feat: add queryMode option

package.json

@@ -74,7 +74,7 @@
     "@langchain/community": "^0.3.4",
     "@langchain/core": "^0.2.9",
     "@langchain/mistralai": "^0.0.28",
-    "@upstash/vector": "^1.1.3",
+    "@upstash/vector": "v1.2.0-canary-hybrid-3",
     "ai": "^3.1.1",
     "cheerio": "^1.0.0-rc.12",
     "d3-dsv": "^3.0.1",
@@ -89,7 +89,6 @@
     "@langchain/openai": "^0.2.8",
     "@upstash/ratelimit": "^1 || ^2",
     "@upstash/redis": "^1.34.0",
-    "@upstash/vector": "^1.1.5",
     "react": "^18 || ^19",
     "react-dom": "^18 || ^19"
   }

src/context-service/index.ts

@@ -89,6 +89,7 @@ export class ContextService {
           topK: optionsWithDefault.topK,
           namespace: optionsWithDefault.namespace,
           contextFilter: optionsWithDefault.contextFilter,
+          queryMode: optionsWithDefault.queryMode,
         });
 
         // Log the result, which will be captured by the outer traceable

src/database.ts

@@ -1,5 +1,5 @@
 import type { WebBaseLoaderParams } from "@langchain/community/document_loaders/web/cheerio";
-import type { Index } from "@upstash/vector";
+import type { Index, QueryMode } from "@upstash/vector";
 import type { RecursiveCharacterTextSplitterParams } from "langchain/text_splitter";
 import { nanoid } from "nanoid";
 import { DEFAULT_SIMILARITY_THRESHOLD, DEFAULT_TOP_K } from "./constants";
@@ -74,6 +74,7 @@ export type VectorPayload = {
   topK?: number;
   namespace?: string;
   contextFilter?: string;
+  queryMode?: QueryMode;
 };
 
 export type ResetOptions = {
@@ -108,6 +109,7 @@ export class Database {
     topK = DEFAULT_TOP_K,
     namespace,
     contextFilter,
+    queryMode,
   }: VectorPayload): Promise<{ data: string; id: string; metadata: TMetadata }[]> {
     const index = this.index;
     const result = await index.query<Record<string, string>>(
@@ -117,6 +119,7 @@ export class Database {
         includeData: true,
         includeMetadata: true,
         ...(typeof contextFilter === "string" && { filter: contextFilter }),
+        queryMode,
       },
       { namespace }
     );

src/rag-chat.test.ts

@@ -3,7 +3,7 @@ import { ChatOpenAI } from "@langchain/openai";
 import { openai } from "@ai-sdk/openai";
 import { Ratelimit } from "@upstash/ratelimit";
 import { Redis } from "@upstash/redis";
-import { Index } from "@upstash/vector";
+import { Index, QueryMode } from "@upstash/vector";
 import { LangChainAdapter, StreamingTextResponse } from "ai";
 import {
   afterAll,
@@ -49,6 +49,11 @@ describe("RAG Chat with advance configs and direct instances", () => {
     url: process.env.UPSTASH_REDIS_REST_URL!,
   });
 
+  const hybridVector = new Index({
+    url: process.env.HYBRID_EMBEDDING_UPSTASH_VECTOR_REST_URL!,
+    token: process.env.HYBRID_EMBEDDING_UPSTASH_VECTOR_REST_TOKEN!,
+  });
+
   const ragChat = new RAGChat({
     model: upstashOpenai("gpt-3.5-turbo"),
     vector,
@@ -69,6 +74,7 @@ describe("RAG Chat with advance configs and direct instances", () => {
     await vector.reset({ namespace });
     await vector.deleteNamespace(namespace);
     await redis.flushdb();
+    await hybridVector.reset({ all: true });
   });
 
   test("should get result without streaming", async () => {
@@ -89,6 +95,42 @@ describe("RAG Chat with advance configs and direct instances", () => {
       "Paris, the capital of France, is renowned for its iconic landmark, the Eiffel Tower, which was completed in 1889 and stands at 330 meters tall."
     );
   });
+
+  test("should retrieve with query mode", async () => {
+    const ragChat = new RAGChat({
+      vector: hybridVector,
+      streaming: true,
+      model: upstash("meta-llama/Meta-Llama-3-8B-Instruct"),
+    });
+
+    await ragChat.context.add({
+      type: "text",
+      data: "foo is bar",
+    });
+    await ragChat.context.add({
+      type: "text",
+      data: "foo is zed",
+    });
+    await awaitUntilIndexed(hybridVector);
+
+    const result = await ragChat.chat<{ unit: string }>("what is foo or bar?", {
+      topK: 1,
+      similarityThreshold: 0,
+      queryMode: QueryMode.SPARSE,
+      onContextFetched(context) {
+        expect(context.length).toBe(1);
+        return context;
+      },
+    });
+
+    expect(result.context).toEqual([
+      {
+        data: "foo is bar",
+        id: expect.any(String) as string,
+        metadata: undefined,
+      },
+    ]);
+  });
 });
 
 describe("RAG Chat with ratelimit", () => {

src/rag-chat.ts

@@ -292,6 +292,7 @@ export class RAGChat {
         ? DEFAULT_PROMPT_WITHOUT_RAG
         : (options?.promptFn ?? this.config.prompt),
       contextFilter: options?.contextFilter ?? undefined,
+      queryMode: options?.queryMode ?? undefined,
     };
   }
 }

src/types.ts

@@ -2,7 +2,7 @@ import type { ChatOpenAI } from "@langchain/openai";
 import type { openai } from "@ai-sdk/openai";
 import type { Ratelimit } from "@upstash/ratelimit";
 import type { Redis } from "@upstash/redis";
-import type { Index } from "@upstash/vector";
+import type { Index, QueryMode } from "@upstash/vector";
 import type { CustomPrompt } from "./rag-chat";
 import type { ChatMistralAI } from "@langchain/mistralai";
 import type { ChatAnthropic } from "@langchain/anthropic";
@@ -92,6 +92,14 @@ export type ChatOptions = {
    * https://upstash.com/docs/vector/features/filtering#metadata-filtering
    */
   contextFilter?: string;
+
+  /**
+   * Query mode to use when querying a hybrid index.
+   *
+   * This is useful if your index is a hybrid index and you want to query the
+   * sparse or dense part when you pass `data`.
+   */
+  queryMode?: QueryMode;
 } & CommonChatAndRAGOptions;
 
 export type PrepareChatResult = { data: string; id: string; metadata: unknown }[];