Documents API

The Documents API allows you to manage, index, and query unstructured data in your Langchain applications. This enables powerful knowledge retrieval, question answering, and content generation based on your custom document collections.

Overview

The Documents API provides a complete solution for:

Uploading and storing documents in various formats
Processing and chunking documents into manageable pieces
Creating and managing document collections
Generating embeddings for semantic search
Retrieving relevant documents based on queries
Building knowledge-powered applications

Document Concepts

Documents

In Langchain, a document is a unit of text with associated metadata. Documents are the basic building blocks for knowledge retrieval. Each document has:

Content: The text content of the document
Metadata: Additional information about the document (source, author, date, etc.)
ID: A unique identifier

Collections

Collections are groups of related documents that have been processed, chunked, and indexed for retrieval. Collections allow you to:

Organize documents by topic, source, or purpose
Apply specific embedding and retrieval settings
Query across related documents efficiently
Manage access and permissions

Embeddings

Embeddings are vector representations of document content that capture semantic meaning. The Langchain API:

Generates embeddings for documents
Stores embeddings in vector databases
Enables semantic similarity search
Supports multiple embedding models

Retrievers

Retrievers are components that fetch relevant documents from collections based on queries. They:

Transform queries into the same vector space as documents
Find semantically similar documents to the query
Apply filters and ranking to results
Return relevant documents with their content and metadata

API Reference

Create Collection

POST https://langchain.moodmnky.com/api/collections
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

Request body:

{
  "name": "Product Documentation",
  "description": "Technical documentation for MOOD MNKY products",
  "embeddingModel": "text-embedding-ada-002",
  "metadata": {
    "category": "technical",
    "department": "support"
  }
}

Response:

{
  "collectionId": "col_01h9f5zk4r9s7l3z8u2y",
  "name": "Product Documentation",
  "description": "Technical documentation for MOOD MNKY products",
  "embeddingModel": "text-embedding-ada-002",
  "documentCount": 0,
  "created": "2023-10-15T14:30:00Z",
  "metadata": {
    "category": "technical",
    "department": "support"
  }
}

Get Collection

GET https://langchain.moodmnky.com/api/collections/{collectionId}
Authorization: Bearer YOUR_API_KEY

Response:

{
  "collectionId": "col_01h9f5zk4r9s7l3z8u2y",
  "name": "Product Documentation",
  "description": "Technical documentation for MOOD MNKY products",
  "embeddingModel": "text-embedding-ada-002",
  "documentCount": 12,
  "created": "2023-10-15T14:30:00Z",
  "lastUpdated": "2023-10-15T16:45:22Z",
  "metadata": {
    "category": "technical",
    "department": "support"
  }
}

List Collections

GET https://langchain.moodmnky.com/api/collections
Authorization: Bearer YOUR_API_KEY

Response:

{
  "collections": [
    {
      "collectionId": "col_01h9f5zk4r9s7l3z8u2y",
      "name": "Product Documentation",
      "description": "Technical documentation for MOOD MNKY products",
      "documentCount": 12,
      "created": "2023-10-15T14:30:00Z"
    },
    {
      "collectionId": "col_02h9g6al5s0t8m4a9v3z",
      "name": "Customer FAQs",
      "description": "Frequently asked questions and answers",
      "documentCount": 45,
      "created": "2023-10-10T09:15:23Z"
    }
  ],
  "pagination": {
    "total": 5,
    "limit": 10,
    "offset": 0,
    "nextOffset": null
  }
}

Upload Document

POST https://langchain.moodmnky.com/api/collections/{collectionId}/documents
Content-Type: multipart/form-data
Authorization: Bearer YOUR_API_KEY

Form data:

file: The document file (PDF, DOCX, TXT, etc.)
metadata (optional): JSON string of metadata
chunkSize (optional): Size of chunks in tokens (default: 1000)
chunkOverlap (optional): Overlap between chunks in tokens (default: 200)

Response:

{
  "documentId": "doc_03j0h7bm6t1u9n5c0w4d",
  "originalFilename": "product_manual.pdf",
  "mimeType": "application/pdf",
  "chunkCount": 15,
  "status": "processing",
  "metadata": {
    "source": "internal",
    "product": "scented_candle",
    "version": "2.1"
  }
}

Add Text Document

POST https://langchain.moodmnky.com/api/collections/{collectionId}/texts
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

Request body:

{
  "text": "MOOD MNKY scented candles are made with 100% natural soy wax and premium essential oils. Each candle is hand-poured and designed to provide an exceptional fragrance experience with a clean, long-lasting burn.",
  "metadata": {
    "source": "product_description",
    "category": "candles",
    "author": "marketing_team"
  },
  "chunkSize": 500,
  "chunkOverlap": 50
}

Response:

{
  "documentId": "doc_04k1i8cn7u2v0o6d1x5e",
  "chunks": 1,
  "status": "complete",
  "metadata": {
    "source": "product_description",
    "category": "candles",
    "author": "marketing_team"
  }
}

Get Document

GET https://langchain.moodmnky.com/api/documents/{documentId}
Authorization: Bearer YOUR_API_KEY

Response:

{
  "documentId": "doc_03j0h7bm6t1u9n5c0w4d",
  "collectionId": "col_01h9f5zk4r9s7l3z8u2y",
  "originalFilename": "product_manual.pdf",
  "mimeType": "application/pdf",
  "chunkCount": 15,
  "status": "complete",
  "created": "2023-10-15T15:12:33Z",
  "lastUpdated": "2023-10-15T15:13:45Z",
  "metadata": {
    "source": "internal",
    "product": "scented_candle",
    "version": "2.1"
  }
}

List Documents in Collection

GET https://langchain.moodmnky.com/api/collections/{collectionId}/documents
Authorization: Bearer YOUR_API_KEY

Response:

{
  "documents": [
    {
      "documentId": "doc_03j0h7bm6t1u9n5c0w4d",
      "originalFilename": "product_manual.pdf",
      "chunkCount": 15,
      "status": "complete",
      "created": "2023-10-15T15:12:33Z"
    },
    {
      "documentId": "doc_04k1i8cn7u2v0o6d1x5e",
      "originalFilename": null,
      "chunkCount": 1,
      "status": "complete",
      "created": "2023-10-15T16:20:15Z"
    }
  ],
  "pagination": {
    "total": 12,
    "limit": 10,
    "offset": 0,
    "nextOffset": 10
  }
}

Delete Document

DELETE https://langchain.moodmnky.com/api/documents/{documentId}
Authorization: Bearer YOUR_API_KEY

Response:

{
  "success": true,
  "message": "Document deleted successfully"
}

Query Collection

POST https://langchain.moodmnky.com/api/collections/{collectionId}/query
Content-Type: application/json
Authorization: Bearer YOUR_API_KEY

Request body:

{
  "query": "How are the scented candles made?",
  "topK": 3,
  "filter": {
    "metadata": {
      "category": "candles"
    }
  },
  "includeMetadata": true
}

Response:

{
  "results": [
    {
      "documentId": "doc_04k1i8cn7u2v0o6d1x5e",
      "content": "MOOD MNKY scented candles are made with 100% natural soy wax and premium essential oils. Each candle is hand-poured and designed to provide an exceptional fragrance experience with a clean, long-lasting burn.",
      "score": 0.89,
      "metadata": {
        "source": "product_description",
        "category": "candles",
        "author": "marketing_team"
      }
    },
    {
      "documentId": "doc_03j0h7bm6t1u9n5c0w4d",
      "content": "Production Process: Our artisanal candles are crafted through a meticulous 8-step process. First, natural soy wax is melted to precisely 180°F. Next, premium essential oils are carefully blended according to our perfumer's formulas to create signature scents.",
      "score": 0.78,
      "metadata": {
        "source": "internal",
        "product": "scented_candle",
        "version": "2.1"
      }
    },
    {
      "documentId": "doc_03j0h7bm6t1u9n5c0w4d",
      "content": "Materials: MOOD MNKY uses only the highest quality materials in our candles. The wax is 100% natural soy, sourced from sustainable American farms. The wicks are cotton, lead-free, and designed for an even burn. Our essential oils are premium grade and sourced from global suppliers known for their quality and ethical practices.",
      "score": 0.72,
      "metadata": {
        "source": "internal",
        "product": "scented_candle",
        "version": "2.1"
      }
    }
  ]
}

Delete Collection

DELETE https://langchain.moodmnky.com/api/collections/{collectionId}
Authorization: Bearer YOUR_API_KEY

Response:

{
  "success": true,
  "message": "Collection deleted successfully"
}

Implementation Examples

Creating and Populating a Collection

// Creating a document collection
async function createProductCollection() {
  const response = await fetch('https://langchain.moodmnky.com/api/collections', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      name: "Candle Product Knowledge Base",
      description: "Documentation about our candle products, manufacturing, and care instructions",
      embeddingModel: "text-embedding-ada-002",
      metadata: {
        department: "product",
        category: "candles",
        purpose: "customer_support"
      }
    })
  });
  
  return await response.json();
}

// Adding a text document to a collection
async function addTextToCollection(collectionId, text, metadata) {
  const response = await fetch(`https://langchain.moodmnky.com/api/collections/${collectionId}/texts`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      text: text,
      metadata: metadata,
      chunkSize: 500,
      chunkOverlap: 50
    })
  });
  
  return await response.json();
}

// Uploading a file to a collection
async function uploadFileToCollection(collectionId, file, metadata) {
  const formData = new FormData();
  formData.append('file', file);
  formData.append('metadata', JSON.stringify(metadata));
  formData.append('chunkSize', '1000');
  formData.append('chunkOverlap', '200');
  
  const response = await fetch(`https://langchain.moodmnky.com/api/collections/${collectionId}/documents`, {
    method: 'POST',
    headers: {
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: formData
  });
  
  return await response.json();
}

// Example usage
async function setupProductKnowledgeBase() {
  // Create a new collection
  const collection = await createProductCollection();
  console.log('Created collection:', collection.collectionId);
  
  // Add product descriptions as text
  const productDescriptions = [
    {
      text: "Lavender Dreams Candle: Our bestselling candle combines the soothing essence of lavender with subtle notes of vanilla and bergamot. Made with 100% soy wax and essential oils, this candle offers 50+ hours of burn time and promotes relaxation and stress relief.",
      metadata: {
        product_id: "LD-100",
        category: "relaxation",
        scent_family: "floral",
        price_range: "premium"
      }
    },
    {
      text: "Ocean Breeze Candle: Transform your space with the fresh, clean scent of ocean air. This invigorating blend combines sea salt, citrus, and light floral notes to create a refreshing atmosphere. Each candle is hand-poured using sustainable soy wax and cotton wicks.",
      metadata: {
        product_id: "OB-200",
        category: "refreshing",
        scent_family: "fresh",
        price_range: "standard"
      }
    }
  ];
  
  for (const product of productDescriptions) {
    await addTextToCollection(collection.collectionId, product.text, product.metadata);
  }
  
  // Upload care instructions PDF
  const careGuideFile = new File(
    // You would typically get this from a file input element
    [/* file content */], 
    "candle_care_guide.pdf", 
    { type: "application/pdf" }
  );
  
  await uploadFileToCollection(
    collection.collectionId,
    careGuideFile,
    {
      document_type: "care_instructions",
      applies_to: "all_candles",
      version: "2023.1"
    }
  );
  
  console.log('Knowledge base setup complete');
}

Querying a Collection

// Querying a collection for relevant documents
async function queryCollection(collectionId, queryText, filters = {}, topK = 3) {
  const response = await fetch(`https://langchain.moodmnky.com/api/collections/${collectionId}/query`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      query: queryText,
      topK: topK,
      filter: filters,
      includeMetadata: true
    })
  });
  
  return await response.json();
}

// Example usage in a customer support chatbot
async function answerProductQuestion(collectionId, question) {
  // Step 1: Retrieve relevant documents
  const queryResults = await queryCollection(collectionId, question);
  
  // Step 2: Format the retrieved contexts for the LLM
  let context = "Information about our products:\n\n";
  queryResults.results.forEach((result, index) => {
    context += `[${index + 1}] ${result.content}\n\n`;
  });
  
  // Step 3: Generate an answer using the retrieved information
  const response = await fetch('https://langchain.moodmnky.com/api/chains/run', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      chainId: 'chain_qa_with_sources',
      inputs: {
        question: question,
        context: context
      }
    })
  });
  
  return await response.json();
}

// Example: Answer a customer question
async function handleCustomerQuery() {
  const question = "What ingredients are in the Lavender Dreams candle?";
  const collectionId = "col_01h9f5zk4r9s7l3z8u2y"; // Product Knowledge Base
  
  const answer = await answerProductQuestion(collectionId, question);
  console.log('Customer question:', question);
  console.log('Answer:', answer.outputs.answer);
  console.log('Sources:', answer.outputs.sources);
}

Building a Document Search Interface

// Class for managing document search
class DocumentSearchManager {
  private apiKey: string;
  private baseUrl: string;
  
  constructor(apiKey: string) {
    this.apiKey = apiKey;
    this.baseUrl = 'https://langchain.moodmnky.com/api';
  }
  
  async getCollections() {
    const response = await fetch(`${this.baseUrl}/collections`, {
      headers: {
        'Authorization': `Bearer ${this.apiKey}`
      }
    });
    
    return await response.json();
  }
  
  async search(collectionId: string, query: string, filters: any = {}, topK: number = 5) {
    const response = await fetch(`${this.baseUrl}/collections/${collectionId}/query`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${this.apiKey}`
      },
      body: JSON.stringify({
        query: query,
        topK: topK,
        filter: filters,
        includeMetadata: true
      })
    });
    
    return await response.json();
  }
  
  async uploadDocument(collectionId: string, file: File, metadata: any = {}) {
    const formData = new FormData();
    formData.append('file', file);
    formData.append('metadata', JSON.stringify(metadata));
    
    const response = await fetch(`${this.baseUrl}/collections/${collectionId}/documents`, {
      method: 'POST',
      headers: {
        'Authorization': `Bearer ${this.apiKey}`
      },
      body: formData
    });
    
    return await response.json();
  }
}

// Example usage in a web application
async function initializeSearchInterface() {
  const searchManager = new DocumentSearchManager('YOUR_API_KEY');
  
  // Get available collections for dropdown
  const collections = await searchManager.getCollections();
  const collectionSelect = document.getElementById('collection-select');
  
  collections.collections.forEach(collection => {
    const option = document.createElement('option');
    option.value = collection.collectionId;
    option.textContent = collection.name;
    collectionSelect.appendChild(option);
  });
  
  // Set up search form
  const searchForm = document.getElementById('search-form');
  const resultsContainer = document.getElementById('search-results');
  
  searchForm.addEventListener('submit', async (e) => {
    e.preventDefault();
    const collectionId = document.getElementById('collection-select').value;
    const query = document.getElementById('search-query').value;
    const categoryFilter = document.getElementById('category-filter').value;
    
    // Create filter object if category is selected
    const filters = categoryFilter ? { metadata: { category: categoryFilter } } : {};
    
    // Perform search
    resultsContainer.innerHTML = '<p>Searching...</p>';
    const results = await searchManager.search(collectionId, query, filters);
    
    // Display results
    resultsContainer.innerHTML = '';
    if (results.results.length === 0) {
      resultsContainer.innerHTML = '<p>No results found</p>';
      return;
    }
    
    results.results.forEach(result => {
      const resultEl = document.createElement('div');
      resultEl.className = 'search-result';
      resultEl.innerHTML = `
        <div class="result-content">${result.content}</div>
        <div class="result-metadata">
          <span class="relevance">Relevance: ${(result.score * 100).toFixed(1)}%</span>
          <span class="source">Source: ${result.metadata.source || 'Unknown'}</span>
          ${result.metadata.product_id ? `<span class="product">Product: ${result.metadata.product_id}</span>` : ''}
        </div>
      `;
      resultsContainer.appendChild(resultEl);
    });
  });
  
  // Set up document upload
  const uploadForm = document.getElementById('upload-form');
  uploadForm.addEventListener('submit', async (e) => {
    e.preventDefault();
    const collectionId = document.getElementById('upload-collection-select').value;
    const file = document.getElementById('document-file').files[0];
    const source = document.getElementById('document-source').value;
    const category = document.getElementById('document-category').value;
    
    if (!file) {
      alert('Please select a file to upload');
      return;
    }
    
    const metadata = { source, category };
    const uploadResult = await searchManager.uploadDocument(collectionId, file, metadata);
    
    alert(`Document uploaded successfully. Document ID: ${uploadResult.documentId}`);
    uploadForm.reset();
  });
}

// Initialize when DOM is ready
document.addEventListener('DOMContentLoaded', initializeSearchInterface);

Using Documents with an Agent

// Create an agent that can search document collections
async function createDocumentAgent(collectionIds) {
  const response = await fetch('https://langchain.moodmnky.com/api/chains', {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      name: "Document Search Agent",
      type: "agent",
      config: {
        llm: {
          provider: "openai",
          model: "gpt-4",
          temperature: 0.2
        },
        tools: [
          {
            name: "search_documents",
            description: "Search document collections for relevant information",
            parameters: {
              type: "object",
              properties: {
                query: {
                  type: "string",
                  description: "The search query"
                },
                collection_id: {
                  type: "string",
                  description: "The ID of the collection to search",
                  enum: collectionIds
                },
                top_k: {
                  type: "integer",
                  description: "Number of results to return",
                  default: 3
                }
              },
              required: ["query", "collection_id"]
            },
            function: async ({ query, collection_id, top_k = 3 }) => {
              const results = await fetch(`https://langchain.moodmnky.com/api/collections/${collection_id}/query`, {
                method: 'POST',
                headers: {
                  'Content-Type': 'application/json',
                  'Authorization': 'Bearer YOUR_API_KEY'
                },
                body: JSON.stringify({
                  query: query,
                  topK: top_k,
                  includeMetadata: true
                })
              }).then(r => r.json());
              
              return {
                results: results.results.map(r => ({
                  content: r.content,
                  metadata: r.metadata,
                  relevance: r.score
                }))
              };
            }
          }
        ],
        memory: {
          type: "conversation",
          config: {
            maxMessages: 10
          }
        },
        systemPrompt: `You are a helpful assistant for MOOD MNKY, a premium self-care and fragrance company. You can search the company's document collections to find accurate information about products, services, and policies.

When answering questions:
1. Search relevant document collections using the search_documents tool
2. Cite your sources by referencing the documents you found
3. If you can't find information, be honest about it
4. Always maintain a helpful, friendly tone aligned with the MOOD MNKY brand

Available document collections:
${collectionIds.join(', ')}`,
        maxIterations: 5
      }
    })
  });
  
  return await response.json();
}

// Using the document agent to answer questions
async function askDocumentAgent(chainId, question) {
  const response = await fetch(`https://langchain.moodmnky.com/api/chains/${chainId}/run`, {
    method: 'POST',
    headers: {
      'Content-Type': 'application/json',
      'Authorization': 'Bearer YOUR_API_KEY'
    },
    body: JSON.stringify({
      inputs: {
        input: question
      }
    })
  });
  
  return await response.json();
}

// Example usage
async function documentAgentDemo() {
  // Available collections
  const collectionIds = [
    "col_01h9f5zk4r9s7l3z8u2y", // Product Knowledge Base
    "col_02h9g6al5s0t8m4a9v3z"  // Customer FAQs
  ];
  
  // Create the agent
  const agent = await createDocumentAgent(collectionIds);
  console.log('Created document agent:', agent.chainId);
  
  // Ask questions
  const questions = [
    "What are the ingredients in your candles?",
    "How long do your candles typically burn?",
    "Do you offer any pet-friendly fragrances?"
  ];
  
  for (const question of questions) {
    console.log(`\nQuestion: ${question}`);
    const response = await askDocumentAgent(agent.chainId, question);
    console.log(`Answer: ${response.outputs.output}`);
  }
}

Best Practices

Document Preparation

Optimize chunking strategies for your content type:
- For articles and documentation: 800-1000 tokens with 100-200 token overlap
- For code snippets: smaller chunks (300-500 tokens) with minimal overlap
- For legal documents: chunk by sections or paragraphs with headers
Structure metadata thoughtfully:
- Include source, author, date, and version information
- Add categorical information for filtering (product type, department, etc.)
- Consider adding importance or priority rankings
- Include relationships to other documents when relevant
Pre-process documents for better results:
- Remove unnecessary boilerplate text
- Clean up formatting artifacts
- Ensure proper encoding and special character handling
- Break up very large documents appropriately

Collection Management

Organize collections logically:
- Create separate collections for significantly different content types
- Use metadata for finer-grained filtering within collections
- Consider the query patterns when structuring collections
Monitor collection size and performance:
- Large collections may require more optimized querying strategies
- Consider periodically reviewing and cleaning up outdated documents
- Monitor query latency and adjust collection structure if needed
Implement versioning strategies:
- Include version information in document metadata
- Consider collection snapshots for major content overhauls
- Maintain an audit trail of document changes

Retrieval Optimization

Craft effective queries:
- Be specific and clear in query formulation
- Include relevant keywords from your domain
- Test different query phrasings to optimize retrieval
Balance precision and recall:
- Adjust topK based on your application needs
- Use filters to narrow down results when appropriate
- Implement re-ranking strategies for better relevance

Implement query preprocessing:

function optimizeQuery(userQuery: string): string {
  // Remove filler words
  let optimized = userQuery.replace(/please|could you|i want to know|tell me about/gi, '');
  
  // Expand common acronyms
  const acronyms: Record<string, string> = {
    'FAQ': 'frequently asked questions',
    'SDS': 'safety data sheet',
    // Add domain-specific acronyms
  };
  
  for (const [acronym, expansion] of Object.entries(acronyms)) {
    const regex = new RegExp(`\\b${acronym}\\b`, 'g');
    optimized = optimized.replace(regex, `${acronym} ${expansion}`);
  }
  
  return optimized.trim();
}

Security and Compliance

Implement proper access controls:
- Restrict collection access based on user roles
- Consider document-level access control for sensitive information
- Audit access to sensitive collections
Handle sensitive information appropriately:
- Don’t store PII or confidential data unless necessary
- Implement data retention policies
- Consider content filtering for uploaded documents
Maintain data lineage:
- Track document sources and modifications
- Implement citation capabilities for retrieved information
- Ensure proper attribution for copyrighted material

Performance Considerations

Optimize embedding generation:
- Batch document processing for efficiency
- Monitor token usage for embeddings
- Consider caching frequently used embeddings

Implement efficient retrieval patterns:

class OptimizedRetriever {
  private collectionId: string;
  private apiKey: string;
  private cache: Map<string, any>;
  
  constructor(collectionId: string, apiKey: string) {
    this.collectionId = collectionId;
    this.apiKey = apiKey;
    this.cache = new Map();
  }
  
  async retrieve(query: string, filters: any = {}, topK: number = 3) {
    // Generate cache key
    const cacheKey = JSON.stringify({ query, filters, topK });
    
    // Check cache first
    if (this.cache.has(cacheKey)) {
      return this.cache.get(cacheKey);
    }
    
    // Optimize query
    const optimizedQuery = this.optimizeQuery(query);
    
    // Perform retrieval
    const results = await fetch(`https://langchain.moodmnky.com/api/collections/${this.collectionId}/query`, {
      method: 'POST',
      headers: {
        'Content-Type': 'application/json',
        'Authorization': `Bearer ${this.apiKey}`
      },
      body: JSON.stringify({
        query: optimizedQuery,
        topK: topK,
        filter: filters,
        includeMetadata: true
      })
    }).then(r => r.json());
    
    // Cache results (with expiration)
    this.cache.set(cacheKey, results);
    setTimeout(() => this.cache.delete(cacheKey), 1000 * 60 * 15); // 15 minute cache
    
    return results;
  }
  
  private optimizeQuery(query: string): string {
    // Implement query optimization
    return query.trim();
  }
}

Implement smart rate limiting:
- Prioritize critical queries
- Implement request queuing for bulk operations
- Consider using webhooks for large document processing

Support & Resources

For additional support:

Documentation Index

​Documents API

​Overview

​Document Concepts

​Documents

​Collections

​Embeddings

​Retrievers

​API Reference

​Create Collection

​Get Collection

​List Collections

​Upload Document

​Add Text Document

​Get Document

​List Documents in Collection

​Delete Document

​Query Collection

​Delete Collection

​Implementation Examples

​Creating and Populating a Collection

​Querying a Collection

​Building a Document Search Interface

​Using Documents with an Agent

​Best Practices

​Document Preparation

​Collection Management

​Retrieval Optimization

​Security and Compliance

​Performance Considerations

​Support & Resources

Documents API

Overview

Document Concepts

Documents

Collections

Embeddings

Retrievers

API Reference

Create Collection

Get Collection

List Collections

Upload Document

Add Text Document

Get Document

List Documents in Collection

Delete Document

Query Collection

Delete Collection

Implementation Examples

Creating and Populating a Collection

Querying a Collection

Building a Document Search Interface

Using Documents with an Agent

Best Practices

Document Preparation

Collection Management

Retrieval Optimization

Security and Compliance

Performance Considerations

Support & Resources