sentence-transformers How to use anaghaj111/codebert-base-code-embed-mrl-langchain-langgraph with sentence-transformers:
from sentence_transformers import SentenceTransformer
model = SentenceTransformer("anaghaj111/codebert-base-code-embed-mrl-langchain-langgraph")
sentences = [
"Explain the __init__ logic",
"async def test_handler_with_async_execution() -> None:\n \"\"\"Test handler works correctly with async tool execution.\"\"\"\n\n @tool\n def async_add(a: int, b: int) -> int:\n \"\"\"Async add two numbers.\"\"\"\n return a + b\n\n def modifying_handler(\n request: ToolCallRequest,\n execute: Callable[[ToolCallRequest], ToolMessage | Command],\n ) -> ToolMessage | Command:\n \"\"\"Handler that modifies arguments.\"\"\"\n # Add 10 to both arguments using override method\n modified_call = {\n **request.tool_call,\n \"args\": {\n **request.tool_call[\"args\"],\n \"a\": request.tool_call[\"args\"][\"a\"] + 10,\n \"b\": request.tool_call[\"args\"][\"b\"] + 10,\n },\n }\n modified_request = request.override(tool_call=modified_call)\n return execute(modified_request)\n\n tool_node = ToolNode([async_add], wrap_tool_call=modifying_handler)\n\n result = await tool_node.ainvoke(\n {\n \"messages\": [\n AIMessage(\n \"adding\",\n tool_calls=[\n {\n \"name\": \"async_add\",\n \"args\": {\"a\": 1, \"b\": 2},\n \"id\": \"call_13\",\n }\n ],\n )\n ]\n },\n config=_create_config_with_runtime(),\n )\n\n tool_message = result[\"messages\"][-1]\n assert isinstance(tool_message, ToolMessage)\n # Original: 1 + 2 = 3, with modifications: 11 + 12 = 23\n assert tool_message.content == \"23\"",
"def __init__(self) -> None:\n self.loads: set[str] = set()\n self.stores: set[str] = set()",
"class InternalServerError(APIStatusError):\n pass"
]
embeddings = model.encode(sentences)
similarities = model.similarity(embeddings, embeddings)
print(similarities.shape)
# [4, 4] 
