From 4bdf34ee4be42f4616aacb6dddb807a4c6e2cad5 Mon Sep 17 00:00:00 2001 From: harshalmore31 Date: Wed, 3 Sep 2025 22:48:51 +0530 Subject: [PATCH] fixed bullets --- docs/rag-vector-databases/chromadb.md | 26 ++++++----------------- docs/rag-vector-databases/faiss.md | 26 ++++++----------------- docs/rag-vector-databases/milvus-cloud.md | 26 ++++++----------------- docs/rag-vector-databases/milvus-local.md | 26 ++++++----------------- docs/rag-vector-databases/pinecone.md | 26 ++++++----------------- 5 files changed, 35 insertions(+), 95 deletions(-) diff --git a/docs/rag-vector-databases/chromadb.md b/docs/rag-vector-databases/chromadb.md index 901b2340..9bf84604 100644 --- a/docs/rag-vector-databases/chromadb.md +++ b/docs/rag-vector-databases/chromadb.md @@ -232,24 +232,12 @@ client = chromadb.PersistentClient( ### Common Issues -1. **Connection Errors** - - Check ChromaDB server status - - Verify network connectivity - - Confirm correct host and port settings - -2. **Performance Issues** - - Monitor collection size and query complexity - - 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 +1. **Connection Errors**: Check ChromaDB server status. Verify network connectivity. Confirm correct host and port settings. + +2. **Performance Issues**: Monitor collection size and query complexity. 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. \ No newline at end of file diff --git a/docs/rag-vector-databases/faiss.md b/docs/rag-vector-databases/faiss.md index 866e6e69..d8b97a3d 100644 --- a/docs/rag-vector-databases/faiss.md +++ b/docs/rag-vector-databases/faiss.md @@ -256,24 +256,12 @@ index = faiss.IndexIVFPQ(quantizer, dimension, nlist, m, nbits) ### Common Issues -1. **Memory Errors** - - Reduce batch sizes or use product quantization - - Consider using memory mapping for large indices - - Monitor system memory usage - -2. **Slow Search Performance** - - Check if IVF index is properly trained - - 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) +1. **Memory Errors**: Reduce batch sizes or use product quantization. Consider using memory mapping for large indices. Monitor system memory usage. + +2. **Slow Search Performance**: Check if IVF index is properly trained. 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. \ No newline at end of file diff --git a/docs/rag-vector-databases/milvus-cloud.md b/docs/rag-vector-databases/milvus-cloud.md index 3f0a69d4..6575b5a7 100644 --- a/docs/rag-vector-databases/milvus-cloud.md +++ b/docs/rag-vector-databases/milvus-cloud.md @@ -275,24 +275,12 @@ def monitored_search(memory, query, **kwargs): ### Common Issues -1. **Connection Errors** - - Verify MILVUS_CLOUD_URI and MILVUS_CLOUD_TOKEN - - Check network connectivity and firewall settings - - Confirm cloud region accessibility - -2. **Performance Issues** - - Monitor collection size and index type appropriateness - - 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 +1. **Connection Errors**: Verify MILVUS_CLOUD_URI and MILVUS_CLOUD_TOKEN. Check network connectivity and firewall settings. Confirm cloud region accessibility. + +2. **Performance Issues**: Monitor collection size and index type appropriateness. 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. \ No newline at end of file diff --git a/docs/rag-vector-databases/milvus-local.md b/docs/rag-vector-databases/milvus-local.md index 1f5a858a..b8f36765 100644 --- a/docs/rag-vector-databases/milvus-local.md +++ b/docs/rag-vector-databases/milvus-local.md @@ -295,24 +295,12 @@ print(f"Document count: {stats['row_count']}") ### Common Issues -1. **Database File Errors** - - Check file permissions and disk space - - Ensure directory exists before creating database - - Handle concurrent access properly - -2. **Performance Issues** - - Monitor database size relative to available memory - - 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 +1. **Database File Errors**: Check file permissions and disk space. Ensure directory exists before creating database. Handle concurrent access properly. + +2. **Performance Issues**: Monitor database size relative to available memory. 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. \ No newline at end of file diff --git a/docs/rag-vector-databases/pinecone.md b/docs/rag-vector-databases/pinecone.md index 61b1cad4..debf7290 100644 --- a/docs/rag-vector-databases/pinecone.md +++ b/docs/rag-vector-databases/pinecone.md @@ -346,25 +346,13 @@ class MonitoredPineconeMemory(PineconeMemory): ### Common Issues -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 - -2. **High Latency** - - Check pod type and consider upgrading - - 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 +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. + +2. **High Latency**: Check pod type and consider upgrading. 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