Structured data story telling by llama_index!!!

[mraguth]

While llamaindex excels in unstructured data mining, I am currently exploring its potential for structured data mining, a domain where most corporates store around 90% of their data in structured mode. I have identified promising capabilities in storytelling by integrating response synthesis with llamaindex to impress management with insightful data narratives. In my approach, I leverage Python and pandas for query pipelines extensively.

However, transitioning this initiative to a production-ready state poses challenges, especially when dealing with tables containing millions of rows. To address this, I'm contemplating whether embedding and storing structured data in a vector database would be a viable solution. I am uncertain if this approach is necessary since pandas typically processes only a few lines of code for execution rather than the entire dataset.

In a previous attempt, I stored my structured data in a vector database but encountered issues where only the top record was retrieved despite multiple records falling under the same category. This experience has led me to seek solutions to two key problems: efficiently handling large tables and ensuring compatibility in storing and querying the data.

I would greatly appreciate your expert advice on how best to address these challenges and elevate this project to a scalable and efficient production level.

[dosubot[bot]]

To address your challenges with large tables and data compatibility in your structured data storytelling project, integrating LlamaIndex with DuckDB offers a promising solution. This approach leverages DuckDB's vector search capabilities, enhancing your project's scalability and efficiency. Here's a concise guide to get you started:

1. Install DuckDB: Ensure DuckDB is correctly installed and operational, as it will serve as the backbone for handling large datasets efficiently.

2. Configure DuckDBVectorStore: Use the DuckDBVectorStore class for integrating DuckDB's vector search into your project. This step is crucial for managing large volumes of data and facilitating fast, relevant search results.

3. Indexing and Data Import: Create indexes on your data within DuckDB and import your data using the methods provided by DuckDBVectorStore. DuckDB's columnar storage is particularly beneficial for analytics and vector operations, making it suitable for your needs.

4. Querying: Take advantage of DuckDB's SQL interface for efficient data querying. Its columnar storage and execution model support complex analytical queries, enhancing your data storytelling capabilities.

5. Maintenance: Although DuckDB is designed to be low-maintenance, regular management of your data and queries is recommended to maintain optimal performance.

This setup not only improves the scalability and efficiency of your project but also broadens your querying capabilities, making it more suitable for a production environment. For detailed implementation, refer to the LlamaIndex integrations directory, specifically the tests and examples provided for DuckDB integration. This will give you a practical understanding of how to apply these concepts to your project.

Details

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-supabase/tests/test_vector_stores_supabase.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-neo4jvector/tests/test_vector_stores_neo4jvector.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-databricks/BUILD
• llama-index-legacy/llama_index/legacy/vector_stores/neo4jvector.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/tests/test_vector_stores_duckdb.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/pyproject.toml
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/tests/test_duckdb.py
• docs/docs/examples/vector_stores/DuckDBDemo.ipynb
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/examples/DuckDBDemo.ipynb
• docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb

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[dosubot]

To resolve the issues you're encountering with DuckDBVectorStore initialization and usage in your structured data storytelling project, follow these steps:

1. Ensure Necessary Packages Are Installed: Make sure you have duckdb and llama-index-vector-stores-duckdb installed. If not, install them using:

   pip install duckdb
   pip install llama-index-vector-stores-duckdb

2. Correct Initialization of DuckDBVectorStore: When initializing DuckDBVectorStore, specify the embed_dim parameter to match the dimensionality of your embeddings. For example:

   from llama_index.vector_stores.duckdb import DuckDBVectorStore
   
   # For 3-dimensional embeddings
   vector_store = DuckDBVectorStore(embed_dim=3)

3. Adding Data: Ensure your data nodes are properly formatted. Use the add method to add your data:

   from llama_index.core.schema import TextNode
   
   text_node_list = [
       TextNode(
           text="Example text",
           id_="example-id",
           embedding=[0.1, 0.2, 0.3],
           metadata={"author": "Example Author"}
       ),
       # Add more TextNode instances as needed
   ]
   
   vector_store.add(text_node_list)

4. Querying Data: Use the query method with a query embedding to retrieve similar items:

   from llama_index.core.vector_stores.types import VectorStoreQuery
   
   query_embedding = [0.1, 0.2, 0.3]  # Example query embedding
   top_k = 5
   result = vector_store.query(VectorStoreQuery(query_embedding=query_embedding, similarity_top_k=top_k))

Ensure each parameter, especially embed_dim, is clearly defined as a keyword argument to avoid TypeError. This approach should help you effectively use DuckDBVectorStore with LlamaIndex for your project.

Details

Sources

• [Bug]: DuckDBVectorStore fails with error: missing 1 required positional argument: 'embed_dim'
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/tests/test_duckdb.py
• llama-index-core/pyproject.toml

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.

To continue the conversation, mention @dosu.

[mraguth]

@dosu Able to make it finally. One last question. In case I like to use this duckdb vectordata via llamaindex pandas query pipe line, I have to convert 'duckdb' vectordb to 'pandas' and flow through 'pandas query pipe line'. Can you cofirm please?

[dosubot]

You don't need to convert DuckDB vector data to pandas DataFrame format to use it with the LlamaIndex pandas query pipeline. The DuckDBVectorStore in LlamaIndex is designed to interact directly with DuckDB, leveraging its SQL capabilities for operations like adding, deleting, and querying nodes based on embeddings. This process bypasses the need for pandas DataFrames, as queries are executed on DuckDB and results are returned as VectorStoreQueryResult objects, streamlining your data processing workflow.

Details

Sources

• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/llama_index/vector_stores/duckdb/base.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/tests/test_duckdb.py
• llama-index-integrations/vector_stores/llama-index-vector-stores-duckdb/examples/DuckDBDemo.ipynb
• docs/docs/examples/vector_stores/DuckDBDemo.ipynb
• docs/docs/examples/index_structs/struct_indices/duckdb_sql_query.ipynb

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.

To continue the conversation, mention @dosu.

[mraguth]

I'm excited about the concept. In our experimentation with mining structured data, the Pandas query pipeline demonstrates significantly higher efficiencies compared to SQL, especially in advanced algorithms (potentially due to the nature of Pandas' efficiency). I'm keen on exploring the connection of a vector database through the Pandas query pipeline. If this isn't feasible, I may need to reconsider this duckdb architecture, and that is ok.