add document search

apps/web/app/(app)/environments/[environmentId]/documents/actions.ts

@@ -0,0 +1,40 @@
+"use server";
+
+import { embed, generateText } from "ai";
+import { z } from "zod";
+import { authenticatedActionClient } from "@formbricks/lib/actionClient";
+import { checkAuthorization } from "@formbricks/lib/actionClient/utils";
+import { embeddingsModel, llmModel } from "@formbricks/lib/ai";
+import { findNearestDocuments, getDocumentsByTypeAndReferenceId } from "@formbricks/lib/document/service";
+import { getQuestionResponseReferenceId } from "@formbricks/lib/document/utils";
+import { getOrganizationIdFromEnvironmentId } from "@formbricks/lib/organization/utils";
+import { ZId } from "@formbricks/types/environment";
+
+const ZSearchDocuments = z.object({
+  environmentId: ZId,
+  searchTerm: z.string(),
+});
+
+export const searchDocumentsAction = authenticatedActionClient
+  .schema(ZSearchDocuments)
+  .action(async ({ ctx, parsedInput }) => {
+    await checkAuthorization({
+      userId: ctx.user.id,
+      organizationId: await getOrganizationIdFromEnvironmentId(parsedInput.environmentId),
+      rules: ["environment", "read"],
+    });
+
+    const { text } = await generateText({
+      model: llmModel,
+      prompt: `Generate 4 example survey feedback responses from users in one line, separated by comma for the following topic: ${parsedInput.searchTerm}`,
+    });
+
+    const { embedding } = await embed({
+      model: embeddingsModel,
+      value: text,
+    });
+
+    const documents = findNearestDocuments(parsedInput.environmentId, embedding, 10);
+
+    return documents;
+  });

apps/web/app/(app)/environments/[environmentId]/documents/components/DocumentSearch.tsx

@@ -0,0 +1,55 @@
+"use client";
+
+import { useState } from "react";
+import { TDocument } from "@formbricks/types/documents";
+import { Button } from "@formbricks/ui/Button";
+import { Card } from "@formbricks/ui/Card";
+import { Input } from "@formbricks/ui/Input";
+import { searchDocumentsAction } from "../actions";
+
+interface DocumentSearchProps {
+  environmentId: string;
+}
+
+export const DocumentSearch = ({ environmentId }: DocumentSearchProps) => {
+  const [searchTerm, setSearchTerm] = useState("");
+  const [documents, setDocuments] = useState<TDocument[]>([]);
+
+  const searchDocuments = async () => {
+    const documents = await searchDocumentsAction({ environmentId, searchTerm });
+    if (documents?.data) {
+      setDocuments(documents.data);
+    } else {
+      console.error(documents);
+    }
+  };
+
+  return (
+    <>
+      <form
+        onSubmit={(e) => {
+          e.preventDefault();
+          searchDocuments();
+        }}
+        className="flex w-full space-x-2">
+        <Input
+          placeholder="What are users thinking the performance of my app?"
+          value={searchTerm}
+          onChange={(v) => setSearchTerm(v.target.value)}
+        />
+        <Button className="h-10">Search</Button>
+      </form>
+      <div className="flex-col space-y-4">
+        {documents.map((document) => (
+          <div className="overflow-hidden rounded-lg bg-white shadow">
+            <div className="whitespace-pre-wrap px-4 py-5 sm:p-6">
+              <p>{document.text}</p>
+              <hr className="my-4 text-slate-300" />
+              <p className="text-xs">Survey Response</p>
+            </div>
+          </div>
+        ))}
+      </div>
+    </>
+  );
+};

apps/web/app/(app)/environments/[environmentId]/documents/loading.tsx

@@ -0,0 +1,7 @@
+import { LoadingSpinner } from "@formbricks/ui/LoadingSpinner";
+
+const Loading = () => {
+  return <LoadingSpinner />;
+};
+
+export default Loading;

apps/web/app/(app)/environments/[environmentId]/documents/page.tsx

@@ -0,0 +1,45 @@
+import { DocumentSearch } from "@/app/(app)/environments/[environmentId]/documents/components/DocumentSearch";
+import { Metadata } from "next";
+import { getServerSession } from "next-auth";
+import { authOptions } from "@formbricks/lib/authOptions";
+import { getOrganizationByEnvironmentId } from "@formbricks/lib/organization/service";
+import { getProductByEnvironmentId } from "@formbricks/lib/product/service";
+import { getUser } from "@formbricks/lib/user/service";
+import { TTemplateRole } from "@formbricks/types/templates";
+import { PageContentWrapper } from "@formbricks/ui/PageContentWrapper";
+import { PageHeader } from "@formbricks/ui/PageHeader";
+
+export const metadata: Metadata = {
+  title: "Your Surveys",
+};
+
+interface SurveyTemplateProps {
+  params: {
+    environmentId: string;
+  };
+  searchParams: {
+    role?: TTemplateRole;
+  };
+}
+
+const Page = async ({ params, searchParams }: SurveyTemplateProps) => {
+  const session = await getServerSession(authOptions);
+
+  if (!session) {
+    throw new Error("Session not found");
+  }
+
+  const user = await getUser(session.user.id);
+  if (!user) {
+    throw new Error("User not found");
+  }
+
+  return (
+    <PageContentWrapper>
+      <PageHeader pageTitle="Documents" />
+      <DocumentSearch environmentId={params.environmentId} />
+    </PageContentWrapper>
+  );
+};
+
+export default Page;

packages/lib/document/service.ts

@@ -9,6 +9,7 @@ import {
   ZDocumentCreateInput,
   ZDocumentType,
 } from "@formbricks/types/documents";
+import { ZId } from "@formbricks/types/environment";
 import { DatabaseError } from "@formbricks/types/errors";
 import { cache } from "../cache";
 import { validateInputs } from "../utils/validate";
@@ -72,9 +73,9 @@ export const getDocumentsByTypeAndReferenceId = reactCache(
               text,
               "referenceId",
               vector::text
-            FROM "Document" e
-            WHERE e."type" = ${type}::"DocumentType"
-              AND e."referenceId" = ${referenceId}
+            FROM "Document" d
+            WHERE d."type" = ${type}::"DocumentType"
+              AND d."referenceId" = ${referenceId}
           `;
 
           const documents = prismaDocuments.map((prismaDocument) => {
@@ -104,3 +105,45 @@ export const getDocumentsByTypeAndReferenceId = reactCache(
       }
     )()
 );
+
+export const findNearestDocuments = async (
+  environmentId: string,
+  vector: number[],
+  limit: number = 5
+): Promise<TDocument[]> => {
+  validateInputs([environmentId, ZId]);
+  const threshold = 0.8; //0.2;
+  // Convert the embedding array to a JSON-like string representation
+  const vectorString = `[${vector.join(",")}]`;
+
+  // Execute raw SQL query to find nearest neighbors and exclude the vector column
+  const prismaDocuments: TPrismaDocument[] = await prisma.$queryRaw`
+    SELECT
+      id,
+      created_at AS "createdAt",
+      updated_at AS "updatedAt",
+      type,
+      text,
+      "referenceId",
+      vector::text
+    FROM "Document" d
+    WHERE d."environmentId" = ${environmentId}
+      AND d."vector" <=> ${vectorString}::vector(512) <= ${threshold}
+    ORDER BY d."vector" <=> ${vectorString}::vector(512)
+    LIMIT ${limit};
+  `;
+
+  const documents = prismaDocuments.map((prismaDocument) => {
+    // Convert the string representation of the vector back to an array of numbers
+    const vector = prismaDocument.vector
+      .slice(1, -1) // Remove the surrounding square brackets
+      .split(",") // Split the string into an array of strings
+      .map(Number); // Convert each string to a number
+    return {
+      ...prismaDocument,
+      vector,
+    };
+  });
+
+  return documents;
+};