What are the best Practices for Handling Large Data Tables via Pandas query pipe line process?

[mraguth]

We utilize structured data queries through a Pandas DataFrame query pipeline and have observed promising results.

However, our data tables are huge, containing millions of rows. What are the best practices for handling these production pipelines?

Should we continue using the same method, or would it be more effective to convert our structured data into unstructured embeddings?

Any suggestions would be greatly appreciated.

[dosubot[bot]]

For handling large data tables via the Pandas query pipeline, here are some best practices:

1. Chunk Processing: Process data in chunks to avoid memory overload. Use pd.read_csv() with the chunksize parameter to read large files in smaller, manageable chunks.

   for chunk in pd.read_csv('large_file.csv', chunksize=100000):
       process(chunk)

2. Efficient Data Types: Optimize memory usage by using appropriate data types. Convert columns to more memory-efficient types, such as category for categorical data and float32 for floating-point numbers.

   df['category_column'] = df['category_column'].astype('category')
   df['float_column'] = df['float_column'].astype('float32')

3. Indexing: Use indexing to speed up query operations. Set an index on columns that are frequently used in queries.

   df.set_index('column_name', inplace=True)

4. Parallel Processing: Utilize parallel processing libraries like Dask or Modin to distribute the workload across multiple cores or machines.

   import dask.dataframe as dd
   ddf = dd.read_csv('large_file.csv')
   result = ddf.groupby('column_name').sum().compute()

5. Memory Management: Use in-place operations and drop unnecessary columns to free up memory.

   df.drop(columns=['unnecessary_column'], inplace=True)

6. Hybrid Search: Combine semantic search (embedding similarity) with keyword search to improve retrieval accuracy. This can be achieved using vector databases with hybrid search functionality or setting up a local hybrid search mechanism with BM25.

Regarding whether to continue using the Pandas query pipeline or convert structured data into unstructured embeddings, it depends on your specific use case:

• Pandas Query Pipeline: Best for structured data where relational operations and complex queries are required.
• Unstructured Embeddings: Useful for semantic search and when dealing with unstructured data or when you need to leverage machine learning models for tasks like classification or clustering.

LlamaIndex provides built-in functionalities for converting structured data into unstructured embeddings, such as the generate_qa_embedding_pairs function and the IntelEmbedding class ^[1][2].

For handling large data tables, LlamaIndex also recommends specific optimizations like adjusting chunk sizes, using hybrid search, and applying metadata filters ^[3].

By following these practices, you can effectively manage and query large datasets using the Pandas query pipeline or consider transitioning to unstructured embeddings based on your needs.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu thank you. But my question is 'what are the best practices for llamaindex to read millions of records in one shot and give better query results?'

[dosubot[bot]]

To use LlamaIndex to read millions of records in one shot and provide better query results, follow these best practices:

1. Decouple Chunks for Retrieval and Synthesis:

   • Embed a document summary: This helps in retrieving relevant documents at a high level before retrieving specific chunks.
   • Embed a sentence: This allows for finer-grained retrieval of relevant context, ensuring enough context for LLM synthesis.

   from llama_index import DocumentSummaryIndex
   index = DocumentSummaryIndex.from_documents(documents)

2. Structured Retrieval for Larger Document Sets:

   • Metadata Filters + Auto Retrieval: Tag each document with metadata and use these tags to filter documents during retrieval.
   • Store Document Hierarchies: Embed document summaries and map them to chunks per document, fetching at the document level first.

   from llama_index import ChromaAutoRetriever
   retriever = ChromaAutoRetriever.from_documents(documents)

3. Dynamically Retrieve Chunks Depending on Your Task:

   • Use LlamaIndex's core abstractions like the router module and data agent module to perform task-specific retrieval.

   from llama_index import RouterQueryEngine
   query_engine = RouterQueryEngine.from_documents(documents)

4. Optimize Context Embeddings:

   • Fine-tune the embedding model to better capture the salient properties of your data corpus.

   from llama_index import EmbeddingFineTuner
   fine_tuner = EmbeddingFineTuner.from_documents(documents)
   fine_tuner.fine_tune()

5. Utilize Specialized Readers and Query Engines:

   • Use specialized readers like SemanticScholarReader to load data efficiently and query engines like CitationQueryEngine to handle large datasets effectively.

   import os
   from llama_index import ServiceContext, StorageContext, VectorStoreIndex, load_index_from_storage
   from llama_index.core.llms import OpenAI
   from llama_index.core.query_engine import CitationQueryEngine
   from llama_index.readers.semanticscholar.base import SemanticScholarReader
   
   s2reader = SemanticScholarReader()
   openai.api_key = os.environ["OPENAI_API_KEY"]
   service_context = ServiceContext.from_defaults(
       llm=OpenAI(model="gpt-3.5-turbo", temperature=0)
   )
   
   query_space = "large language models"
   query_string = "limitations of using large language models"
   full_text = True
   total_papers = 50
   
   persist_dir = "./citation_" + query_space + "_" + str(total_papers) + "_" + str(full_text)
   
   if not os.path.exists(persist_dir):
       documents = s2reader.load_data(query_space, total_papers, full_text=full_text)
       index = VectorStoreIndex.from_documents(documents, service_context=service_context)
       index.storage_context.persist(persist_dir=persist_dir)
   else:
       index = load_index_from_storage(
           StorageContext.from_defaults(persist_dir=persist_dir),
           service_context=service_context,
       )
   
   query_engine = CitationQueryEngine.from_args(
       index,
       similarity_top_k=3,
       citation_chunk_size=512,
   )
   
   response = query_engine.query(query_string)
   print("Answer: ", response)
   print("Source nodes: ")
   for node in response.source_nodes:
       print(node.node.metadata)

By implementing these techniques, you can optimize your retrieval and generation performance, ensuring accurate and efficient query results over large datasets ^[1][2].

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu. This is great. However, how I do this for my pandas query pipeline? I have pandas dataframe to input. Can you give me a similar sample code that you have given above?

[dosubot[bot]]

To integrate LlamaIndex with your Pandas query pipeline, you can follow these steps to handle large datasets efficiently. Here's a sample code that demonstrates how to use LlamaIndex with a Pandas DataFrame:

1. Load Data in Chunks: Read the data in chunks to avoid memory overload.
2. Convert DataFrame to Documents: Convert each chunk of the DataFrame into a list of documents.
3. Create and Use an Index: Create an index from the documents and use it for querying.

Here's a sample code:

import pandas as pd
from llama_index import VectorStoreIndex, SimpleDirectoryReader, Document

# Step 1: Load data in chunks
chunksize = 100000
data_chunks = pd.read_csv('large_file.csv', chunksize=chunksize)

# Step 2: Convert DataFrame chunks to documents
documents = []
for chunk in data_chunks:
    for _, row in chunk.iterrows():
        # Convert each row to a document
        doc = Document(text=row.to_string())
        documents.append(doc)

# Step 3: Create an index from the documents
index = VectorStoreIndex.from_documents(documents)

# Step 4: Query the index
query_engine = index.as_query_engine()
response = query_engine.query("Your query here")
print(response)

Explanation:

1. Loading Data in Chunks: The pd.read_csv function with the chunksize parameter reads the CSV file in smaller chunks.
2. Converting DataFrame to Documents: Each row of the DataFrame is converted to a Document object.
3. Creating and Using an Index: The VectorStoreIndex is created from the list of documents, and the query_engine is used to perform queries.

This approach ensures that you can handle large datasets efficiently by processing them in manageable chunks and leveraging LlamaIndex for querying.

To continue talking to Dosu, mention @dosu.

[mraguth]

@dosu. Thank you again. This is my 'pandas query pipe line' code. How I incorporate your snippet into my code? Sorry, I am new and learning fast :-)

My code:

pandas_prompt = PromptTemplate(pandas_prompt_str).partial_format(
    instruction_str=instruction_str, df_str=df.head(5)
)
pandas_output_parser = PandasInstructionParser(df)
response_synthesis_prompt = PromptTemplate(response_synthesis_prompt_str)


vector_index = VectorStoreIndex(df_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)


vector_index = VectorStoreIndex(doc_nodes + df_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)


recursive_retriever = RecursiveRetriever(
    "vector",
    retriever_dict={"vector": vector_retriever},
    query_engine_dict=df_id_query_engine_mapping,
    verbose=False,                                         
)

response_synthesizer = get_response_synthesizer(response_mode="compact")

[dosubot[bot]]

To incorporate the LlamaIndex snippet into your existing Pandas query pipeline code, you can follow these steps. I'll integrate the necessary parts from the provided snippet into your code:

1. Load Data in Chunks: Read the data in chunks to avoid memory overload.
2. Convert DataFrame to Documents: Convert each chunk of the DataFrame into a list of documents.
3. Create and Use an Index: Create an index from the documents and use it for querying.

Here's how you can modify your code:

import pandas as pd
from llama_index import VectorStoreIndex, Document, RecursiveRetriever, get_response_synthesizer
from llama_index.experimental.query_engine.pandas.output_parser import PandasInstructionParser
from llama_index.legacy.prompts.base import PromptTemplate

# Sample data and prompt strings
pandas_prompt_str = "Your pandas prompt string here"
response_synthesis_prompt_str = "Your response synthesis prompt string here"
instruction_str = "Your instruction string here"

# Step 1: Load data in chunks
chunksize = 100000
data_chunks = pd.read_csv('large_file.csv', chunksize=chunksize)

# Step 2: Convert DataFrame chunks to documents
documents = []
for chunk in data_chunks:
    for _, row in chunk.iterrows():
        # Convert each row to a document
        doc = Document(text=row.to_string())
        documents.append(doc)

# Step 3: Create an index from the documents
vector_index = VectorStoreIndex.from_documents(documents)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)

# Your existing code
pandas_prompt = PromptTemplate(pandas_prompt_str).partial_format(
    instruction_str=instruction_str, df_str=df.head(5)
)
pandas_output_parser = PandasInstructionParser(df)
response_synthesis_prompt = PromptTemplate(response_synthesis_prompt_str)

# Combine document nodes and dataframe nodes if needed
doc_nodes = [Document(text=doc.text) for doc in documents]
df_nodes = [Document(text=row.to_string()) for _, row in df.iterrows()]
vector_index = VectorStoreIndex(doc_nodes + df_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)

# Create RecursiveRetriever
recursive_retriever = RecursiveRetriever(
    "vector",
    retriever_dict={"vector": vector_retriever},
    query_engine_dict=df_id_query_engine_mapping,
    verbose=False,
)

# Get response synthesizer
response_synthesizer = get_response_synthesizer(response_mode="compact")

# Example query
query = "Your query here"
response = recursive_retriever.retrieve_all(query)
print(response)

Explanation:

1. Loading Data in Chunks: The pd.read_csv function with the chunksize parameter reads the CSV file in smaller chunks.
2. Converting DataFrame to Documents: Each row of the DataFrame is converted to a Document object.
3. Creating and Using an Index: The VectorStoreIndex is created from the list of documents, and the vector_retriever is used to perform queries.
4. Integrating with Existing Code: The RecursiveRetriever and get_response_synthesizer are used to handle the retrieval and response synthesis.

This approach ensures that you can handle large datasets efficiently by processing them in manageable chunks and leveraging LlamaIndex for querying.

To continue talking to Dosu, mention @dosu.