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Mark-TanbowuMark-Tanbowu
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[fel] update llama selector tools meta (#245)
* [fit]Update plugins metadata registration * 修改检视意见
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‎framework/fel/python/plugins/fel_llama_selector_tools/callable_registers.py‎

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This file was deleted.

‎framework/fel/python/plugins/fel_llama_selector_tools/llama_selector.py‎

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Original file line numberDiff line numberDiff line change
@@ -4,23 +4,24 @@
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# Licensed under the MIT License. See License.txt in the project root for license information.
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# ======================================================================================================================
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import traceback
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from typing import Tuple, List, Any, Callable
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from typing import List
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from fitframework import fit_logger
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from fitframework import fit_logger, fitable
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from llama_index.core.base.base_selector import SingleSelection
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from llama_index.core.selectors import EmbeddingSingleSelector
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from llama_index.embeddings.openai import OpenAIEmbedding
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from .callable_registers import register_callable_tool
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from .types.embedding_choice_selector import EmbeddingChoiceSelectorOptions
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def embedding_choice_selector(choice: List[str], query_str: str, **kwargs) -> List[SingleSelection]:
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@fitable("llama.tools.embedding_choice_selector", "default")
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def embedding_choice_selector(choice: List[str], query_str: str , options:EmbeddingChoiceSelectorOptions) -> List[SingleSelection]:
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""" Embedding selector that chooses one out of many options."""
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if len(choice) == 0:
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return []
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api_key = kwargs.get("api_key") or "EMPTY"
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model_name = kwargs.get("model_name") or "bge-large-zh"
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api_base = kwargs.get("api_base") or None
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api_key = options.api_key
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model_name = options.model_name
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api_base = options.api_base
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embed_model = OpenAIEmbedding(model_name=model_name, api_base=api_base, api_key=api_key)
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selector = EmbeddingSingleSelector.from_defaults(embed_model=embed_model)
@@ -30,19 +31,3 @@ def embedding_choice_selector(choice: List[str], query_str: str, **kwargs) -> Li
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fit_logger.error("Invoke embedding choice selector failed.")
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traceback.print_exc()
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return []
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# Tuple 结构： (tool_func, config_args, return_description)
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selector_toolkit: List[Tuple[Callable[..., Any], List[str], str]] = [
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(embedding_choice_selector, ["model_name", "api_key", "api_base", "prompt", "mode"], "The selected choice."),
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]
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for tool in selector_toolkit:
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register_callable_tool(tool, embedding_choice_selector.__module__, "llama_index.rag.toolkit")
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if __name__ == '__main__':
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import time
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from .llama_schema_helper import dump_llama_schema
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current_timestamp = time.strftime('%Y%m%d%H%M%S')
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dump_llama_schema(selector_toolkit, f"./llama_tool_schema-{str(current_timestamp)}.json")

‎framework/fel/python/plugins/fel_llama_selector_tools/tools.json‎

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{
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"tools": [
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{
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"tags": [
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"LlamaIndex"
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],
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"runnables": {
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"LlamaIndex": {
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"genericableId": "llama_index.rag.toolkit",
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"fitableId": "embedding_choice_selector"
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}
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},
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"schema": {
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"name": "embedding_choice_selector",
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"description": " Embedding selector that chooses one out of many options.",
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"parameters": {
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"type": "object",
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"properties": {
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"choice": {
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"title": "Choice",
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"type": "array",
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"items": {
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"type": "string"
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}
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},
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"query_str": {
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"title": "Query Str",
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"type": "string"
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},
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"model_name": {
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"type": "string",
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"description": "model_name"
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},
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"api_key": {
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"type": "string",
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"description": "api_key"
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},
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"api_base": {
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"type": "string",
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"description": "api_base"
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"version" : "1.0.0",
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"definitionGroups" : [ {
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"name" : "llm_selector_tools",
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"summary" : "LLM选择器工具组",
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"description" : "基于嵌入相似度的智能选择器工具，用于从多个选项中选择与查询最相关的选项。支持配置不同的嵌入模型和API端点。",
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"extensions" : { },
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"definitions" : [ {
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"schema" : {
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"name" : "EmbeddingChoiceSelectorTool",
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"description" : "嵌入选择器，从多个选项中选择一个。输入选项列表和查询字符串，返回选择结果列表。",
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"parameters" : {
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"type" : "object",
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"properties" : {
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"choice" : {
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"defaultValue" : "",
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"description" : "可选项列表",
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"name" : "choice",
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"type" : "array",
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"items" : {
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"type" : "string"
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},
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"example" : ""
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},
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"prompt": {
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"type": "string",
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"description": "prompt"
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"query_str" : {
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"defaultValue" : "",
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"description" : "查询字符串",
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"name" : "query_str",
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"type" : "string",
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"example" : ""
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},
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"mode": {
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"type": "string",
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"description": "mode"
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"options" : {
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"defaultValue" : "",
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"description" : "嵌入选择器配置",
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"name" : "options",
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"type" : "object",
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"properties" : {
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"api_key" : {
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"type" : "string"
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},
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"model_name" : {
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"type" : "string"
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},
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"api_base" : {
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"type" : "string"
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}
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},
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"example" : "",
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"required" : [ "api_key", "model_name", "api_base" ]
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}
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},
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"required": [
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"choice",
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"query_str"
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]
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"required" : [ "choice", "query_str", "options" ]
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},
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"return": {
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"title": "The Selected Choice.",
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"type": "array",
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"items": {
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"title": "SingleSelection",
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"description": "A single selection of a choice.",
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"type": "object",
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"properties": {
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"index": {
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"title": "Index",
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"type": "integer"
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"order" : [ "choice", "query_str", "options" ],
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"return" : {
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"type" : "array",
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"items" : {
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"type" : "object",
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"properties" : {
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"content" : {
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"type" : "string"
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},
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"reason": {
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"title": "Reason",
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"type": "string"
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"score" : {
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"type" : "number"
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}
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}
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},
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"convertor" : ""
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}
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}
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} ]
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} ],
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"toolGroups" : [ {
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"name" : "default",
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"summary" : "默认工具组",
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"description" : "包含嵌入选择器工具的默认工具组，提供基于语义相似度的智能选择功能。",
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"extensions" : { },
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"definitionGroupName" : "llm_selector_tools",
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"tools" : [ {
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"namespace" : "embedding_choice_selector",
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"schema" : {
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"name" : "EmbeddingChoiceSelectorTool",
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"description" : "基于嵌入相似度的智能选择器，通过计算查询字符串与选项列表的语义相似度，返回最相关的选项及其相似度分数。",
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"parameters" : {
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"type" : "object",
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"properties" : {
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"choice" : {
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"name" : "choice",
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"description" : "待选择的选项列表，工具将计算每个选项与查询字符串的语义相似度",
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"type" : "array",
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"items" : {
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"type" : "string"
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}
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},
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"required": [
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"index",
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"reason"
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]
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}
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"query_str" : {
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"name" : "query_str",
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"description" : "查询字符串，用于与选项列表进行语义相似度匹配",
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"type" : "string"
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},
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"options" : {
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"name" : "options",
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"description" : "嵌入模型配置参数，包括API密钥、模型名称和API基础URL",
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"type" : "object",
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"properties" : {
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"api_key" : {
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"description" : "用于访问嵌入模型的API密钥",
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"type" : "string"
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},
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"model_name" : {
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"description" : "要使用的嵌入模型名称",
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"type" : "string"
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},
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"api_base" : {
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"description" : "嵌入模型API的基础URL地址",
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"type" : "string"
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}
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},
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"required" : [ "api_key", "model_name", "api_base" ]
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}
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},
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"required" : [ ]
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},
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"parameterExtensions": {
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"config": [
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"model_name",
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"api_key",
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"api_base",
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"prompt",
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"mode"
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]
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"order" : [ "choice", "query_str", "options" ],
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"return" : {
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"name" : "selection_results",
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"description" : "返回选择结果列表，包含每个选项的内容和对应的相似度分数，按相似度降序排列",
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"type" : "array",
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"items" : {
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"type" : "object",
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"properties" : {
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"content" : {
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"description" : "选项的文本内容",
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"type" : "string"
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},
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"score" : {
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"description" : "该选项与查询字符串的语义相似度分数（0-1之间，分数越高表示越相似）",
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"type" : "number"
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}
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}
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},
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"convertor" : ""
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}
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}
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}
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]
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},
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"runnables" : {
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"FIT" : {
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"genericableId" : "llama.tools.embedding_choice_selector",
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"fitableId" : "default"
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}
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},
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"extensions" : {
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"tags" : [ "llama" ]
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},
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"definitionName" : "EmbeddingChoiceSelectorTool"
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} ]
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} ]
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}

‎framework/fel/python/plugins/fel_llama_selector_tools/types/__init__.py‎

Whitespace-only changes.

‎framework/fel/python/plugins/fel_llama_selector_tools/types/embedding_choice_selector.py‎

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# -- encoding: utf-8 --
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# Copyright (c) 2025 Huawei Technologies Co., Ltd. All Rights Reserved.
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# This file is a part of the ModelEngine Project.
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# Licensed under the MIT License. See License.txt in the project root for license information.
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# ======================================================================================================================
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class EmbeddingChoiceSelectorOptions(object):
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def __init__(self , model_name : str , api_key : str , api_base : str):
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self.model_name = model_name
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self.api_key = api_key
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self.api_base = api_base
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def __eq__(self, other):
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if not isinstance(other, self.__class__):
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return False
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return self.__dict__ == other.__dict__
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def __hash__(self):
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return hash(tuple(self.__dict__.values()))
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def __repr__(self):
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return str((self.__dict__.values()))

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