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🔧 chore(loading.py): add instantiate_llm function to handle LLM instantiation and set ChatConfig.streaming based on openai_api_base parameter

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This commit adds a new function `instantiate_llm` to handle LLM (Language Model) instantiation. It also sets the `ChatConfig.streaming` attribute based on the `openai_api_base` parameter. This is a workaround to ensure that JinaChat works until streaming is implemented.
This commit is contained in:
Gabriel Luiz Freitas Almeida 2023-06-27 10:16:19 -03:00
commit 7c6845b15e
1 changed files with 13 additions and 0 deletions
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13
src/backend/langflow/interface/initialize/loading.py
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@ -6,6 +6,7 @@ from langchain.agents import agent as agent_module
from langchain.agents.agent import AgentExecutor from langchain.agents.agent import AgentExecutor
from langchain.agents.agent_toolkits.base import BaseToolkit from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.agents.tools import BaseTool from langchain.agents.tools import BaseTool
from langflow.interface.initialize.vector_store import vecstore_initializer from langflow.interface.initialize.vector_store import vecstore_initializer
from pydantic import ValidationError from pydantic import ValidationError
@ -20,6 +21,7 @@ from langchain.chains.base import Chain
from langchain.vectorstores.base import VectorStore from langchain.vectorstores.base import VectorStore
from langchain.document_loaders.base import BaseLoader from langchain.document_loaders.base import BaseLoader
from langchain.prompts.base import BasePromptTemplate from langchain.prompts.base import BasePromptTemplate
from langflow.chat.config import ChatConfig
def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any: def instantiate_class(node_type: str, base_type: str, params: Dict) -> Any:
@ -76,10 +78,21 @@ def instantiate_based_on_type(class_object, base_type, node_type, params):
return instantiate_utility(node_type, class_object, params) return instantiate_utility(node_type, class_object, params)
elif base_type == "chains": elif base_type == "chains":
return instantiate_chains(node_type, class_object, params) return instantiate_chains(node_type, class_object, params)
elif base_type == "llms":
return instantiate_llm(node_type, class_object, params)
else: else:
return class_object(**params) return class_object(**params)
def instantiate_llm(node_type, class_object, params: Dict):
# This is a workaround so JinaChat works until streaming is implemented
# if "openai_api_base" in params and "jina" in params["openai_api_base"]:
# False if condition is True
ChatConfig.streaming = "jina" not in params.get("openai_api_base", "")
return class_object(**params)
def instantiate_chains(node_type, class_object: Type[Chain], params: Dict): def instantiate_chains(node_type, class_object: Type[Chain], params: Dict):
if "retriever" in params and hasattr(params["retriever"], "as_retriever"): if "retriever" in params and hasattr(params["retriever"], "as_retriever"):
params["retriever"] = params["retriever"].as_retriever() params["retriever"] = params["retriever"].as_retriever()