Fix/localai (langgenius#2840)

api/core/model_runtime/model_providers/localai/llm/llm.py

@@ -1,6 +1,5 @@
 from collections.abc import Generator
 from typing import cast
-from urllib.parse import urljoin
 
 from httpx import Timeout
 from openai import (
@@ -19,6 +18,7 @@
 from openai.types.chat import ChatCompletion, ChatCompletionChunk
 from openai.types.chat.chat_completion_message import FunctionCall
 from openai.types.completion import Completion
+from yarl import URL
 
 from core.model_runtime.entities.common_entities import I18nObject
 from core.model_runtime.entities.llm_entities import LLMMode, LLMResult, LLMResultChunk, LLMResultChunkDelta
@@ -181,7 +181,7 @@ def validate_credentials(self, model: str, credentials: dict) -> None:
  UserPromptMessage(content='ping')
  ], model_parameters={
  'max_tokens': 10,
- }, stop=[])
+ }, stop=[], stream=False)
  except Exception as ex:
  raise CredentialsValidateFailedError(f'Invalid credentials {str(ex)}')
 
@@ -227,14 +227,20 @@ def get_customizable_model_schema(self, model: str, credentials: dict) -> AIMode
  )
  ]
 
+ model_properties = { 
+ ModelPropertyKey.MODE: completion_model,
+ } if completion_model else {}
+
+ model_properties[ModelPropertyKey.CONTEXT_SIZE] = int(credentials.get('context_size', '2048'))
+
  entity = AIModelEntity(
  model=model,
  label=I18nObject(
  en_US=model
  ),
  fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
  model_type=ModelType.LLM,
- model_properties={ ModelPropertyKey.MODE: completion_model } if completion_model else {},
+ model_properties=model_properties,
  parameter_rules=rules
  )
 
@@ -319,7 +325,7 @@ def _to_client_kwargs(self, credentials: dict) -> dict:
  client_kwargs = {
  "timeout": Timeout(315.0, read=300.0, write=10.0, connect=5.0),
  "api_key": "1",
- "base_url": urljoin(credentials['server_url'], 'v1'),
+ "base_url": str(URL(credentials['server_url']) / 'v1'),
  }
 
  return client_kwargs

api/core/model_runtime/model_providers/localai/localai.yaml

@@ -56,3 +56,12 @@ model_credential_schema:
  placeholder:
  zh_Hans: 在此输入LocalAI的服务器地址，如 http://192.168.1.100:8080
  en_US: Enter the url of your LocalAI, e.g. http://192.168.1.100:8080
+ - variable: context_size
+ label:
+ zh_Hans: 上下文大小
+ en_US: Context size
+ placeholder:
+ zh_Hans: 输入上下文大小
+ en_US: Enter context size
+ required: false
+ type: text-input

api/core/model_runtime/model_providers/localai/text_embedding/text_embedding.py

@@ -1,11 +1,12 @@
 import time
 from json import JSONDecodeError, dumps
-from os.path import join
 from typing import Optional
 
 from requests import post
+from yarl import URL
 
-from core.model_runtime.entities.model_entities import PriceType
+from core.model_runtime.entities.common_entities import I18nObject
+from core.model_runtime.entities.model_entities import AIModelEntity, FetchFrom, ModelPropertyKey, ModelType, PriceType
 from core.model_runtime.entities.text_embedding_entities import EmbeddingUsage, TextEmbeddingResult
 from core.model_runtime.errors.invoke import (
  InvokeAuthorizationError,
@@ -57,7 +58,7 @@ def _invoke(self, model: str, credentials: dict,
  }
 
  try:
- response = post(join(url, 'embeddings'), headers=headers, data=dumps(data), timeout=10)
+ response = post(str(URL(url) / 'embeddings'), headers=headers, data=dumps(data), timeout=10)
  except Exception as e:
  raise InvokeConnectionError(str(e))
 
@@ -113,6 +114,27 @@ def get_num_tokens(self, model: str, credentials: dict, texts: list[str]) -> int
  # use GPT2Tokenizer to get num tokens
  num_tokens += self._get_num_tokens_by_gpt2(text)
  return num_tokens
+
+ def _get_customizable_model_schema(self, model: str, credentials: dict) -> AIModelEntity | None:
+ """
+ Get customizable model schema
+
+ :param model: model name
+ :param credentials: model credentials
+ :return: model schema
+ """
+ return AIModelEntity(
+ model=model,
+ label=I18nObject(zh_Hans=model, en_US=model),
+ model_type=ModelType.TEXT_EMBEDDING,
+ features=[],
+ fetch_from=FetchFrom.CUSTOMIZABLE_MODEL,
+ model_properties={
+ ModelPropertyKey.CONTEXT_SIZE: int(credentials.get('context_size', '512')),
+ ModelPropertyKey.MAX_CHUNKS: 1,
+ },
+ parameter_rules=[]
+ )
 
  def validate_credentials(self, model: str, credentials: dict) -> None:
  """