import { FluxClient, SimpleKeyAuth } from '@foxnose/sdk';
import { FoxNoseRetriever } from '@foxnose/langchain';

const client = new FluxClient({
  baseUrl: 'https://<env_key>.fxns.io',
  apiPrefix: 'content',
  auth: new SimpleKeyAuth('YOUR_PUBLIC_KEY', 'YOUR_SECRET_KEY'),
});

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'hybrid',
  topK: 5,
});

const docs = await retriever.invoke('How do I reset my password?');
for (const doc of docs) {
  console.log(doc.pageContent.slice(0, 200));
}

import { FoxNoseLoader } from '@foxnose/langchain';

const loader = new FoxNoseLoader({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  batchSize: 50,
});

const docs = await loader.load();
console.log(`Loaded ${docs.length} documents`);

import { createFoxNoseTool } from '@foxnose/langchain';

const tool = createFoxNoseTool({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  name: 'search_knowledge_base',
  description: 'Search the knowledge base for relevant articles.',
});

// Use with any LangChain agent
const result = await tool.invoke({ query: 'vector search best practices' });

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'vector',
  topK: 10,
  similarityThreshold: 0.7,
});

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'hybrid',
  hybridConfig: {
    vectorWeight: 0.7,
    textWeight: 0.3,
    rerankResults: true,
  },
});

import { OpenAIEmbeddings } from '@langchain/openai';

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'vector',
  embeddings: new OpenAIEmbeddings({ model: 'text-embedding-3-small' }),
  vectorField: 'embedding',
  topK: 10,
});

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'vector',
  queryVector: [0.1, 0.2, 0.3, /* ... */],
  vectorField: 'embedding',
});

const retriever = new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  searchMode: 'hybrid',
  where: {
    $: {
      all_of: [
        { status__eq: 'published' },
        { category__in: ['tech', 'science'] },
      ],
    },
  },
});

new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
});

new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentFields: ['title', 'body'],
  pageContentSeparator: '\n\n',  // default
});

new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentMapper: (result) =>
    `# ${result.data?.title}\n\n${result.data?.body}`,
});

// Whitelist specific fields
new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  metadataFields: ['title', 'category'],
});

// Blacklist specific fields
new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  excludeMetadataFields: ['internal_notes'],
});

// Disable system metadata (_sys fields)
new FoxNoseRetriever({
  client,
  folderPath: 'articles',
  pageContentField: 'body',
  includeSysMetadata: false,
});