outsourced Chunk-Size & -Overlap; Fixed missing vectorDB after deletion

__tests__/manualREST/ollama.rest

@@ -104,10 +104,15 @@ Accept: application/json
 Content-Type: application/json
 
 {
-  "input": "Under what path could members of the working group can find the exam git directory?",
+  "input": "What's the diameter of mercury",
   "model": "llama3"
 }
 
+# {
+#   "input": "Under what path could members of the working group can find the exam git directory?",
+#   "model": "llama3"
+# }
+
 ### generate a follow up question
 # @name resumeChat
 POST {{host}}/ai/chat

controllers/Embeddings.js

@@ -5,7 +5,7 @@ import { ChromaClient } from "chromadb";
 // embeddings
 import { Chroma } from "@langchain/community/vectorstores/chroma";
 import { OllamaEmbeddings } from "@langchain/community/embeddings/ollama";
-import { RecursiveCharacterTextSplitter } from "langchain/text_splitter";
+import { CharacterTextSplitter, RecursiveCharacterTextSplitter } from "langchain/text_splitter";
 // loaders - https://js.langchain.com/v0.1/docs/modules/data_connection/document_loaders/
 import { DirectoryLoader } from "langchain/document_loaders/fs/directory";
 import {
@@ -18,7 +18,6 @@ import fs from 'fs';
 import path from 'path';
 import { PDFLoader } from '@langchain/community/document_loaders/fs/pdf';
 import { MultiFileLoader } from "langchain/document_loaders/fs/multi_file";
-import { ScoreThresholdRetriever } from 'langchain/retrievers/score_threshold';
 
 
 // PROVIDE OLLAMA CONNECTION
@@ -39,28 +38,29 @@ export const chromaVSsettings = {
     "hnsw:space": "cosine"
   }
 };
-// PROVIDE VECTOR STORE CONNECTION
-// predefine vectorStoreConnection in global scope
-let vectorStoreConnection;
+
+/** *******************************************************
+ * PROVIDE VECTOR STORE CONNECTION
+ */
+export const vectorStoreConnection = async () => {
   try {
     // check if cectorDB is reachable
     await chroma.heartbeat();
     // create connection
-  vectorStoreConnection = await Chroma.fromExistingCollection(embeddings, chromaVSsettings);
+    return await Chroma.fromExistingCollection(embeddings, chromaVSsettings);
   } catch (error) {
     // throw error if connection can't be established
     throw new Error(`Error creating VectorDB connection on ${process.env['VECTOR_API_URL']}`);
   }
-// export vectorStoreConnection
-export default vectorStoreConnection;
-// PROVIDE RETRIEVER
-export const retriever = vectorStoreConnection.asRetriever();
-// export const retriever = vectorStoreConnection.asRetriever(1);
-// export const retriever = ScoreThresholdRetriever.fromVectorStore(vectorStoreConnection, {
-//   minSimilarityScore: 0.1, // Finds results with at least this similarity score
-//   maxK: 100, // The maximum K value to use. Use it based to your chunk size to make sure you don't run out of tokens
-//   kIncrement: 2, // How much to increase K by each time. It'll fetch N results, then N + kIncrement, then N + kIncrement * 2, etc.
-// });
+};
+
+/** *******************************************************
+ * PROVIDE RETRIEVER
+ */
+export const retriever = async () => {
+  let vectorStore = await vectorStoreConnection();
+  return vectorStore.asRetriever();
+};
 
 
 
@@ -362,8 +362,19 @@ export const fileLoader = async (docs = []) => {
 * EMBED GIVEN DOCS
 */
 export const embedder = async (docs) => {
+
+  console.log("🚀 ~ embedder ~ process.env.CHUNK_SIZE:", process.env.CHUNK_SIZE);
+  console.log("🚀 ~ embedder ~ process.env.CHUNK_OVERLAP:", process.env.CHUNK_OVERLAP);
+
+
   // chunk docs 
-  const splitter = new RecursiveCharacterTextSplitter();
+  const splitter = new RecursiveCharacterTextSplitter({
+    chunkSize: Number(process.env.CHUNK_SIZE),
+    chunkOverlap: Number(process.env.CHUNK_OVERLAP),
+    separators: ['\n\n', '\n', ' ', '']
+  });
+
+
   const chunks = await splitter.splitDocuments(docs);
 
   // add unix timestamp to metadata of all chunks

utils/handleAI.js

@@ -2,13 +2,14 @@ import urlExist from "url-exist";
 import { Ollama } from 'ollama';
 import { ChatOllama } from "@langchain/community/chat_models/ollama";
 import { ChatPromptTemplate, PromptTemplate, MessagesPlaceholder } from "@langchain/core/prompts";
-import { isCollectionAvailable, retriever } from "../controllers/Embeddings.js";
+import { isCollectionAvailable, retriever, vectorStoreConnection } from "../controllers/Embeddings.js";
 import { createHistoryAwareRetriever } from "langchain/chains/history_aware_retriever";
 import { createStuffDocumentsChain } from "langchain/chains/combine_documents";
 import { createRetrievalChain } from "langchain/chains/retrieval";
 import { HumanMessage, AIMessage } from "@langchain/core/messages";
 import { extendChat } from "./handleDB.js";
 import * as path from 'path';
+import { MultiQueryRetriever } from "langchain/retrievers/multi_query";
 
 
 // PROVIDE OLLAMA CONNECTION TO ALL ROUTES
@@ -103,7 +104,31 @@ export const summarizeText = async (model, input) => {
 /** *******************************************************
  * GENERATE CHAT
  */
+
 export const chat = async (req, res, next) => {
+  // create chat model
+  const llm = new ChatOllama({
+    baseUrl: process.env['AI_API_URL'],
+    model: req.body.model,
+    temperature: Number(process.env['AI_TEMPERATURE'])
+  });
+
+  let vectorStore = await vectorStoreConnection();
+
+  const retriever = MultiQueryRetriever.fromLLM({
+    llm: llm,
+    retriever: vectorStore.asRetriever(),
+    verbose: true,
+  });
+
+  const query = req.body.input;
+  const retrievedDocs = await retriever.invoke(query);
+
+  res.json({ message: 'hi', retrievedDocs });
+};
+
+
+export const chat_OLD = async (req, res, next) => {
   // FEATURE apply citations or sources
   // sources: https://js.langchain.com/v0.1/docs/use_cases/question_answering/sources/#adding-sources
   // citations: https://js.langchain.com/v0.1/docs/use_cases/question_answering/citations/