")
if env_api_url:
env_api_url = env_api_url.rstrip("/")
kwargs["api_url"] = env_api_url + "/rerank"

10
dbgpt/serve/rag/service/service.py
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@ -506,6 +506,7 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes
name=space.name,
embedding_fn=embedding_fn,
max_chunks_once_load=CFG.KNOWLEDGE_MAX_CHUNKS_ONCE_LOAD,
max_threads=CFG.KNOWLEDGE_MAX_THREADS,
llm_client=self.llm_client,
model_name=None,
)
@ -567,6 +568,10 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes
doc.chunk_size = len(chunk_docs)
vector_ids = [chunk.chunk_id for chunk in chunk_docs]
else:
max_chunks_once_load = (
vector_store_connector._index_store_config.max_chunks_once_load
)
max_threads = vector_store_connector._index_store_config.max_threads
assembler = await EmbeddingAssembler.aload_from_knowledge(
knowledge=knowledge,
index_store=vector_store_connector.index_client,
@ -575,7 +580,10 @@ class Service(BaseService[KnowledgeSpaceEntity, SpaceServeRequest, SpaceServeRes
chunk_docs = assembler.get_chunks()
doc.chunk_size = len(chunk_docs)
vector_ids = await assembler.apersist()
vector_ids = await assembler.apersist(
max_chunks_once_load=max_chunks_once_load,
max_threads=max_threads,
)
doc.status = SyncStatus.FINISHED.name
doc.result = "document persist into index store success"
if vector_ids is not None: