diff --git a/arctl/README.md b/arctl/README.md index a695aa673..b78653fec 100644 --- a/arctl/README.md +++ b/arctl/README.md @@ -8,7 +8,7 @@ arctl(arcadia command line tool) go install github.com/kubeagi/arcadia/arctl@latest ``` -If build succeeded, `arctl` will be built into `bin/arctl` under `arcadia` +Now have a try!❤️ ```shell ❯ arctl -h @@ -30,8 +30,47 @@ Flags: Use "arctl [command] --help" for more information about a command. ``` +### Build from local code + +1. Clone `arcadia` + +```shell +git clone https://github.com/kubeagi/arcadia.git +``` + +2. Build + +```shell +make arctl +``` + +3. Have a try! ❤️ + +```shell +❯ ./bin/arctl chat -h +Do LLM chat with similarity search(optional) + +Usage: + arctl chat [usage] [flags] + +Flags: + --enable-embedding-search enable embedding similarity search + -h, --help help for chat + --llm-apikey string apiKey to access embedding/llm service.Must required when embedding similarity search is enabled + --llm-type string llm type to use for embedding & chat(Only zhipuai,openai supported now) (default "zhipuai") + --method string Invoke method used when access LLM service(invoke/sse-invoke) (default "sse-invoke") + --model string which model to use: chatglm_lite/chatglm_std/chatglm_pro (default "chatglm_lite") + --namespace string namespace/collection to query from (default "arcadia") + --num-docs int number of documents to be returned with SimilarSearch (default 5) + --question string question text to be asked + --score-threshold float score threshold for similarity search(Higher is better) + --temperature float32 temperature for chat (default 0.95) + --top-p float32 top-p for chat (default 0.7) + --vector-store string vector stores to use(Only chroma supported now) (default "http://localhost:8000") +``` + ## Usage -### Load documents into vector store +### Load documents into vector store with embedding service ```shell ❯ arctl load -h @@ -41,24 +80,25 @@ Usage: arctl load [usage] [flags] Flags: - --chunk-overlap int chunk overlap for embedding (default 30) - --chunk-size int chunk size for embedding (default 300) - --document string path of the document to load - --document-language string language of the document(Only text,html,csv supported now) (default "text") - --embedding-llm-apikey string apiKey to access embedding service - --embedding-llm-type string llm type to use(Only zhipuai,openai supported now) (default "zhipuai") - -h, --help help for load - --namespace string namespace/collection of the document to load into (default "arcadia") - --vector-store string vector stores to use(Only chroma supported now) (default "http://localhost:8000") + --chunk-overlap int chunk overlap for embedding (default 30) + --chunk-size int chunk size for embedding (default 300) + --document string path of the document to load + --document-language string language of the document(Only text,html,csv supported now) (default "text") + -h, --help help for load + --llm-apikey string apiKey to access embedding service + --llm-type string llm type to use(Only zhipuai,openai supported now) (default "zhipuai") + --namespace string namespace/collection of the document to load into (default "arcadia") + --vector-store string vector stores to use(Only chroma supported now) (default "http://localhost:8000") ``` Required Arguments: -- `--embedding-llm-apikey` +- `--llm-apikey` - `--document` For example: +> This will load `./README.md` into vectorstore(chromadb http://localhost:8000) with help of embedding service `zhipuai` and its apikey `26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm` ```shell -arctl load --embedding-llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --document ./README.md +arctl load --llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --document ./README.md ``` ### Chat with LLM @@ -70,38 +110,37 @@ Usage: arctl chat [usage] [flags] Flags: - --chat-llm-apikey string apiKey to access embedding service - --chat-llm-type string llm type to use(Only zhipuai,openai supported now) (default "zhipuai") - --embedding-llm-apikey string apiKey to access embedding service.Must required when embedding similarity search is enabled - --embedding-llm-type string llm type to use(Only zhipuai,openai supported now) (default "zhipuai") - --enable-embedding-search enable embedding similarity search - -h, --help help for chat - --method string Invoke method used when access LLM service(invoke/sse-invoke) (default "sse-invoke") - --model string which model to use: chatglm_lite/chatglm_std/chatglm_pro (default "chatglm_lite") - --namespace string namespace/collection to query from (default "arcadia") - --num-docs int number of documents to be returned with SimilarSearch (default 3) - --question string question text to be asked - --score-threshold float score threshold for similarity search(Higher is better) - --temperature float32 temperature for chat (default 0.95) - --top-p float32 top-p for chat (default 0.7) - --vector-store string vector stores to use(Only chroma supported now) (default "http://localhost:8000") + --enable-embedding-search enable embedding similarity search(false by default) + -h, --help help for chat + --llm-apikey string apiKey to access embedding/llm service.Must required when embedding similarity search is enabled + --llm-type string llm type to use for embedding & chat(Only zhipuai,openai supported now) (default "zhipuai") + --method string Invoke method used when access LLM service(invoke/sse-invoke) (default "sse-invoke") + --model string which model to use: chatglm_lite/chatglm_std/chatglm_pro (default "chatglm_lite") + --namespace string namespace/collection to query from (default "arcadia") + --num-docs int number of documents to be returned with SimilarSearch (default 5) + --question string question text to be asked + --score-threshold float score threshold for similarity search(Higher is better) + --temperature float32 temperature for chat (default 0.95) + --top-p float32 top-p for chat (default 0.7) + --vector-store string vector stores to use(Only chroma supported now) (default "http://localhost:8000") ``` Now `arctl chat` has two modes which is controlled by flag `--enable-embedding-search`: - normal chat without embedding search(default) - enable similarity search with embedding -#### Normal chat(Without embedding) +#### Chat without embedding + +> This will chat with LLM `zhipuai` with its apikey by using model `chatglm_pro` without embedding ```shell -arctl chat --chat-llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --model chatglm_pro --question "介绍一下Arcadia" +arctl chat --llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --model chatglm_pro --question "介绍一下Arcadia" ``` Required Arguments: -- `--chat-llm-apikey` +- `--llm-apikey` - `--question` - **Output:** ```shell Prompts: [{user 介绍一下Arcadia}] @@ -116,16 +155,17 @@ Arcadia 开发的游戏中最知名的作品之一是《Second Life》（第二 总的来说，Arcadia 是一家在游戏行业具有较高声誉和影响力的公司，他们不断推出新的游戏作品，为玩家带来更多精彩的游戏体验。 ``` -#### Enable Similarity Search +#### Chat with embedding + +> This will chat with LLM `zhipuai` with its apikey by using model `chatglm_pro` with embedding enabled ```shell -arctl chat --enable-embedding-search --chat-llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --embedding-llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --model chatglm_pro --num-docs 10 --question "介绍一下Arcadia" +arctl chat --enable-embedding-search --llm-apikey 26b2bc55fae40752055cadfc4792f9de.wagA4NIwg5aZJWhm --model chatglm_pro --num-docs 10 --question "介绍一下Arcadia" ``` Required Arguments: - `--enable-embedding-search` -- `--embedding-llm-apikey` -- `--chat-llm-apikey` +- `--llm-apikey` - `--question` **Output:** @@ -161,7 +201,6 @@ Arcadia 项目包含了一些用于增强 Golang 中 AI 功能的软件包。其 3. Embedding Service - ✅ zhipuai -- ✅ openai 4. LLM Service diff --git a/arctl/chat.go b/arctl/chat.go index ba6c25ed8..61886e1d2 100644 --- a/arctl/chat.go +++ b/arctl/chat.go @@ -35,9 +35,8 @@ import ( var ( question string + // chat with LLM - chatLLMType string - chatAPIKey string model string method string temperature float32 @@ -102,25 +101,24 @@ func NewChatCmd() *cobra.Command { } // For similarity search - cmd.Flags().BoolVar(&enableSimilaritySearch, "enable-embedding-search", false, "enable embedding similarity search") - cmd.Flags().StringVar(&embeddingLLMType, "embedding-llm-type", string(llms.ZhiPuAI), "llm type to use(Only zhipuai,openai supported now)") - cmd.Flags().StringVar(&embeddingLLMApiKey, "embedding-llm-apikey", "", "apiKey to access embedding service.Must required when embedding similarity search is enabled") + cmd.Flags().BoolVar(&enableSimilaritySearch, "enable-embedding-search", false, "enable embedding similarity search(false by default)") cmd.Flags().StringVar(&vectorStore, "vector-store", "http://localhost:8000", "vector stores to use(Only chroma supported now)") + // Similarity search params cmd.Flags().StringVar(&nameSpace, "namespace", "arcadia", "namespace/collection to query from") cmd.Flags().Float64Var(&scoreThreshold, "score-threshold", 0, "score threshold for similarity search(Higher is better)") cmd.Flags().IntVar(&numDocs, "num-docs", 5, "number of documents to be returned with SimilarSearch") - cmd.Flags().StringVar(&question, "question", "", "question text to be asked") - // For LLM chat - cmd.Flags().StringVar(&chatLLMType, "chat-llm-type", string(llms.ZhiPuAI), "llm type to use(Only zhipuai,openai supported now)") - cmd.Flags().StringVar(&chatAPIKey, "chat-llm-apikey", "", "apiKey to access embedding service") + cmd.Flags().StringVar(&llmType, "llm-type", string(llms.ZhiPuAI), "llm type to use for embedding & chat(Only zhipuai,openai supported now)") + cmd.Flags().StringVar(&apiKey, "llm-apikey", "", "apiKey to access embedding/llm service.Must required when embedding similarity search is enabled") + cmd.Flags().StringVar(&question, "question", "", "question text to be asked") + // LLM Chat params cmd.PersistentFlags().StringVar(&model, "model", string(zhipuai.ZhiPuAILite), "which model to use: chatglm_lite/chatglm_std/chatglm_pro") cmd.PersistentFlags().StringVar(&method, "method", "sse-invoke", "Invoke method used when access LLM service(invoke/sse-invoke)") cmd.PersistentFlags().Float32Var(&temperature, "temperature", 0.95, "temperature for chat") cmd.PersistentFlags().Float32Var(&topP, "top-p", 0.7, "top-p for chat") - if err = cmd.MarkFlagRequired("chat-llm-apikey"); err != nil { + if err = cmd.MarkFlagRequired("llm-apikey"); err != nil { panic(err) } if err = cmd.MarkFlagRequired("question"); err != nil { @@ -134,14 +132,14 @@ func SimilaritySearch(ctx context.Context) ([]schema.Document, error) { var embedder embeddings.Embedder var err error - if embeddingLLMApiKey == "" { + if apiKey == "" { return nil, errors.New("embedding-llm-apikey is required when embedding similarity search is enabled") } - switch embeddingLLMType { + switch llmType { case "zhipuai": embedder, err = zhipuaiembeddings.NewZhiPuAI( - zhipuaiembeddings.WithClient(*zhipuai.NewZhiPuAI(embeddingLLMApiKey)), + zhipuaiembeddings.WithClient(*zhipuai.NewZhiPuAI(apiKey)), ) if err != nil { return nil, err @@ -169,7 +167,7 @@ func SimilaritySearch(ctx context.Context) ([]schema.Document, error) { func Chat(ctx context.Context, similarDocs []schema.Document) error { // Only for zhipuai - client := zhipuai.NewZhiPuAI(chatAPIKey) + client := zhipuai.NewZhiPuAI(apiKey) params := zhipuai.DefaultModelParams() params.Model = zhipuai.Model(model) diff --git a/arctl/load.go b/arctl/load.go index da05f0718..428952da7 100644 --- a/arctl/load.go +++ b/arctl/load.go @@ -35,8 +35,8 @@ import ( ) var ( - embeddingLLMType string - embeddingLLMApiKey string + llmType string + apiKey string document string language string @@ -61,8 +61,8 @@ func NewLoadCmd() *cobra.Command { }, } - cmd.Flags().StringVar(&embeddingLLMType, "embedding-llm-type", string(llms.ZhiPuAI), "llm type to use(Only zhipuai,openai supported now)") - cmd.Flags().StringVar(&embeddingLLMApiKey, "embedding-llm-apikey", "", "apiKey to access embedding service") + cmd.Flags().StringVar(&llmType, "llm-type", string(llms.ZhiPuAI), "llm type to use(Only zhipuai,openai supported now)") + cmd.Flags().StringVar(&apiKey, "llm-apikey", "", "apiKey to access embedding service") cmd.Flags().StringVar(&vectorStore, "vector-store", "http://localhost:8000", "vector stores to use(Only chroma supported now)") @@ -73,7 +73,7 @@ func NewLoadCmd() *cobra.Command { cmd.Flags().IntVar(&chunkSize, "chunk-size", 300, "chunk size for embedding") cmd.Flags().IntVar(&chunkOverlap, "chunk-overlap", 30, "chunk overlap for embedding") - if err = cmd.MarkFlagRequired("embedding-llm-apikey"); err != nil { + if err = cmd.MarkFlagRequired("llm-apikey"); err != nil { panic(err) } if err = cmd.MarkFlagRequired("document"); err != nil { @@ -112,10 +112,10 @@ func EmbedDocuments(ctx context.Context, documents []schema.Document) error { var embedder embeddings.Embedder var err error - switch embeddingLLMType { + switch llmType { case "zhipuai": embedder, err = zhipuaiembeddings.NewZhiPuAI( - zhipuaiembeddings.WithClient(*zhipuai.NewZhiPuAI(embeddingLLMApiKey)), + zhipuaiembeddings.WithClient(*zhipuai.NewZhiPuAI(apiKey)), ) if err != nil { return err diff --git a/pkg/llms/zhipuai/api.go b/pkg/llms/zhipuai/api.go index 01dd7d9de..4026b5d65 100644 --- a/pkg/llms/zhipuai/api.go +++ b/pkg/llms/zhipuai/api.go @@ -201,6 +201,10 @@ func (z *ZhiPuAI) CreateEmbedding(ctx context.Context, inputTexts []string) ([][ if err != nil { return nil, err } + if postResponse.Code != 200 { + return nil, fmt.Errorf("embedding failed: %s", postResponse.String()) + } + embeddings = append(embeddings, postResponse.Data.Embedding) } diff --git a/pkg/llms/zhipuai/response.go b/pkg/llms/zhipuai/response.go index da596dc39..9be365707 100644 --- a/pkg/llms/zhipuai/response.go +++ b/pkg/llms/zhipuai/response.go @@ -22,13 +22,6 @@ import ( "github.com/kubeagi/arcadia/pkg/llms" ) -type Response struct { - Code int `json:"code"` - Data *Data `json:"data"` - Msg string `json:"msg"` - Success bool `json:"success"` -} - type EmbeddingResponse struct { Code int `json:"code"` Data *EmbeddingData `json:"data"` @@ -36,6 +29,33 @@ type EmbeddingResponse struct { Success bool `json:"success"` } +func (embeddingResp *EmbeddingResponse) Unmarshall(bytes []byte) error { + return json.Unmarshal(embeddingResp.Bytes(), embeddingResp) +} + +func (embeddingResp *EmbeddingResponse) Type() llms.LLMType { + return llms.ZhiPuAI +} + +func (embeddingResp *EmbeddingResponse) Bytes() []byte { + bytes, err := json.Marshal(embeddingResp) + if err != nil { + return []byte{} + } + return bytes +} + +func (embeddingResp *EmbeddingResponse) String() string { + return string(embeddingResp.Bytes()) +} + +type Response struct { + Code int `json:"code"` + Data *Data `json:"data"` + Msg string `json:"msg"` + Success bool `json:"success"` +} + func (response *Response) Unmarshall(bytes []byte) error { return json.Unmarshal(response.Bytes(), response) } diff --git a/pkg/llms/zhipuai/sse_client.go b/pkg/llms/zhipuai/sse_client.go index cbd0c7eb8..a3b388707 100644 --- a/pkg/llms/zhipuai/sse_client.go +++ b/pkg/llms/zhipuai/sse_client.go @@ -65,6 +65,7 @@ func Stream(apiURL, token string, params ModelParams, timeout time.Duration, han } defer resp.Body.Close() + fmt.Printf("%v", resp) // parse response body as stream events eventChan, errorChan := NewSSEClient().Events(resp)