How to Use PydanticOutputParser with GraphCypherQAChain

[Zaeem6100]

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Example Code

Model

class Course(BaseModel):
    name: str = Field(description="Name of the course")
    duration: str = Field(description="Duration of the course")
    fee: str = Field(description="Fee of the course")
    intake: str = Field(description="Intake of the course")
    deadline: str = Field(description="Deadline of the course")
    eligibility: str = Field(description="Eligibility of the course")
    courseType: str = Field(description="Type of the course")
    courseLevel: str = Field(description="Level of the course")
    courseDescription: str = Field(description="Description of the course")
    courseLink: str = Field(description="Link of the course")


class UserChat(BaseModel):
    answer: str = Field(
        description="Answer of the following question that was asked by the user"
    )
    recommendedCountry: Optional[List[str]] = Field(
        description="Here are the list of recommended countries for the user to apply based on the user's interest"
    )
    recommendedUniversities: Optional[List[str]] = Field(
        description="Here are the list of recommended universities for the user to apply based on the user's interest"
    )
    recommendedCourse: Optional[List[Course]] = Field(
        description="Here are the list of recommended courses for that  universities for "
        " user to apply based on the user's interest"
    )

graph = Neo4jGraph(
                url=config_DB["uri"],
                username=config_DB["user"],
                password=config_DB["password"]);

def CypherQueryPromptTemplate():
    prompt = """
        Task:Generate Cypher statement to query a graph database.
        Instructions:
        You have to generate a cypher query to find a course based on the student's background and preferences.
        Always Try to Recommend the Priority University Courses. 
        IF there is no priority university course then recommend the normal university course.
        The Course Degree Level and Taught Language are also provided in the database.
        Further Details for the course can be found in the embedding of the course. 
        The Schema for the Database is provided below. 
        {schema}
        The question is:
        {question}"""

    return PromptTemplate(
        template=prompt,
        input_variables=["schema", "question"]
    )


chain = GraphCypherQAChain.from_llm(
                llm=llm,
                graph=graph,
                verbose=True,
                use_function_response=True,
                output_parser=StrOutputParser(),
                runnable=RunnablePassthrough(),
                cypher_prompt=CypherQueryPromptTemplate(),
                return_intermediate_steps=True,
                validate_cypher=True,
                function_response_system="Respond as a Student Assistant !",
            )
chain.invoke({"query": question})

Description

neo4J Course Node

Node properties:
Country {name: STRING}
University {name: STRING, priority: BOOLEAN}
Course {degree_level: STRING, taught_language: STRING, embedding: STRING, name: STRING, course_link: STRING}
Relationship properties:

The relationships:
(:Country)-[:HAS_UNIVERSITY]->(:University)
(:University)-[:HAS_COURSE]->(:Course)

I am creating the Embedding using embeddings = OpenAIEmbeddings().embed_query(cleaned_html)

I am Phasing 2 Problems

1. My Pydentic Output parser has some fields that are not in the Graph Node So I need to get those too from the embeddings.
2. How I will add the pedantic parser to the GraphCypherQAChain?

How the output prompts are going to handle in the GraphCypherQAChain.

System Info

System Information

OS: Windows
OS Version: 10.0.22631
Python Version: 3.12.3 (tags/v3.12.3:f6650f9, Apr 9 2024, 14:05:25) [MSC v.1938 64 bit (AMD64)]

Package Information

langchain_core: 0.2.11
langchain: 0.2.6
langchain_community: 0.2.6
langsmith: 0.1.83
langchain_openai: 0.1.14
langchain_text_splitters: 0.2.2

[dosubot[bot]]

Hello, @Zaeem6100! I'm here to help you with any bugs, questions, or contributions you might have. Let's tackle this together!

To address your issues with using PydanticOutputParser in GraphCypherQAChain and handling fields not present in the Graph Node, follow these steps:

1. Define your Pydantic model with optional fields:
   This allows you to handle fields that may not always be present in the Graph Node.

2. Use PydanticOutputParser to parse the output:
   Integrate the PydanticOutputParser into your GraphCypherQAChain.

Here's an example of how you can achieve this:

Step 1: Define Your Pydantic Model

from typing import Optional
from pydantic import BaseModel

class CourseModel(BaseModel):
    name: str
    degree_level: Optional[str]
    taught_language: Optional[str]
    embedding: Optional[str]
    course_link: Optional[str]
    additional_field: Optional[str]  # Example of an additional field not in the Graph Node

Step 2: Use PydanticOutputParser in GraphCypherQAChain

from langchain_core.output_parsers import PydanticOutputParser
from langchain_core.outputs import Generation
from langchain_openai import ChatOpenAI
from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain

# Initialize the parser with your Pydantic model
parser = PydanticOutputParser(pydantic_object=CourseModel)

# Example result from GraphCypherQAChain
result = [Generation(text='{"name": "Course Name", "additional_field": "Extra Info"}')]

# Parse the result
parsed_output = parser.parse_result(result)
print(parsed_output)

# Define your LLM and GraphCypherQAChain
llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
graph = Neo4jGraph(
    url=config_DB["uri"],
    username=config_DB["user"],
    password=config_DB["password"]
)

def CypherQueryPromptTemplate():
    prompt = """
        Task: Generate Cypher statement to query a graph database.
        Instructions:
        You have to generate a cypher query to find a course based on the student's background and preferences.
        Always Try to Recommend the Priority University Courses. 
        IF there is no priority university course then recommend the normal university course.
        The Course Degree Level and Taught Language are also provided in the database.
        Further Details for the course can be found in the embedding of the course. 
        The Schema for the Database is provided below. 
        {schema}
        The question is:
        {question}"""

    return PromptTemplate(
        template=prompt,
        input_variables=["schema", "question"]
    )

chain = GraphCypherQAChain.from_llm(
    llm=llm,
    graph=graph,
    verbose=True,
    use_function_response=True,
    output_parser=parser,  # Use PydanticOutputParser here
    runnable=RunnablePassthrough(),
    cypher_prompt=CypherQueryPromptTemplate(),
    return_intermediate_steps=True,
    validate_cypher=True,
    function_response_system="Respond as a Student Assistant !",
)

response = chain.invoke({"query": question})
print(response)

Explanation

1. Pydantic Model: The CourseModel class defines the structure of the expected output, including optional fields.
2. PydanticOutputParser: The PydanticOutputParser is initialized with the CourseModel and used to parse the output from the GraphCypherQAChain.
3. GraphCypherQAChain: The output_parser parameter is set to the PydanticOutputParser instance, ensuring that the output is parsed according to the defined Pydantic model.

This setup allows you to handle fields that may not be present in the Graph Node and ensures that the output is parsed correctly ^[1][2].

To continue talking to Dosu, mention @dosu.

[Zaeem6100]

@dosu

[dosubot[bot]]

To use PydanticOutputParser with GraphCypherQAChain and handle fields not present in the Graph Node, follow these steps:

Step 1: Define Your Pydantic Model

from typing import Optional, List
from pydantic import BaseModel, Field

class CourseModel(BaseModel):
    name: str
    degree_level: Optional[str] = Field(default=None)
    taught_language: Optional[str] = Field(default=None)
    embedding: Optional[str] = Field(default=None)
    course_link: Optional[str] = Field(default=None)
    additional_field: Optional[str] = Field(default=None)  # Example of an additional field not in the Graph Node

Step 2: Use PydanticOutputParser in GraphCypherQAChain

from langchain_core.output_parsers import PydanticOutputParser
from langchain_openai import ChatOpenAI
from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
from langchain_community.graphs.neo4j import Neo4jGraph
from langchain.prompts import PromptTemplate

# Initialize the parser with your Pydantic model
parser = PydanticOutputParser(pydantic_object=CourseModel)

# Define your LLM and GraphCypherQAChain
llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
graph = Neo4jGraph(
    url=config_DB["uri"],
    username=config_DB["user"],
    password=config_DB["password"]
)

def CypherQueryPromptTemplate():
    prompt = """
        Task: Generate Cypher statement to query a graph database.
        Instructions:
        You have to generate a cypher query to find a course based on the student's background and preferences.
        Always Try to Recommend the Priority University Courses. 
        IF there is no priority university course then recommend the normal university course.
        The Course Degree Level and Taught Language are also provided in the database.
        Further Details for the course can be found in the embedding of the course. 
        The Schema for the Database is provided below. 
        {schema}
        The question is:
        {question}"""

    return PromptTemplate(
        template=prompt,
        input_variables=["schema", "question"]
    )

chain = GraphCypherQAChain.from_llm(
    llm=llm,
    graph=graph,
    verbose=True,
    use_function_response=True,
    output_parser=parser,  # Use PydanticOutputParser here
    cypher_prompt=CypherQueryPromptTemplate(),
    return_intermediate_steps=True,
    validate_cypher=True,
    function_response_system="Respond as a Student Assistant!",
)

response = chain.invoke({"query": question})
print(response)

Explanation

1. Pydantic Model: The CourseModel class defines the structure of the expected output, including optional fields.
2. PydanticOutputParser: The PydanticOutputParser is initialized with the CourseModel and used to parse the output from the GraphCypherQAChain.
3. GraphCypherQAChain: The output_parser parameter is set to the PydanticOutputParser instance, ensuring that the output is parsed according to the defined Pydantic model.

This setup allows you to handle fields that may not be present in the Graph Node and ensures that the output is parsed correctly ^[1].

To continue talking to Dosu, mention @dosu.

[Zaeem6100]

It Didnt Return the Parsed Object

[dosubot[bot]]

To ensure that the GraphCypherQAChain returns the parsed object correctly when using PydanticOutputParser, you need to handle the validation errors that arise due to missing fields. The PydanticOutputParser class includes mechanisms to raise exceptions when validation fails.

Here is a detailed approach to ensure the parsed object is returned correctly:

Step 1: Define Your Pydantic Model with Optional Fields

Define your Pydantic model with optional fields to handle cases where some fields might not be present in the Graph Node.

from typing import Optional, List
from pydantic import BaseModel, Field

class CourseModel(BaseModel):
    name: str
    degree_level: Optional[str] = Field(default=None)
    taught_language: Optional[str] = Field(default=None)
    embedding: Optional[str] = Field(default=None)
    course_link: Optional[str] = Field(default=None)
    additional_field: Optional[str] = Field(default=None)  # Example of an additional field not in the Graph Node

Step 2: Use PydanticOutputParser in GraphCypherQAChain

Integrate the PydanticOutputParser into your GraphCypherQAChain.

from langchain_core.output_parsers import PydanticOutputParser
from langchain_openai import ChatOpenAI
from langchain_community.chains.graph_qa.cypher import GraphCypherQAChain
from langchain_community.graphs.neo4j import Neo4jGraph
from langchain.prompts import PromptTemplate

# Initialize the parser with your Pydantic model
parser = PydanticOutputParser(pydantic_object=CourseModel)

# Define your LLM and GraphCypherQAChain
llm = ChatOpenAI(model="gpt-4-0125-preview", temperature=0)
graph = Neo4jGraph(
    url=config_DB["uri"],
    username=config_DB["user"],
    password=config_DB["password"]
)

def CypherQueryPromptTemplate():
    prompt = """
        Task: Generate Cypher statement to query a graph database.
        Instructions:
        You have to generate a cypher query to find a course based on the student's background and preferences.
        Always Try to Recommend the Priority University Courses. 
        IF there is no priority university course then recommend the normal university course.
        The Course Degree Level and Taught Language are also provided in the database.
        Further Details for the course can be found in the embedding of the course. 
        The Schema for the Database is provided below. 
        {schema}
        The question is:
        {question}"""

    return PromptTemplate(
        template=prompt,
        input_variables=["schema", "question"]
    )

chain = GraphCypherQAChain.from_llm(
    llm=llm,
    graph=graph,
    verbose=True,
    use_function_response=True,
    output_parser=parser,  # Use PydanticOutputParser here
    cypher_prompt=CypherQueryPromptTemplate(),
    return_intermediate_steps=True,
    validate_cypher=True,
    function_response_system="Respond as a Student Assistant!",
)

response = chain.invoke({"query": question})
print(response)

Handling Validation Errors

The PydanticOutputParser will raise an OutputParserException if the parsing fails due to validation errors. Ensure that your model fields are defined as optional if they might not be present in the Graph Node.

Example of Handling Missing Fields

Here is an example of how the PydanticOutputParser handles missing fields:

class PydanticOutputParser(JsonOutputParser, Generic[TBaseModel]):
    """Parse an output using a pydantic model."""

    pydantic_object: Type[TBaseModel]  # type: ignore
    """The pydantic model to parse."""

    def _parse_obj(self, obj: dict) -> TBaseModel:
        try:
            return self.pydantic_object.parse_obj(obj)
        except pydantic.ValidationError as e:
            raise self._parser_exception(e, obj)

    def _parser_exception(
        self, e: Exception, json_object: dict
    ) -> OutputParserException:
        json_string = json.dumps(json_object)
        name = self.pydantic_object.__name__
        msg = f"Failed to parse {name} from completion {json_string}. Got: {e}"
        return OutputParserException(msg, llm_output=json_string)

This code attempts to parse the object using the specified Pydantic model and raises an OutputParserException if the parsing fails due to validation errors. The exception message includes details about the failure, which can help in debugging and ensuring that the fields are correctly handled.

By defining fields as optional and handling the OutputParserException, you can ensure that the parsed object is returned correctly even when some fields are not present in the Graph Node ^[1][2][3].

To continue talking to Dosu, mention @dosu.

[jais001]

@dosu Hi,

I have also tried using pydantic output parser for the GraphCypherQAChain, but is not working for me... My langchain version is langchain==0.1.16,
Can anyone provide any insights on how to use pydantic models for getting a structured output from this chain.

[jais001]

@dosu Hi,

I have also tried using output parser for the GraphCypherQAChain, but is not working for me... My langchain version is langchain==0.1.16,
Can anyone provide any insights on how to use pydantic models for getting a structured output from this chain.