Collect the data from Chroma db to analyze the data via pandas query pipe line.

[mraguth]

As a Data Scientist with a passion for Python, I find myself captivated by the capabilities of the pandas query pipeline. The dynamic retrieval and contextual summarization of data findings within this pipeline are truly remarkable. In the example provided in the link, structured data (or CSV via pandas DataFrame) is utilized.

My question pertains to whether it is feasible to gather data from ChromaDB and apply the same pandas pipeline methodology. We have a specific use case where all our structured and unstructured data is stored in ChromaDB. Now we like to collect the data from Chromadb and analyze via 'Pandas query pipe line'. Please help.

https://docs.llamaindex.ai/en/stable/examples/pipeline/query_pipeline_pandas/?h=pandas+query

[dosubot[bot]]

To analyze data from ChromaDB using the pandas query pipeline, follow these steps:

1. Initialize ChromaReader: Create an instance of ChromaReader from the llama_index.readers.chroma.base module, specifying your collection name and, if applicable, the directory where your collection is persisted.

   from llama_index.readers.chroma.base import ChromaReader
   
   reader = ChromaReader(collection_name='your_collection_name', persist_directory='/path/to/persisted/collection')

2. Load Data: Use the load_data method with your query to retrieve data. This method allows filtering by metadata or document content.

   loaded_data = reader.load_data(query='your_query_string', limit=10)

3. Convert to Pandas DataFrame: Transform the loaded data into a list of dictionaries, then create a DataFrame.

   import pandas as pd
   
   data_dicts = [{'id': doc.id_, 'text': doc.text, 'metadata': doc.metadata} for doc in loaded_data]
   df = pd.DataFrame(data_dicts)

4. Apply Pandas Query Pipeline: With your data in a DataFrame, you can now use pandas' functionality to filter, group, and analyze your data as needed.

   filtered_df = df.query('metadata["metadata_field"] == "desired_value"')

This process combines ChromaDB's data retrieval capabilities with pandas' powerful data analysis tools, allowing for efficient data processing and analysis tailored to your specific needs.

Details

Sources

• llama-index-packs/llama-index-packs-chroma-autoretrieval/llama_index/packs/chroma_autoretrieval/base.py
• llama-index-legacy/llama_index/legacy/readers/chroma.py
• llama-index-integrations/readers/llama-index-readers-chroma/llama_index/readers/chroma/base.py
• llama-index-integrations/readers/llama-index-readers-chroma/pyproject.toml
• llama-index-core/llama_index/core/query_engine/pandas/init.py
• llama-index-legacy/llama_index/legacy/indices/struct_store/pandas.py
• llama-index-legacy/llama_index/legacy/query_engine/pandas/init.py
• llama-index-integrations/readers/llama-index-readers-chroma/llama_index/readers/chroma/init.py

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