How to json serialize a response using pydantic class while using it in a query pipeline

[sohanasarah]

I wrote query pipeline like below: I want to extract the SQL from the metadata so I wrote the output parser with Pydantic class. Now the problem I am using is that the response & the metadata I am passing into output parser has special characters so I get validation error. Below is the code snippets. How to resolve it?

class TableData(BaseModel):
    data: Optional[List[Dict[str, Any]]] = None
    answer: Optional[Union[str, None]] = None

class Answer(BaseModel):
    answer: Optional[Union[TableData, str, None]]
    sql: Union[str, None, Dict]
    
 def query_pipeline():
     def run_vn_fn(query_str: str):
        response = st.session_state.vn_query_engine.custom_query(query_str)
        return response

    vn_component = FnComponent(fn=run_vn_fn)

 # it doesn't work
    def escape_special_characters(data):
        if isinstance(data, str):
            # Replace newlines and other potentially problematic characters
            return (
                data.replace("\n", "\\n")
                .replace("\t", "\\t")
                .replace(":", " ")
                .replace("{", "{{")
                .replace("}", "}}")
                .replace("'", '"')
            )
        elif isinstance(data, dict):
            return {k: escape_special_characters(v) for k, v in data.items()}
        elif isinstance(data, list):
            return [escape_special_characters(item) for item in data]
        else:
            return data  # Return as is if it's not a string, list, or dict

    def get_response(response):
        # Convert response to string and escape special characters
        response_str = response.response
        response_str = escape_special_characters(response_str)
        return response_str

    response_component = FnComponent(fn=get_response)

    def get_sql(response):
        # Initialize an empty string for SQL
        clean_sql = ""

        # Check if metadata is present and contains 'sql'
        if response.metadata and "sql" in response.metadata:
            # Retrieve SQL from metadata
            sql = response.metadata["sql"]
            # Remove newlines and extra spaces
            clean_sql = escape_special_characters(sql)
            if isinstance(clean_sql, dict):
                clean_sql = json.dumps(clean_sql)
            else:
                # clean_sql = clean_sql.strip()
                clean_sql = str(clean_sql)
        return clean_sql

    sql_component = FnComponent(fn=get_sql)

    response_synthesis_prompt_str = """ Given an input question, carefully synthesize an accurate response from the SQL result. 
                     Convert table data into a dictionary format and summarize the key findings in a clear, analytical narrative.
                     If the result is not in a table format, convert it into a readable string format.
                     Provide detailed analysis of the result, identifying trends, anomalies, or significant figures.
                     Do not omit any critical information from the result.
                     Contextualize the data with relevant background information to enhance understanding.
                     Suggest follow-up questions or further data queries based on the initial findings.
                     Use precise measurements and specify units where applicable.

                    Question: {query_str}
                    SQL Result: {response_str}
                    SQL: {sql_str}

                    If table data is present: Return a JSON object with a 'data' key containing an array of records, and an 'answer' key providing a narrative analysis.
                    If no table data: Return a plain text answer using the SQL results and analysis.
                    The final response should be a JSON object with 'answer' containing your response
                    and 'sql' key containing the SQL.
                    Your final response should be in the following json format:"""

    response_synthesis_prompt_str = output_parser.format(response_synthesis_prompt_str)

    response_synthesis_prompt = PromptTemplate(response_synthesis_prompt_str)

    if "query_engine_status" not in st.session_state:
        st.session_state.chat_engine_status = False

    if not st.session_state.chat_engine_status:
        qp = QP(
            modules={
                "input": InputComponent(),
                "vn_component": vn_component,
                "vn_response": response_component,
                "vn_sql": sql_component,
                "response_synthesis_prompt": response_synthesis_prompt,
                "response_synthesis_llm": llm,
                "output_parser": output_parser,
            },
            verbose=True,
        )

        qp.add_link("input", "vn_component", dest_key="query_str")
        qp.add_link("vn_component", "vn_response", dest_key="response")
        qp.add_link("vn_component", "vn_sql", dest_key="response")
        qp.add_link("vn_response", "response_synthesis_prompt", dest_key="response_str")
        qp.add_link("vn_sql", "response_synthesis_prompt", dest_key="sql_str")
        qp.add_link("input", "response_synthesis_prompt", dest_key="query_str")
        qp.add_chain(
            ["response_synthesis_prompt", "response_synthesis_llm", "output_parser"]
        )

[dosubot[bot]]

To address the validation errors you're encountering when trying to serialize response and SQL data with Pydantic, especially with special characters, it's crucial to ensure that the data being serialized conforms to your Pydantic model schema. Here are specific steps to help you troubleshoot and resolve these issues:

1. Review Your Pydantic Model Schema: Double-check that your Answer model accurately reflects the structure and types of the data you're working with. This includes ensuring that optional fields and data types are correctly defined.

2. Manually Validate Input Data: Before serialization, manually inspect the data to ensure it matches the expected schema. This can help pinpoint where the discrepancies that lead to validation errors are.

3. Leverage Pydantic's Detailed Errors: Pydantic provides comprehensive error messages that pinpoint which fields are causing validation issues. Use these messages to adjust your data or model accordingly.

4. Implement Custom Validation: For complex validation needs that go beyond Pydantic's built-in capabilities, consider adding custom validation methods to your model. These can clean, validate, or transform data pre-validation.

5. Refine Data Parsing Logic: If necessary, refine your get_response and get_sql methods to ensure they properly preprocess data for Pydantic parsing. This may involve additional data cleaning or transformation steps.

6. Ensure Proper Escaping of Special Characters: Your current approach to escaping special characters is crucial for avoiding JSON parsing errors. Make sure this logic is correctly applied to all relevant data before serialization.

By following these steps and ensuring your input data strictly adheres to your Pydantic model's schema, you should be able to resolve the validation errors you're facing. For further guidance, the Pydantic documentation is an excellent resource for best practices and advanced usage scenarios.

Details

Sources

• llama-index-legacy/llama_index/legacy/output_parsers/pydantic.py
• llama-index-core/llama_index/core/base/response/schema.py
• llama-index-core/tests/output_parsers/test_pydantic.py
• llama-index-legacy/tests/output_parsers/test_pydantic.py
• llama-index-packs/llama-index-packs-code-hierarchy/tests/test_code_parse_nodes_special_characters.py
• llama-index-core/llama_index/core/response_synthesizers/base.py
• llama-index-core/llama_index/core/indices/struct_store/sql_retriever.py
• llama-index-legacy/llama_index/legacy/indices/struct_store/sql_query.py
• llama-index-legacy/llama_index/legacy/indices/struct_store/sql_retriever.py
• llama-index-core/llama_index/core/indices/struct_store/sql_query.py

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[sohanasarah]

@dosu can I show me an example

[dosubot]

Certainly! To further illustrate how you can manage special characters in your Pydantic models and ensure proper serialization, let's delve into a more detailed example. This will demonstrate how to define your models to accurately handle fields that may contain special characters, leveraging Pydantic's built-in serialization capabilities.

Consider a scenario where you're dealing with text data that includes various special characters, such as quotes or backslashes. The goal is to serialize this data into JSON format without encountering validation errors due to these characters. Here's how you can achieve this:

from pydantic import BaseModel, constr

class MyModel(BaseModel):
    text_field: constr(strip_whitespace=True)

# Example usage
model_instance = MyModel(text_field='Some text with special characters like "quotes" and \\ backslashes.')
json_output = model_instance.json()
print(json_output)

In this example, we use the constr type from Pydantic to define a string field (text_field) with specific constraints—in this case, automatically stripping any leading or trailing whitespace. When you call the .json() method on an instance of MyModel, Pydantic automatically serializes the text_field to JSON, correctly escaping any special characters such as quotes and backslashes. This ensures that the output is a valid JSON string, ready for use in your application or for storage.

This approach simplifies handling special characters, as Pydantic's serialization process takes care of the necessary escaping for you. By defining your model fields with the appropriate data types and constraints, you can leverage Pydantic's powerful serialization and validation features to manage complex data more effectively.

Remember, the key to successfully using Pydantic models is to ensure that your data structures and types accurately reflect the data you're working with. This allows Pydantic to perform its validation and serialization tasks efficiently, reducing the likelihood of encountering errors due to mismatched data types or improperly handled special characters.

Details

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.