I have multiple CSV files and data dictionary describin...

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I have multiple CSV files and data dictionary describing about each column . I want to use only open source llm and Rag to create conversational chatbot with memory. It should be able to perform aggregations also on my CSV data files

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kkapa.ai

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kkapa.ai

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Based on the information provided in the extracts, you can use LlamaIndex and RAG (Retrieval-Augmented Generation) to create a conversational chatbot with memory. However, the specific details on how to perform aggregations on your CSV data files are not provided in the extracts. Here's a general approach based on the information available:

Loading: Load your CSV data into your pipeline. LlamaHub provides hundreds of connectors to choose from, but the specific method for CSV files is not mentioned in the extracts. You might need to convert your CSV files into a format that LlamaIndex can work with.

Indexing: Create a data structure that allows for querying the data. For LLMs this nearly always means creating vector embeddings, numerical representations of the meaning of your data, as well as numerous other metadata strategies to make it easy to accurately find contextually relevant data.

Storing: Once your data is indexed you will almost always want to store your index, as well as other metadata, to avoid having to re-index it.

Querying: For any given indexing strategy there are many ways you can utilize LLMs and LlamaIndex data structures to query, including sub-queries, multi-step queries and hybrid strategies.

Evaluation: A critical step in any pipeline is checking how effective it is relative to other strategies, or when you make changes. Evaluation provides objective measures of how accurate, faithful and fast your responses to queries are.

For the chatbot functionality, you can use the llm.chat() function as shown in the extracts. However, the specific details on how to integrate this with RAG and your CSV data are not provided in the extracts.

kkapa.ai

For more detailed instructions, you might want to refer to the LlamaIndex documentation () and the example notebooks on GitHub ().

Please note that this is a high-level approach and the specific implementation details might vary based on your exact requirements and the structure of your CSV files.
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TTech explorer

How can I pass data dictionary, so my CSV columns makes more sense and understands what each column refers to

TTech explorer

@kapa.ai How can I pass data dictionary, so my CSV columns makes more sense and understands what each column refers to

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the information provided in the extracts, LlamaIndex supports structured data and can work with data dictionaries. However, the specific details on how to pass a data dictionary to make your CSV columns more understandable are not provided in the extracts.

In general, you would need to create a structured data format that includes both your CSV data and the data dictionary. This could be done by creating a DataFrame or a similar data structure where each column is annotated with the corresponding description from the data dictionary.

Here's an example of how you might structure your data based on the information provided in the extracts:

Plain Text

DataFrame with args: {
  "columns": [
    {
      "column_name": "Name",
      "column_desc": "Name of the person"
    },
    {
      "column_name": "Age",
      "column_desc": "Age of the person"
    },
    ...
  ],
  "rows": [
    {
      "row_values": ["John", 25, ...]
    },
    ...
  ]
}

In this example, each column in the DataFrame is associated with a description ("column_desc") from the data dictionary.

Please note that this is a high-level approach and the specific implementation details might vary based on your exact requirements and the structure of your CSV files and data dictionary.

For more detailed instructions, you might want to refer to the LlamaIndex documentation () and the example notebooks on GitHub ().
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TTech explorer

@kapa.ai how to include memory also. Like how can my pandas query engine takes previous question and answers into context

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the information provided in the extracts, LlamaIndex supports the concept of "deep memory" which can be used to improve the accuracy of the model by taking into account the context of previous questions and answers.

In the provided extracts, the "deep memory" feature is demonstrated with the vector_index.as_query_engine() method, where the vector_store_kwargs parameter is set to {"deep_memory": True}. Here's the example code:

Plain Text

query_engine = vector_index.as_query_engine(
    vector_store_kwargs={"deep_memory": True}
)
response = query_engine.query(
    "How can you connect your own storage to the deeplake?"
)
print(response)

In this example, the query engine is set up with "deep memory" enabled, which means it will take into account the context of previous questions and answers when generating responses.

However, the specific details on how to use "deep memory" with the PandasQueryEngine are not provided in the extracts. You might need to refer to the LlamaIndex documentation () and the example notebooks on GitHub () for more detailed instructions.

Please note that this is a high-level approach and the specific implementation details might vary based on your exact requirements.
----------------------
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TTech explorer

@kapa.ai how to set Query pipeline to use max number of workers available

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the information provided in the extracts, you can set the number of workers for parallel execution in the pipeline.run() method. Here's an example:

Plain Text

nodes = pipeline.run(documents=documents, num_workers=4)

In this example, num_workers is set to 4, which means the pipeline will use 4 workers for parallel execution.

However, the specific details on how to set num_workers to the maximum number of workers available are not provided in the extracts. In Python, you can usually get the number of available processors with os.cpu_count(), but it's not clear from the extracts whether you can pass this value directly to num_workers.

Here's an example of how you might do it based on general Python knowledge:

Plain Text

import os

num_workers = os.cpu_count()
nodes = pipeline.run(documents=documents, num_workers=num_workers)

Please note that this is a high-level approach and the specific implementation details might vary based on your exact requirements.

For more detailed instructions, you might want to refer to the LlamaIndex documentation () and the example notebooks on GitHub ().
----------------------
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TTech explorer

@kapa.ai
How to restrict pandas query engine to answer only questions related to data and not hallucinating

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

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Based on the information provided in the extracts, the PandasQueryEngine in LlamaIndex is designed to convert natural language queries into Pandas python code and execute them on a given DataFrame. This inherently restricts the engine to only answer questions related to the data in the DataFrame.

However, the specific details on how to prevent the engine from "hallucinating", or generating responses that are not based on the data, are not provided in the extracts.

In general, the accuracy of the responses generated by the PandasQueryEngine will depend on the quality of the LLM model used, as well as the quality and relevance of the data in the DataFrame.

It's also worth noting that the PandasQueryEngine has measures in place to enforce safety and prevent arbitrary code execution. For instance, it does not allow the execution of private/dunder methods, and it restricts access to a limited set of globals.

For more detailed instructions, you might want to refer to the LlamaIndex documentation () and the example notebooks on GitHub ().
----------------------
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----------------------

TTech explorer

@kapa.ai how can I make chat_engine execution fast by using all cpu cores

kkapa.ai

Let me check the knowledge base for you. One moment! :books:

kkapa.ai

@Tech explorer That's a great question, but unfortunately I am only capable of responding to a maximum of five questions per thread. Don't hesitate to create a new thread for any further questions you have though. I'm here to help!

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I have multiple CSV files and data dictionary describing about each column . I want to use