refactor: qna to interface

Author: glorat

src/lib/ai/langchainWrapper.ts

@@ -1,33 +1,6 @@
-import {getOpenAIChat} from 'src/lib/ai/config';
-import {ConversationalRetrievalQAChain} from 'langchain/chains';
-import {BufferMemory} from 'langchain/memory';
+import {useQnaStore} from '../../stores/qnaStore'
 
-import {useMultiFileStore} from 'stores/multiFileStore';
-
-
-
-const memory =  new BufferMemory({
-  memoryKey: 'chat_history',
-  inputKey: 'question',
-  outputKey: 'text',
-  returnMessages: true
-})
-
-const currentVectorStore = () => {
-  const multiFileStore = useMultiFileStore()
-  return multiFileStore.vectorStore
-}
 
 export const performVectorStoreQnaDirect = async (args: {question:string}) => {
-
-  const model = getOpenAIChat()
-  const chain = ConversationalRetrievalQAChain.fromLLM(model, currentVectorStore().asRetriever(), {
-    returnSourceDocuments: true,
-    memory,
-    questionGeneratorChainOptions:{
-      llm: model
-    }
-  })
-  const res = await chain.call(args)
-  return res
+  return useQnaStore().performVectorStoreQnaDirect(args)
 }

src/lib/ai/openaiFacade.ts

@@ -46,7 +46,3 @@ export async function createImage(arg: {request:CreateImageRequest}): Promise<Im
 export async function sendChatMessage(arg:{message:string, clientOptions?:any, chatOptions?:any, cache: any}): Promise<ReturnType<typeof sendChatMessageDirect>> {
   return await doGeneric(arg, 'sendChatMessage', sendChatMessageDirect)
 }
-
-export async function performVectorStoreQna(arg:{question:string}): Promise<ReturnType<typeof performVectorStoreQnaDirect>> {
-  return await doGeneric(arg, 'performVectorStoreQnaDirect', performVectorStoreQnaDirect)
-}

src/pages/RetrievalQnaPage.vue

@@ -5,7 +5,7 @@
     <div style="background-color: white; height: 50px"></div>
   </q-page>
   <q-page-sticky position="bottom">
-    <chat-entry v-model="newMessage" @message="sendMessage"></chat-entry>
+    <chat-entry v-model="newMessage" @message="sendMessage" @reset="onReset"></chat-entry>
   </q-page-sticky>
 </template>
 
@@ -16,7 +16,7 @@ import {v4} from 'uuid';
 import MultiFileManager from 'components/MultiFileManager.vue';
 import ChatComponent from 'components/ChatComponent.vue';
 import ChatEntry from 'components/ChatEntry.vue';
-import {performVectorStoreQna} from 'src/lib/ai/openaiFacade';
+import {useQnaStore} from '../stores/qnaStore'
 
 const busy = computed(() => loading.value || multiFileStore.processing)
 const loading = ref(false)
@@ -32,13 +32,16 @@ const sendMessage = async () => {
     const msg = newMessage.value.trim()
 
     messages.push({id: v4(), message: msg, role: 'User'})
-    const res = await performVectorStoreQna( {question:msg} )
+    const res = await useQnaStore().performVectorStoreQna({question:msg})
     messages.push({id: v4(), message: res.text, role: 'Other'})
 
     newMessage.value = ''
   }
 }
 
+const onReset = () => {
+
+}
 
 </script>
 

src/stores/multiFileStore.ts

@@ -74,3 +74,4 @@ export const useMultiFileStore = defineStore('multiFile', {
     }
   },
 })
+

src/stores/qnaStore.ts

@@ -0,0 +1,41 @@
+import {defineStore} from 'pinia'
+import {BufferMemory} from 'langchain/memory'
+import {markRaw} from 'vue'
+import {ConversationalRetrievalQAChain} from 'langchain/chains'
+import {getOpenAIChat} from '../lib/ai/config'
+import {useMultiFileStore} from './multiFileStore'
+
+const newBufferMemory = () => {
+  return new BufferMemory({
+    memoryKey: 'chat_history',
+    inputKey: 'question',
+    outputKey: 'text',
+    returnMessages: true
+  })
+}
+
+export const useQnaStore = defineStore('qnaStore', {
+  state: () => ({
+    memory: markRaw(newBufferMemory())
+  }),
+  actions: {
+    async performVectorStoreQna (args: {question:string}) {
+      const vs = useMultiFileStore().vectorStore
+
+      const model = getOpenAIChat()
+      const memory = this.memory
+      const chain = ConversationalRetrievalQAChain.fromLLM(model, vs.asRetriever(), {
+        returnSourceDocuments: true,
+        memory,
+        questionGeneratorChainOptions:{
+          llm: model
+        }
+      })
+      const res = await chain.call(args)
+      return res
+    },
+    resetChat() {
+      this.memory = markRaw(newBufferMemory())
+    }
+  }
+})

test/vitest/__tests__/retrieveQna.vitest.test.ts

@@ -6,6 +6,7 @@ import {useMultiFileStore} from 'stores/multiFileStore';
 import {createPinia, setActivePinia} from 'pinia';
 
 import {anyBufferToText} from 'src/lib/ai/unstructured';
+import {useQnaStore} from 'stores/qnaStore'
 
 describe('retrieveQna', () => {
   setActivePinia(createPinia())
@@ -37,7 +38,7 @@ describe('retrieveQna', () => {
 
   it('should answer from store', async () => {
     const msg = 'What is the answer to the ultimate question?'
-    const res = await performVectorStoreQna( {question:msg} )
+    const res = await useQnaStore().performVectorStoreQna( {question:msg} )
     expect (res.text).toContain('43')
   }, 20000)