add docs

.gitmodules

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-[submodule "apps/docs"]
-	path = apps/docs
-	url = https://github.com/dhravya/docs

docs/api/search.md

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-# Search
-
-This endpoint provides semantic search capabilities across your saved memories using state-of-the-art embeddings. It returns relevant content ranked by similarity.
-
-```http
-POST /api/search
-```
-
-## Request Body
-
-```typescript
-{
-  // The search query to find relevant content
-  query: string,
-
-  // Maximum number of results to return (1-50, default: 10)
-  limit?: number,
-
-  // Minimum similarity threshold (0-1, default: 0)
-  threshold?: number
-}
-```
-
-### Field Descriptions
-
-| Field       | Type   | Required | Description                                   |
-| ----------- | ------ | -------- | --------------------------------------------- |
-| `query`     | string | Yes      | Search query text (minimum 1 character)       |
-| `limit`     | number | No       | Maximum number of results (1-50, default: 10) |
-| `threshold` | number | No       | Minimum similarity score (0-1, default: 0)    |
-
-## Response
-
-The endpoint returns an array of search results, sorted by relevance:
-
-```typescript
-{
-  results: Array<{
-    // Document identifiers
-    id: string;
-    uuid: string;
-
-    // Content fields
-    content: string; // Full document content
-    chunkContent: string; // Matching content chunk
-
-    // Metadata
-    createdAt: string; // ISO timestamp
-
-    // Relevance score (0-1)
-    similarity: number; // Rounded to 4 decimal places
-  }>;
-}
-```
-
-### Error Responses
-
-#### 400 Bad Request
-
-```json
-{
-  "error": "Search query cannot be empty"
-}
-```
-
-#### 401 Unauthorized
-
-```json
-{
-  "error": "Unauthorized"
-}
-```
-
-#### 500 Internal Server Error
-
-```json
-{
-  "error": "Search failed",
-  "details": "Error details (in development mode)"
-}
-```
-
-## Features
-
-### Semantic Search
-
-- Uses BAAI BGE base embeddings model
-- Computes cosine similarity between query and content
-- Returns similarity scores between 0 and 1
-- Supports partial matching and semantic understanding
-
-### Performance
-
-- Results limited to specified threshold
-- Efficient vector search using PostgreSQL
-- Chunked content for better matching
-- Optimized similarity calculations
-
-### Content Processing
-
-- Automatic query embedding
-- Smart content chunking
-- Relevance scoring
-- Result deduplication
-
-## Examples
-
-### Basic Search
-
-```json
-{
-  "query": "machine learning concepts"
-}
-```
-
-### Advanced Search with Filters
-
-```json
-{
-  "query": "python programming",
-  "limit": 20,
-  "threshold": 0.5
-}
-```
-
-### Response Example
-
-```json
-{
-  "results": [
-    {
-      "id": "doc-123",
-      "uuid": "abc-456",
-      "content": "Python is a versatile programming language...",
-      "chunkContent": "...particularly useful for machine learning...",
-      "createdAt": "2024-03-20T12:00:00Z",
-      "similarity": 0.8754
-    },
-    {
-      "id": "doc-124",
-      "uuid": "def-789",
-      "content": "Programming basics include...",
-      "chunkContent": "...Python syntax is straightforward...",
-      "createdAt": "2024-03-19T15:30:00Z",
-      "similarity": 0.7123
-    }
-  ]
-}
-```
-
-## Notes
-
-1. Authentication is required
-2. Results are sorted by similarity score (descending)
-3. Similarity scores are normalized between 0 and 1
-4. Content is automatically chunked for better matching
-5. Performance optimizations include:
-   - Vector indexing
-   - Similarity thresholding
-   - Result limiting
-   - Score normalization
-6. The API uses the BGE base embeddings model for semantic understanding
-7. Supports both exact and semantic matching
-8. Results include both full content and relevant chunks