[Question]: How to speed up loading&creating VectorStoreIndex?

[TianyuFan0504]

Question Validation

• [X] I have searched both the documentation and discord for an answer.

Question

@dosu-bot

As the external data gets bigger and bigger the VectorStoreIndex seems to be getting very slow to create and read, I understand that it takes time to create but why is reading so slow as well? Is there any way to speed it up?

For example, I have a total of 1.5G of text files under '../datasets', which takes about 4000 seconds to create, and about 500s to save and read the created VectorStoreIndex.

Here is my code and settings

llm = OpenAI(temperature=0, 
             model="gpt-3.5-turbo"
             )
embed_model = HuggingFaceEmbedding(model_name='facebook/contriever')
Settings.llm = llm
Settings.embed_model = embed_model
Settings.node_parser = SentenceSplitter(chunk_size=2048, chunk_overlap=20)
Settings.context_window = 8192
Settings.num_output = 512
Settings.tokenizer = tiktoken.encoding_for_model("gpt-3.5-turbo").encode

documents = SimpleDirectoryReader("../datasets/").load_data()
nodes = Settings.node_parser.get_nodes_from_documents(documents)
#Creating:
index = VectorStoreIndex(nodes,include_embeddings=True)#it takes 4000s
index.storage_context.persist(persist_dir="../storage/")
#Reading:
index = load_index_from_storage(StorageContext.from_defaults(persist_dir="../storage/"))#it takes 500s

[dosubot[bot]]

To address the slow creation and reading times of the VectorStoreIndex with large datasets, consider the following optimizations:

1. Parallel Processing: Utilize parallel processing to expedite the index creation. This involves dividing your data into smaller chunks and processing them simultaneously across multiple CPU cores.

2. Incremental Indexing: Build the index incrementally by adding data in smaller batches, which can be more efficient than processing everything at once.

3. Optimize Reading: Improve read times by using techniques like memory mapping (mmap) to map the index file into memory, which can significantly reduce loading times.

4. Efficient Data Structures: Employ data structures that support fast search and insertion operations to enhance both creation and reading speeds.

5. Hardware Acceleration: If available, leverage GPUs for hardware acceleration to further speed up processing.

Here's a practical example of implementing parallel processing and incremental indexing:

from multiprocessing import Pool
from llama_index.core import VectorStoreIndex

def create_index(nodes):
    return VectorStoreIndex(nodes, include_embeddings=True)

# Split nodes into manageable chunks
chunks = [nodes[i:i + 10000] for i in range(0, len(nodes), 10000)]

# Process chunks in parallel
with Pool(processes=4) as pool:
    indices = pool.map(create_index, chunks)

# Merge the processed indices
index = VectorStoreIndex.merge(indices)

# Persist the index
index.storage_context.persist(persist_dir="../storage/")

These strategies are aimed at optimizing performance by leveraging parallel processing, managing data more efficiently, and utilizing hardware capabilities. Testing different approaches to find the best fit for your specific needs is crucial, as optimizations can vary based on the dataset and system configuration.

Sources

• docs/docs/optimizing/basic_strategies/basic_strategies.md
• llama-index-core/llama_index/core/indices/vector_store/base.py
• llama-index-core/tests/indices/test_loading.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[TianyuFan0504]

To address the slow creation and reading times of the VectorStoreIndex with large datasets, consider the following optimizations:

1. Parallel Processing: Utilize parallel processing to expedite the index creation. This involves dividing your data into smaller chunks and processing them simultaneously across multiple CPU cores.
2. Incremental Indexing: Build the index incrementally by adding data in smaller batches, which can be more efficient than processing everything at once.
3. Optimize Reading: Improve read times by using techniques like memory mapping (mmap) to map the index file into memory, which can significantly reduce loading times.
4. Efficient Data Structures: Employ data structures that support fast search and insertion operations to enhance both creation and reading speeds.
5. Hardware Acceleration: If available, leverage GPUs for hardware acceleration to further speed up processing.

Here's a practical example of implementing parallel processing and incremental indexing:

from multiprocessing import Pool
from llama_index.core import VectorStoreIndex

def create_index(nodes):
    return VectorStoreIndex(nodes, include_embeddings=True)

# Split nodes into manageable chunks
chunks = [nodes[i:i + 10000] for i in range(0, len(nodes), 10000)]

# Process chunks in parallel
with Pool(processes=4) as pool:
    indices = pool.map(create_index, chunks)

# Merge the processed indices
index = VectorStoreIndex.merge(indices)

# Persist the index
index.storage_context.persist(persist_dir="../storage/")

These strategies are aimed at optimizing performance by leveraging parallel processing, managing data more efficiently, and utilizing hardware capabilities. Testing different approaches to find the best fit for your specific needs is crucial, as optimizations can vary based on the dataset and system configuration.

Sources

• docs/docs/optimizing/basic_strategies/basic_strategies.md
• llama-index-core/llama_index/core/indices/vector_store/base.py
• llama-index-core/tests/indices/test_loading.py

About Dosu

This response is meant to be useful and save you time. It is not meant to be a precise solution, but rather a starting point for your own research.

Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant.

If you want to continue the conversation, start your reply with @dosu-bot.

@dosu-bot What about an acceleration method when reading VectorStoreIndex? Do you have any suggestions?

[dosubot[bot]]

To enhance the reading speed of VectorStoreIndex, consider these targeted strategies:

1. Batch Processing Enhancements: Fine-tune the batch sizes during data processing to balance memory usage and read operations, optimizing for your specific workload and hardware.

2. Caching Strategies: Implement caching for frequently accessed data to minimize disk reads and expensive computations, though this will increase memory usage.

3. Data Compression: If disk I/O or network bandwidth is a bottleneck, compressing the data in the vector store can reduce data transfer volumes, using algorithms like LZ4 or Snappy for a balance of speed and compression ratio.

4. Optimize Data Layout: Reorganize the data layout in the vector store for more efficient reads, potentially grouping related vectors together to reduce read overhead.

5. Profiling and Hotspot Analysis: Use profiling tools to identify and optimize bottlenecks in the reading process, focusing on slow functions or lines of code.

6. Database and Storage Engine Tuning: Ensure the underlying database or storage engine is optimally configured for caching, indexing, and concurrency, or consider switching to a more suitable engine based on your access patterns.

7. Efficient Serialization/Deserialization Formats: Use efficient formats like Protocol Buffers or FlatBuffers for faster serialization and deserialization compared to JSON or XML.

8. Algorithmic Improvements: Explore advanced data structures and algorithms, such as KD-trees, R-trees, or approximate nearest neighbor (ANN) algorithms, for potential speedups in indexing and querying.

These optimizations should be tested for their impact on performance in the context of your specific application, balancing optimization efforts with maintainability and complexity.

Sources

• llama-index-core/llama_index/core/indices/vector_store/base.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-docarray/llama_index/vector_stores/docarray/init.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[TianyuFan0504]

@dosu-bot The main time consumption seems to be in the part where getting nodes from documen (see the code), do you have any suggestions about speeding up this part?

documents = SimpleDirectoryReader("../datasets/").load_data()
nodes = Settings.node_parser.get_nodes_from_documents(documents)

[dosubot[bot]]

To speed up the get_nodes_from_documents part of your process, consider these targeted optimizations:

1. Parallel Processing: Use Python's multiprocessing or concurrent.futures modules to distribute document processing across multiple CPU cores, significantly reducing overall processing time.

2. Efficient Text Processing: Optimize the text processing logic within your node parsing to minimize complex operations and avoid unnecessary computations. Batch common preprocessing steps before indexing to save time.

3. Data Loading and Storage: Load documents in chunks to manage memory usage better, and consider using memory-mapped files or efficient databases for large datasets that don't fit into memory.

4. Profiling and Monitoring: Employ profiling tools like Python's cProfile or line_profiler to pinpoint slow function calls within get_nodes_from_documents. Monitoring system resources during processing can also highlight performance bottlenecks.

For implementation specifics, especially around parallel processing and efficient text handling, refer to the relevant sections of your project's documentation or source code. These optimizations should be tailored to your dataset and processing environment for best results.

Sources

• llama-index-core/tests/indices/vector_store/test_retrievers.py
• llama-index-legacy/tests/readers/test_file.py
• llama-index-legacy/llama_index/legacy/node_parser/interface.py

#### About Dosu

This response is meant to be useful and save you time. It is **not meant to be a precise solution**, but rather a starting point for your own research. Help me be more useful! Please leave a 👍 if this is helpful and 👎 if it is irrelevant. If you want to continue the conversation, start your reply with @dosu-bot.

[logan-markewich]

@TianyuFan0504 You are using a local model, so it is going to be limited by your hardaware. If you are running on CPU, it will be very slow (it should be running on GPU automatically if you have CUDA installed)

You can also try increasing the batch size for embeddings, but note you may run into memory issues if its too big

embed_model = HuggingFaceEmbedding(model_name='facebook/contriever', embed_batch_size=20)

[logan-markewich]

Oh, is the main time is in loading documents?

It really depends on what kinds of documents you are loading. You can try increasing the num workers

documents = SimpleDirectoryReader("../datasets/").load_data(num_workers=4)

[TianyuFan0504]

Oh, is the main time is in loading documents?

It really depends on what kinds of documents you are loading. You can try increasing the num workers

documents = SimpleDirectoryReader("../datasets/").load_data(num_workers=4)

hi @logan-markewich, thanks for your help, which helps me lot : )

After further testing, I found that the function get_node_from_documents consumes almost 60% of the time, similar to what is mentioned in these 2 issues: https://github.com/run-llama/llama_index/issues/11025 and https://github.com/run-llama/llama_index/issues/11844.

If I understand correctly, it seems that this function doesn't support running in parallel? I'm trying to build a parallelizable version of this function, but I'm not sure if it's a duplicate of what's already out there.

documents = SimpleDirectoryReader("../datasets").load_data()
nodes = Settings.node_parser.get_nodes_from_documents(documents,show_progress = True)#consumes almost 60% of the time
index = VectorStoreIndex(nodes,include_embeddings=True)

BTW, I found that as the number of documents gets progressively larger, the time savings from specifying or not specifying num_workers in the function SimpleDirectoryReader.load_data() becomes less and less. I'm looking into the cause of this.

[ksjadeja]

You are using simple SentenceSplitter and you can use pipeline(IngestionPipeline) to use the num_workers parameter and there would be significant speedup. The reason I am facing this issue is because I want to use SemanticSplitterNodeParser and the way SemanticSplitterNodeParser is implemented and hence multiprocessing fails.

[TianyuFan0504]

It works! Thank you @ksjadeja : )