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Feat Add redis as vector database (#145)
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,107 @@ | ||
| package main | ||
|
|
||
| import ( | ||
| "context" | ||
| "fmt" | ||
|
|
||
| "github.com/RediSearch/redisearch-go/v2/redisearch" | ||
| openaiembedder "github.com/henomis/lingoose/embedder/openai" | ||
| "github.com/henomis/lingoose/index" | ||
| indexoption "github.com/henomis/lingoose/index/option" | ||
| "github.com/henomis/lingoose/index/vectordb/redis" | ||
| "github.com/henomis/lingoose/llm/openai" | ||
| "github.com/henomis/lingoose/loader" | ||
| "github.com/henomis/lingoose/prompt" | ||
| "github.com/henomis/lingoose/textsplitter" | ||
| ) | ||
|
|
||
| // download https://raw.githubusercontent.com/hwchase17/chat-your-data/master/state_of_the_union.txt | ||
|
|
||
| func main() { | ||
| index := index.New( | ||
| redis.New( | ||
| redis.Options{ | ||
| RedisearchClient: redisearch.NewClient("localhost:6379", "test"), | ||
| CreateIndex: &redis.CreateIndexOptions{ | ||
| Dimension: 1536, | ||
| Distance: redis.DistanceCosine, | ||
| }, | ||
| }, | ||
| ), | ||
| openaiembedder.New(openaiembedder.AdaEmbeddingV2), | ||
| ).WithIncludeContents(true) | ||
|
|
||
| indexIsEmpty, err := index.IsEmpty(context.Background()) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
|
|
||
| if indexIsEmpty { | ||
| err = ingestData(index) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
| } | ||
|
|
||
| query := "What is the purpose of the NATO Alliance?" | ||
| similarities, err := index.Query( | ||
| context.Background(), | ||
| query, | ||
| indexoption.WithTopK(3), | ||
| ) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
|
|
||
| content := "" | ||
| for _, similarity := range similarities { | ||
| fmt.Printf("Similarity: %f\n", similarity.Score) | ||
| fmt.Printf("Document: %s\n", similarity.Content()) | ||
| fmt.Println("Metadata: ", similarity.Metadata) | ||
| fmt.Println("ID: ", similarity.ID) | ||
| fmt.Println("----------") | ||
| content += similarity.Content() + "\n" | ||
| } | ||
|
|
||
| llmOpenAI := openai.NewCompletion().WithVerbose(true) | ||
|
|
||
| prompt1 := prompt.NewPromptTemplate( | ||
| "Based on the following context answer to the question.\n\nContext:\n{{.context}}\n\nQuestion: {{.query}}"). | ||
| WithInputs( | ||
| map[string]string{ | ||
| "query": query, | ||
| "context": content, | ||
| }, | ||
| ) | ||
|
|
||
| err = prompt1.Format(nil) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
|
|
||
| _, err = llmOpenAI.Completion(context.Background(), prompt1.String()) | ||
| if err != nil { | ||
| panic(err) | ||
| } | ||
| } | ||
|
|
||
| func ingestData(redisIndex *index.Index) error { | ||
| documents, err := loader.NewDirectoryLoader(".", ".txt").Load(context.Background()) | ||
| if err != nil { | ||
| return err | ||
| } | ||
|
|
||
| textSplitter := textsplitter.NewRecursiveCharacterTextSplitter(1000, 20) | ||
|
|
||
| documentChunks := textSplitter.SplitDocuments(documents) | ||
|
|
||
| for _, doc := range documentChunks { | ||
| fmt.Println(doc.Content) | ||
| fmt.Println("----------") | ||
| fmt.Println(doc.Metadata) | ||
| fmt.Println("----------") | ||
| fmt.Println() | ||
| } | ||
|
|
||
| return redisIndex.LoadFromDocuments(context.Background(), documentChunks) | ||
| } |
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