fixed bullets

docs/rag-vector-databases/chromadb.md

@@ -232,24 +232,12 @@ client = chromadb.PersistentClient(
 
 ### Common Issues
 
-1. **Connection Errors**
+1. **Connection Errors**: Check ChromaDB server status. Verify network connectivity. Confirm correct host and port settings.
-   - Check ChromaDB server status
+
-   - Verify network connectivity
+2. **Performance Issues**: Monitor collection size and query complexity. Consider collection partitioning. Optimize metadata queries.
-   - Confirm correct host and port settings
+
-
+3. **Memory Issues**: Adjust HNSW parameters. Use persistent storage instead of in-memory. Implement proper cleanup procedures.
-2. **Performance Issues**
+
-   - Monitor collection size and query complexity
+4. **Embedding Errors**: Verify LiteLLM configuration. Check API keys and quotas. Handle rate limiting properly.
-   - Consider collection partitioning
-   - Optimize metadata queries
-
-3. **Memory Issues**
-   - Adjust HNSW parameters
-   - Use persistent storage instead of in-memory
-   - Implement proper cleanup procedures
-
-4. **Embedding Errors**
-   - Verify LiteLLM configuration
-   - Check API keys and quotas
-   - Handle rate limiting properly
 
 This comprehensive guide provides everything needed to integrate ChromaDB with Swarms agents for powerful RAG applications using the unified LiteLLM embeddings approach.

docs/rag-vector-databases/faiss.md

@@ -256,24 +256,12 @@ index = faiss.IndexIVFPQ(quantizer, dimension, nlist, m, nbits)
 
 ### Common Issues
 
-1. **Memory Errors**
+1. **Memory Errors**: Reduce batch sizes or use product quantization. Consider using memory mapping for large indices. Monitor system memory usage.
-   - Reduce batch sizes or use product quantization
+
-   - Consider using memory mapping for large indices
+2. **Slow Search Performance**: Check if IVF index is properly trained. Adjust nprobe parameter (higher = slower but more accurate). Consider using GPU acceleration.
-   - Monitor system memory usage
+
-
+3. **Low Search Accuracy**: Increase nlist for IVF indices. Adjust efSearch for HNSW indices. Verify embedding normalization.
-2. **Slow Search Performance**
+
-   - Check if IVF index is properly trained
+4. **Index Loading Issues**: Check file permissions and disk space. Verify FAISS version compatibility. Ensure consistent data types (float32).
-   - Adjust nprobe parameter (higher = slower but more accurate)
-   - Consider using GPU acceleration
-
-3. **Low Search Accuracy**
-   - Increase nlist for IVF indices
-   - Adjust efSearch for HNSW indices  
-   - Verify embedding normalization
-
-4. **Index Loading Issues**
-   - Check file permissions and disk space
-   - Verify FAISS version compatibility
-   - Ensure consistent data types (float32)
 
 This comprehensive guide provides everything needed to integrate FAISS with Swarms agents for high-performance RAG applications using the unified LiteLLM embeddings approach.

docs/rag-vector-databases/milvus-cloud.md

@@ -275,24 +275,12 @@ def monitored_search(memory, query, **kwargs):
 
 ### Common Issues
 
-1. **Connection Errors**
+1. **Connection Errors**: Verify MILVUS_CLOUD_URI and MILVUS_CLOUD_TOKEN. Check network connectivity and firewall settings. Confirm cloud region accessibility.
-   - Verify MILVUS_CLOUD_URI and MILVUS_CLOUD_TOKEN
+
-   - Check network connectivity and firewall settings
+2. **Performance Issues**: Monitor collection size and index type appropriateness. Check query complexity and filter expressions. Review auto-scaling configuration.
-   - Confirm cloud region accessibility
+
-
+3. **Search Accuracy Issues**: Verify embedding model consistency. Check vector normalization if using cosine similarity. Review index parameters and search parameters.
-2. **Performance Issues**
+
-   - Monitor collection size and index type appropriateness
+4. **Quota and Billing Issues**: Monitor usage against plan limits. Review auto-scaling settings. Check billing alerts and notifications.
-   - Check query complexity and filter expressions
-   - Review auto-scaling configuration
-
-3. **Search Accuracy Issues**
-   - Verify embedding model consistency
-   - Check vector normalization if using cosine similarity
-   - Review index parameters and search parameters
-
-4. **Quota and Billing Issues**
-   - Monitor usage against plan limits
-   - Review auto-scaling settings
-   - Check billing alerts and notifications
 
 This comprehensive guide provides everything needed to integrate Milvus Cloud with Swarms agents for enterprise-scale RAG applications using the unified LiteLLM embeddings approach.

docs/rag-vector-databases/milvus-local.md

@@ -295,24 +295,12 @@ print(f"Document count: {stats['row_count']}")
 
 ### Common Issues
 
-1. **Database File Errors**
+1. **Database File Errors**: Check file permissions and disk space. Ensure directory exists before creating database. Handle concurrent access properly.
-   - Check file permissions and disk space
+
-   - Ensure directory exists before creating database
+2. **Performance Issues**: Monitor database size relative to available memory. Consider index type optimization for dataset size. Batch operations for better throughput.
-   - Handle concurrent access properly
+
-
+3. **Memory Usage**: Use appropriate index parameters for available RAM. Monitor system memory usage. Consider data compression techniques.
-2. **Performance Issues**
+
-   - Monitor database size relative to available memory
+4. **Data Corruption**: Implement proper backup and recovery procedures. Handle application crashes gracefully. Use database validation tools.
-   - Consider index type optimization for dataset size
-   - Batch operations for better throughput
-
-3. **Memory Usage**
-   - Use appropriate index parameters for available RAM
-   - Monitor system memory usage
-   - Consider data compression techniques
-
-4. **Data Corruption**
-   - Implement proper backup and recovery procedures
-   - Handle application crashes gracefully
-   - Use database validation tools
 
 This comprehensive guide provides everything needed to integrate Milvus Lite with Swarms agents for local, lightweight RAG applications using the unified LiteLLM embeddings approach.

docs/rag-vector-databases/pinecone.md

@@ -346,25 +346,13 @@ class MonitoredPineconeMemory(PineconeMemory):
 
 ### Common Issues
 
-1. **API Quota Exceeded**
+1. **API Quota Exceeded**: Monitor API usage and implement rate limiting. Consider upgrading plan or optimizing query patterns. Use batch operations to reduce API calls.
-   - Monitor API usage and implement rate limiting
+
-   - Consider upgrading plan or optimizing query patterns
+2. **High Latency**: Check pod type and consider upgrading. Verify regional configuration. Optimize query complexity and top_k values.
-   - Use batch operations to reduce API calls
+
-
+3. **Index Capacity Issues**: Monitor index fullness metrics. Consider scaling up pod type or adding shards. Implement data archival strategies.
-2. **High Latency**
+
-   - Check pod type and consider upgrading
+4. **Connection Errors**: Verify API key and environment configuration. Check network connectivity and firewall settings. Implement retry logic with exponential backoff.
-   - Verify regional configuration
-   - Optimize query complexity and top_k values
-
-3. **Index Capacity Issues**
-   - Monitor index fullness metrics
-   - Consider scaling up pod type or adding shards
-   - Implement data archival strategies
-
-4. **Connection Errors**
-   - Verify API key and environment configuration
-   - Check network connectivity and firewall settings
-   - Implement retry logic with exponential backoff
 
 ### Performance Tuning
 ```python