fix: claude function call

app/client/platforms/anthropic.ts

@@ -1,4 +1,9 @@
-import { Anthropic, ApiPath } from "@/app/constant";
+import {
+  Anthropic,
+  ApiPath,
+  REQUEST_TIMEOUT_MS,
+  ServiceProvider,
+} from "@/app/constant";
 import {
   AgentChatOptions,
   ChatOptions,
@@ -22,6 +27,11 @@ import { preProcessImageContent, stream } from "@/app/utils/chat";
 import { cloudflareAIGatewayUrl } from "@/app/utils/cloudflare";
 import { RequestPayload } from "./openai";
 import { fetch } from "@/app/utils/stream";
+import {
+  EventStreamContentType,
+  fetchEventSource,
+} from "@fortaine/fetch-event-source";
+import Locale from "../../locales";
 
 export type MultiBlockContent = {
   type: "image" | "text";
@@ -85,9 +95,163 @@ export class ClaudeApi implements LLMApi {
   transcription(options: TranscriptionOptions): Promise<string> {
     throw new Error("Method not implemented.");
   }
-  toolAgentChat(options: AgentChatOptions): Promise<void> {
-    throw new Error("Method not implemented.");
+  async toolAgentChat(options: AgentChatOptions) {
+    const visionModel = isVisionModel(options.config.model);
+    const messages: AgentChatOptions["messages"] = [];
+    for (const v of options.messages) {
+      const content = visionModel
+        ? await preProcessImageContent(v.content)
+        : getMessageTextContent(v);
+      messages.push({ role: v.role, content });
+    }
+
+    const modelConfig = {
+      ...useAppConfig.getState().modelConfig,
+      ...useChatStore.getState().currentSession().mask.modelConfig,
+      ...{
+        model: options.config.model,
+      },
+    };
+    const accessStore = useAccessStore.getState();
+    let baseUrl = accessStore.anthropicUrl;
+    const requestPayload = {
+      chatSessionId: options.chatSessionId,
+      messages,
+      isAzure: false,
+      azureApiVersion: accessStore.azureApiVersion,
+      stream: options.config.stream,
+      model: modelConfig.model,
+      temperature: modelConfig.temperature,
+      presence_penalty: modelConfig.presence_penalty,
+      frequency_penalty: modelConfig.frequency_penalty,
+      top_p: modelConfig.top_p,
+      baseUrl: baseUrl,
+      maxIterations: options.agentConfig.maxIterations,
+      returnIntermediateSteps: options.agentConfig.returnIntermediateSteps,
+      useTools: options.agentConfig.useTools,
+      provider: ServiceProvider.Anthropic,
+    };
+
+    console.log("[Request] anthropic payload: ", requestPayload);
+
+    const shouldStream = true;
+    const controller = new AbortController();
+    options.onController?.(controller);
+
+    try {
+      let path = "/api/langchain/tool/agent/";
+      const enableNodeJSPlugin = !!process.env.NEXT_PUBLIC_ENABLE_NODEJS_PLUGIN;
+      path = enableNodeJSPlugin ? path + "nodejs" : path + "edge";
+      const chatPayload = {
+        method: "POST",
+        body: JSON.stringify(requestPayload),
+        signal: controller.signal,
+        headers: getHeaders(),
+      };
+
+      // make a fetch request
+      const requestTimeoutId = setTimeout(
+        () => controller.abort(),
+        REQUEST_TIMEOUT_MS,
+      );
+      // console.log("shouldStream", shouldStream);
+
+      if (shouldStream) {
+        let responseText = "";
+        let finished = false;
+
+        const finish = () => {
+          if (!finished) {
+            options.onFinish(responseText);
+            finished = true;
+          }
+        };
+
+        controller.signal.onabort = finish;
+
+        fetchEventSource(path, {
+          ...chatPayload,
+          async onopen(res) {
+            clearTimeout(requestTimeoutId);
+            const contentType = res.headers.get("content-type");
+            console.log(
+              "[OpenAI] request response content type: ",
+              contentType,
+            );
+
+            if (contentType?.startsWith("text/plain")) {
+              responseText = await res.clone().text();
+              return finish();
+            }
+
+            if (
+              !res.ok ||
+              !res.headers
+                .get("content-type")
+                ?.startsWith(EventStreamContentType) ||
+              res.status !== 200
+            ) {
+              const responseTexts = [responseText];
+              let extraInfo = await res.clone().text();
+              console.warn(`extraInfo: ${extraInfo}`);
+
+              if (res.status === 401) {
+                responseTexts.push(Locale.Error.Unauthorized);
+              }
+
+              if (extraInfo) {
+                responseTexts.push(extraInfo);
+              }
+
+              responseText = responseTexts.join("\n\n");
+
+              return finish();
+            }
+          },
+          onmessage(msg) {
+            let response = JSON.parse(msg.data);
+            if (!response.isSuccess) {
+              console.error("[Request]", msg.data);
+              responseText = msg.data;
+              throw Error(response.message);
+            }
+            if (msg.data === "[DONE]" || finished) {
+              return finish();
+            }
+            try {
+              if (response && !response.isToolMessage) {
+                responseText += response.message;
+                options.onUpdate?.(responseText, response.message);
+              } else {
+                options.onToolUpdate?.(response.toolName!, response.message);
+              }
+            } catch (e) {
+              console.error("[Request] parse error", response, msg);
+            }
+          },
+          onclose() {
+            finish();
+          },
+          onerror(e) {
+            options.onError?.(e);
+            throw e;
+          },
+          openWhenHidden: true,
+        });
+      } else {
+        const res = await fetch(path, chatPayload);
+        clearTimeout(requestTimeoutId);
+
+        const resJson = await res.json();
+        const message = this.extractMessage(resJson);
+        options.onFinish(message);
+      }
+    } catch (e) {
+      console.log("[Request] failed to make a chat reqeust", e);
+      options.onError?.(e as Error);
+    }
   }
+
   createRAGStore(options: CreateRAGStoreOptions): Promise<string> {
     throw new Error("Method not implemented.");
   }

app/utils.ts

@@ -318,6 +318,9 @@ export function isFunctionCallModel(modelName: string) {
     "claude-3-opus-20240229",
     "claude-3-haiku-20240307",
     "claude-3-5-sonnet-20240620",
+    "claude-3-5-sonnet-20241022",
+    "claude-3-5-sonnet-latest",
+    "claude-3-5-haiku-latest",
   ];
   if (specialModels.some((keyword) => modelName === keyword)) return true;
   return DEFAULT_MODELS.filter(