更新python后端
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@@ -0,0 +1,114 @@
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 21,
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"id": "d029ad67",
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/plain": [
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"[460823023525530114, 460823023525530115]"
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]
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},
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"execution_count": 21,
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"metadata": {},
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"output_type": "execute_result"
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}
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],
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"source": [
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"from langchain_milvus import BM25BuiltInFunction, Milvus\n",
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"URI = \"http://10.10.10.9:19530\"\n",
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"tongyiKey = \"sk-9464b2498c184982a9fe9d2c2e725ab5\"\n",
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"from langchain_community.embeddings import DashScopeEmbeddings\n",
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"embeddings = DashScopeEmbeddings(\n",
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" model=\"text-embedding-v3\",\n",
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" dashscope_api_key= tongyiKey, \n",
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")\n",
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"vectorstore = Milvus(\n",
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" embedding_function=embeddings,\n",
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" connection_args={\"uri\": URI, \"token\": \"root:Milvus\", \"db_name\": \"bbit_ai_lab\"},\n",
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" collection_name=\"knowledge\",\n",
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" index_params={\"index_type\": \"FLAT\", \"metric_type\": \"L2\"},\n",
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" consistency_level=\"Strong\",\n",
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" auto_id=True,\n",
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"\n",
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" primary_field = \"id\",\n",
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" text_field=\"text\",\n",
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" vector_field=\"vector\",\n",
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" partition_key_field = \"kn_id\",\n",
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" enable_dynamic_field = True,\n",
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" drop_old=False, # set to True if seeking to drop the collection with that name if it exists\n",
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")\n",
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"\n",
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"from langchain.schema import Document\n",
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"\n",
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"docs = [\n",
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" Document(\n",
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" page_content=\"这是第一条文本\",\n",
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" metadata={\n",
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" \"kn_id\": \"8ecd1179-4194-4b80-bc39-5addc678df4b\",\n",
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" \"is_active\": True,\n",
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" }\n",
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" ),\n",
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" Document(\n",
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" page_content=\"这是第二条文本\",\n",
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" metadata={\n",
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" \"kn_id\": \"8ecd1179-4194-4b80-bc39-5addc678df4b\",\n",
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" \"is_active\": True,\n",
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" }\n",
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" )\n",
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"]\n",
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"\n",
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"vectorstore.add_documents(docs)\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"id": "a480053b",
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"*这是第一条文本 [{'kn_id': '8ecd1179-4194-4b80-bc39-5addc678df4b', 'id': 460823023525530108, 'is_active': True}]\n",
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"*这是第一条文本 [{'kn_id': '8ecd1179-4194-4b80-bc39-5addc678df4b', 'id': 460823023525530110, 'is_active': True}]\n"
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]
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}
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],
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"source": [
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"results = vectorstore.similarity_search(\n",
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" \"\",\n",
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" k=2,\n",
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" expr='kn_id == \"8ecd1179-4194-4b80-bc39-5addc678df4b\"',\n",
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")\n",
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"for res in results:\n",
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" print(f\"*{res.page_content} [{res.metadata}]\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "lang",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.10.18"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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