Function addContextFromMemory

packages/baseai/src/utils/memory/lib.ts:136–196

({
	messages,
	memoryNames
}: {
	messages: Message[];
	memoryNames: string[];
})

};

export const addContextFromMemory = async ({
	messages,
	memoryNames
}: {
	messages: Message[];
	memoryNames: string[];
}) => {
	try {
		// Check if there are no memory names.
		const isMemoryAttached = memoryNames.length > 0;
		logger('memory', isMemoryAttached, 'Memory attached');

		// Check if there are no messages.
		const messagesExist = messages.length > 0;

		// Return the messages if there are no memory names or messages.
		if (!isMemoryAttached || !messagesExist) return;

		// This will be the user prompt.
		const lastUserMsg = [...messages]
			.reverse()
			.find(m => m.role === 'user');
		const userPrompt = lastUserMsg?.content;

		// If there is no user prompt, return the messages.
		if (!userPrompt) return;

		// 1- Generate the embeddings of the user prompt.
		// Read config to determine which embedding to use.
		const config = await loadConfig();
		const useLocalEmbeddings = config.memory?.useLocalEmbeddings || false;

		let embeddings = [];
		if (useLocalEmbeddings) {
			// Use local embeddings
			dlog('Generating local embeddings');
			embeddings = await generateLocalEmbeddings([userPrompt]);
		} else {
			// Use OpenAI embeddings
			dlog('Generating OpenAI embeddings');
			embeddings = await getOpenAIEmbeddings([userPrompt]);
		}

		// 2- Get all the memorysets from the db.
		const memoryChunks = await getDocumentsFromMemory(memoryNames);
		if (memoryChunks.length === 0) return;

		// 3- Get similar chunks from the memorysets.
		const similarChunks = cosineSimilaritySearch({
			chunks: memoryChunks,
			queryEmbedding: embeddings[0].embedding,
			topK: MEMORYSETS.MAX_CHUNKS_ATTACHED_TO_LLM
		});
		if (similarChunks.length === 0) return;
		logger('memory.similarChunks', similarChunks);

		return similarChunks;
	} catch (error: any) {