Vectordb-bench

1. Add Chroma and Redis Client
2. Add 6 more cases and results

|cases|dataset|filter|description|
|-- | -- | -- | --|
Search Performance Case | OpenAI generated 500K vectors, 1536 dimensions | N/A | Index building time, recall, latency, maximum QPS
Search Performance Case | OpenAI generated 5M vectors, 1536 dimensions | N/A | Index building time, recall, latency, maximum QPS
Filtering Search Performance Case | OpenAI generated 500K vectors, 1536 dimensions | 1% vectors | Index building time, recall, latency, maximum QPS
Filtering Search Performance Case | OpenAI generated 5M vectors, 1536 dimensions | 1% vectors | Index building time, recall, latency, maximum QPS
Filtering Search Performance Case | OpenAI generated 500K vectors, 1536 dimensions | 99% vectors | Index building time, recall, latency, maximum QPS
Filtering Search Performance Case | OpenAI generated 5M vectors, 1536 dimensions | 99% vectors | Index building time, recall, latency, maximum QPS

What's New
1. Add pgvector client and benchmark results
2. Support Leaderborad: https://zilliz.com/benchmark
3. Retest and update qdrant, pinecone, milvus, and zillizcloud results
4. Add Timeout for all cases

Enhancement
1. Sync all process before multi-processing search starts.
2. Refine DataSet

- Add Pinecone results
- Remove results of 100K
- Enable to save results as one image
- Enable to select all, deselect all filters