vault backup: 2026-04-29 18:36:58
This commit is contained in:
@@ -0,0 +1,446 @@
|
||||
---
|
||||
Description: ""
|
||||
date: "2026-01-20"
|
||||
lastmod: ""
|
||||
tags: []
|
||||
title: Indexer 使用说明
|
||||
weight: 5
|
||||
---
|
||||
|
||||
## **基本介绍**
|
||||
|
||||
Indexer 组件是一个用于存储和索引文档的组件。它的主要作用是将文档及其向量表示存储到后端存储系统中，并提供高效的检索能力。这个组件在以下场景中发挥重要作用：
|
||||
|
||||
- 构建向量数据库，以用于语义关联搜索
|
||||
|
||||
## **组件定义**
|
||||
|
||||
### **接口定义**
|
||||
|
||||
> 代码位置：eino/components/indexer/interface.go
|
||||
|
||||
```go
|
||||
type Indexer interface {
|
||||
Store(ctx context.Context, docs []*schema.Document, opts ...Option) (ids []string, err error)
|
||||
}
|
||||
```
|
||||
|
||||
#### **Store 方法**
|
||||
|
||||
- 功能：存储文档并建立索引
|
||||
- 参数：
|
||||
- ctx：上下文对象，用于传递请求级别的信息，同时也用于传递 Callback Manager
|
||||
- docs：待存储的文档列表
|
||||
- opts：存储选项，用于配置存储行为
|
||||
- 返回值：
|
||||
- ids：存储成功的文档 ID 列表
|
||||
- error：存储过程中的错误信息
|
||||
|
||||
### **公共 Option**
|
||||
|
||||
Indexer 组件使用 IndexerOption 来定义可选参数，Indexer 定义了如下的公共 option。另外，每个具体的实现可以定义自己的特定 Option，通过 WrapIndexerImplSpecificOptFn 函数包装成统一的 IndexerOption 类型。
|
||||
|
||||
```go
|
||||
type Options struct {
|
||||
// SubIndexes 是要建立索引的子索引列表
|
||||
SubIndexes []string
|
||||
// Embedding 是用于生成文档向量的组件
|
||||
Embedding embedding.Embedder
|
||||
}
|
||||
```
|
||||
|
||||
可以通过以下方式设置选项：
|
||||
|
||||
```go
|
||||
// 设置子索引
|
||||
WithSubIndexes(subIndexes []string) Option
|
||||
// 设置向量生成组件
|
||||
WithEmbedding(emb embedding.Embedder) Option
|
||||
```
|
||||
|
||||
## **使用方式**
|
||||
|
||||
### **单独使用**
|
||||
|
||||
#### VikingDB 示例
|
||||
|
||||
```go
|
||||
import (
|
||||
"github.com/cloudwego/eino/schema"
|
||||
"github.com/cloudwego/eino-ext/components/indexer/volc_vikingdb"
|
||||
)
|
||||
|
||||
collectionName := "eino_test"
|
||||
|
||||
/*
|
||||
* 下面示例中提前构建了一个名为 eino_test 的数据集 (collection)，字段配置为:
|
||||
* 字段名称 字段类型 向量维度
|
||||
* ID string
|
||||
* vector vector 1024
|
||||
* sparse_vector sparse_vector
|
||||
* content string
|
||||
* extra_field_1 string
|
||||
*
|
||||
* component 使用时注意:
|
||||
* 1. ID / vector / sparse_vector / content 的字段名称与类型与上方配置一致
|
||||
* 2. vector 向量维度需要与 ModelName 对应的模型所输出的向量维度一致
|
||||
* 3. 部分模型不输出稀疏向量，此时 UseSparse 需要设置为 false，collection 可以不设置 sparse_vector 字段
|
||||
*/
|
||||
|
||||
cfg := &volc_vikingdb.IndexerConfig{
|
||||
// https://api-vikingdb.volces.com （华北）
|
||||
// https://api-vikingdb.mlp.cn-shanghai.volces.com（华东）
|
||||
// https://api-vikingdb.mlp.ap-mya.byteplus.com（海外-柔佛）
|
||||
Host: "api-vikingdb.volces.com",
|
||||
Region: "cn-beijing",
|
||||
AK: ak,
|
||||
SK: sk,
|
||||
Scheme: "https",
|
||||
ConnectionTimeout: 0,
|
||||
Collection: collectionName,
|
||||
EmbeddingConfig: volc_vikingdb.EmbeddingConfig{
|
||||
UseBuiltin: true,
|
||||
ModelName: "bge-m3",
|
||||
UseSparse: true,
|
||||
},
|
||||
AddBatchSize: 10,
|
||||
}
|
||||
|
||||
volcIndexer, _ := volc_vikingdb.NewIndexer(ctx, cfg)
|
||||
|
||||
doc := &schema.Document{
|
||||
ID: "mock_id_1",
|
||||
Content: "A ReAct prompt consists of few-shot task-solving trajectories, with human-written text reasoning traces and actions, as well as environment observations in response to actions",
|
||||
}
|
||||
volc_vikingdb.SetExtraDataFields(doc, map[string]interface{}{"extra_field_1": "mock_ext_abc"})
|
||||
volc_vikingdb.SetExtraDataTTL(doc, 1000)
|
||||
|
||||
docs := []*schema.Document{doc}
|
||||
resp, _ := volcIndexer.Store(ctx, docs)
|
||||
|
||||
fmt.Printf("vikingDB store success, docs=%v, resp ids=%v\n", docs, resp)
|
||||
```
|
||||
|
||||
#### Milvus 示例
|
||||
|
||||
```go
|
||||
package main
|
||||
|
||||
import (
|
||||
"github.com/cloudwego/eino/schema"
|
||||
"github.com/milvus-io/milvus/client/v2/milvusclient"
|
||||
"github.com/cloudwego/eino-ext/components/indexer/milvus2"
|
||||
)
|
||||
|
||||
// 创建索引器
|
||||
indexer, err := milvus2.NewIndexer(ctx, &milvus2.IndexerConfig{
|
||||
ClientConfig: &milvusclient.ClientConfig{
|
||||
Address: addr,
|
||||
Username: username,
|
||||
Password: password,
|
||||
},
|
||||
Collection: "my_collection",
|
||||
Dimension: 1024, // 与 embedding 模型维度匹配
|
||||
MetricType: milvus2.COSINE,
|
||||
IndexBuilder: milvus2.NewHNSWIndexBuilder().WithM(16).WithEfConstruction(200),
|
||||
Embedding: emb,
|
||||
})
|
||||
|
||||
// 索引文档
|
||||
docs := []*schema.Document{
|
||||
{
|
||||
ID: "doc1",
|
||||
Content: "EINO is a framework for building AI applications",
|
||||
},
|
||||
}
|
||||
ids, err := indexer.Store(ctx, docs)
|
||||
```
|
||||
|
||||
#### ElasticSearch 7 示例
|
||||
|
||||
```go
|
||||
import (
|
||||
"github.com/cloudwego/eino/components/embedding"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
elasticsearch "github.com/elastic/go-elasticsearch/v7"
|
||||
"github.com/cloudwego/eino-ext/components/indexer/es7"
|
||||
)
|
||||
|
||||
client, _ := elasticsearch.NewClient(elasticsearch.Config{
|
||||
Addresses: []string{"http://localhost:9200"},
|
||||
Username: username,
|
||||
Password: password,
|
||||
})
|
||||
|
||||
// 创建 ES 索引器组件
|
||||
indexer, _ := es7.NewIndexer(ctx, &es7.IndexerConfig{
|
||||
Client: client,
|
||||
Index: indexName,
|
||||
BatchSize: 10,
|
||||
DocumentToFields: func(ctx context.Context, doc *schema.Document) (field2Value map[string]es7.FieldValue, err error) {
|
||||
return map[string]es7.FieldValue{
|
||||
fieldContent: {
|
||||
Value: doc.Content,
|
||||
EmbedKey: fieldContentVector, // 对文档内容进行向量化并保存到 "content_vector" 字段
|
||||
},
|
||||
fieldExtraLocation: {
|
||||
Value: doc.MetaData[docExtraLocation],
|
||||
},
|
||||
}, nil
|
||||
},
|
||||
Embedding: emb,
|
||||
})
|
||||
|
||||
// 索引文档
|
||||
docs := []*schema.Document{
|
||||
{
|
||||
ID: "doc1",
|
||||
Content: "EINO is a framework for building AI applications",
|
||||
},
|
||||
}
|
||||
ids, err := indexer.Store(ctx, docs)
|
||||
```
|
||||
|
||||
#### OpenSearch 2 示例
|
||||
|
||||
```go
|
||||
package main
|
||||
|
||||
import (
|
||||
"github.com/cloudwego/eino/schema"
|
||||
opensearch "github.com/opensearch-project/opensearch-go/v2"
|
||||
"github.com/cloudwego/eino-ext/components/indexer/opensearch2"
|
||||
)
|
||||
|
||||
client, err := opensearch.NewClient(opensearch.Config{
|
||||
Addresses: []string{"http://localhost:9200"},
|
||||
Username: username,
|
||||
Password: password,
|
||||
})
|
||||
|
||||
// 创建 opensearch 索引器组件
|
||||
indexer, _ := opensearch2.NewIndexer(ctx, &opensearch2.IndexerConfig{
|
||||
Client: client,
|
||||
Index: "your_index_name",
|
||||
BatchSize: 10,
|
||||
DocumentToFields: func(ctx context.Context, doc *schema.Document) (map[string]opensearch2.FieldValue, error) {
|
||||
return map[string]opensearch2.FieldValue{
|
||||
"content": {
|
||||
Value: doc.Content,
|
||||
EmbedKey: "content_vector",
|
||||
},
|
||||
}, nil
|
||||
},
|
||||
Embedding: emb,
|
||||
})
|
||||
|
||||
// 索引文档
|
||||
docs := []*schema.Document{
|
||||
{
|
||||
ID: "doc1",
|
||||
Content: "EINO is a framework for building AI applications",
|
||||
},
|
||||
}
|
||||
ids, err := indexer.Store(ctx, docs)
|
||||
```
|
||||
|
||||
### **在编排中使用**
|
||||
|
||||
```go
|
||||
// 在 Chain 中使用
|
||||
chain := compose.NewChain[[]*schema.Document, []string]()
|
||||
chain.AppendIndexer(indexer)
|
||||
|
||||
// 在 Graph 中使用
|
||||
graph := compose.NewGraph[[]*schema.Document, []string]()
|
||||
graph.AddIndexerNode("indexer_node", indexer)
|
||||
```
|
||||
|
||||
## **Option 和 Callback 使用**
|
||||
|
||||
### **Option 使用示例**
|
||||
|
||||
```go
|
||||
// 使用选项 (单独使用时)
|
||||
ids, err := indexer.Store(ctx, docs,
|
||||
// 设置子索引
|
||||
indexer.WithSubIndexes([]string{"kb_1", "kb_2"}),
|
||||
// 设置向量生成组件
|
||||
indexer.WithEmbedding(embedder),
|
||||
)
|
||||
```
|
||||
|
||||
### **Callback 使用示例**
|
||||
|
||||
> 代码位置：eino-ext/components/indexer/volc_vikingdb/examples/builtin_embedding
|
||||
|
||||
```go
|
||||
import (
|
||||
"context"
|
||||
"fmt"
|
||||
"log"
|
||||
"os"
|
||||
|
||||
"github.com/cloudwego/eino/callbacks"
|
||||
"github.com/cloudwego/eino/components/indexer"
|
||||
"github.com/cloudwego/eino/compose"
|
||||
"github.com/cloudwego/eino/schema"
|
||||
callbacksHelper "github.com/cloudwego/eino/utils/callbacks"
|
||||
|
||||
"github.com/cloudwego/eino-ext/components/indexer/volc_vikingdb"
|
||||
)
|
||||
|
||||
handler := &callbacksHelper.IndexerCallbackHandler{
|
||||
OnStart: func(ctx context.Context, info *callbacks.RunInfo, input *indexer.CallbackInput) context.Context {
|
||||
log.Printf("input access, len: %v, content: %s\n", len(input.Docs), input.Docs[0].Content)
|
||||
return ctx
|
||||
},
|
||||
OnEnd: func(ctx context.Context, info *callbacks.RunInfo, output *indexer.CallbackOutput) context.Context {
|
||||
log.Printf("output finished, len: %v, ids=%v\n", len(output.IDs), output.IDs)
|
||||
return ctx
|
||||
},
|
||||
// OnError
|
||||
}
|
||||
|
||||
// 使用 callback handler
|
||||
helper := callbacksHelper.NewHandlerHelper().
|
||||
Indexer(handler).
|
||||
Handler()
|
||||
|
||||
chain := compose.NewChain[[]*schema.Document, []string]()
|
||||
chain.AppendIndexer(volcIndexer)
|
||||
|
||||
// 在运行时使用
|
||||
run, _ := chain.Compile(ctx)
|
||||
|
||||
outIDs, _ := run.Invoke(ctx, docs, compose.WithCallbacks(helper))
|
||||
|
||||
fmt.Printf("vikingDB store success, docs=%v, resp ids=%v\n", docs, outIDs)
|
||||
```
|
||||
|
||||
## **已有实现**
|
||||
|
||||
- Volc VikingDB Indexer: 基于火山引擎 VikingDB 实现的向量数据库索引器 [Indexer - VikingDB](/zh/docs/eino/ecosystem_integration/indexer/indexer_volc_vikingdb)
|
||||
- Milvus v2.5+ Indexer: 基于 Milvus 实现的向量数据库索引器 [Indexer - Milvus 2 (v2.5+)](/zh/docs/eino/ecosystem_integration/indexer/indexer_milvusv2)
|
||||
- Milvus v2.4- Indexer: 基于 Milvus 实现的向量数据库索引器 [Indexer - Milvus (v2.4-)](/zh/docs/eino/ecosystem_integration/indexer/indexer_milvus)
|
||||
- Elasticsearch 8 Indexer: 基于 ES8 实现的通用搜索引擎索引器 [Indexer - ElasticSearch 8](/zh/docs/eino/ecosystem_integration/indexer/indexer_es8)
|
||||
- ElasticSearch 7 Indexer: 基于 ES7 实现的通用搜索引擎索引器 [Indexer - Elasticsearch 7 ](/zh/docs/eino/ecosystem_integration/indexer/indexer_elasticsearch7)
|
||||
- OpenSearch 3 Indexer: 基于 OpenSearch 3 实现的通用搜索引擎索引器 [Indexer - OpenSearch 3](/zh/docs/eino/ecosystem_integration/indexer/indexer_opensearch3)
|
||||
- OpenSearch 2 Indexer: 基于 OpenSearch 2 实现的通用搜索引擎索引器 [Indexer - OpenSearch 2](/zh/docs/eino/ecosystem_integration/indexer/indexer_opensearch2)
|
||||
|
||||
## **自行实现参考**
|
||||
|
||||
实现自定义的 Indexer 组件时，需要注意以下几点：
|
||||
|
||||
1. 注意对公共 option 的处理以及组件实现级的 option 处理
|
||||
2. 注意对 callback 的处理
|
||||
|
||||
### **Option 机制**
|
||||
|
||||
自定义 Indexer 可根据需要实现自己的 Option：
|
||||
|
||||
```go
|
||||
// 定义 Option 结构体
|
||||
type MyIndexerOptions struct {
|
||||
BatchSize int
|
||||
MaxRetries int
|
||||
}
|
||||
|
||||
// 定义 Option 函数
|
||||
func WithBatchSize(size int) indexer.Option {
|
||||
return indexer.WrapIndexerImplSpecificOptFn(func(o *MyIndexerOptions) {
|
||||
o.BatchSize = size
|
||||
})
|
||||
}
|
||||
```
|
||||
|
||||
### **Callback 处理**
|
||||
|
||||
Indexer 实现需要在适当的时机触发回调。框架已经定义了标准的回调输入输出结构体：
|
||||
|
||||
```go
|
||||
// CallbackInput 是 indexer 回调的输入
|
||||
type CallbackInput struct {
|
||||
// Docs 是待索引的文档列表
|
||||
Docs []*schema.Document
|
||||
// Extra 是回调的额外信息
|
||||
Extra map[string]any
|
||||
}
|
||||
|
||||
// CallbackOutput 是 indexer 回调的输出
|
||||
type CallbackOutput struct {
|
||||
// IDs 是索引器返回的文档 ID 列表
|
||||
IDs []string
|
||||
// Extra 是回调的额外信息
|
||||
Extra map[string]any
|
||||
}
|
||||
```
|
||||
|
||||
### **完整实现示例**
|
||||
|
||||
```go
|
||||
type MyIndexer struct {
|
||||
batchSize int
|
||||
embedder embedding.Embedder
|
||||
}
|
||||
|
||||
func NewMyIndexer(config *MyIndexerConfig) (*MyIndexer, error) {
|
||||
return &MyIndexer{
|
||||
batchSize: config.DefaultBatchSize,
|
||||
embedder: config.DefaultEmbedder,
|
||||
}, nil
|
||||
}
|
||||
|
||||
func (i *MyIndexer) Store(ctx context.Context, docs []*schema.Document, opts ...indexer.Option) ([]string, error) {
|
||||
// 1. 处理选项
|
||||
options := &indexer.Options{},
|
||||
options = indexer.GetCommonOptions(options, opts...)
|
||||
|
||||
// 2. 获取 callback manager
|
||||
cm := callbacks.ManagerFromContext(ctx)
|
||||
|
||||
// 3. 开始存储前的回调
|
||||
ctx = cm.OnStart(ctx, info, &indexer.CallbackInput{
|
||||
Docs: docs,
|
||||
})
|
||||
|
||||
// 4. 执行存储逻辑
|
||||
ids, err := i.doStore(ctx, docs, options)
|
||||
|
||||
// 5. 处理错误和完成回调
|
||||
if err != nil {
|
||||
ctx = cm.OnError(ctx, info, err)
|
||||
return nil, err
|
||||
}
|
||||
|
||||
ctx = cm.OnEnd(ctx, info, &indexer.CallbackOutput{
|
||||
IDs: ids,
|
||||
})
|
||||
|
||||
return ids, nil
|
||||
}
|
||||
|
||||
func (i *MyIndexer) doStore(ctx context.Context, docs []*schema.Document, opts *indexer.Options) ([]string, error) {
|
||||
// 实现文档存储逻辑 (注意处理公共option的参数)
|
||||
// 1. 如果设置了 Embedding 组件，生成文档的向量表示
|
||||
if opts.Embedding != nil {
|
||||
// 提取文档内容
|
||||
texts := make([]string, len(docs))
|
||||
for j, doc := range docs {
|
||||
texts[j] = doc.Content
|
||||
}
|
||||
// 生成向量
|
||||
vectors, err := opts.Embedding.EmbedStrings(ctx, texts)
|
||||
if err != nil {
|
||||
return nil, err
|
||||
}
|
||||
// 将向量存储到文档的 MetaData 中
|
||||
for j, doc := range docs {
|
||||
doc.WithVector(vectors[j])
|
||||
}
|
||||
}
|
||||
|
||||
// 2. 其他自定义逻辑
|
||||
return ids, nil
|
||||
}
|
||||
```
|
||||
Reference in New Issue
Block a user