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+---
+title: Optimizing RAG Performance in LibreChat
+date: 2025/04/25
+description: 'Optimize Retrieval-Augmented Generation (RAG) performance for LibreChat'
+tags:
+  - rag
+  - performance
+  - optimization
+  - guide
+authorid: anon
+ogImage: https://firebasestorage.googleapis.com/v0/b/superb-reporter-407417.appspot.com/o/rag_blog_image_04_25_25.png?alt=media
+---
+
+import { BlogHeader } from '@/components/blog/BlogHeader'
+
+<BlogHeader />
+
+> **Note**: This is a guest post. The information provided may not be accurate if the underlying RAG API changes in future LibreChat versions. Always refer to the official documentation for the most up-to-date information.
+
+# Optimizing RAG Performance in LibreChat
+
+This guide walks you through optimizing Retrieval-Augmented Generation (RAG) performance in your LibreChat setup. Always change **only one major setting at a time** and test results carefully.
+
+---
+
+## 1. Optimize Database (vectordb - PostgreSQL/pgvector)
+
+Improving database performance is crucial for RAG speed, especially during indexing and retrieval.
+
+### 1.1. Verify/Create Metadata & Filter Indexes (**CRITICAL**)
+
+Missing indexes for filtering can drastically degrade performance.
+
+#### Connect to the Database:
+
+```bash
+docker exec -it vectordb psql -U myuser -d mydatabase
+# Enter 'mypassword' (or your secure password)
+```
+
+#### Check for existing indexes:
+
+```sql
+\di langchain_pg_embedding*
+```
+
+You should see:
+
+- `custom_id_idx`
+- `idx_cmetadata_file_id_text`
+- A vector index like `langchain_pg_embedding_embedding_idx`
+
+If missing, run:
+
+```sql
+-- Internal lookup by custom_id
+CREATE INDEX CONCURRENTLY IF NOT EXISTS custom_id_idx ON langchain_pg_embedding (custom_id);
+
+-- Filter by file_id inside metadata
+CREATE INDEX CONCURRENTLY IF NOT EXISTS idx_cmetadata_file_id_text ON langchain_pg_embedding ((cmetadata ->> 'file_id'));
+```
+
+#### Exit:
+
+```sql
+\q
+```
+
+---
+
+### 1.2. Verify/Tune Vector Index
+
+The pgvector extension typically creates an index on the `embedding` column.
+
+Check with `\di` again. Look for a `hnsw` or `ivfflat` index type.
+
+> ⚙️ **Advanced**: You can tune index parameters like `lists`, `m`, `ef_search`, and `ef_construction` (see [pgvector README](https://github.com/pgvector/pgvector)).
+
+---
+
+### 1.3. Monitor/Adjust Server Resources
+
+```bash
+docker stats vectordb
+```
+
+Watch for memory and/or CPU saturation. PostgreSQL benefits from abundant RAM.
+
+#### Optional: Set resource limits in `docker-compose.override.yml`
+
+```yaml
+deploy:
+  resources:
+    limits:
+      memory: 4G
+      cpus: '1.0'
+```
+
+---
+
+### 1.4. Perform Database Maintenance
+
+Run regularly:
+
+```sql
+VACUUM (VERBOSE, ANALYZE) langchain_pg_embedding;
+```
+
+---
+
+### 1.5. Advanced PostgreSQL Tuning
+
+Consider tuning:
+
+- `shared_buffers`
+- `work_mem`
+- `maintenance_work_mem`
+- `effective_cache_size`
+
+These live in `postgresql.conf` (inside the container). Only touch them if you know what you're doing.
+
+---
+
+## 2. Tune Chunking Strategy (`.env`)
+
+Impacts upload speed and retrieval precision.
+
+### 2.1. Open the main `.env` file:
+
+```bash
+nano ~/LibreChat/.env
+```
+
+### 2.2. Modify chunk settings:
+
+```env
+CHUNK_SIZE=1500
+CHUNK_OVERLAP=150
+```
+
+Try other combinations like:
+
+- `1000/100`
+- `500/50`
+
+Trade-offs:  
+- **Larger chunks** = faster processing, lower precision  
+- **Smaller chunks** = slower, more precise
+
+### 2.3. Save, exit, and restart:
+
+```bash
+docker-compose down
+docker-compose up -d --force-recreate
+```
+
+---
+
+### 2.4. Delete Old Embeddings
+
+- **Easiest**: Delete files via UI
+- **Advanced**: Delete from DB
+
+```sql
+DELETE FROM langchain_pg_embedding WHERE cmetadata ->> 'file_id' = 'YOUR_FILE_ID';
+```
+
+> 🔁 Safer method: Use a **new test file** for each config test
+
+---
+
+### 2.5. Re-upload & test performance
+
+---
+
+## 3. Optimize Embedding Process
+
+Set provider/model in `.env`.
+
+### Examples:
+
+#### OpenAI:
+
+```env
+EMBEDDINGS_PROVIDER=openai
+EMBEDDINGS_MODEL=text-embedding-ada-002
+```
+
+#### Azure:
+
+```env
+EMBEDDINGS_PROVIDER=azure
+AZURE_OPENAI_API_KEY=...
+AZURE_OPENAI_ENDPOINT=...
+AZURE_OPENAI_EMBEDDINGS_DEPLOYMENT_NAME=...
+```
+
+#### Ollama (local):
+
+```env
+EMBEDDINGS_PROVIDER=ollama
+OLLAMA_BASE_URL=http://ollama:11434
+EMBEDDINGS_MODEL=nomic-embed-text
+```
+
+### Restart and re-upload:
+
+```bash
+docker-compose down
+docker-compose up -d --force-recreate
+```
+
+---
+
+## 4. Tune Retrieval Strategy
+
+How many chunks are retrieved affects both relevance and API limits.
+
+### 4.1. In `.env`:
+
+```env
+RAG_API_TOP_K=3
+# RAG_USE_FULL_CONTEXT=True
+```
+
+- Lower `TOP_K` = safer, faster  
+- Higher `TOP_K` = more context, risk of hitting token limits
+
+---
+
+## 5. Monitor LibreChat API Logs
+
+Check for truncation or token overflows.
+
+### 5.1. Run a large query, then:
+
+```bash
+docker logs <apiN_container_name> --tail 300
+```
+
+Search for:
+
+```text
+... [truncated]
+```
+
+If present, reduce `TOP_K` or `CHUNK_SIZE`.
+
+---
+
+## 6. Manage Server Load & Isolation
+
+### 6.1. Monitor:
+
+```bash
+htop
+docker stats
+```
+
+### 6.2. Reduce Load (temporarily):
+
+```bash
+# Stop unused APIs or admin panels
+docker-compose stop api2 api3
+sudo systemctl stop lcadmin3.service
+```
+
+### 6.3. Upgrade Hardware
+
+- More RAM/CPU
+- Use SSD (preferably NVMe)
+- GPU boosts embedding (if using local models)
+
+### 6.4. Advanced: Separate Services
+
+You can host `vectordb` and `rag_api` on separate machines for heavy workloads.
+
+---
+
+## Summary
+
+Start with **index optimization**. Then move on to:
+
+1. Chunk tuning (`CHUNK_SIZE`, `CHUNK_OVERLAP`)
+2. Retrieval strategy (`RAG_API_TOP_K`)
+3. Embedding configuration
+4. API log monitoring
+
+Test each change independently. Always monitor API logs and resource usage. If issues persist, consider model/hardware upgrades.
diff --git a/pages/docs/features/image_gen.mdx b/pages/docs/features/image_gen.mdx
index daf16a8..6b659e5 100644
--- a/pages/docs/features/image_gen.mdx
+++ b/pages/docs/features/image_gen.mdx
@@ -18,10 +18,12 @@ Each has its own look, price-point, and setup step (usually just an API key or U
 | **MCP** | Bring-your-own-Image-Generators | MCP server with image output support |
 
 **Notes:**
+- Image Outputs are directly sent to the LLM as part of the immediate chat context following generation.
+  - See [Image Storage and Handling](#image-storage-and-handling) for more details.
 - API keys can be omitted in favor of allowing the user to enter their own key from the UI.
 - Azure OpenAI does not yet support the latest OpenAI GPT-Image-1.
 - MCP Server tool image outputs are supported, which may output images similarly to LC's built-in tools.
-  - Note: MCP servers may or may not use the correct response format when outputting images. See details in the [MCP section below](#5--model-context-protocol-mcp).
+  - Note: MCP servers may or may not use the correct format when outputting images. See details in the [MCP section below](#5--model-context-protocol-mcp).
 
 ---
 
@@ -258,6 +260,11 @@ mcpServers:
 All generated images are:
 1. Saved according to the configured [**`fileStrategy`**](/docs/configuration/librechat_yaml/object_structure/config#filestrategy)
 2. Displayed directly in the chat interface
+3. Image Outputs are directly sent to the LLM as part of the immediate chat context following generation.
+  - This may create issues if you are using an LLM that does not support image inputs.
+  - There will be an option to disable this behavior on a per-agent-basis in the future.
+  - The outputs are only directly sent to the LLM upon generation, not on every message.
+  - To include the image in the chat, you can directly attach it to the message from the side panel.
 
 ---
 
@@ -276,6 +283,7 @@ If any of the tools encounter an error, they will return an error message explai
 - Content policy violations
 - Proxy/network issues
 - Invalid parameters
+- Unsupported image payload (see [Image Storage and Handling](#image-storage-and-handling) above)
 
 ---