diff --git a/fern/docs.yml b/fern/docs.yml
index cd46f3f35..7ba3888c4 100644
--- a/fern/docs.yml
+++ b/fern/docs.yml
@@ -228,9 +228,6 @@ navigation:
                   - page: Custom knowledge base
                     path: knowledge-base/custom-knowledge-base.mdx
                     icon: fa-light fa-server
-                  - page: Migrating from Trieve
-                    path: knowledge-base/migrating-from-trieve.mdx
-                    icon: fa-light fa-triangle-exclamation
               - page: Argument encryption
                 path: tools/arguments-encryption.mdx
                 icon: fa-light fa-lock
@@ -1014,8 +1011,6 @@ redirects:
     destination: "/assistants/custom-tools"
   - source: "/knowledgebase"
     destination: "/knowledge-base"
-  - source: "/customization/bring-your-own-vectors/trieve"
-    destination: "/knowledge-base/integrating-with-trieve"
   - source: "/quickstart/billing"
     destination: "/billing/billing-faq"
   - source: /assistants/default-tools
diff --git a/fern/knowledge-base/custom-knowledge-base.mdx b/fern/knowledge-base/custom-knowledge-base.mdx
index af7bbeefc..c6049d754 100644
--- a/fern/knowledge-base/custom-knowledge-base.mdx
+++ b/fern/knowledge-base/custom-knowledge-base.mdx
@@ -529,7 +529,6 @@ app.post('/kb/search', async (req, res) => {
 Now that you have a custom knowledge base implementation:
 
 - **[Query Tool Configuration](/knowledge-base/using-query-tool):** Learn advanced query tool configurations
-- **[Vector Databases](/knowledge-base/integrating-with-trieve):** Explore vector database integrations
 - **[Assistant Configuration](/assistants):** Optimize your assistant's use of knowledge bases
 
 <Note>
diff --git a/fern/knowledge-base/integrating-with-trieve.mdx b/fern/knowledge-base/integrating-with-trieve.mdx
deleted file mode 100644
index 2358dbea6..000000000
--- a/fern/knowledge-base/integrating-with-trieve.mdx
+++ /dev/null
@@ -1,334 +0,0 @@
----
-title: Bring your own chunks/vectors from Trieve
-subtitle: Using Trieve for improved RAG with Vapi
-slug: knowledge-base/integrating-with-trieve
----
-
-<Frame>
-  <div class="video-embed-wrapper">
-    <iframe
-      src="https://www.youtube.com/embed/KZkYPSJPPk8?si=IFyBAL1zAXrUwv2L"
-      title='An embedded YouTube video titled "Quickstart: Building a Hotel Voice Agent with Vapi and Trieve"'
-      frameborder="0"
-      allow="fullscreen; accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share"
-      allowfullscreen
-      referrerpolicy="strict-origin-when-cross-origin"
-    />
-  </div>
-</Frame>
-
-# Using Trieve with Vapi
-
-Vapi integrates with [Trieve](https://trieve.ai) through the BYOD (Bring Your Own Dataset) approach, allowing you to use your Trieve API key to import your existing Trieve datasets into Vapi.
-
-## Integrating with Trieve
-
-The BYOD approach offers flexibility and control over your datasets. You can:
-
-- Fully manage your datasets in Trieve's native interface
-- Use Trieve's advanced features like:
-  - Custom chunking rules
-  - Search playground testing
-  - Manual chunk editing
-  - Website crawling
-  - Dataset visualization
-
-### Step 1: Set Up Trieve Dataset
-
-1. Create an account at [Trieve](https://trieve.ai)
-2. Create a new dataset using Trieve's dashboard
-
-![Create dataset in Trieve](../static/images/knowledge-base/create-dataset.png)
-
-When creating your dataset in Trieve, selecting the right embedding model is crucial for optimizing performance and accuracy. Here are some of the available options:
-
-### jina-base-en
-
-- **Provider**: Jina AI (Hosted by Trieve)
-- **Performance**: Fast
-- **Description**: This model is designed for speed and efficiency, making it suitable for applications where quick response times are critical. It provides a good balance of performance and accuracy for general use cases.
-
-### text-embedding-3-small
-
-- **Provider**: OpenAI
-- **Performance**: Moderate
-- **Description**: A smaller model from OpenAI that offers a compromise between speed and accuracy. It is suitable for applications that require a balance between computational efficiency and the quality of embeddings.
-
-### text-embedding-3-large
-
-- **Provider**: OpenAI
-- **Performance**: Slow
-- **Description**: This larger model provides the highest accuracy among the options but at the cost of slower processing times. It is ideal for applications where the quality of embeddings is prioritized over speed.
-
-3. Add content through various methods:
-
-#### Upload Documents
-
-Upload documents directly through Trieve's interface:
-
-![Upload files in Trieve](../static/images/knowledge-base/upload-files.png)
-
-When uploading files, you can configure advanced chunking options:
-
-![Upload files advanced options in Trieve](../static/images/knowledge-base/upload-files-advanced.png)
-
-#### Edit Individual Chunks
-
-After uploading documents, you can edit individual chunks to refine their content:
-
-![Edit chunk interface in Trieve](../static/images/knowledge-base/edit-chunk.png)
-
-##### Editing Options
-
-- **Chunk Content**: Modify the text directly in the rich text editor
-
-  - Fix formatting issues
-  - Correct errors or typos
-  - Split or combine chunks manually
-  - Add or remove content
-
-- **Metadata Fields**:
-  - Date: Update document timestamps
-  - Number Value: Adjust numeric metadata for filtering
-  - Location: Set or modify geographical coordinates
-  - Weight: Fine-tune search relevance with custom weights
-  - Fulltext Boost: Add terms to enhance search visibility
-  - Semantic Boost: Adjust vector embedding influence
-
-##### Best Practices for Chunk Editing
-
-1. **Content Length**
-
-   - Keep chunks between 200-1000 tokens
-   - Maintain logical content boundaries
-   - Ensure complete thoughts within each chunk
-
-2. **Metadata Optimization**
-
-   - Use consistent date formats
-   - Add relevant numeric values for filtering
-   - Apply weights strategically for important content
-
-3. **Search Enhancement**
-   - Use boost terms for critical keywords
-   - Balance semantic and fulltext boosts
-   - Test search results after significant edits
-
-### Advanced Chunking Options
-
-#### Metadata
-
-- Add custom metadata as JSON to associate with your chunks
-  - Useful for filtering and organizing content (e.g., `{"author": "John Doe", "category": "technical"}`)
-  - Keep metadata concise and relevant to avoid storage overhead
-  - Use consistent keys across related documents for better searchability
-
-#### Date
-
-- Specify the creation or relevant date for the document
-  - Important for version control and content freshness
-  - Helps with filtering outdated information
-  - Use actual document creation dates when possible
-
-#### Split Delimiters
-
-- Define custom delimiters (e.g., ".,?\n") to control where chunks are split
-  - Recommended defaults: ".,?\n" for general content
-  - Add semicolons (;) for technical documentation
-  - Use "\n\n" for markdown or structured content
-  - Avoid over-aggressive splitting that might break context
-
-#### Target Splits Per Chunk
-
-- Set the desired number of splits per chunk
-  - Default: 20 splits
-  - Recommended ranges:
-    - 15-25 for general content
-    - 10-15 for technical documentation
-    - 25-30 for narrative content
-  - Lower values create more granular chunks, better for precise retrieval
-  - Higher values maintain more context but may retrieve irrelevant information
-
-#### Rebalance Chunks
-
-- Enable to redistribute content evenly across chunks
-  - Recommended for documents with varying section lengths
-  - Helps maintain consistent chunk sizes
-  - May slightly impact natural content boundaries
-  - Best used with technical documentation or structured content
-
-#### Use gpt4o chunking
-
-- Enable GPT-4 optimized chunking for improved semantic coherence
-  - Recommended for:
-    - Complex technical documentation
-    - Content with intricate relationships
-    - Documents where context preservation is crucial
-  - Note: Increases processing time and cost
-  - Best for high-value content where accuracy is paramount
-
-#### Heading Based Chunking
-
-- Split content based on document headings
-  - Ideal for well-structured documents (e.g., documentation, reports)
-  - Works best with consistent heading hierarchy
-  - Consider enabling for:
-    - Technical documentation
-    - User manuals
-    - Research papers
-  - May create uneven chunk sizes based on section lengths
-
-#### System Prompt
-
-- Provide custom instructions for the chunking process
-  - Optional but powerful for specific use cases
-  - Example prompts:
-    - "Preserve code blocks as single chunks"
-    - "Keep API endpoint descriptions together"
-    - "Maintain question-answer pairs in the same chunk"
-  - Keep prompts clear and specific
-  - Test different prompts with sample content to optimize results
-
-#### Website Crawling
-
-Trieve offers powerful website crawling capabilities with extensive configuration options:
-
-![Website crawling in Trieve](../static/images/knowledge-base/crawl.png)
-
-##### Crawl Configuration Options
-
-- **Crawl Interval**: Set how often to refresh content
-
-  - Options: Daily, Weekly, Monthly
-  - Recommended: Daily for frequently updated content
-
-- **Page Limit**: Control the maximum number of pages to crawl
-
-  - Default: 1000 pages
-  - Adjust based on your site size and content relevance
-
-- **URL Patterns**
-
-  - Include/Exclude specific URL patterns using regex
-  - Example includes: `https://docs.example.com/*`
-  - Example excludes: `https://example.com/internal/*`
-
-- **Query Selectors**
-
-  - Include specific HTML elements for targeted content extraction
-  - Exclude navigation, footers, and other non-content elements
-  - Common excludes: `navbar`, `footer`, `aside`, `nav`, `form`
-
-- **Special Content Types**
-
-  - OpenAPI Spec: Toggle for API documentation crawling
-  - Shopify: Enable for e-commerce content
-  - YouTube Channel: Include video transcripts and descriptions
-
-- **Advanced Options**
-  - Boost Titles: Increase weight of page titles in search results
-  - Allow External Links: Include content from linked domains
-  - Ignore Sitemap: Skip sitemap-based crawling
-  - Remove Strings: Clean up headers and body content
-
-##### Best Practices for Crawling
-
-1. **Start Small**
-
-   - Begin with a low page limit
-   - Test with specific sections of your site
-   - Gradually expand coverage
-
-2. **Optimize Selectors**
-
-   - Remove navigation and UI elements
-   - Focus on main content areas
-   - Use browser inspector to identify key selectors
-
-3. **Monitor Performance**
-   - Check crawl logs regularly
-   - Adjust patterns based on results
-   - Balance frequency with server load
-
-### Step 2: Test and Refine
-
-Use Trieve's search playground to:
-
-- Test semantic search queries
-- Adjust chunk sizes
-- Edit chunks manually
-
-- Visualize vector embeddings
-- Fine-tune relevance scores
-
-![Search playground in Trieve](../static/images/knowledge-base/search-playground.png)
-
-### Step 3: Import to Vapi
-
-1. Create your Trieve API key from [Trieve's dashboard](https://dashboard.trieve.ai/org/keys)
-2. Add your Trieve API key to Vapi [Provider Credentials](https://dashboard.vapi.ai/keys)
-   ![Add Trieve API key in Vapi](../static/images/knowledge-base/trieve-credential.png)
-3. Once your dataset is optimized in Trieve, import it to Vapi via POST request to the [create knowledge base route](/api-reference/knowledge-bases/create):
-
-```json
-{
-  "name": "trieve-dataset",
-  "provider": "trieve",
-  "searchPlan": {
-    "scoreThreshold": 0.2,
-    "searchType": "semantic"
-  },
-  "createPlan": {
-    "type": "import",
-    "providerId": "<Your Trieve Dataset ID>"
-  }
-}
-```
-
-## Best Practices
-
-1. **Dataset Organization**
-
-   - Segment datasets by domain knowledge boundaries
-   - Use semantic-based dataset naming (e.g., "api-docs-v2", "user-guides-2024")
-   - Version control chunking configurations in your codebase
-
-2. **Content Quality**
-
-   - Implement text normalization (Unicode normalization, whitespace standardization)
-   - Use regex patterns to clean formatting artifacts
-   - Validate chunk semantic coherence through embedding similarity scores
-
-3. **Performance Optimization**
-
-   - Target chunk sizes: 200-1000 tokens (optimal for current embedding models)
-   - Configure hybrid search with BM25 boost = 0.3 for technical content
-   - Set score thresholds dynamically based on embedding model (0.2 for text-embedding-3-small, 0.25 for text-embedding-3-large)
-
-4. **Maintenance**
-   - Implement automated content refresh cycles via Trieve's API
-   - Track search result relevance metrics (MRR, NDCG)
-   - Rotate API keys on 90-day cycles
-
-## Troubleshooting
-
-Common issues and solutions:
-
-1. **Search Relevance Issues**
-
-   - Implement cross-encoder reranking for critical queries
-   - Fine-tune BM25 vs semantic weights (recommended ratio: 0.3:0.7)
-   - Analyze chunk boundary overlap percentage (aim for 15-20%)
-
-2. **Integration Errors**
-
-   - Validate dataset permissions (READ_DATASET scope required)
-   - Check for dataset ID format compliance (UUID v4)
-   - Monitor rate limits (default: 100 requests/min)
-
-3. **Performance Optimization**
-   - Implement chunk size normalization (max variance: 20%)
-   - Enable query caching for frequent searches
-   - Use batch operations for bulk updates (max 100 chunks/request)
-
-Need help? Contact [support@vapi.ai](mailto:support@vapi.ai) for assistance.
diff --git a/fern/knowledge-base/migrating-from-trieve.mdx b/fern/knowledge-base/migrating-from-trieve.mdx
deleted file mode 100644
index 520921ac1..000000000
--- a/fern/knowledge-base/migrating-from-trieve.mdx
+++ /dev/null
@@ -1,413 +0,0 @@
----
-title: Migrating from Trieve
-subtitle: Essential migration guide for Trieve users - service ending November 1st, 2025
-slug: knowledge-base/migrating-from-trieve
----
-
-<Warning>
-  **URGENT: Trieve is shutting down on November 1st, 2025.** All Trieve-based knowledge bases must be migrated before this date to avoid service disruption.
-</Warning>
-
-## Overview
-
-Trieve [is shutting down their cloud service](https://www.trieve.ai/blog/trieve-is-being-acquired-by-mintlify) on November 1st, 2025. If you're currently using Trieve for your Vapi knowledge bases, you need to migrate before this date to avoid service disruption.
-
-This guide provides two migration paths to ensure your assistant continues working seamlessly.
-
-**Available migration options:**
-- **Custom Knowledge Base**: Full control with your own search infrastructure
-- **Google Knowledge Base**: Managed solution using Google's Gemini models
-
-<Note>
-  **Important Behavioral Difference**: Google knowledge bases work as tools that the assistant calls when needed based on your prompt instructions. Custom knowledge bases are queried on every user request automatically. Choose based on whether you want selective or automatic knowledge retrieval.
-</Note>
-
-<Tip>
-  **Recommendation**: To maintain the same assistant behavior as your previous Trieve setup, we recommend migrating to a **Custom Knowledge Base**. This ensures your assistant continues to automatically query your knowledge on every user message, just like it did with Trieve.
-</Tip>
-
-## Migration Options Comparison
-
-Choose the migration path that best fits your needs:
-
-<CardGroup cols={2}>
-  <Card title="Custom Knowledge Base" icon="server" href="#migrating-to-custom-knowledge-base">
-    **Best for:** Advanced users wanting automatic knowledge retrieval
-    
-    - Queried on every user message
-    - Full control over search logic
-    - Use any vector database or search system
-    - Requires server development
-  </Card>
-  <Card title="Google Knowledge Base" icon="brain" href="#migrating-to-google-knowledge-base">
-    **Best for:** Quick migration with selective knowledge use
-    
-    - Works as a tool called when needed
-    - Simple file upload process  
-    - No server maintenance required
-    - Assistant decides when to use knowledge
-  </Card>
-</CardGroup>
-
-### Decision Matrix
-
-| Factor | Custom Knowledge Base | Google Knowledge Base |
-|--------|----------------------|----------------------|
-| **Retrieval Behavior** | Queried on every user message | Called as tool when needed by assistant |
-| **Latency** | Lower (direct database query) | Higher (Google Gemini model processing) |
-| **Technical Complexity** | High | Low |
-| **Ongoing Maintenance** | Server management required | None |
-| **Customization** | Complete flexibility | Limited |
-| **Cost** | Server hosting costs | Included in Vapi |
-| **Search Quality** | Depends on implementation | Google Gemini quality |
-
-## Migrating to Custom Knowledge Base
-
-### Step 1: Export Your Data
-
-Your export process depends on how your Trieve knowledge base was originally created:
-
-#### Option A: User-Managed Trieve Dataset (Import Plan)
-If you created your knowledge base with your own Trieve dataset:
-
-1. Access your Trieve dashboard directly
-2. Export your datasets in your preferred format
-3. Download all document chunks and metadata
-4. Save any custom embeddings if applicable
-
-#### Option B: Vapi-Managed Trieve Dataset (Create Plan)  
-If Vapi created and managed your Trieve dataset:
-
-1. Navigate to the **Files** page in your Vapi dashboard
-2. Download the individual files that were uploaded to create the knowledge base
-3. These files are stored in Vapi and are not affected by the Trieve shutdown
-
-<Note>
-  **Files uploaded to Vapi are safe**: If your knowledge base was created using files uploaded to Vapi, those files remain available in your Vapi account and are not affected by the Trieve shutdown.
-</Note>
-
-### Step 2: Set Up Your Custom Server
-
-Choose your infrastructure and implement the knowledge base endpoint:
-
-<CodeBlocks>
-```typescript title="Node.js Example"
-import express from 'express';
-import { PineconeClient } from '@pinecone-database/pinecone';
-
-const app = express();
-const pinecone = new PineconeClient();
-
-app.post('/kb/search', async (req, res) => {
-  try {
-    const { message } = req.body;
-    const userMessages = message.messages.filter(msg => msg.role === 'user');
-    const latestQuery = userMessages[userMessages.length - 1]?.content || '';
-
-    // Search your vector database
-    const index = pinecone.Index('your-knowledge-base');
-    const searchResults = await index.query({
-      vector: await getEmbedding(latestQuery),
-      topK: 5,
-      includeMetadata: true
-    });
-
-    // Format response
-    const documents = searchResults.matches.map(match => ({
-      content: match.metadata.content,
-      similarity: match.score,
-      uuid: match.id
-    }));
-
-    res.json({ documents });
-  } catch (error) {
-    res.status(500).json({ error: 'Search failed' });
-  }
-});
-
-app.listen(3000);
-```
-
-```python title="Python Example"
-from fastapi import FastAPI
-import weaviate
-from sentence_transformers import SentenceTransformer
-
-app = FastAPI()
-client = weaviate.Client("http://localhost:8080")
-
-@app.post("/kb/search")
-async def search_knowledge_base(request: dict):
-    try:
-        message = request.get('message', {})
-        user_messages = [msg for msg in message.get('messages', []) if msg.get('role') == 'user']
-        latest_query = user_messages[-1].get('content', '') if user_messages else ''
-
-        # Search your vector database
-        result = client.query.get("Document", ["content", "title"]) \
-            .with_near_text({"concepts": [latest_query]}) \
-            .with_limit(5) \
-            .with_additional(["certainty"]) \
-            .do()
-
-        documents = []
-        for doc in result['data']['Get']['Document']:
-            documents.append({
-                "content": doc['content'],
-                "similarity": doc['_additional']['certainty'],
-                "uuid": doc.get('title', 'unknown')
-            })
-
-        return {"documents": documents}
-    except Exception as error:
-        return {"error": str(error)}
-```
-</CodeBlocks>
-
-### Step 3: Create Custom Knowledge Base in Vapi
-
-<CodeBlocks>
-```bash title="cURL"
-curl --location 'https://api.vapi.ai/knowledge-base' \
---header 'Content-Type: application/json' \
---header 'Authorization: Bearer YOUR_VAPI_API_KEY' \
---data '{
-    "provider": "custom-knowledge-base",
-    "server": {
-        "url": "https://your-domain.com/kb/search",
-        "secret": "your-webhook-secret"
-    }
-}'
-```
-
-```typescript title="TypeScript SDK"
-const knowledgeBase = await vapi.knowledgeBases.create({
-  provider: "custom-knowledge-base",
-  server: {
-    url: "https://your-domain.com/kb/search",
-    secret: "your-webhook-secret"
-  }
-});
-```
-</CodeBlocks>
-
-### Step 4: Update Your Assistant
-
-Replace your Trieve knowledge base with the custom one:
-
-<CodeBlocks>
-```bash title="cURL"
-curl --location --request PATCH 'https://api.vapi.ai/assistant/YOUR_ASSISTANT_ID' \
---header 'Content-Type: application/json' \
---header 'Authorization: Bearer YOUR_VAPI_API_KEY' \
---data '{
-    "model": {
-        "model": "gpt-4o",
-        "provider": "openai",
-        "messages": [
-            {
-                "role": "system",
-                "content": "Your existing system prompt..."
-            }
-        ],
-        "knowledgeBaseId": "YOUR_NEW_KNOWLEDGE_BASE_ID"
-    }
-}'
-```
-
-```typescript title="TypeScript SDK"
-// Get existing configuration
-const existingAssistant = await vapi.assistants.get("YOUR_ASSISTANT_ID");
-
-// Update with new knowledge base
-const updatedAssistant = await vapi.assistants.update("YOUR_ASSISTANT_ID", {
-  model: {
-    ...existingAssistant.model,
-    knowledgeBaseId: knowledgeBase.id
-  }
-});
-```
-</CodeBlocks>
-
-## Migrating to Google Knowledge Base
-
-### Step 1: Prepare Your Files
-
-Your file preparation depends on how your Trieve knowledge base was originally created:
-
-#### Option A: User-Managed Trieve Dataset (Import Plan)
-If you created your knowledge base with your own Trieve dataset:
-
-1. Export your Trieve datasets as individual files
-2. Organize files by topic or domain
-3. Ensure files are in supported formats (`.txt`, `.pdf`, `.docx`, `.md`, etc.)
-4. Keep individual files under 300KB for optimal processing
-5. **Upload these files to Vapi** (you'll need them in Vapi to create the query tool)
-
-#### Option B: Vapi-Managed Trieve Dataset (Create Plan)
-If Vapi created and managed your Trieve dataset:
-
-1. Navigate to the **Files** page in your Vapi dashboard
-2. Locate the files that were used to create your Trieve knowledge base
-3. Note the **file IDs** - you'll use these directly to create your query tool
-4. **No need to download or re-upload** - the files are already in Vapi
-
-<Note>
-  **Files uploaded to Vapi are safe**: If your knowledge base was created using files uploaded to Vapi, those files remain available and are not affected by the Trieve shutdown.
-</Note>
-
-### Step 2: Upload Files to Vapi (Option A Only)
-
-<Note>
-  **This step is only required for Option A (User-Managed Trieve)**. If you're using Option B (Vapi-Managed), skip to Step 3 as your files are already in Vapi.
-</Note>
-
-If you exported files from your own Trieve dataset (Option A):
-
-1. Navigate to **Build > Files** in your Vapi dashboard
-2. Click **Upload** and select your exported files
-3. Wait for processing and note the generated file IDs
-
-### Step 3: Create Query Tool
-
-Create a query tool using your file IDs:
-
-- **Option A**: Use the file IDs from the files you just uploaded in Step 2
-- **Option B**: Use the existing file IDs you noted from your Vapi Files page
-
-For detailed instructions, see our [Query Tool documentation](/knowledge-base/using-query-tool).
-
-<Warning>
-  **Don't forget**: You must update your assistant's system prompt to explicitly name the tool to use. Add instructions like: "When users ask about products or support topics, use the 'knowledge-search' tool to search the knowledge base." Replace 'knowledge-search' with your actual tool function name.
-</Warning>
-
-<CodeBlocks>
-```bash title="cURL"
-curl --location 'https://api.vapi.ai/tool' \
---header 'Content-Type: application/json' \
---header 'Authorization: Bearer YOUR_VAPI_API_KEY' \
---data '{
-    "type": "query",
-    "function": {
-        "name": "knowledge-search"
-    },
-    "knowledgeBases": [
-        {
-            "provider": "google",
-            "name": "product-support-documentation",
-            "description": "Contains comprehensive information about our products, services, features, pricing, and technical support documentation. Includes troubleshooting guides and company information.",
-            "fileIds": [
-                "FILE_ID_1",
-                "FILE_ID_2",
-                "FILE_ID_3"
-            ]
-        }
-    ]
-}'
-```
-
-```typescript title="TypeScript SDK"
-const queryTool = await vapi.tools.create({
-  type: "query",
-  function: {
-    name: "knowledge-search"
-  },
-  knowledgeBases: [
-    {
-      provider: "google",
-      name: "product-support-documentation",
-      description: "Contains comprehensive information about our products, services, features, pricing, and technical support documentation. Includes troubleshooting guides and company information.",
-      fileIds: [
-        "FILE_ID_1", 
-        "FILE_ID_2", 
-        "FILE_ID_3"
-      ]
-    }
-  ]
-});
-```
-</CodeBlocks>
-
-### Step 4: Update Your Assistant
-
-Remove Trieve tools and add your new query tool:
-
-<CodeBlocks>
-```bash title="cURL"
-curl --location --request PATCH 'https://api.vapi.ai/assistant/YOUR_ASSISTANT_ID' \
---header 'Content-Type: application/json' \
---header 'Authorization: Bearer YOUR_VAPI_API_KEY' \
---data '{
-    "model": {
-        "model": "gpt-4o",
-        "provider": "openai",
-        "messages": [
-            {
-                "role": "system", 
-                "content": "Your existing system prompt..."
-            }
-        ],
-        "toolIds": ["YOUR_NEW_QUERY_TOOL_ID"]
-    }
-}'
-```
-
-```typescript title="TypeScript SDK"
-// Get existing assistant
-const existingAssistant = await vapi.assistants.get("YOUR_ASSISTANT_ID");
-
-// Update with new tool
-const updatedAssistant = await vapi.assistants.update("YOUR_ASSISTANT_ID", {
-  model: {
-    ...existingAssistant.model,
-    toolIds: [queryTool.id]
-  }
-});
-```
-</CodeBlocks>
-
-## Post-Migration Checklist
-
-After completing your migration:
-
-- [ ] **Test all assistants** to ensure knowledge retrieval is working
-- [ ] **Monitor response quality** and adjust configurations if needed
-- [ ] **Remove the old Trieve knowledge base** from Vapi using API.
-- [ ] **Remove Trieve credentials** from Vapi dashboard
-
-## Troubleshooting Common Issues
-
-### Poor Search Quality After Migration
-
-**Custom Knowledge Base:**
-- Verify your embedding model matches or exceeds Trieve's quality
-- Adjust similarity thresholds based on your model
-- Test different chunking strategies
-- Consider implementing hybrid search (semantic + keyword)
-
-**Google Knowledge Base:**
-- Review file formatting and structure
-- Optimize knowledge base descriptions
-- Test with different query phrasings
-- Consider splitting large files into smaller, focused documents
-
-### Performance Issues
-
-**Custom Knowledge Base:**
-- Implement caching for frequently searched content
-- Optimize your vector database configuration
-- Consider using faster embedding models for latency-critical applications
-- Monitor and optimize server response times
-
-**Google Knowledge Base:**
-- Ensure files are under 300KB each
-- Use clear, well-structured file formats
-- Minimize the number of files per knowledge base where possible
-
-## Next Steps
-
-Once you've completed your migration:
-
-- **[Custom Knowledge Base](/knowledge-base/custom-knowledge-base):** Learn advanced customization options
-- **[Query Tool Configuration](/knowledge-base/using-query-tool):** Optimize your Google knowledge base setup
-- **[Assistant Configuration](/assistants):** Fine-tune your assistant's knowledge base usage
diff --git a/fern/knowledgebase.mdx b/fern/knowledgebase.mdx
index a67c92667..bff7dfa69 100644
--- a/fern/knowledgebase.mdx
+++ b/fern/knowledgebase.mdx
@@ -20,76 +20,7 @@ Using a Knowledge Base with your voice AI assistant offers several benefits:
 
 ## **How to Create a Knowledge Base**
 
-To create a Knowledge Base with Trieve, follow these steps:
-
-### **Step 1: Create a Knowledge Base with Trieve**
-
-Vapi integrates with [Trieve](https://trieve.ai) using the BYOD (Bring Your Own Dataset) approach. First, create and optimize your dataset in Trieve (see our [Integrating with Trieve guide](knowledge-base/integrating-with-trieve) for detailed instructions), then import it to Vapi:
-
-```bash
-curl --location 'https://api.vapi.ai/knowledge-base' \
---header 'Content-Type: text/plain' \
---header 'Authorization: Bearer <YOUR_API_KEY>' \
---data '{
-    "name": "trieve-dataset",
-    "provider": "trieve",
-    "searchPlan": {
-        "searchType": "semantic",
-        "topK": 3,
-        "removeStopWords": true,
-        "scoreThreshold": 0.7
-    },
-    "createPlan": {
-        "type": "import",
-        "providerId": "<YOUR_TRIEVE_DATASET_ID>"
-    }
-}'
-```
-
-#### Configuration Options
-
-##### Search Plan Options
-
-- **searchType** (required): The search method used for finding relevant chunks. Available options:
-  - `fulltext`: Traditional text search
-  - `semantic`: Semantic similarity search
-  - `hybrid`: Combines fulltext and semantic search
-  - `bm25`: BM25 ranking algorithm
-- **topK** (optional): Number of top chunks to return. Default varies by implementation
-- **removeStopWords** (optional): When true, removes common stop words from the search query. Default: `false`
-- **scoreThreshold** (optional): Filters out chunks based on their similarity score:
-  - For cosine distance: Excludes chunks below the threshold
-  - For Manhattan Distance, Euclidean Distance, and Dot Product: Excludes chunks above the threshold
-  - Set to 0 or omit for no threshold
-
-##### Import Options
-
-- **providerId** (required): The ID of your Trieve dataset that you want to import
-- **type** (required): Must be set to "import" for the BYOD approach
-
-### **Step 2: Create an Assistant**
-
-Create a new assistant in Vapi and, on the right sidebar menu. Add the Knowledge Base to your assistant via the PATCH endpoint. Also make sure you customize your assistant's system prompt to utilize the Knowledge Base for responding to user queries.
-
-```bash
-curl --location --request PATCH 'https://api.vapi.ai/assistant/<ASSISTANT_ID>' \
---header 'Content-Type: text/plain' \
---header 'Authorization: Bearer <YOUR_API_KEY>' \
---data '{
-  "model": {
-    "knowledgeBaseId": "<KNOWLEDGE_BASE_ID>",
-    "temperature": 0.2,
-    "provider": "openai",
-    "model": "gpt-4o",
-    "messages": [
-      {
-        "content": "You are a smart assistant who responds to user queries using the information you know, or information supplied by outside context.",
-        "role": "system"
-      }
-    ]
-  }
-}'
-```
+For step-by-step instructions on creating and configuring a knowledge base, see the [Knowledge Base guide](/knowledge-base).
 
 ## **Best Practices for Creating Effective Knowledge Bases**