feat: 使用百炼的嵌入模型服务
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@@ -71,4 +71,9 @@ ai_configs = {
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'url': 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions',
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'url': 'https://dashscope.aliyuncs.com/compatible-mode/v1/chat/completions',
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'key': env.llm_key_bailian,
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'key': env.llm_key_bailian,
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},
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},
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'bailian-embedding': {
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'model': 'text-embedding-v4',
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'url': 'https://dashscope.aliyuncs.com/compatible-mode/v1/embeddings',
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'key': env.llm_key_bailian,
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},
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}
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}
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@@ -37,7 +37,7 @@ def create_llama_index_llm(platform_code='huoshan-doubao', temperature=0.5):
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)
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)
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def create_embeddings(platform_code="huoshan-embedding-240715"):
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def create_embeddings(platform_code="bailian-embedding"):
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"""
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"""
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创建自定义嵌入模型实例
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创建自定义嵌入模型实例
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@@ -1,3 +1,4 @@
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import asyncio
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from typing import List, Optional
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from typing import List, Optional
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from llama_index.core import Document, VectorStoreIndex
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from llama_index.core import Document, VectorStoreIndex
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@@ -45,7 +46,9 @@ class MilvusService:
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)
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)
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from llama_index.core.node_parser import HierarchicalNodeParser
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from llama_index.core.node_parser import HierarchicalNodeParser
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parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
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parser = HierarchicalNodeParser.from_defaults(chunk_sizes=[2048, 512, 128])
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nodes = await parser.aget_nodes_from_documents(documents)
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# nodes = await parser.aget_nodes_from_documents(documents)
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# 使用同步方法并包装在 asyncio.to_thread 中
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nodes = await asyncio.to_thread(parser.get_nodes_from_documents,documents)
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await vector_index.ainsert_nodes(nodes)
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await vector_index.ainsert_nodes(nodes)
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return vector_index
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return vector_index
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