Can I use llama_index as JSON parser to move the JSON to structured data?

[mraguth]

I am referring link below. Is that possible to parse the entire JSON and create pandas data frame? thoughts?

https://docs.llamaindex.ai/en/stable/examples/query_engine/JSONalyze_query_engine/

[dosubot[bot]]

Yes, you can use llama_index to parse JSON data and convert it into structured data, such as a pandas DataFrame. The JSONReader class in llama_index is designed to facilitate this process.

Here is an example of how you might use JSONReader to parse JSON data and convert it into a pandas DataFrame:

import json
import pandas as pd
from llama_index.readers.json.base import JSONReader

# Example JSON content
json_content = """
{
    "items": [
        {
            "Column1": "Value1",
            "Column2": "Value2"
        },
        {
            "Column1": "Value3",
            "Column2": "Value4"
        }
    ]
}
"""

# Load JSON content
data = json.loads(json_content)

# Convert JSON to DataFrame
df = pd.DataFrame(data["items"])

# Initialize the JSONReader
json_reader = JSONReader()

# Print the DataFrame
print(df)

This code will parse the JSON content, extract the data, and convert it into a pandas DataFrame using the JSONReader class from llama_index ^[1][2][3].

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[mraguth]

@dosu I am looking this blog.
https://docs.llamaindex.ai/en/stable/examples/query_engine/json_query_engine/

How I do above via LLM? instead of simple llamda parser? Any thoughts?

[dosubot[bot]]

To perform JSON queries using an LLM instead of a simple JSON parser, you can follow the steps outlined in the JSON Query Engine with LLM blog. Here is a detailed example:

1. Install the necessary packages:

   %pip install llama-index-llms-openai
   !pip install llama-index
   !pip install jsonpath-ng

2. Set up logging and environment variables:

   import logging
   import sys
   import os
   import openai
   
   logging.basicConfig(stream=sys.stdout, level=logging.INFO)
   logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
   
   os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE"

3. Prepare your JSON data and schema:

   json_value = {
       "blogPosts": [
           {
               "id": 1,
               "title": "First blog post",
               "content": "This is my first blog post",
           },
           {
               "id": 2,
               "title": "Second blog post",
               "content": "This is my second blog post",
           },
       ],
       "comments": [
           {
               "id": 1,
               "content": "Nice post!",
               "username": "jerry",
               "blogPostId": 1,
           },
           {
               "id": 2,
               "content": "Interesting thoughts",
               "username": "simon",
               "blogPostId": 2,
           },
           {
               "id": 3,
               "content": "Loved reading this!",
               "username": "simon",
               "blogPostId": 2,
           },
       ],
   }
   
   json_schema = {
       "$schema": "http://json-schema.org/draft-07/schema#",
       "description": "Schema for a very simple blog post app",
       "type": "object",
       "properties": {
           "blogPosts": {
               "description": "List of blog posts",
               "type": "array",
               "items": {
                   "type": "object",
                   "properties": {
                       "id": {
                           "description": "Unique identifier for the blog post",
                           "type": "integer",
                       },
                       "title": {
                           "description": "Title of the blog post",
                           "type": "string",
                       },
                       "content": {
                           "description": "Content of the blog post",
                           "type": "string",
                       },
                   },
                   "required": ["id", "title", "content"],
               },
           },
           "comments": {
               "description": "List of comments on blog posts",
               "type": "array",
               "items": {
                   "type": "object",
                   "properties": {
                       "id": {
                           "description": "Unique identifier for the comment",
                           "type": "integer",
                       },
                       "content": {
                           "description": "Content of the comment",
                           "type": "string",
                       },
                       "username": {
                           "description": "Username of the commenter (lowercased)",
                           "type": "string",
                       },
                       "blogPostId": {
                           "description": "Identifier for the blog post to which the comment belongs",
                           "type": "integer",
                       },
                   },
                   "required": ["id", "content", "username", "blogPostId"],
               },
           },
       },
       "required": ["blogPosts", "comments"],
   }

4. Initialize the JSON Query Engine with the LLM:

   from llama_index.llms.openai import OpenAI
   from llama_index.core.query_engine.jsonalyze_query_engine import JSONalyzeQueryEngine
   
   llm = OpenAI(model="gpt-4")
   
   nl_query_engine = JSONalyzeQueryEngine(
       list_of_dict=json_value,
       llm=llm,
   )
   raw_query_engine = JSONalyzeQueryEngine(
       list_of_dict=json_value,
       llm=llm,
       synthesize_response=False,
   )

5. Perform the query:

   nl_response = nl_query_engine.query(
       "What comments has Jerry been writing?",
   )
   raw_response = raw_query_engine.query(
       "What comments has Jerry been writing?",
   )

6. Display the responses:

   from IPython.display import Markdown, display
   
   display(
       Markdown(f"<h1>Natural language Response</h1><br><b>{nl_response}</b>")
   )
   display(Markdown(f"<h1>Raw JSON Response</h1><br><b>{raw_response}</b>"))

7. Get the JSON Path query string:

   # get the json path query string. Same would apply to raw_response
   print(nl_response.metadata["sql_query"])

This setup allows you to use an LLM to convert natural language queries into structured SQL queries and retrieve the relevant data from your JSON documents ^[1][2][3].

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[mraguth]

@dosu. Here my objective is get JSON entire schema in to sturctured pandas dataframe. Please help.

[dosubot[bot]]

To convert an entire JSON schema into a structured pandas DataFrame using the Llama Index platform and an LLM, you can utilize the LabelledSimpleDataset class from the llama_index.core.llama_dataset.simple module. Here is an example of how you might achieve this:

from llama_index.core.llama_dataset.simple import LabelledSimpleDataset, LabelledSimpleDataExample
import pandas as pd

# Assuming you have a JSON schema as a dictionary
json_schema = {
    "reference_label": "Label1",
    "text": "Sample text",
    "text_by": "Generated by LLM"
    # Add more fields as needed
}

# Create a list of LabelledSimpleDataExample instances
examples = [LabelledSimpleDataExample(**json_schema)]

# Create a LabelledSimpleDataset instance
labelled_simple_dataset = LabelledSimpleDataset(examples=examples)

# Convert the dataset to a pandas DataFrame
df = labelled_simple_dataset.to_pandas()

# Display the DataFrame
print(df)

This code snippet demonstrates how to use the LabelledSimpleDataset class to convert a JSON schema into a pandas DataFrame. Adjust the json_schema dictionary to match your actual JSON schema ^[1].

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