Upload 3 files

rag/qdrant.js

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+const { QdrantVectorStore } = require("@langchain/qdrant");
+const { OpenAIEmbeddings } = require("@langchain/openai");
+const { QdrantClient } = require("@qdrant/js-client-rest");
+
+let client = null;
+
+try {
+  const url = process.env.QDRANT_URL;
+  if (url && (url.startsWith('http://') || url.startsWith('https://'))) {
+      client = new QdrantClient({
+        url: url,
+        apiKey: process.env.QDRANT_API_KEY,
+      });
+      console.log("Qdrant Client Initialized Successfully with URL:", url);
+  } else {
+      console.warn("Warning: Invalid or missing QDRANT_URL. RAG functionality will be disabled.");
+  }
+} catch (e) {
+  console.error("Failed to initialize Qdrant Client:", e.message);
+}
+
+async function getVectorStore(collectionName) {
+  if (!client) {
+      throw new Error("Qdrant client is not initialized. Check QDRANT_URL in .env");
+  }
+
+  const embeddings = new OpenAIEmbeddings({
+    modelName: "text-embedding-3-small", 
+  });
+
+  return await QdrantVectorStore.fromExistingCollection(
+    embeddings,
+    {
+      client,
+      collectionName,
+    }
+  ).catch(async (e) => {
+      // If error is about missing collection, try to create it
+      try {
+        const collections = await client.getCollections();
+        const exists = collections.collections.some(c => c.name === collectionName);
+        
+        if (!exists) {
+            console.log(`Creating collection: ${collectionName}`);
+            await client.createCollection(collectionName, {
+                vectors: {
+                    size: 1536, // OpenAI text-embedding-3-small dimension
+                    distance: 'Cosine'
+                }
+            });
+        }
+      } catch (err) {
+        console.warn(`Could not verify/create collection '${collectionName}':`, err.message);
+        // Fallthrough to attempt returning the store anyway or re-throw
+      }
+
+    return new QdrantVectorStore(embeddings, {
+      client,
+      collectionName,
+    });
+  });
+}
+
+module.exports = { getVectorStore, client };

rag/researchRag.js

@@ -0,0 +1,52 @@
+const { getVectorStore } = require('./qdrant');
+const { RecursiveCharacterTextSplitter } = require("@langchain/textsplitters");
+const { model } = require('../model');
+const { HumanMessage, SystemMessage } = require("@langchain/core/messages");
+
+const COLLECTION_NAME = "research_collection";
+
+async function storeResearch(text) {
+  try {
+    const vectorStore = await getVectorStore(COLLECTION_NAME);
+    const splitter = new RecursiveCharacterTextSplitter({
+      chunkSize: 500,
+      chunkOverlap: 50,
+    });
+    
+    const documents = await splitter.createDocuments([text]);
+    await vectorStore.addDocuments(documents);
+    console.log(`[Research RAG] Stored ${documents.length} chunks.`);
+  } catch (error) {
+    console.error("[Research RAG] Storage Error:", error);
+  }
+}
+
+async function chatResearch(query, history = []) {
+    try {
+        const vectorStore = await getVectorStore(COLLECTION_NAME);
+        const results = await vectorStore.similaritySearch(query, 3);
+        const context = results.map(r => r.pageContent).join("\n\n");
+
+        const systemPrompt = `You are a Research Assistant. Answer the user's question based strictly on the provided context.
+        
+        Context:
+        ${context}`;
+
+        // Convert simple history to messages if needed, here assuming just query for now or simple chat
+        // Ideally we append history messages.
+        const messages = [
+            new SystemMessage(systemPrompt),
+            ...history.map(msg => msg.role === 'user' ? new HumanMessage(msg.content) : new SystemMessage(msg.content)), // simplified
+            new HumanMessage(query)
+        ];
+
+        const response = await model.invoke(messages);
+        return response.content;
+
+    } catch (error) {
+        console.error("[Research RAG] Chat Error:", error);
+        return "I encountered an error accessing the research data.";
+    }
+}
+
+module.exports = { storeResearch, chatResearch };

rag/synthesisRag.js

@@ -0,0 +1,50 @@
+const { getVectorStore } = require('./qdrant');
+const { RecursiveCharacterTextSplitter } = require("@langchain/textsplitters");
+const { model } = require('../model');
+const { HumanMessage, SystemMessage } = require("@langchain/core/messages");
+
+const COLLECTION_NAME = "synthesis_collection";
+
+async function storeInsights(text) {
+  try {
+    const vectorStore = await getVectorStore(COLLECTION_NAME);
+    const splitter = new RecursiveCharacterTextSplitter({
+      chunkSize: 500,
+      chunkOverlap: 50,
+    });
+    
+    const documents = await splitter.createDocuments([text]);
+    await vectorStore.addDocuments(documents);
+    console.log(`[Synthesis RAG] Stored ${documents.length} chunks.`);
+  } catch (error) {
+    console.error("[Synthesis RAG] Storage Error:", error);
+  }
+}
+
+async function chatInsights(query, history = []) {
+    try {
+        const vectorStore = await getVectorStore(COLLECTION_NAME);
+        const results = await vectorStore.similaritySearch(query, 3);
+        const context = results.map(r => r.pageContent).join("\n\n");
+
+        const systemPrompt = `You are a Strategy Consultant. Answer the user's question based strictly on the provided context.
+        
+        Context:
+        ${context}`;
+
+        const messages = [
+            new SystemMessage(systemPrompt),
+            ...history.map(msg => msg.role === 'user' ? new HumanMessage(msg.content) : new SystemMessage(msg.content)),
+            new HumanMessage(query)
+        ];
+
+        const response = await model.invoke(messages);
+        return response.content;
+
+    } catch (error) {
+        console.error("[Synthesis RAG] Chat Error:", error);
+        return "I encountered an error accessing the strategy insights.";
+    }
+}
+
+module.exports = { storeInsights, chatInsights };