Langchain is pointless json This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. How to split JSON data. The langchain agent currently fetches results from tools and runs another round of LLM on the tool’s results which changes the format (json for instance) and sometimes worsen the results before sending it as “final answer”. tavily_search import TavilySearchResults from langchain_openai import ChatOpenAI This example shows how to load and use an agent with a JSON toolkit. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. This supports JSON schema definition as input and enforces the model to produce a conforming JSON output. tools. It attempts to keep nested json objects whole but will split them if needed to keep chunks between a minchunksize and the maxchunksize. RecursiveJsonSplitter ([max_chunk_size, ]). from langchain_core. This will result in an AgentAction being returned. Here is an example of how to use JSON mode with OpenAI: JSON files. It provides good abstractions, code snippets, and tool integrations for building demos. The following JSON validators provide functionality to check your model's output consistently. JSONFormer is a library that wraps local Hugging Face pipeline models for structured decoding of a subset of the JSON Schema. But what we end up with a mediocre DAG framework where all the instructions/data passing through is just garbage. If the output signals that an action should be taken, should be in the below format. class Joke (BaseModel): setup: str = Field (description = "question to set up a joke") Sep 21, 2024 · Understanding JSON and Its Importance in LangChain. a couple of bulletpoints of "here are the problems this solves that langchain doesn't" or "ways this is different from langchain" would go a long way. from langchain. g. You can find a table of model providers that support JSON mode here. 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. This is likely the cause of the JSONDecodeError you're encountering. No JSON pointer example The most simple way of using it is to specify no JSON pointer. The JSON loader use JSON pointer to target keys in your JSON files you want to target. The JSON approach works great out of the box with GPT4 but breaks down with 3. ): Important integrations have been split into lightweight packages that are co-maintained by the LangChain team and the integration developers. Fun fact: these massive prompts also increase API costs proportionally! Langchain is attempting to set up abstractions to reuse everything. agents import AgentExecutor, create_json_chat_agent from langchain_community . tools . Templates are no more useful than calling . replace () on a string. BaseModel. output_parsers import JsonOutputParser from langchain_core. If the value is not a nested json, but rather a very large string the string will not be split. JSON parser. This json splitter splits json data while allowing control over chunk sizes. Jul 14, 2023 · When looking at the LangChain code, it turns out that tool selection is done by requiring the output to be valid JSON through prompt engineering, and just hoping everything goes well. Aug 27, 2023 · TL;DR - Not pointless for building quick cool demos BUT not worth learning for building real applications. Mar 20, 2024 · Based on the code you've shared, it seems like the LineListOutputParser is expecting a JSON string as input to its parse method. 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. agents. Source code for langchain_text_splitters. This is useful when you want to answer questions about a JSON blob that's too large to fit in the context window of an LLM. The JSON loader uses JSON pointer to target keys in your JSON files you want to target. 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). The nests can get very complicated so manually creating schema/functions is not an option. Credentials No credentials are required to use the JSONLoader class. tool. I was able to solve for it by doing something that looks a lot like the new StructuredChat agent, so I’ll probably switch so subclassing that; I’m also excited about the output parser wjth retries. Mar 6, 2024 · I have a json file that has many nested json/dicts within it. JSON is a lightweight data interchange format that is easy to read and write for humans and machines alike. documents import Document. Expects output to be in one of two formats. If you need a hard cap on the chunk size considder following this with a How to parse JSON output. The documentation is out-of-date and inconsistent. . No JSON pointer example The most simple way of using it, is to specify no JSON pointer. `` ` To access JSON document loader you'll need to install the langchain-community integration package as well as the jq python package. "texts" are just strings and "documents" are just a pointless dict that contain "texts. Tool for listing keys in a JSON spec. Splits JSON data into smaller, structured chunks while preserving hierarchy. The README is both grandiose and vague. Parses tool invocations and final answers in JSON format. It focused on optimizing interactions with LLMs to Integration packages (e. group (2) return _parse_json (json_str, parser JSONFormer. param args_schema: Optional [TypeBaseModel] = None ¶ Pydantic model class to validate and parse the tool’s input arguments. from __future__ import annotations import copy import json from typing import Any, Dict, List, Optional from langchain_core. While some model providers support built-in ways to return structured output, not all do. Keep in mind that large language models are leaky abstractions! You'll have to use an LLM with sufficient capacity to generate well-formed JSON. Jun 28, 2024 · Summary: This blog provided a comprehensive guide on leveraging LangChain to ensure precise JSON responses from any Large Language Model (LLM). Dec 9, 2024 · JSONDecodeError: # Try to find JSON string within triple backticks match = _json_markdown_re. Example JSON file: class langchain_community. JsonListKeysTool [source] ¶ Bases: BaseTool. output_parsers. " Just load the strings from your datasource yourself. Initialize the tool. JSON Lines is a file format where each line is a valid JSON value. langchain already has a lot of adoption so you're fighting an uphill battle to begin with. A lot of the data is not necessary, and this holds true for other jsons from the same source. search (json_string) # If no match found, assume the entire string is a JSON string if match is None: json_str = json_string else: # If match found, use the content within the backticks json_str = match. However, the output from the ChatOpenAI model is not a JSON string, but a list of strings. The loader will load all strings it finds in the JSON object. LangChain implements a JSONLoader to convert JSON and JSONL data into LangChain Document objects. Args schema should be either: A subclass of pydantic. This notebook showcases an agent interacting with large JSON/dict objects. Each json differs drastically. It traverses json data depth first and builds smaller json chunks. json. The longer the chain, the more garbage you find at the output. langchain: Chains, agents, and retrieval strategies that make up an application's cognitive architecture. 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 JSON. langchain-openai, langchain-anthropic, etc. you may have a lot of insightful and useful modifications in your design, but if you don't communicate what those are, you're just assuming everyone is as JSON mode In addition to tool calling, some model providers support a feature called JSON mode. If you want to get automated best in-class tracing of your model calls you can also set your LangSmith API key by uncommenting below: json. Assistant is designed to be able to assist with a wide range of tasks, from answering simple questions to providing in-depth explanations and discussions on a wide range of topics. It uses a specified jq schema to parse the JSON files, allowing for the extraction of specific fields into the content and metadata of the LangChain Document. prompts import PromptTemplate from langchain_openai import ChatOpenAI from pydantic import BaseModel, Field model = ChatOpenAI (temperature = 0) # Define your desired data structure. It consists of key-value pairs and This example shows how to load and use an agent with a JSON toolkit. JSON Toolkit. Dec 9, 2024 · class langchain. Below is an example of a json. It works by filling in the structure tokens and then sampling the content tokens from the model. JSONAgentOutputParser [source] ¶ Bases: AgentOutputParser. Example JSON file: This json splitter traverses json data depth first and builds smaller json chunks. fvysovz vawwt qqfctj bcbpkb cnvj lajd mph slqyfi ynvwf qxvjkyrf