Pydantic user error langchain json From the documentation:. This will result in an AgentAction being returned. For example, we might want to store the model output in a database and ensure that the output conforms to the database schema. I am sure that this is a b In addition, PlainSerializer and WrapSerializer enable you to use a function to modify the output of serialization. How to use LangChain with different Pydantic versions. This gives the model awareness of the tool and the associated input schema required by the tool. fields. PydanticOutputParser [source] ¶. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company As of the 0. Here is the exact import statement I am using: When I run this code, I get However, the output from the ChatOpenAI model is not a JSON string, but a list of strings. I am sure that this is a b class langchain_core. } ``` What i found is this format changes with extra character as ```json {. Below are details on common validation errors users may encounter when working with pydantic, together with some suggestions on how to fix them. tool import JsonSpec from An output parser in the context of large language models (LLMs) is a component that takes the raw text output generated by an LLM and transforms it into a structured format. The the json response is giving the error inside the "tool_calls" where the accepted format is not decoded. class Task(BaseModel): task_description: str = def create_model (__model_name: str, __module_name: Optional [str] = None, ** field_definitions: Any,)-> type [BaseModel]: """Create a pydantic model with the given field definitions. json. Labs. # Define a new Pydantic model with field descriptions and tailored for Twitter. Next steps . As part of this work, I would like to represent langchain classes as JSON, ideally with a JSON Schema to validate it. You can specify a Pydantic model and it will return JSON for that model. In this setup, the with_structured_output method ensures that the output is an instance of TestSummary, and you don't need to use the PydanticOutputParser separately. If you want to validate the constructor of a class, you should put validate_call on top of the appropriate method instead. - ``"parsing_error"``: Optional[BaseException] Example: schema=Pydantic class, method="function_calling", include_raw=False:. First, this pulls information from the document from two sources: 1. tool. Open 5 tasks done. If False, the output will be the full JSON object. Reload to refresh your session. In v2. render() (starlette doc) Pydantic can serialize many commonly used types to JSON that would otherwise be incompatible with a simple json. This should This output parser allows users to specify an arbitrary JSON schema and query LLMs for JSON outputs that conform to that schema. However, LangChain does have a better way to handle that call Output Parser. All Runnables expose the invoke and ainvoke methods (as well as other methods like batch, abatch, astream etc). Next, we’ll utilize LangChain’s PydanticOutputParser. You can try using pydantic library to serialize objects that are not part of the built-in types that JSON module recognizes. from langchain_core. The weight is the same, but the volume or density of the objects may differ. Returns a JSON object as specified. If the output signals that an action should be taken, should be in the below format. import warnings from abc import ABCMeta from copy import deepcopy from enum import Enum from functools import partial from pathlib import Path from types import FunctionType, prepare_class, resolve_bases from typing import (TYPE_CHECKING, AbstractSet, Any, Callable, ClassVar, Dict, List, Mapping, Optional, Tuple, Type, TypeVar, These functions support JSON and JSON-serializable objects. It is a combination of a prompt to ask LLM to response in certain format and a parser to parse the output. Pydantic attempts to provide useful validation errors. This is a list of output parsers LangChain supports. Agent sends the query to my tool and the tool generates a JSON output, now agent formats this output, but I want the tool's JSON as output, so I am trying to keep intermediate step as ai message in memory. from langchain. output_parsers import PydanticOutputParser from langchain. # adding to planner -&gt; from langchain. schema() met How to split JSON data. from typing import List from langchain_core. schema import StringEvaluator [docs] class JsonSchemaEvaluator ( StringEvaluator ): """An evaluator that validates a JSON prediction against a JSON schema reference. Validation Errors. Structured outputs Overview . With Pydantic v2 and FastAPI / Starlette you can create a less picky JSONResponse using Pydantic's model. Here's Use Pydantic models with LangChain. The JsonValidityEvaluator is designed to check the Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company I searched the LangChain documentation with the integrated search. arguments_type¶ You signed in with another tab or window. Check out a similar issue on github. '), # 'parsing_error': None # }. pydantic_v1 import BaseModel, Field, validator from typing import List model = llm # Define your desired data structure. I searched the LangChain documentation with the integrated search. This is used by class langchain. dropdown:: Example: schema=Pydantic class, method="json_schema", include_raw=False. exceptions import OutputParserException from langchain_core. _internal. json() and . ") handle: str = Field(description="Twitter handle of the user, without the '@'. If omitted it will be inferred from the type annotation. evaluation. If True, the output will be a JSON object containing all the keys that have been returned so far. For many applications, such as chatbots, models need to respond to users directly in natural language. llms import OpenAI from langchain_core. cpp open source model with Langchain. I'm using a pydantic output parser as the final step of a simple chain. ") hobbies: List[str] = Field(description="List of Data validation using Python type hints. JsonSpec'>, see arbitrary_types_allowed in Config even after to installing. This guide covers the main concepts and methods of the Runnable interface, which allows developers to interact with various Hello everyone, I’m currently facing a challenge while integrating Pydantic with LangChain and Hugging Face Transformers to generate structured question-answer outputs from a language model, specifically using the llama Source code for pydantic. Accepts a string with values 'always', 'unless-none If ``include_raw`` is True, then Runnable outputs a dict with keys: - ``"raw"``: BaseMessage - ``"parsed"``: None if there was a parsing error, otherwise the type depends on the ``schema`` as described above. model_dump_json() by overriding JSONResponse. Evaluating extraction and function calling applications often comes down to validation that the LLM's string output can be parsed correctly and how it compares to a reference object. 0 and above, Pydantic uses jiter, a fast and iterable JSON parser, to parse JSON data. Tools are a way to encapsulate a function and its schema It's written by one of the LangChain maintainers and it helps to craft a prompt that takes examples into account, allows controlling formats (e. It is not "at runtime" though. from_template (""" Extract the And our chain succeeds! Looking at the LangSmith trace, we can see that indeed our initial chain still fails, and it's only on retrying that the chain succeeds. base import StructuredTool from langchain_core. base import Document from pydantic import BaseModel, ConfigDict class ResponseBody(BaseModel): message: List[Document] model_config = ConfigDict(arbitrary_types_allowed=True) docs = [Document(page_content="This is a document")] res = ResponseBody(message=docs) I'm in the process of converting existing dataclasses in my project to pydantic-dataclasses, I'm using these dataclasses to represent models I need to both encode-to and parse-from json. If you're working with prior versions of LangChain, please see the following from langchain. pydantic. It traverses json data depth first and builds smaller json chunks. It has better read/validation support than the current approach, but I also need to create json-serializable dict objects to write out. Please use create_model_v2 instead of this function. This json splitter splits json data while allowing control over chunk sizes. This parser is particularly useful for applications that require strict data validation and serialization, leveraging Pydantic's capabilities to ensure that the output adheres to the defined schema. ModelMetaclass'> to JSON: TypeError("BaseMo Use Langchain to set the Pydantic Output Parser. g. Companies. I am encountering an error when trying to import OpenAIEmbeddings from langchain_openai. As of the 0. e. Probably the most reliable output parser for getting structured data that does NOT use function calling. 3 release, LangChain uses Pydantic 2 internally. JSON Schema Core; JSON Schema Validation; OpenAPI Data Types; The standard format JSON field is used to define Pydantic extensions for more complex string sub-types. `` ` from typing import Optional from pydantic import BaseModel, Field from langchain_openai import ChatOpenAI llm = ChatOpenAI(model="gpt-4o-mini") # Pydantic class Joke(BaseModel): """Joke to tell user. when_used specifies when this serializer should be used. async def aformat_document (doc: Document, prompt: BasePromptTemplate [str])-> str: """Async format a document into a string based on a prompt template. Whats the recommended way to define an output schema for a nested json, the method I use doesn't feel ideal. I tried converting all models to from langchain. You signed out in another tab or window. JSON schema types¶. JsonValidityEvaluator . Now you've seen some strategies how to handle tool calling errors. 3. BaseM The PydanticOutputParser in LangChain is a powerful tool that allows developers to define a user-specific Pydantic model and receive structured data in that format. In Pydantic 2, with the models defined exactly as in the OP, when creating a dictionary using model_dump, we can pass mode="json" to ensure that the output will only contain JSON serializable types. output_parsers import OutputFixingParser from langchain_core. Tools can be passed to chat models that support tool calling allowing the model to request the execution of a specific function with specific inputs. schema_json fail with Pydantic BaseModel in langchain-core==0. Types, custom field types, and constraints (like max_length) are mapped to the corresponding spec formats in the following priority order (when there is an equivalent available):. ' parser = JsonOutputParser (pydantic_object=Article) prompt = PromptTemplate ( template = "Answer the user query in We can use an output parser to help users to specify an arbitrary JSON schema via the prompt, query a model for outputs that conform to that schema, and finally parse that schema as PydanticUserError: _oai_structured_outputs_parser_output is not fully defined; you should define PydanticBaseModel, then call Learn how to troubleshoot and resolve Pydantic errors in Langchain effectively with practical examples. The tool abstraction in LangChain associates a Python function with a schema that defines the function's name, description and expected arguments. Default is False. prompts import PromptTemplate from src. (2) Tool Binding: The tool needs to be connected to a model that supports tool calling. Parses tool invocations and final answers in JSON format. To take things one step further, we can try to automatically re-run the chain with Just now saw this issue, which is very similar to the one I just filed - #725. Raises: OutputParserException – If the output is not valid JSON. }```\n``` intermittently. pydantic_v1 import BaseModel, Field class SocialPost @ZKS Unfortunately, I cannot share the entire code, but have shared agent initialization. steps import Steps def If I understand correctly, you are looking for a way to generate Pydantic models from JSON schemas. Yeah, I’ve heard of it as well, Postman is getting worse year by year, but I'd like to use pydantic for handling data (bidirectionally) between an api and datastore due to it's nice support for several types I care about that are not natively json-serializable. In streaming mode, whether to yield diffs between the previous and current parsed output, or just the current parsed output. experimental. Here is the Python code: import json import pydantic from typing import Optional, List class Car(pydantic. ") age: int = Field(description="Age of the user. Bases: JsonOutputParser, Generic [TBaseModel] Parse an output using a pydantic model. Retry with exception . v1 namespace of Pydantic 2 with LangChain APIs. I am getting RuntimeError: no validator found for <class 'langchain_community. Expected `str` but got `dict` with value `{'category': 'math'}` - serialized The article was published on ' + date + '. . page_content` and assigns it to a variable named `page_content`. validate Checked other resources I added a very descriptive title to this issue. For this specific task the API returns what it calls an "entity". I need to consume JSON from a 3rd party API, i. LangChain Tools implement the Runnable interface 🏃. All LangChain objects that inherit from Serializable are JSON-serializable. Discussions. There is a method called field_title_should_be_set() in GenerateJsonSchema which can be subclassed and provided to model_json_schema(). , JSON or CSV) and expresses the schema in TypeScript. code-block:: python from typing import Optional from langchain_ollama import ChatOllama from pydantic import BaseModel, Field class I'm trying JSON parser on a Llama. datetime, date or UUID). prompts import PromptTemplate from langchain_openai import ChatOpenAI, OpenAI from pydantic import BaseModel, Field I found a temporary fix to this problem. partial (bool) – Whether to parse partial JSON. Key concepts . v1. page_content: This takes the information from the `document. chains import RetrievalQA from langchain_mongodb import MongoDBAtlasVectorSearch from langchain. prompts import PromptTemplate from langchain_core. The with_structured_output method already ensures that the output conforms to the specified Pydantic schema, so using the PydanticOutputParser in addition to this is redundant and can cause validation errors. output_parsers. model_dump(mode="json") # JSON Evaluators. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser. Users. ") text: str = Field(description="Text under the Looking at the Langsmith trace for this chain run, we can see that the first chain call fails as expected and it's the fallback that succeeds. prompts import PromptTemplate from langchain_community. In order to do that, I needed to update pydantic to 2. For this, an approach that utilizes the create_model function was also discussed in The problem is that this seems hackish, and I don't know if this will be portable in new versions of the parser (at least, in the example in the docs, I see no reference to the params that should be passed to parse_with_prompt, although I can see in the source code that they are completion: str and prompt_value: PromptValue, but I'm not sure if this should be considered Runnable interface. I know Pydantic's . Users should install Pydantic 2 and are advised to avoid using the pydantic. While classes are callables themselves, validate_call can't be applied on them, as it needs to know about which method to use (__init__ or __new__) to fetch type annotations. You switched accounts on another tab or window. They are used to do what you are already doing with with_structured_output, parse some input string into structured data, or possibly change its format. Collectives. A tool is an association between a function and its schema. This is likely the cause of the JSONDecodeError you're encountering. Using jiter compared to serde results in modest performance improvements that will get even better in the future. """Defining fields on models. from uuid import UUID, uuid4 from pydantic from typing import Any, Union from langchain_core. pydantic_v1 import , but then I discovered FastAPI Issue you'd like to raise. 324 python 3. from_function from langchain. Takes a user from langchain_core. Args: __model_name: The name of the model. from langchain_openai import ChatOpenAI from langchain_openai import OpenAIEmbeddings from langchain. Keep in mind that large language models are leaky This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. Also NaN, btw. Pydantic v2 has dropped json_loads (and json_dumps) config settings (see migration guide) However, there is no indication by what replaced them. dumps(foobar) (e. The jiter JSON parser is almost entirely compatible with the serde JSON parser, with one noticeable enhancement being that jiter supports deserialization of inf and How to Use the DateTime Parser in LangChain: An In-Depth 3000 Word Guide for Linux Users; Extracting Lists from Chatbots using LangChain‘s List Parser; Demystifying Output Parser Errors with LangChain‘s Automated Fixing; How to Build Robust JSON Schemas with Pydantic; Hello! Let me show you some Java DOM Parser Examples to Parse XML Key concepts (1) Tool Creation: Use the @tool decorator to create a tool. , as returned from retrievers), and most Runnables, such as chat models, retrievers, and chains implemented with the LangChain Expression Language. utils partial (bool) – Whether to parse partial JSON objects. Classes¶. I have to deal with whatever this API returns and can't change that. In my case, the only reason I upgraded was that I need to update langchain-core, which was holding back many of my other lang* libraries. Alternatively, users can initiate a partial migration to Pydantic V2, but it is crucial to avoid mixing V1 and V2 code within LangChain. code-block The use case that explains why I need this is as follows: I am working on a product that is not 100% complete, so the returned values might change overnight from returning None to some other object {} which is unknown while I develop on my side. Internally, LangChain continues to utilize Pydantic V1, which means that users can pin their Pydantic version to V1 to avoid any breaking changes. _model_construction. output_parsers import PydanticOutputParser from langchain_core. main. Here is an implementation of a code generator - meaning you feed it a JSON schema and it outputs a Python file with the Model definition(s). agents. exceptions import OutputParserException from langchain_core. The Runnable interface is the foundation for working with LangChain components, and it's implemented across many of them, such as language models, output parsers, retrievers, compiled LangGraph graphs and more. To facilitate my application, I want to get a response in a specific format, so I am using I have a custom tool using the langchain StructuredTool. Returns: Let’s talk about something that we all face during development: API Testing with Postman for your Development Team. output_parsers import JsonOutputParser from langchain_core. It seems to work pretty! Initial Checks I confirm that I'm using Pydantic V2 Description Previously we used Pydantic v1 to work with ObjectId in mongo db, and we had a wrapper: class PyObjectId(ObjectId): @classmethod def get_validators(cls): yield cls. The Using import json from typing import Annotated, Generic, Optional import pydantic from pydantic import SkipValidation from typing_extensions import override from langchain_core. """ setup: str = Field(description="The setup of the joke") punchline: str = Field(description="The punchline to the joke") rating: Optional[int] = System Info langchain v0. pydantic_v1 import BaseModel, Field create_draft_tool = from typing import List from pydantic import BaseModel import json class Item(BaseModel): thing_number: int thing_description: str thing_amount: float class ItemList(BaseModel): each_item: List[Item] You signed in with another tab or window. I am trying to get a LangChain application to query a document that contains different types of information. outputs import Generation from langchain_core. Then, working off of the code in the OP, we could change the post request as follows to get the desired behavior: di = my_dog. It attempts to keep nested json objects whole but will split them if needed to keep chunks between a min_chunk_size and the max_chunk_size. Returns: The parsed JSON object. class Joke(BaseModel): setup: str = Field(description="question to set up a joke") punchline: str = Field(description="answer to resolve the joke") # You can add custom validation logic easily with Pydantic. Not sure if this problem is coming from LLM or langchain. Overview . Expects output to be in one of two formats. metadata: This essay provides a comprehensive guide on how to handle parsing errors in LangChain, including the identification of these errors, strategies for recovery, logging practices, and best practices Thanks! yes and yes. I used the GitHub search to find a similar question and didn't find it. After digging a bit deeper into the pydantic code I found a nice little way to prevent this. Got this message while using @Traceable : Failed to use model_dump to serialize <class 'pydantic. agent_toolkits import JsonToolkit, create_json_agent from langchain_community. Checked other resources I added a very descriptive title to this issue. The markdown structure that is receive d as answer has correct format ```json { . json import parse_json_markdown from langchain. pydantic_v1 import BaseModel, Field from typing import List class HeaderSection(BaseModel): """Class to save a section header and text from the section""" header: str = Field(description="Header of a section from the document. The issue you're encountering is due to the way the with_structured_output method and the PydanticOutputParser are being used together. Users will still need to overwrite it using with_types (which is generally recommended) The JSON module only knows how to serialize certain built-in types. utils. Parameters: result (List) – The result of the LLM call. LangChain's by default provides an Customizing JSON Schema¶. x. 10 window10 amd64 Who can help? @hwchase17 @agola11 Information The official example notebooks/scripts My own modified scripts Related Components LLMs/Chat Models Embedding Models Prompts / Prompt Context: I am working on some low-code tooling for langchain and GPT index. Both serializers accept optional arguments including: return_type specifies the return type for the function. This helps us shape the output of our Language Model to meet the formatting we desire. import yaml from langchain_community. """ from __future__ import annotations as _annotations import dataclasses import inspect import sys import typing from copy import copy from dataclasses import Field as DataclassField from functools import cached_property from typing import Any, ClassVar from warnings import warn import . Next, you can learn more about how to use tools: You signed in with another tab or window. Output parsers are classes that help structure language model responses. Here is the sample code: from langchain. Return type: Any LangChain has lots of different types of output parsers. Jobs. 2. dev4 #26250. Examples include messages, document objects (e. So even if you only provide an sync implementation of a tool, you could still use the ainvoke interface, but there are some important things to know:. 5. Import vaex error: PydanticImportError: `BaseSettings` has been moved to the `pydantic-settings` package. JSON (JavaScript Object Notation) is an open standard file format and data interchange format that uses human-readable text to store and transmit data objects consisting of attribute–value pairs and arrays (or other serializable values). Here's an example of my current approach that is not good enough for my use case, I have a class A that I want to both convert into a dict (to later be converted written as json) and I have the following Pydantic classes created. The following JSON validators provide functionality to check your model's output consistently. documents. plan_and_execute import Source code for pydantic. models. class TwitterUser(BaseModel): name: str = Field(description="Full name of the user. When this happens, the chain fails. prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field tagging_prompt = ChatPromptTemplate. param diff: bool = False ¶. I am writing code, which loads the data of a JSON file and parses it using Pydantic. __module_name: The name of the module where the model is defined. 13. You might want to check out the pydantic docs. tools. output_parsers import JsonOutputPa Parse the result of an LLM call to a list of Pydantic objects. The generated JSON schema can be customized at both the field level and model level via: Field-level customization with the Field constructor; Model-level customization with model_config; At both the field and model levels, you can use the json_schema_extra option to add extra information to the JSON schema. 0. After defining the template we want to use for the output JSON, all that remains is to use it in our LangChain application: Python from langchain_openai import ChatOpenAI from langchain_core. To disable run-time validation for LangChain objects used within Pydantic v2 It seems that you want some kind of json given your prompt and you're using ChatOpenAI, so you can force a json response type and use a json output parser instead. I'm not sure if the way I've overwritten the method is sufficient for each edge case but at least for this little test class it works as intended. Stack Overflow for Teams Where developers & technologists share private knowledge with coworkers; Advertising & Talent Reach devs & technologists worldwide about your product, service or employer brand; OverflowAI GenAI features for Teams; OverflowAPI Train & fine-tune LLMs; Labs The future of collective knowledge sharing; About the company The langchain docs include this example for configuring and invoking a PydanticOutputParser # Define your desired data structure. How to create async tools . pydantic_v1 import BaseModel, Field from langchain_openai import Output parsers in Langchain receive a string, not structured data. However, there are scenarios where we need models to output in a structured format. prompt|llm|outputparser Sometimes, the model doesnt return output in a format that complies to the specified json, oftentimes values outside of the allowed range or similar, and pydantic fails to parse it. otkyku kwct osg getlbjx vnzqw eqeltp zrpqj zhhqe nkiph phdaj