feedback

Author: correa-brian

client/Chat.jsx

@@ -38,7 +38,7 @@ const Chat = () => {
 
       try {
         setLoading(true);
-          const response = await chatServices.chatWithLLM({ userInput });
+          const { response } = await chatServices.chatWithLLM({ userInput });
           setAnswer(response);
         } catch (err) {
           setError(err);

server/handlers/chat_handler.js

@@ -1,12 +1,4 @@
-import path from 'path';
-import { HNSWLib } from "langchain/vectorstores/hnswlib";
-import { RetrievalQAChain } from 'langchain/chains';
-import { OpenAIEmbeddings } from "langchain/embeddings/openai";
-import { ContextualCompressionRetriever } from "langchain/retrievers/contextual_compression";
-import { LLMChainExtractor } from "langchain/retrievers/document_compressors/chain_extract";
-
 import { MODEL_STORES, enabledModel } from '../config/model_store_constants.js';
-import { getFileLoader } from '../utils/documentLoader.js';
 
 class ChatService {
   constructor () {
@@ -20,31 +12,7 @@ class ChatService {
   }
 
   async ingestFile(data) {
-    const { files } = data;
-    const { originalFilename, filepath } = files['chat-file'];
-    const fileExtension = path.extname(originalFilename);
-    
-    const loader = getFileLoader(fileExtension, filepath);
-    if (!loader) {
-      throw Error('bad');
-    }
-
-    const docs = await loader.load();
-    this.modelService.vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
-
-    const baseCompressor = LLMChainExtractor.fromLLM(this.modelService.model);
-    this.modelService.vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
-    this.modelService.retriever = new ContextualCompressionRetriever({
-      baseCompressor,
-      baseRetriever: this.modelService.vectorStore.asRetriever(),
-    });
-
-    this.modelService.chain = RetrievalQAChain.fromLLM(
-      this.modelService.model, 
-      this.modelService.retriever, 
-      { returnSourceDocuments: true }
-    );
-    return { success: true };
+    return this.modelService.ingestFile(data);
   }
 }
 

server/services/hf.js

@@ -8,6 +8,10 @@ class HuggingFaceService {
       this.model = new HfInference(HUGGINGFACEHUB_API_KEY);
     }
 
+    ingestFile() {
+      console.log('this is not implement for HuggingFace yet');
+    }
+
     async call(userInput) {
       // TO DO: pass in past_user_inputs for context
       const response = await this.model.conversational({

server/services/openai.js

@@ -1,7 +1,14 @@
+import path from 'path';
+import { HNSWLib } from "langchain/vectorstores/hnswlib";
+import { RetrievalQAChain } from 'langchain/chains';
+import { OpenAIEmbeddings } from "langchain/embeddings/openai";
+import { ContextualCompressionRetriever } from "langchain/retrievers/contextual_compression";
+import { LLMChainExtractor } from "langchain/retrievers/document_compressors/chain_extract";
 import { ConversationChain } from 'langchain/chains';
 import { PromptTemplate } from 'langchain/prompts';
 import { ConversationSummaryMemory } from 'langchain/memory';
 import { OpenAI } from 'langchain/llms/openai';
+import { getFileLoader } from '../utils/documentLoader.js';
 
 class OpenAiService {
   constructor () {
@@ -35,14 +42,42 @@ class OpenAiService {
     return { chain, inputType: 'input', responseType: 'response' };
   }
 
+  async ingestFile(data) {
+    const { files } = data;
+    const { originalFilename, filepath } = files['chat-file'];
+    const fileExtension = path.extname(originalFilename);
+    
+    const loader = getFileLoader(fileExtension, filepath);
+    if (!loader) {
+      throw Error('bad');
+    }
+
+    const docs = await loader.load();
+    this.vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
+
+    const baseCompressor = LLMChainExtractor.fromLLM(this.model);
+    this.vectorStore = await HNSWLib.fromDocuments(docs, new OpenAIEmbeddings());
+    this.retriever = new ContextualCompressionRetriever({
+      baseCompressor,
+      baseRetriever: this.vectorStore.asRetriever(),
+    });
+
+    this.chain = RetrievalQAChain.fromLLM(
+      this.model, 
+      this.retriever, 
+      { returnSourceDocuments: true }
+    );
+    return { success: true };
+  }
+
   call = async (userInput) => {  
     const { chain, inputType, responseType } = this.assembleChain();
 
     const { [responseType]: response } = await chain.call({
       [inputType]: userInput,
     });
 
-   return response;
+   return { response };
   }
 }