add conversationId functionality to map to customId in ingestion (#499)

### TL;DR

Added support for conversation grouping in Supermemory middleware through a new `conversationId` parameter.

### What changed?

- Added a new `conversationId` option to the `withSupermemory` function to group messages into a single document for contextual memory generation
- Updated the middleware to use this conversation ID when adding memories, using a `customId` format of `conversation:{conversationId}`
- Created a new `getConversationContent` function that extracts the full conversation content from the prompt parameters
- Enhanced memory storage to save entire conversations rather than just the last user message
- Updated documentation and examples to demonstrate the new parameter usage

### How to test?

1. Import the `withSupermemory` function from the package
2. Create a model with memory using the new `conversationId` parameter:
   ```typescript
   const modelWithMemory = withSupermemory(openai("gpt-4"), "user-123", {
     conversationId: "conversation-456",
     mode: "full",
     addMemory: "always"
   })
   ```
3. Use the model in a conversation and verify that messages are grouped by the conversation ID
4. Check that memories are being stored with the custom ID format `conversation:{conversationId}`

### Why make this change?

This enhancement improves the contextual understanding of the AI by allowing related messages to be grouped together as a single conversation document. By using a conversation ID, the system can maintain coherent memory across multiple interactions within the same conversation thread, providing better context retrieval and more relevant responses.

packages/tools/src/vercel/index.ts

@@ -13,6 +13,7 @@ import { createSupermemoryMiddleware } from "./middleware"
  * @param model - The language model to wrap with supermemory capabilities
  * @param containerTag - The container tag/identifier for memory search (e.g., user ID, project ID)
  * @param options - Optional configuration options for the middleware
+ * @param options.conversationId - Optional conversation ID to group messages into a single document for contextual memory generation
  * @param options.verbose - Optional flag to enable detailed logging of memory search and injection process (default: false)
  * @param options.mode - Optional mode for memory search: "profile" (default), "query", or "full"
  * @param options.addMemory - Optional mode for memory search: "always" (default), "never"
@@ -24,7 +25,11 @@ import { createSupermemoryMiddleware } from "./middleware"
  * import { withSupermemory } from "@supermemory/tools/ai-sdk"
  * import { openai } from "@ai-sdk/openai"
  *
- * const modelWithMemory = withSupermemory(openai("gpt-4"), "user-123")
+ * const modelWithMemory = withSupermemory(openai("gpt-4"), "user-123", {
+ *   conversationId: "conversation-456",
+ *   mode: "full",
+ *   addMemory: "always"
+ * })
  *
  * const result = await generateText({
  *   model: modelWithMemory,
@@ -38,8 +43,9 @@ import { createSupermemoryMiddleware } from "./middleware"
 const wrapVercelLanguageModel = (
 	model: LanguageModelV2,
 	containerTag: string,
-	options?: { 
-		verbose?: boolean; 
+	options?: {
+		conversationId?: string;
+		verbose?: boolean;
 		mode?: "profile" | "query" | "full";
 		addMemory?: "always" | "never";
 	},
@@ -50,13 +56,14 @@ const wrapVercelLanguageModel = (
 		throw new Error("SUPERMEMORY_API_KEY is not set")
 	}
 
+	const conversationId = options?.conversationId
 	const verbose = options?.verbose ?? false
 	const mode = options?.mode ?? "profile"
 	const addMemory = options?.addMemory ?? "never"
 
 	const wrappedModel = wrapLanguageModel({
 		model,
-		middleware: createSupermemoryMiddleware(containerTag, verbose, mode, addMemory),
+		middleware: createSupermemoryMiddleware(containerTag, conversationId, verbose, mode, addMemory),
 	})
 
 	return wrappedModel

packages/tools/src/vercel/middleware.ts

@@ -132,20 +132,36 @@ const addSystemPrompt = async (
 	}
 }
 
+const getConversationContent = (params: LanguageModelV2CallOptions) => {
+	return params.prompt
+		.map((msg) => {
+			const role = msg.role === "user" ? "User" : "Assistant"
+			const content = msg.content
+				.filter((c) => c.type === "text")
+				.map((c) => c.text)
+				.join(" ")
+			return `${role}: ${content}`
+		})
+		.join("\n\n")
+}
+
 const addMemoryTool = async (
 	client: Supermemory,
 	containerTag: string,
 	content: string,
+	customId: string | undefined,
 	logger: Logger,
 ): Promise<void> => {
 	try {
 		const response = await client.memories.add({
 			content,
 			containerTags: [containerTag],
+			customId,
 		})
 
 		logger.info("Memory saved successfully", {
 			containerTag,
+			customId,
 			contentLength: content.length,
 			memoryId: response.id,
 		})
@@ -158,6 +174,7 @@ const addMemoryTool = async (
 
 export const createSupermemoryMiddleware = (
 	containerTag: string,
+	conversationId?: string,
 	verbose = false,
 	mode: "profile" | "query" | "full" = "profile",
 	addMemory: "always" | "never" = "never"
@@ -173,7 +190,14 @@ export const createSupermemoryMiddleware = (
 			const userMessage = getLastUserMessage(params)
 
 			if (addMemory === "always" && userMessage && userMessage.trim()) {
-				addMemoryTool(client, containerTag, userMessage, logger)
+				const content = conversationId
+					? getConversationContent(params)
+					: userMessage
+				const customId = conversationId
+					? `conversation:${conversationId}`
+					: undefined
+
+				addMemoryTool(client, containerTag, content, customId, logger)
 			}
 
 			if (mode !== "profile") {
@@ -185,6 +209,7 @@ export const createSupermemoryMiddleware = (
 
 			logger.info("Starting memory search", {
 				containerTag,
+				conversationId,
 				mode,
 			})