feat: 增加全文检索和混合检索方式

apps/application/chat_pipeline/step/search_dataset_step/i_search_dataset_step.py

@@ -6,15 +6,16 @@
     @date：2024/1/9 18:10
     @desc: 检索知识库
 """
+import re
 from abc import abstractmethod
 from typing import List, Type
 
+from django.core import validators
 from rest_framework import serializers
 
 from application.chat_pipeline.I_base_chat_pipeline import IBaseChatPipelineStep, ParagraphPipelineModel
 from application.chat_pipeline.pipeline_manage import PipelineManage
 from common.util.field_message import ErrMessage
-from dataset.models import Paragraph
 
 
 class ISearchDatasetStep(IBaseChatPipelineStep):
@@ -38,6 +39,10 @@ class ISearchDatasetStep(IBaseChatPipelineStep):
         # 相似度 0-1之间
         similarity = serializers.FloatField(required=True, max_value=1, min_value=0,
                                             error_messages=ErrMessage.float("引用分段数"))
+        search_mode = serializers.CharField(required=True, validators=[
+            validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                      message="类型只支持register|reset_password", code=500)
+        ], error_messages=ErrMessage.char("检索模式"))
 
     def get_step_serializer(self, manage: PipelineManage) -> Type[InstanceSerializer]:
         return self.InstanceSerializer
@@ -50,6 +55,7 @@ class ISearchDatasetStep(IBaseChatPipelineStep):
     @abstractmethod
     def execute(self, problem_text: str, dataset_id_list: list[str], exclude_document_id_list: list[str],
                 exclude_paragraph_id_list: list[str], top_n: int, similarity: float, padding_problem_text: str = None,
+                search_mode: str = None,
                 **kwargs) -> List[ParagraphPipelineModel]:
         """
         关于 用户和补全问题 说明: 补全问题如果有就使用补全问题去查询 反之就用用户原始问题查询
@@ -60,6 +66,7 @@ class ISearchDatasetStep(IBaseChatPipelineStep):
         :param exclude_document_id_list:           需要排除的文档id
         :param exclude_paragraph_id_list:          需要排除段落id
         :param padding_problem_text                补全问题
+        :param search_mode                         检索模式
         :return: 段落列表
         """
         pass

apps/application/chat_pipeline/step/search_dataset_step/impl/base_search_dataset_step.py

@@ -17,6 +17,7 @@ from common.config.embedding_config import VectorStore, EmbeddingModel
 from common.db.search import native_search
 from common.util.file_util import get_file_content
 from dataset.models import Paragraph
+from embedding.models import SearchMode
 from smartdoc.conf import PROJECT_DIR
 
 
@@ -24,13 +25,14 @@ class BaseSearchDatasetStep(ISearchDatasetStep):
 
     def execute(self, problem_text: str, dataset_id_list: list[str], exclude_document_id_list: list[str],
                 exclude_paragraph_id_list: list[str], top_n: int, similarity: float, padding_problem_text: str = None,
+                search_mode: str = None,
                 **kwargs) -> List[ParagraphPipelineModel]:
         exec_problem_text = padding_problem_text if padding_problem_text is not None else problem_text
         embedding_model = EmbeddingModel.get_embedding_model()
         embedding_value = embedding_model.embed_query(exec_problem_text)
         vector = VectorStore.get_embedding_vector()
-        embedding_list = vector.query(embedding_value, dataset_id_list, exclude_document_id_list,
-                                      exclude_paragraph_id_list, True, top_n, similarity)
+        embedding_list = vector.query(exec_problem_text, embedding_value, dataset_id_list, exclude_document_id_list,
+                                      exclude_paragraph_id_list, True, top_n, similarity, SearchMode(search_mode))
         if embedding_list is None:
             return []
         paragraph_list = self.list_paragraph([row.get('paragraph_id') for row in embedding_list], vector)

apps/application/models/application.py

@@ -19,7 +19,7 @@ from users.models import User
 
 
 def get_dataset_setting_dict():
-    return {'top_n': 3, 'similarity': 0.6, 'max_paragraph_char_number': 5000}
+    return {'top_n': 3, 'similarity': 0.6, 'max_paragraph_char_number': 5000, 'search_mode': 'embedding'}
 
 
 def get_model_setting_dict():

apps/application/serializers/application_serializers.py

@@ -8,12 +8,13 @@
 """
 import hashlib
 import os
+import re
 import uuid
 from functools import reduce
 from typing import Dict
 
 from django.contrib.postgres.fields import ArrayField
-from django.core import cache
+from django.core import cache, validators
 from django.core import signing
 from django.db import transaction, models
 from django.db.models import QuerySet
@@ -32,6 +33,7 @@ from common.util.field_message import ErrMessage
 from common.util.file_util import get_file_content
 from dataset.models import DataSet, Document
 from dataset.serializers.common_serializers import list_paragraph
+from embedding.models import SearchMode
 from setting.models import AuthOperate
 from setting.models.model_management import Model
 from setting.models_provider.constants.model_provider_constants import ModelProvideConstants
@@ -77,6 +79,10 @@ class DatasetSettingSerializer(serializers.Serializer):
                                         error_messages=ErrMessage.float("相识度"))
     max_paragraph_char_number = serializers.IntegerField(required=True, min_value=500, max_value=10000,
                                                          error_messages=ErrMessage.integer("最多引用字符数"))
+    search_mode = serializers.CharField(required=True, validators=[
+        validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                  message="类型只支持register|reset_password", code=500)
+    ], error_messages=ErrMessage.char("检索模式"))
 
 
 class ModelSettingSerializer(serializers.Serializer):
@@ -291,6 +297,10 @@ class ApplicationSerializer(serializers.Serializer):
                                               error_messages=ErrMessage.integer("topN"))
         similarity = serializers.FloatField(required=True, max_value=1, min_value=0,
                                             error_messages=ErrMessage.float("相关度"))
+        search_mode = serializers.CharField(required=True, validators=[
+            validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                      message="类型只支持register|reset_password", code=500)
+        ], error_messages=ErrMessage.char("检索模式"))
 
         def is_valid(self, *, raise_exception=False):
             super().is_valid(raise_exception=True)
@@ -312,6 +322,7 @@ class ApplicationSerializer(serializers.Serializer):
             hit_list = vector.hit_test(self.data.get('query_text'), dataset_id_list, exclude_document_id_list,
                                        self.data.get('top_number'),
                                        self.data.get('similarity'),
+                                       SearchMode(self.data.get('search_mode')),
                                        EmbeddingModel.get_embedding_model())
             hit_dict = reduce(lambda x, y: {**x, **y}, [{hit.get('paragraph_id'): hit} for hit in hit_list], {})
             p_list = list_paragraph([h.get('paragraph_id') for h in hit_list])

apps/application/serializers/chat_message_serializers.py

@@ -77,6 +77,8 @@ class ChatInfo:
             'model_id': self.application.model.id if self.application.model is not None else None,
             'problem_optimization': self.application.problem_optimization,
             'stream': True,
+            'search_mode': self.application.dataset_setting.get(
+                'search_mode') if 'search_mode' in self.application.dataset_setting else 'embedding'
 
         }
 
@@ -184,9 +186,9 @@ class ChatMessageSerializer(serializers.Serializer):
             pipeline_manage_builder.append_step(BaseResetProblemStep)
         # 构建流水线管理器
         pipeline_message = (pipeline_manage_builder.append_step(BaseSearchDatasetStep)
-                           .append_step(BaseGenerateHumanMessageStep)
-                           .append_step(BaseChatStep)
-                           .build())
+                            .append_step(BaseGenerateHumanMessageStep)
+                            .append_step(BaseChatStep)
+                            .build())
         exclude_paragraph_id_list = []
         # 相同问题是否需要排除已经查询到的段落
         if re_chat:

apps/application/swagger_api/application_api.py

@@ -161,6 +161,8 @@ class ApplicationApi(ApiMixin):
                                                  default=0.6),
                     'max_paragraph_char_number': openapi.Schema(type=openapi.TYPE_NUMBER, title='最多引用字符数',
                                                                 description="最多引用字符数", default=3000),
+                    'search_mode': openapi.Schema(type=openapi.TYPE_STRING, title='检索模式',
+                                                  description="embedding|keywords|blend", default='embedding'),
                 }
             )
 

apps/application/views/application_views.py

@@ -343,7 +343,8 @@ class Application(APIView):
                 ApplicationSerializer.HitTest(data={'id': application_id, 'user_id': request.user.id,
                                                     "query_text": request.query_params.get("query_text"),
                                                     "top_number": request.query_params.get("top_number"),
-                                                    'similarity': request.query_params.get('similarity')}).hit_test(
+                                                    'similarity': request.query_params.get('similarity'),
+                                                    'search_mode': request.query_params.get('search_mode')}).hit_test(
                 ))
 
     class Operate(APIView):

apps/common/swagger_api/common_api.py

@@ -33,6 +33,13 @@ class CommonApi:
                                       default=0.6,
                                       required=True,
                                       description='相关性'),
+                    openapi.Parameter(name='search_mode',
+                                      in_=openapi.IN_QUERY,
+                                      type=openapi.TYPE_STRING,
+                                      default="embedding",
+                                      required=True,
+                                      description='检索模式embedding|keywords|blend'
+                                      )
                     ]
 
         @staticmethod

apps/common/util/ts_vecto_util.py

@@ -0,0 +1,107 @@
+# coding=utf-8
+"""
+    @project: maxkb
+    @Author：虎
+    @file： ts_vecto_util.py
+    @date：2024/4/16 15:26
+    @desc:
+"""
+import re
+import uuid
+from typing import List
+
+import jieba
+from jieba import analyse
+
+from common.util.split_model import group_by
+
+jieba_word_list_cache = [chr(item) for item in range(38, 84)]
+
+for jieba_word in jieba_word_list_cache:
+    jieba.add_word('#' + jieba_word + '#')
+# r"(?i)\b(?:https?|ftp|tcp|file)://[^\s]+\b",
+# 某些不分词数据
+# r'"([^"]*)"'
+word_pattern_list = [r"v\d+.\d+.\d+",
+                     r"[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Z|a-z]{2,}"]
+
+remove_chars = '\n , :\'<>！@#￥%……&*（）!@#$%^&*()： ；，/"./-'
+
+
+def get_word_list(text: str):
+    result = []
+    for pattern in word_pattern_list:
+        word_list = re.findall(pattern, text)
+        for child_list in word_list:
+            for word in child_list if isinstance(child_list, tuple) else [child_list]:
+                # 不能有: 所以再使用: 进行分割
+                if word.__contains__(':'):
+                    item_list = word.split(":")
+                    for w in item_list:
+                        result.append(w)
+                else:
+                    result.append(word)
+    return result
+
+
+def replace_word(word_dict, text: str):
+    for key in word_dict:
+        text = re.sub('(?<!#)' + word_dict[key] + '(?!#)', key, text)
+    return text
+
+
+def get_word_key(text: str, use_word_list):
+    for j_word in jieba_word_list_cache:
+        if not text.__contains__(j_word) and not use_word_list.__contains__(j_word):
+            return j_word
+    j_word = str(uuid.uuid1())
+    jieba.add_word(j_word)
+    return j_word
+
+
+def to_word_dict(word_list: List, text: str):
+    word_dict = {}
+    for word in word_list:
+        key = get_word_key(text, set(word_dict))
+        word_dict['#' + key + '#'] = word
+    return word_dict
+
+
+def get_key_by_word_dict(key, word_dict):
+    v = word_dict.get(key)
+    if v is None:
+        return key
+    return v
+
+
+def to_ts_vector(text: str):
+    # 获取不分词的数据
+    word_list = get_word_list(text)
+    # 获取关键词关系
+    word_dict = to_word_dict(word_list, text)
+    # 替换字符串
+    text = replace_word(word_dict, text)
+    # 分词
+    result = jieba.tokenize(text, mode='search')
+    result_ = [{'word': get_key_by_word_dict(item[0], word_dict), 'index': item[1]} for item in result]
+    result_group = group_by(result_, lambda r: r['word'])
+    return " ".join(
+        [f"{key.lower()}:{','.join([str(item['index'] + 1) for item in result_group[key]][:20])}" for key in
+         result_group if
+         not remove_chars.__contains__(key) and len(key.strip()) >= 0])
+
+
+def to_query(text: str):
+    # 获取不分词的数据
+    word_list = get_word_list(text)
+    # 获取关键词关系
+    word_dict = to_word_dict(word_list, text)
+    # 替换字符串
+    text = replace_word(word_dict, text)
+    extract_tags = analyse.extract_tags(text, topK=5, withWeight=True, allowPOS=('ns', 'n', 'vn', 'v', 'eng'))
+    result = " ".join([get_key_by_word_dict(word, word_dict) for word, score in extract_tags if
+                       not remove_chars.__contains__(word)])
+    # 删除词库
+    for word in word_list:
+        jieba.del_word(word)
+    return result

apps/dataset/serializers/dataset_serializers.py

@@ -37,6 +37,7 @@ from common.util.split_model import get_split_model
 from dataset.models.data_set import DataSet, Document, Paragraph, Problem, Type, ProblemParagraphMapping
 from dataset.serializers.common_serializers import list_paragraph, MetaSerializer
 from dataset.serializers.document_serializers import DocumentSerializers, DocumentInstanceSerializer
+from embedding.models import SearchMode
 from setting.models import AuthOperate
 from smartdoc.conf import PROJECT_DIR
 
@@ -457,6 +458,10 @@ class DataSetSerializers(serializers.ModelSerializer):
                                               error_messages=ErrMessage.char("响应Top"))
         similarity = serializers.FloatField(required=True, max_value=1, min_value=0,
                                             error_messages=ErrMessage.char("相似度"))
+        search_mode = serializers.CharField(required=True, validators=[
+            validators.RegexValidator(regex=re.compile("^embedding|keywords|blend$"),
+                                      message="类型只支持register|reset_password", code=500)
+        ], error_messages=ErrMessage.char("检索模式"))
 
         def is_valid(self, *, raise_exception=True):
             super().is_valid(raise_exception=True)
@@ -474,6 +479,7 @@ class DataSetSerializers(serializers.ModelSerializer):
             hit_list = vector.hit_test(self.data.get('query_text'), [self.data.get('id')], exclude_document_id_list,
                                        self.data.get('top_number'),
                                        self.data.get('similarity'),
+                                       SearchMode(self.data.get('search_mode')),
                                        EmbeddingModel.get_embedding_model())
             hit_dict = reduce(lambda x, y: {**x, **y}, [{hit.get('paragraph_id'): hit} for hit in hit_list], {})
             p_list = list_paragraph([h.get('paragraph_id') for h in hit_list])

apps/dataset/views/dataset.py

@@ -111,7 +111,8 @@ class Dataset(APIView):
                 DataSetSerializers.HitTest(data={'id': dataset_id, 'user_id': request.user.id,
                                                  "query_text": request.query_params.get("query_text"),
                                                  "top_number": request.query_params.get("top_number"),
-                                                 'similarity': request.query_params.get('similarity')}).hit_test(
+                                                 'similarity': request.query_params.get('similarity'),
+                                                 'search_mode': request.query_params.get('search_mode')}).hit_test(
                 ))
 
     class Operate(APIView):

apps/embedding/migrations/0002_embedding_search_vector.py

@@ -0,0 +1,54 @@
+# Generated by Django 4.1.13 on 2024-04-16 11:43
+
+import django.contrib.postgres.search
+from django.db import migrations
+
+from common.util.common import sub_array
+from common.util.ts_vecto_util import to_ts_vector
+from dataset.models import Status
+from embedding.models import Embedding
+
+
+def update_embedding_search_vector(embedding, paragraph_list):
+    paragraphs = [paragraph for paragraph in paragraph_list if paragraph.id == embedding.get('paragraph')]
+    if len(paragraphs) > 0:
+        content = paragraphs[0].title + paragraphs[0].content
+        return Embedding(id=embedding.get('id'), search_vector=to_ts_vector(content))
+    return Embedding(id=embedding.get('id'), search_vector="")
+
+
+def save_keywords(apps, schema_editor):
+    document = apps.get_model("dataset", "Document")
+    embedding = apps.get_model("embedding", "Embedding")
+    paragraph = apps.get_model('dataset', 'Paragraph')
+    db_alias = schema_editor.connection.alias
+    document_list = document.objects.using(db_alias).all()
+    for document in document_list:
+        document.status = Status.embedding
+        document.save()
+        paragraph_list = paragraph.objects.using(db_alias).filter(document=document).all()
+        embedding_list = embedding.objects.using(db_alias).filter(document=document).values('id', 'search_vector',
+                                                                                            'paragraph')
+        embedding_update_list = [update_embedding_search_vector(embedding, paragraph_list) for embedding
+                                 in embedding_list]
+        child_array = sub_array(embedding_update_list, 20)
+        for c in child_array:
+            try:
+                embedding.objects.using(db_alias).bulk_update(c, ['search_vector'])
+            except Exception as e:
+                print(e)
+
+
+class Migration(migrations.Migration):
+    dependencies = [
+        ('embedding', '0001_initial'),
+    ]
+
+    operations = [
+        migrations.AddField(
+            model_name='embedding',
+            name='search_vector',
+            field=django.contrib.postgres.search.SearchVectorField(default='', verbose_name='分词'),
+        ),
+        migrations.RunPython(save_keywords)
+    ]

apps/embedding/models/embedding.py

@@ -10,6 +10,7 @@ from django.db import models
 
 from common.field.vector_field import VectorField
 from dataset.models.data_set import Document, Paragraph, DataSet
+from django.contrib.postgres.search import SearchVectorField
 
 
 class SourceType(models.TextChoices):
@@ -19,6 +20,12 @@ class SourceType(models.TextChoices):
     TITLE = 2, '标题'
 
 
+class SearchMode(models.TextChoices):
+    embedding = 'embedding'
+    keywords = 'keywords'
+    blend = 'blend'
+
+
 class Embedding(models.Model):
     id = models.CharField(max_length=128, primary_key=True, verbose_name="主键id")
 
@@ -37,6 +44,8 @@ class Embedding(models.Model):
 
     embedding = VectorField(verbose_name="向量")
 
+    search_vector = SearchVectorField(verbose_name="分词", default="")
+
     meta = models.JSONField(verbose_name="元数据", default=dict)
 
     class Meta:

apps/embedding/sql/blend_search.sql

@@ -0,0 +1,26 @@
+SELECT
+	paragraph_id,
+	comprehensive_score,
+	comprehensive_score AS similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		( "paragraph_id" ) ( similarity ),* ,
+		similarity AS comprehensive_score
+	FROM
+		(
+		SELECT
+			*,
+			(( 1 - ( embedding.embedding <=>  %s ) )+ts_rank_cd( embedding.search_vector, websearch_to_tsquery('simple', %s ), 32 )) AS similarity
+		FROM
+			embedding ${embedding_query}
+		) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE
+	comprehensive_score >%s
+ORDER BY
+	comprehensive_score DESC
+	LIMIT %s

apps/embedding/sql/keywords_search.sql

@@ -0,0 +1,17 @@
+SELECT
+    paragraph_id,
+	comprehensive_score,
+	comprehensive_score as similarity
+FROM
+	(
+	SELECT DISTINCT ON
+		("paragraph_id") ( similarity ),* ,similarity AS comprehensive_score
+	FROM
+		( SELECT *,ts_rank_cd(embedding.search_vector,websearch_to_tsquery('simple',%s),32) AS similarity  FROM embedding ${keywords_query}) TEMP
+	ORDER BY
+		paragraph_id,
+		similarity DESC
+	) DISTINCT_TEMP
+WHERE comprehensive_score>%s
+ORDER BY comprehensive_score DESC
+LIMIT %s

apps/embedding/vector/base_vector.py

@@ -14,7 +14,7 @@ from langchain_community.embeddings import HuggingFaceEmbeddings
 
 from common.config.embedding_config import EmbeddingModel
 from common.util.common import sub_array
-from embedding.models import SourceType
+from embedding.models import SourceType, SearchMode
 
 lock = threading.Lock()
 
@@ -113,13 +113,16 @@ class BaseVectorStore(ABC):
         return result[0]
 
     @abstractmethod
-    def query(self, query_embedding: List[float], dataset_id_list: list[str], exclude_document_id_list: list[str],
-              exclude_paragraph_list: list[str], is_active: bool, top_n: int, similarity: float):
+    def query(self, query_text:str,query_embedding: List[float],  dataset_id_list: list[str],
+              exclude_document_id_list: list[str],
+              exclude_paragraph_list: list[str], is_active: bool, top_n: int, similarity: float,
+              search_mode: SearchMode):
         pass
 
     @abstractmethod
     def hit_test(self, query_text, dataset_id: list[str], exclude_document_id_list: list[str], top_number: int,
                  similarity: float,
+                 search_mode: SearchMode,
                  embedding: HuggingFaceEmbeddings):
         pass
 

apps/embedding/vector/pg_vector.py

@@ -9,6 +9,7 @@
 import json
 import os
 import uuid
+from abc import ABC, abstractmethod
 from typing import Dict, List
 
 from django.db.models import QuerySet
@@ -18,7 +19,8 @@ from common.config.embedding_config import EmbeddingModel
 from common.db.search import generate_sql_by_query_dict
 from common.db.sql_execute import select_list
 from common.util.file_util import get_file_content
-from embedding.models import Embedding, SourceType
+from common.util.ts_vecto_util import to_ts_vector, to_query
+from embedding.models import Embedding, SourceType, SearchMode
 from embedding.vector.base_vector import BaseVectorStore
 from smartdoc.conf import PROJECT_DIR
 
@@ -57,7 +59,8 @@ class PGVector(BaseVectorStore):
                               paragraph_id=paragraph_id,
                               source_id=source_id,
                               embedding=text_embedding,
-                              source_type=source_type)
+                              source_type=source_type,
+                              search_vector=to_ts_vector(text))
         embedding.save()
         return True
 
@@ -71,13 +74,15 @@ class PGVector(BaseVectorStore):
                                     is_active=text_list[index].get('is_active', True),
                                     source_id=text_list[index].get('source_id'),
                                     source_type=text_list[index].get('source_type'),
-                                    embedding=embeddings[index]) for index in
+                                    embedding=embeddings[index],
+                                    search_vector=to_ts_vector(text_list[index]['text'])) for index in
                           range(0, len(text_list))]
         QuerySet(Embedding).bulk_create(embedding_list) if len(embedding_list) > 0 else None
         return True
 
     def hit_test(self, query_text, dataset_id_list: list[str], exclude_document_id_list: list[str], top_number: int,
                  similarity: float,
+                 search_mode: SearchMode,
                  embedding: HuggingFaceEmbeddings):
         if dataset_id_list is None or len(dataset_id_list) == 0:
             return []
@@ -87,17 +92,14 @@ class PGVector(BaseVectorStore):
         if exclude_document_id_list is not None and len(exclude_document_id_list) > 0:
             exclude_dict.__setitem__('document_id__in', exclude_document_id_list)
         query_set = query_set.exclude(**exclude_dict)
-        exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
-                                                           select_string=get_file_content(
-                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
-                                                                            'hit_test.sql')),
-                                                           with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(embedding_query), *exec_params, similarity, top_number])
-        return embedding_model
+        for search_handle in search_handle_list:
+            if search_handle.support(search_mode):
+                return search_handle.handle(query_set, query_text, embedding_query, top_number, similarity, search_mode)
 
-    def query(self, query_embedding: List[float], dataset_id_list: list[str], exclude_document_id_list: list[str],
-              exclude_paragraph_list: list[str], is_active: bool, top_n: int, similarity: float):
+    def query(self, query_text: str, query_embedding: List[float], dataset_id_list: list[str],
+              exclude_document_id_list: list[str],
+              exclude_paragraph_list: list[str], is_active: bool, top_n: int, similarity: float,
+              search_mode: SearchMode):
         exclude_dict = {}
         if dataset_id_list is None or len(dataset_id_list) == 0:
             return []
@@ -107,14 +109,9 @@ class PGVector(BaseVectorStore):
         if exclude_paragraph_list is not None and len(exclude_paragraph_list) > 0:
             exclude_dict.__setitem__('paragraph_id__in', exclude_paragraph_list)
         query_set = query_set.exclude(**exclude_dict)
-        exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
-                                                           select_string=get_file_content(
-                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
-                                                                            'embedding_search.sql')),
-                                                           with_table_name=True)
-        embedding_model = select_list(exec_sql,
-                                      [json.dumps(query_embedding), *exec_params, similarity, top_n])
-        return embedding_model
+        for search_handle in search_handle_list:
+            if search_handle.support(search_mode):
+                return search_handle.handle(query_set, query_text, query_embedding, top_n, similarity, search_mode)
 
     def update_by_source_id(self, source_id: str, instance: Dict):
         QuerySet(Embedding).filter(source_id=source_id).update(**instance)
@@ -141,3 +138,81 @@ class PGVector(BaseVectorStore):
 
     def delete_by_paragraph_id(self, paragraph_id: str):
         QuerySet(Embedding).filter(paragraph_id=paragraph_id).delete()
+
+
+class ISearch(ABC):
+    @abstractmethod
+    def support(self, search_mode: SearchMode):
+        pass
+
+    @abstractmethod
+    def handle(self, query_set, query_text, query_embedding, top_number: int,
+               similarity: float, search_mode: SearchMode):
+        pass
+
+
+class EmbeddingSearch(ISearch):
+    def handle(self,
+               query_set,
+               query_text,
+               query_embedding,
+               top_number: int,
+               similarity: float,
+               search_mode: SearchMode):
+        exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
+                                                           select_string=get_file_content(
+                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                                            'embedding_search.sql')),
+                                                           with_table_name=True)
+        embedding_model = select_list(exec_sql,
+                                      [json.dumps(query_embedding), *exec_params, similarity, top_number])
+        return embedding_model
+
+    def support(self, search_mode: SearchMode):
+        return search_mode.value == SearchMode.embedding.value
+
+
+class KeywordsSearch(ISearch):
+    def handle(self,
+               query_set,
+               query_text,
+               query_embedding,
+               top_number: int,
+               similarity: float,
+               search_mode: SearchMode):
+        exec_sql, exec_params = generate_sql_by_query_dict({'keywords_query': query_set},
+                                                           select_string=get_file_content(
+                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                                            'keywords_search.sql')),
+                                                           with_table_name=True)
+        embedding_model = select_list(exec_sql,
+                                      [to_query(query_text), *exec_params, similarity, top_number])
+        return embedding_model
+
+    def support(self, search_mode: SearchMode):
+        return search_mode.value == SearchMode.keywords.value
+
+
+class BlendSearch(ISearch):
+    def handle(self,
+               query_set,
+               query_text,
+               query_embedding,
+               top_number: int,
+               similarity: float,
+               search_mode: SearchMode):
+        exec_sql, exec_params = generate_sql_by_query_dict({'embedding_query': query_set},
+                                                           select_string=get_file_content(
+                                                               os.path.join(PROJECT_DIR, "apps", "embedding", 'sql',
+                                                                            'blend_search.sql')),
+                                                           with_table_name=True)
+        embedding_model = select_list(exec_sql,
+                                      [json.dumps(query_embedding), to_query(query_text), *exec_params, similarity,
+                                       top_number])
+        return embedding_model
+
+    def support(self, search_mode: SearchMode):
+        return search_mode.value == SearchMode.blend.value
+
+
+search_handle_list = [EmbeddingSearch(), KeywordsSearch(), BlendSearch()]

ui/src/views/application/CreateAndSetting.vue

@@ -145,74 +145,12 @@
                   <div class="flex-between">
                     <span>关联知识库</span>
                     <div>
-                      <el-popover :visible="popoverVisible" :width="214" trigger="click">
-                        <template #reference>
-                          <el-button type="primary" link @click="datasetSettingChange('open')">
-                            <AppIcon iconName="app-operation" class="mr-4"></AppIcon>参数设置
-                          </el-button>
-                        </template>
-                        <div class="dataset_setting">
-                          <div class="form-item mb-16">
-                            <div class="title flex align-center mb-8">
-                              <span style="margin-right: 4px">相似度高于</span>
-                              <el-tooltip
-                                effect="dark"
-                                content="相似度越高相关性越强。"
-                                placement="right"
-                              >
-                                <AppIcon iconName="app-warning" class="app-warning-icon"></AppIcon>
-                              </el-tooltip>
-                            </div>
-                            <div @click.stop>
-                              <el-input-number
-                                v-model="dataset_setting.similarity"
-                                :min="0"
-                                :max="1"
-                                :precision="3"
-                                :step="0.1"
-                                controls-position="right"
-                                style="width: 180px"
-                              />
-                            </div>
-                          </div>
-                          <div class="form-item mb-16">
-                            <div class="title mb-8">引用分段数 TOP</div>
-                            <div @click.stop>
-                              <el-input-number
-                                v-model="dataset_setting.top_n"
-                                :min="1"
-                                :max="10"
-                                controls-position="right"
-                                style="width: 180px"
-                              />
-                            </div>
-                          </div>
-
-                          <div class="form-item mb-16">
-                            <div class="title mb-8">最多引用字符数</div>
-                            <div class="flex align-center">
-                              <el-slider
-                                v-model="dataset_setting.max_paragraph_char_number"
-                                show-input
-                                :show-input-controls="false"
-                                :min="500"
-                                :max="10000"
-                                style="width: 180px"
-                                class="custom-slider"
-                              />
-                            </div>
-                          </div>
-                        </div>
-                        <div class="text-right">
-                          <el-button @click="popoverVisible = false">取消</el-button>
-                          <el-button type="primary" @click="datasetSettingChange('close')"
-                            >确认</el-button
-                          >
-                        </div>
-                      </el-popover>
-                      <el-button type="primary" link @click="openDatasetDialog"
-                        ><el-icon class="mr-4"><Plus /></el-icon>添加</el-button
-                      >
+                      <el-button type="primary" link @click="openParamSettingDialog">
+                        <AppIcon iconName="app-operation" class="mr-4"></AppIcon>参数设置
+                      </el-button>
+                      <el-button type="primary" link @click="openDatasetDialog">
+                        <el-icon class="mr-4"><Plus /></el-icon>添加
+                      </el-button>
                     </div>
                   </div>
                 </template>
@@ -221,13 +159,6 @@
                     >关联的知识库展示在这里</el-text
                   >
                   <el-row :gutter="12" v-else>
-                    <!-- <el-col :xs="24" :sm="24" :md="12" :lg="12" :xl="12" class="mb-8">
-                      <CardAdd
-                        title="关联知识库"
-                        @click="openDatasetDialog"
-                        style="min-height: 50px; font-size: 14px"
-                      />
-                    </el-col> -->
                     <el-col
                       :xs="24"
                       :sm="24"
@@ -311,6 +242,7 @@
       </el-col>
     </el-row>
 
+    <ParamSettingDialog ref="ParamSettingDialogRef" @refresh="refreshParam" />
     <AddDatasetDialog
       ref="AddDatasetDialogRef"
       @addData="addDataset"
@@ -318,6 +250,8 @@
       @refresh="refresh"
       :loading="datasetLoading"
     />
+
+    <!-- 添加模版 -->
     <CreateModelDialog
       ref="createModelRef"
       @submit="getModel"
@@ -329,7 +263,8 @@
 <script setup lang="ts">
 import { reactive, ref, watch, onMounted } from 'vue'
 import { useRouter, useRoute } from 'vue-router'
-import { groupBy, cloneDeep } from 'lodash'
+import { groupBy } from 'lodash'
+import ParamSettingDialog from './components/ParamSettingDialog.vue'
 import AddDatasetDialog from './components/AddDatasetDialog.vue'
 import CreateModelDialog from '@/views/template/component/CreateModelDialog.vue'
 import SelectProviderDialog from '@/views/template/component/SelectProviderDialog.vue'
@@ -363,6 +298,8 @@ const defaultPrompt = `已知信息：
 问题：
 {question}
 `
+
+const ParamSettingDialogRef = ref<InstanceType<typeof ParamSettingDialog>>()
 const createModelRef = ref<InstanceType<typeof CreateModelDialog>>()
 const selectProviderRef = ref<InstanceType<typeof SelectProviderDialog>>()
 
@@ -384,7 +321,8 @@ const applicationForm = ref<ApplicationFormType>({
   dataset_setting: {
     top_n: 3,
     similarity: 0.6,
-    max_paragraph_char_number: 5000
+    max_paragraph_char_number: 5000,
+    search_mode: 'embedding'
   },
   model_setting: {
     prompt: defaultPrompt
@@ -392,8 +330,6 @@ const applicationForm = ref<ApplicationFormType>({
   problem_optimization: false
 })
 
-const popoverVisible = ref(false)
-
 const rules = reactive<FormRules<ApplicationFormType>>({
   name: [{ required: true, message: '请输入应用名称', trigger: 'blur' }],
   model_id: [
@@ -408,17 +344,6 @@ const rules = reactive<FormRules<ApplicationFormType>>({
 const modelOptions = ref<any>(null)
 const providerOptions = ref<Array<Provider>>([])
 const datasetList = ref([])
-const dataset_setting = ref<any>({})
-
-function datasetSettingChange(val: string) {
-  if (val === 'open') {
-    popoverVisible.value = true
-    dataset_setting.value = cloneDeep(applicationForm.value.dataset_setting)
-  } else if (val === 'close') {
-    popoverVisible.value = false
-    applicationForm.value.dataset_setting = cloneDeep(dataset_setting.value)
-  }
-}
 
 const submit = async (formEl: FormInstance | undefined) => {
   if (!formEl) return
@@ -438,6 +363,14 @@ const submit = async (formEl: FormInstance | undefined) => {
   })
 }
 
+const openParamSettingDialog = () => {
+  ParamSettingDialogRef.value?.open(applicationForm.value.dataset_setting)
+}
+
+function refreshParam(data: any) {
+  applicationForm.value.dataset_setting = data
+}
+
 const openCreateModel = (provider?: Provider) => {
   if (provider && provider.provider) {
     createModelRef.value?.open(provider)
@@ -560,13 +493,4 @@ onMounted(() => {
 .prologue-md-editor {
   height: 150px;
 }
-.dataset_setting {
-  color: var(--el-text-color-regular);
-  font-weight: 400;
-}
-.custom-slider {
-  :deep(.el-input-number.is-without-controls .el-input__wrapper) {
-    padding: 0 !important;
-  }
-}
 </style>

ui/src/views/application/components/ParamSettingDialog.vue

@@ -0,0 +1,152 @@
+<template>
+  <el-dialog title="参数设置" class="param-dialog" v-model="dialogVisible" style="width: 550px">
+    <div class="dialog-max-height">
+      <el-scrollbar>
+        <div class="p-16">
+          <el-form label-position="top" ref="paramFormRef" :model="form">
+            <el-form-item label="检索模式">
+              <el-radio-group v-model="form.search_mode" class="card__radio">
+                <el-card
+                  shadow="never"
+                  class="mb-16"
+                  :class="form.search_mode === 'embedding' ? 'active' : ''"
+                >
+                  <el-radio value="embedding" size="large">
+                    <p class="mb-4">向量检索</p>
+                    <el-text type="info">通过向量距离计算与用户问题最相似的文本分段</el-text>
+                  </el-radio>
+                </el-card>
+                <el-card
+                  shadow="never"
+                  class="mb-16"
+                  :class="form.search_mode === 'keywords' ? 'active' : ''"
+                >
+                  <el-radio value="keywords" size="large">
+                    <p class="mb-4">全文检索</p>
+                    <el-text type="info">通过关键词检索，返回包含关键词最多的文本分段</el-text>
+                  </el-radio>
+                </el-card>
+                <el-card
+                  shadow="never"
+                  class="mb-16"
+                  :class="form.search_mode === 'blend' ? 'active' : ''"
+                >
+                  <el-radio value="blend" size="large">
+                    <p class="mb-4">混合检索</p>
+                    <el-text type="info"
+                      >同时执行全文检索和向量检索，再进行重排序，从两类查询结果中选择匹配用户问题的最佳结果</el-text
+                    >
+                  </el-radio>
+                </el-card>
+              </el-radio-group>
+            </el-form-item>
+            <el-form-item>
+              <template #label>
+                <div class="flex align-center">
+                  <span class="mr-4">相似度高于</span>
+                  <el-tooltip effect="dark" content="相似度越高相关性越强。" placement="right">
+                    <AppIcon iconName="app-warning" class="app-warning-icon"></AppIcon>
+                  </el-tooltip>
+                </div>
+              </template>
+              <el-input-number
+                v-model="form.similarity"
+                :min="0"
+                :max="1"
+                :precision="3"
+                :step="0.1"
+                controls-position="right"
+              />
+            </el-form-item>
+            <el-form-item label="引用分段数 TOP">
+              <el-input-number v-model="form.top_n" :min="1" :max="10" controls-position="right" />
+            </el-form-item>
+            <el-form-item label="最多引用字符数">
+              <el-slider
+                v-model="form.max_paragraph_char_number"
+                show-input
+                :show-input-controls="false"
+                :min="500"
+                :max="10000"
+                class="custom-slider"
+              />
+            </el-form-item>
+          </el-form>
+        </div>
+      </el-scrollbar>
+    </div>
+    <template #footer>
+      <span class="dialog-footer">
+        <el-button @click.prevent="dialogVisible = false"> 取消 </el-button>
+        <el-button type="primary" @click="submit(paramFormRef)" :loading="loading">
+          保存
+        </el-button>
+      </span>
+    </template>
+  </el-dialog>
+</template>
+<script setup lang="ts">
+import { ref, watch } from 'vue'
+
+import type { FormInstance, FormRules } from 'element-plus'
+
+const emit = defineEmits(['refresh'])
+
+const paramFormRef = ref()
+const form = ref<any>({
+  search_mode: 'embedding',
+  top_n: 3,
+  similarity: 0.6,
+  max_paragraph_char_number: 5000
+})
+
+const dialogVisible = ref<boolean>(false)
+const loading = ref(false)
+
+watch(dialogVisible, (bool) => {
+  if (!bool) {
+    form.value = {
+      search_mode: 'embedding',
+      top_n: 3,
+      similarity: 0.6,
+      max_paragraph_char_number: 5000
+    }
+  }
+})
+
+const open = (data: any) => {
+  form.value = { ...form.value, ...data }
+  dialogVisible.value = true
+}
+
+const submit = async (formEl: FormInstance | undefined) => {
+  if (!formEl) return
+  await formEl.validate((valid, fields) => {
+    if (valid) {
+      emit('refresh', form.value)
+      dialogVisible.value = false
+    }
+  })
+}
+
+defineExpose({ open })
+</script>
+<style lang="scss" scope>
+.param-dialog {
+  padding: 8px;
+  .el-dialog__header {
+    padding: 16px 16px 0 16px;
+  }
+  .el-dialog__body {
+    padding: 0 !important;
+  }
+  .dialog-max-height {
+    height: calc(100vh - 260px);
+  }
+  .custom-slider {
+    .el-input-number.is-without-controls .el-input__wrapper {
+      padding: 0 !important;
+    }
+  }
+}
+</style>

ui/src/views/hit-test/index.vue

@@ -78,11 +78,47 @@
       <ParagraphDialog ref="ParagraphDialogRef" :title="title" @refresh="refresh" />
     </LayoutContainer>
     <div class="hit-test__operate p-24 pt-0">
-      <el-popover :visible="popoverVisible" placement="right-end" :width="180" trigger="click">
+      <el-popover :visible="popoverVisible" placement="right-end" :width="500" trigger="click">
         <template #reference>
           <el-button icon="Setting" class="mb-8" @click="settingChange('open')">参数设置</el-button>
         </template>
-
+        <div class="mb-16">
+          <div class="title mb-8">检索模式</div>
+          <el-radio-group v-model="cloneForm.search_mode" class="card__radio">
+            <el-card
+              shadow="never"
+              class="mb-16"
+              :class="cloneForm.search_mode === 'embedding' ? 'active' : ''"
+            >
+              <el-radio value="embedding" size="large">
+                <p class="mb-4">向量检索</p>
+                <el-text type="info">通过向量距离计算与用户问题最相似的文本分段</el-text>
+              </el-radio>
+            </el-card>
+            <el-card
+              shadow="never"
+              class="mb-16"
+              :class="cloneForm.search_mode === 'keywords' ? 'active' : ''"
+            >
+              <el-radio value="keywords" size="large">
+                <p class="mb-4">全文检索</p>
+                <el-text type="info">通过关键词检索，返回包含关键词最多的文本分段</el-text>
+              </el-radio>
+            </el-card>
+            <el-card
+              shadow="never"
+              class="mb-16"
+              :class="cloneForm.search_mode === 'blend' ? 'active' : ''"
+            >
+              <el-radio value="blend" size="large">
+                <p class="mb-4">混合检索</p>
+                <el-text type="info"
+                  >同时执行全文检索和向量检索，再进行重排序，从两类查询结果中选择匹配用户问题的最佳结果</el-text
+                >
+              </el-radio>
+            </el-card>
+          </el-radio-group>
+        </div>
         <div class="mb-16">
           <div class="title mb-8">相似度高于</div>
           <el-input-number
@@ -92,7 +128,6 @@
             :precision="3"
             :step="0.1"
             controls-position="right"
-            style="width: 145px"
           />
         </div>
         <div class="mb-16">
@@ -103,7 +138,6 @@
             :min="1"
             :max="10"
             controls-position="right"
-            style="width: 145px"
           />
         </div>
         <div class="text-right">
@@ -161,7 +195,8 @@ const title = ref('')
 const inputValue = ref('')
 const formInline = ref({
   similarity: 0.6,
-  top_number: 5
+  top_number: 5,
+  search_mode: 'embedding'
 })
 
 // 第一次加载
@@ -213,8 +248,7 @@ function sendChatHandle(event: any) {
 function getHitTestList() {
   const obj = {
     query_text: inputValue.value,
-    similarity: formInline.value.similarity,
-    top_number: formInline.value.top_number
+    ...formInline.value
   }
   if (isDataset.value) {
     datasetApi.getDatasetHitTest(id, obj, loading).then((res) => {