试验性的记忆功能 (#812)

* feat: memory basic

* fix: chaite ver

* fix: update prompt

* fix: memory cursor and extract prompt

* fix: memory retrieval bug

* fix: memory retrieval bug

* fix: one more attempt by codex

* fix: messages prompt error

* fix: one more time by codex

* fix: metrics by codex

* fix: memory forward

* fix: memory show update time

models/chaite/vectorizer.js

@@ -0,0 +1,89 @@
+import { Chaite, ChaiteContext, GeminiClient, OpenAIClient } from 'chaite'
+
+async function getIClientByChannel (channel) {
+  await channel.ready()
+  const baseLogger = global.logger || console
+  if (channel.options?.setLogger) {
+    channel.options.setLogger(baseLogger)
+  }
+  const context = new ChaiteContext(baseLogger)
+  context.setChaite(Chaite.getInstance())
+  switch (channel.adapterType) {
+    case 'openai':
+      return new OpenAIClient(channel.options, context)
+    case 'gemini':
+      return new GeminiClient(channel.options, context)
+    case 'claude':
+      throw new Error('claude does not support embedding')
+    default:
+      throw new Error(`Unsupported adapter ${channel.adapterType}`)
+  }
+}
+
+async function resolveChannelForModel (model) {
+  const manager = Chaite.getInstance().getChannelsManager()
+  const channels = await manager.getChannelByModel(model)
+  if (channels.length === 0) {
+    throw new Error('No channel found for model: ' + model)
+  }
+  return channels[0]
+}
+
+export async function getClientForModel (model) {
+  const channel = await resolveChannelForModel(model)
+  const client = await getIClientByChannel(channel)
+  return { client, channel }
+}
+
+/**
+ * 创建一个基于Chaite渠道的向量器
+ * @param {string} model
+ * @param {number} dimensions
+ * @returns {{ textToVector: (text: string) => Promise<number[]>, batchTextToVector: (texts: string[]) => Promise<number[][]> }}
+ */
+export function createChaiteVectorizer (model, dimensions) {
+  return {
+    async textToVector (text) {
+      const { client } = await getClientForModel(model)
+      const options = { model }
+      if (Number.isFinite(dimensions) && dimensions > 0) {
+        options.dimensions = dimensions
+      }
+      const result = await client.getEmbedding(text, options)
+      return result.embeddings[0]
+    },
+    async batchTextToVector (texts) {
+      const manager = Chaite.getInstance().getChannelsManager()
+      const channels = await manager.getChannelsByModel(model, texts.length)
+      if (channels.length === 0) {
+        throw new Error('No channel found for model: ' + model)
+      }
+      const clients = await Promise.all(channels.map(({ channel }) => getIClientByChannel(channel)))
+      const results = []
+      let startIndex = 0
+      for (let i = 0; i < channels.length; i++) {
+        const { quantity } = channels[i]
+        const slice = texts.slice(startIndex, startIndex + quantity)
+        const options = { model }
+        if (Number.isFinite(dimensions) && dimensions > 0) {
+          options.dimensions = dimensions
+        }
+        const embeddings = await clients[i].getEmbedding(slice, options)
+        results.push(...embeddings.embeddings)
+        startIndex += quantity
+      }
+      return results
+    }
+  }
+}
+
+export async function embedTexts (texts, model, dimensions) {
+  if (!texts || texts.length === 0) {
+    return []
+  }
+  const vectorizer = createChaiteVectorizer(model, dimensions)
+  if (texts.length === 1) {
+    return [await vectorizer.textToVector(texts[0])]
+  }
+  return await vectorizer.batchTextToVector(texts)
+}