Langchain json agent python example

Langchain json agent python example. 3 days ago · A Runnable sequence representing an agent. LangChain provides output parser tools for just this purpose. com/docs/integrations/toolkits/json The JSON toolkit used in this example uses davinci:003, which is soon-to-be-deprecated and costs a whopping $0. ChatGPT Plugins. For a complete list of supported models and model variants, see the Ollama model Source code for langchain_community. For example, this toolkit can be used to delete data exposed via an OpenAPI compliant API. Apr 9, 2023 · LangChain provides a standard interface for memory, a collection of memory implementations, and examples of chains/agents that use memory. One comprises tools to interact with json: one tool to list the keys of a json object and another tool to get the value for a given key. GmailToolkit. 3 days ago · Here's an example:. This example goes over how to load data from JSONLines or JSONL files. Returning Structured Output. 3 days ago · Source code for langchain_core. dumps() . g. If the output signals that an action should be taken, should be in the below format. In this method, all differences between sentences are calculated, and then any difference greater than the X percentile is split. This notebook walks through connecting a LangChain email to the Gmail API. . For this example, we will give the agent access to two tools: The retriever we just created. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. As you may know, GPT models have been trained on data up until 2021, which can be a significant limitation. This will result in an AgentAction being returned. Examples using initialize_agent ¶ AINetwork Function-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. """ Jan 30, 2024 · Regarding multi-agent communication, it can be implemented in the LangChain framework by creating multiple instances of the AgentExecutor class, each with its own agent and set of tools. agents import AgentType, initialize_agent. It also highlights the challenges and limitations of using LLMs in agent systems. ConversationalChatAgent [source] ¶ Bases: Agent [Deprecated] An agent designed to hold a conversation in addition to using tools. Once you’ve downloaded the credentials. You can subscribe to these events by using the callbacks argument 3 days ago · Either 'force' or 'generate'. the state of a service; e. agents import AgentExecutor, create_react_agent prompt = hub. 0: Use Use new agent constructor methods like create_react_agent, create_json_agent, create_structured_chat_agent, etc. JSON files. Quickstart Many APIs are already compatible with OpenAI function calling. import { OpenAI , OpenAIEmbeddings } from "@langchain/openai" ; Learn how to create a LangChain agent, a powerful tool for natural language processing, using Azure OpenAI and Python with the ReAct approach. NOTE: for this example we will only show how to create an agent using OpenAI models, as local models are not reliable enough yet. json. # Select the LLM to use. It can often be useful to have an agent return something with more structure. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – HumanMessagePromptTemplate, SystemMessagePromptTemplate, ) from langchain_openai import ChatOpenAI. It optimizes setup and configuration details, including GPU usage. from langchain import OpenAI, ConversationChain llm = OpenAI(temperature=0) conversation = ConversationChain(llm=llm, verbose=True) conversation. Nov 27, 2023 · langchain python agent react differently, for one prompt, it can import scanpy library, but not for the other one. As these applications get more and more complex, it becomes crucial to be able to inspect what exactly is going on inside your chain or agent. info. 0. , by creating, deleting, or updating, reading underlying data. Once this is done, we’ll install the required libraries. Jun 5, 2023 · Whats the recommended way to define an output schema for a nested json, the method I use doesn't feel ideal. So, let's get started! How to Load a JSON File in Langchain in Python? Loading a JSON file into Langchain using Python is a straightforward process. Portable Document Format (PDF), standardized as ISO 32000, is a file format developed by Adobe in 1992 to present documents, including text formatting and images, in a manner independent of application software, hardware, and operating systems. {. 1. OpenAIEmbeddings(), breakpoint_threshold_type="percentile". Head to Integrations for documentation on built-in callbacks integrations with 3rd-party tools. Your input to the tools should be in the form of `data ["key"] [0]` where `data` is the JSON blob you are interacting with, and the syntax used is Python. pip install chromadb. Modifications. SQL. # Function to create a consistent LLM connector. chains import RetrievalQA. The former takes as input multiple texts, while the latter takes a single text. gmail. langchain. The loader will load all strings it finds in the JSON object. One of the most common types of databases that we can build Q&A systems for are SQL databases. A prompt for a language model is a set of instructions or input provided by a user to guide the model's response, helping it understand the context and generate relevant and coherent language-based output, such as answering questions, completing sentences, or engaging in a conversation. For a list of agent types and which ones work with more complicated inputs, please see this documentation. Override init to support instantiation by position for backward compat. You can find more details in the LangChain repository. The core idea of agents is to use a language model to choose a sequence of actions to take. Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. This walkthrough uses the chroma vector database, which runs on your local machine as a library. qa_chain = RetrievalQA. Jan 2, 2024 · I believe this code as printed in the book "Generative AI with LangChain" relies on and older version of langchain. Python agent - an agent capable of producing and executing Python code. toolkit. Finally, set the OPENAI_API_KEY environment variable to the token value. , MySQL, PostgreSQL, Oracle SQL, Databricks, SQLite). Each agent can then be run in a loop, with the output of one agent being passed as input to the next agent. JSON Mode: Some LLMs are can be forced to 3 days ago · Create an agent that uses JSON to format its logic, build for Chat Models. Nov 26, 2023 · Updated example of JSON agent? https://python. A retriever is an interface that returns documents given an unstructured query. These LLMs can structure output according to a given schema. The JSON loader use JSON pointer to target keys in your JSON files you want to target. From the basics to practical examples, we've got you covered. agent_toolkits Langchain Semantic Search: Search and indexing your own Google Drive Files using GPT3, LangChain, and Python; GPT Political Compass; llm-grovers-search-party: Leveraging Qiskit, OpenAI and LangChain to demonstrate Grover's algorithm; TextWorld ReAct Agent; LangChain <> Wolfram Alpha; BYO Knowledge Graph; Large Language Models Course Jan 21, 2024 · Let’s write a sample agent that will summarize the meeting notes and preserve the action items. openapi. By default, most of the agents return a single string. as_retriever(), chain_type_kwargs={"prompt": prompt} 1 day ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). This article will guide you through the steps of setting up the environment, designing the prompt template, and testing the agent's reasoning and acting skills. In Chains, a sequence of actions is hardcoded. If you would rather manually specify your API key and/or organization ID, use the following code: Mar 13, 2024 · langchain_community. Next, use the DefaultAzureCredential class to get a token from AAD by calling get_token as shown below. 4 days ago · class langchain_core. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Mar 17, 2024 · In this guide, we will delve deep into the world of Langchain and JSON. , by reading, creating, updating, deleting data associated with this service. Recursively split JSON. We use a simple ConversationBufferWindowMemory for this example that keeps a rolling window of the last two conversation turns. An agent that breaks down a complex question into a series of simpler questions. requests import Requests. First, import dependencies and load the LLM. 5-turbo-instruct. llms import OpenAI from langchain. Jun 18, 2023 · Need some help. agents import create_json_agent from langchain. """ json_agent: Any requests_wrapper: TextRequestsWrapper allow_dangerous_requests: bool = False """Allow dangerous requests. conversational_chat. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be Mar 18, 2024 · Generate a JSON representation of the model, include and exclude arguments as per dict(). A full description of an action for an ActionAgent to execute. LangChain provides a callbacks system that allows you to hook into the various stages of your LLM application. Expects output to be in one of two formats. It takes as input all the same input variables as the prompt passed in does. Parameters. prompt import PromptTemplate. This notebook goes through how to create your own custom agent. 5 days ago · The prompt in the LLMChain MUST include a variable called “agent_scratchpad” where the agent can put its intermediary work. NOTE: for this example we will only show how to create an agent using OpenAI models, as local models runnable on consumer hardware are not reliable enough yet. Review all integrations for many great hosted offerings. 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 covers how to load PDF documents into the Document format that we use downstream. Bases: AgentAction. Importantly, the name, description, and JSON schema (if used) are all used in the Extend the LLM Agent with the ability to retain a memory and use it as context as it continues the conversation. Parameters Percentile. OpenAPIToolkit. To use this toolkit, you will need to set up your credentials explained in the Gmail API docs . agents import load_tools. """ from __future__ import annotations from typing import TYPE_CHECKING, Any, Dict, List, Optional from langchain_core. predict(input="Hi there!") 2nd example: “json explorer” agent Here’s an agent that’s not particularly practical, but neat! The agent has access to 2 toolkits. I suggest setting up a conda environment for the book as there seemed to be breaking changes. examples = [. Namely, it comes with: simple syntax for binding functions to models. It can optionally first compress, or collapse, the mapped documents to make sure that they fit in the combine documents chain . 284 to be exact. agents. plan_and_execute import JSONLines files. The config supports standard keys like ‘tags’, ‘metadata’ for tracing purposes, ‘max_concurrency’ for controlling how much work to do in parallel, and other keys. Note 1: This currently only works for plugins with no auth. First, it can be used to audit what exactly the LLM predicted to lead to this 3 days ago · langchain. Notes LangChain结合了大型语言模型、知识库和计算逻辑，可以用于快速开发强大的AI应用。这个仓库包含了我对LangChain的学习和实践经验，包括教程和代码案例。让我们一起探索LangChain的可能性，共同推动人工智能领域的进步！ - aihes/LangChain-Tutorials-and-Examples It explores the components of such agents, including planning, memory, and tool use. For example, Klarna has a YAML file that describes its API and allows OpenAI to interact with it: Sep 10, 2023 · langchainのAgentは言語モデルに使用する関数（tool)を決定させるためのクラスです。. The final thing we will create is an agent - where the LLM decides what steps to take. The simpler the input to a tool is, the easier it is for an LLM to be able to use it. """handle_parsing_errors:Union[bool,str,Callable[ [OutputParserException],str]]=False"""How to handle errors Create the example set. Vector stores can be used as the backbone of a retriever, but there are other types of retrievers as well. Whether this agent is intended for Chat Models (takes in messages, outputs message) or LLMs (takes in string, outputs string). The JSON loader uses JSON pointer to pnpm add @langchain/openai @langchain/community This example shows how to load and use an agent with a vectorstore toolkit. Functions: For example, OpenAI functions is one popular means of doing this. LangChain comes with a number of built-in chains and agents that are compatible with any SQL dialect supported by SQLAlchemy (e. Assuming that you can write a prompt that will get the AI to consistently provide a response in a suitable format, you need a way to handle that output. Chroma. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. callbacks import BaseCallbackManager from langchain_core. Here, the formatted examples will match the format expected for the OpenAI tool calling API since that’s what we’re using. To walk through an example, suppose a user asks How many emergency visits were there in 2023? 5 days ago · Bases: AgentOutputParser. LangChain is a framework for developing applications powered by language models. # RetrievalQA. This json splitter traverses json data depth first and builds smaller json chunks. . Note 2: There are almost certainly other ways to do this, this is just a first pass. prompt (ChatPromptTemplate) – The prompt to use. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Retrievers. It returns as output either an AgentAction or AgentFinish. agent_toolkits. Here's a quick step-by-step guide with sample code: Gmail. import os. Class Jan 23, 2024 · In this way, the supervisor can also be thought of an agent whose tools are other agents! Hierarchical Agent Teams. There are many great vector store options, here are a few that are free, open-source, and run entirely on your local machine. Pandas DataFrame agent - an agent capable of question-answering over Pandas dataframes, builds on top This example shows how to load and use an agent with a JSON toolkit. It then passes all the new documents to a separate combine documents chain to get a single output (the Reduce step). from langchain_openai import OpenAI. LCEL is great for constructing your own chains, but it’s also nice to have chains that you can use off-the-shelf. "} {"html": "This is another sentence. agents import ZeroShotAgent. Here, we use gpt-3. You can use an agent with a different type of model than it is intended for, but it likely won't produce results of the same quality. It is more general than a vector store. from langchain. `"force"` returns a string saying that it stopped because it met a time or iteration limit. 1 day ago · Deprecated since version 0. ChatOllama. agents ¶ Agent is a class that uses an LLM to choose a sequence of actions to take. [RequestsGetTool(name='requests_get', description='A portal to the internet. Please refer to the RunnableConfig for more details. Generally, this approach is the easiest to work with and is expected to yield good results. Examples: Python; JS; This is similar to the above example, but now the agents in the nodes are actually other langgraph objects themselves. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Mar 26, 2023 · Building a Math Application with LangChain Agents A tutorial on why LLMs struggle with math, and how to resolve these limitations using LangChain Agents, OpenAI and Chainlit 12 min read · 4 days ago The primary supported way to do this is with LCEL. llm (BaseLanguageModel) – LLM to use as the agent. 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 Introduction. Important LangChain primitives like LLMs, parsers, prompts, retrievers, and agents implement the LangChain Runnable Interface. The hospital name is passed as input to a Python function that gets wait times, and the wait time is returned to the agent. The text splitters in Lang Chain have 2 methods — create documents and split documents. This log can be used in a few ways. In chains, a sequence of actions is hardcoded (in code). param log: str [Required] ¶. See Prompt section below for more. This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. Toolkit for interacting with Gmail. A retriever does not need to be able to store documents, only to return (or retrieve) them. instead. The reason for having these as two separate methods is that some embedding providers have different embedding methods for documents (to be 4 days ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). This agent executor uses existing LangChain agents. No JSON pointer example The most simple way of using it, is to specify no JSON pointer. Example JSONLines file: {"html": "This is a sentence. llm, retriever=vectorstore. First, it can be used to audit what exactly the LLM predicted to lead to this (tool Apr 21, 2023 · An agent has access to an LLM and a suite of tools for example Google Search, Python REPL, math calculator, weather APIs, etc. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Here is an example from the movie agent using this structure. base. langchain[docarray]==0. Intended Model Type. # adding to planner -&gt; from langchain. js and gpt to parse , store and answer question such as for example: "find me jobs with 2 year experience The map reduce documents chain first applies an LLM chain to each document individually (the Map step), treating the chain output as a new document. This interface provides two general approaches to stream content: sync stream and async astream : a default implementation of streaming that streams the final output from the chain. 2 days ago · langchain_core 0. The article provides case studies and proof-of-concept examples of LLM-powered agents in various domains, such as scientific discovery and generative agents simulation. Agents select and use Tools and Toolkits for actions. prompts. One of the first things to do when building an agent is to decide what tools it should have access to. Agents select and use **Tools** and **Toolkits** for actions. , tool calling or JSON mode etc. 33¶ langchain_core. Lance. prompt import JSON_PREFIX, JSON_SUFFIX from langchain In order to easily let LLMs interact with that information, we provide a wrapper around the Python Requests module that takes in a URL and fetches data from that URL. Agentはtoolを決定するだけで実行はしません。. dumps(), other arguments as per json. If you have better ideas, please open a PR! For example, if the goal is to generate a dataset, you'd want the response to be provided in a specific format like CSV or JSON. FAISS. Vectorstore agent - an agent capable of interacting with vector stores. For example, the support tool should be used to optimize or debug a Cypher statement and the input to the tool should be a fully formed question. This notebook covers how to have an agent return a structured output. PDF. AgentActionMessageLog [source] ¶. Mar 1, 2023 · Other agent toolkit examples: JSON agent - an agent capable of interacting with a large JSON blob. Parses tool invocations and final answers in JSON format. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. Getting Started Notebook: Walks through creating this type of executor from scratch; High Level Entrypoint: Walks through how to use the high level entrypoint for the chat agent executor. stop_sequence (Union[bool, List[str]]) – bool or list of str. language_models import BaseLanguageModel from langchain_community. Memory is needed to enable conversation. One document will be created for each JSON object in the file. It's offered in Python or JavaScript (TypeScript) packages. `"generate"` calls the agent's LLM Chain one final time to generate a final answer based on the previous steps. The second argument is a JSONPointer to the property to extract from each JSON object in the file. It simplifies the process of programming and integration with external data sources and software workflows. from_chain_type(. dumps(). Custom agent. 2 days ago · This agent has access to a document store that allows it to look up relevant information to answering the question. With the data added to the vectorstore, we can initialize the chain. The main advantages of using the SQL Agent are: It can answer questions based on the databases’ schema as well as on the databases’ content (like describing a specific table). In this example, we will use OpenAI Tool Calling to create this agent. My question is how to make sure to import the correct library without problem. See documentation for details. To get started, create a list of few-shot examples. Additional information to log about the action. Luckily, LangChain has a built-in output parser of the Agents. ) Reason: rely on a language model to reason (about how to answer based on There are 3 broad approaches for information extraction using LLMs: Tool/Function Calling Mode: Some LLMs support a tool or function calling mode. Ollama allows you to run open-source large language models, such as Llama 2, locally. Both have the same logic under the hood but one takes in a list of text Mar 6, 2024 · Wait Times Function: Similar to the logic in Step 1, the LangChain agent tries to extract a hospital name from the user query. Apr 19, 2023 · The description of a tool is used by an agent to identify when and how to use a tool. We will first create it WITHOUT memory, but we will then show how to add memory in. LangChain has other memory options, with different tradeoffs suitable for different use cases. They enable use cases such as: 2 days ago · langchain_core. memory import TextMemory. Then, set OPENAI_API_TYPE to azure_ad. This agent uses a search tool to look up answers to the simpler questions in order to answer the original complex question. LLM-generated interface: Use an LLM with access to API documentation to create an interface. Example JSON file: Aug 7, 2023 · Types of Splitters in LangChain. You should only use keys that you JSON parser. The output of the runnable. I have the following JSON content in a file and would like to use langchain. prompts import PromptTemplate. We also have a lot of examples highlighting how to slightly modify the base chat agent May 30, 2023 · Since we are dealing with reading from a JSON, I used the already defined json agent from the langchain library: from langchain. 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. requests_tools = load_tools(["requests_all"]) requests_tools. It can recover from errors by running a generated The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. """Json agent. agent = initialize_agent(. If the value is not a nested json, but rather a very large string the string will not be split. This is generally the most reliable way to create agents. 5 days ago · langchain_community. experimental. ). The last thing we need to do is to initialize the agent. LangChain comes with a number of utilities to make function-calling easy. This provides even more flexibility than using LangChain AgentExecutor as the agent runtime. few_shot import FewShotPromptTemplate. タスクを完了するためにはtoolを実行し、その実行結果を言語モデルに渡す必要があり、その処理はAgentではなくAgentExecutorという Jun 1, 2023 · LangChain is an open source framework that allows AI developers to combine Large Language Models (LLMs) like GPT-4 with external data. The main thing this affects is the prompting strategy used. LangChain has a SQL Agent which provides a more flexible way of interacting with SQL Databases than a chain. from langchain_community. In this case, LangChain offers a higher-level constructor method. This example shows how to use ChatGPT Plugins within LangChain abstractions. 6 days ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). A good example of this is an agent tasked with doing question-answering over some sources. Notes Deprecated since version langchain==0. text_splitter = SemanticChunker(. The base Embeddings class in LangChain provides two methods: one for embedding documents and one for embedding a query. “action”: “search”, “action_input”: “2+2”. Since one of the available tools of the agent is a recommender tool, it decided to utilize the recommender tool by providing the JSON syntax to define its input. This is useful for logging, monitoring, streaming, and other tasks. pull The below example is a bit more advanced - the format of the example needs to match the API used (e. chat = ChatOpenAI(temperature=0) The above cell assumes that your OpenAI API key is set in your environment variables. agent_toolkits import NLAToolkit. tools (Sequence) – Tools this agent has access to. Toolkit for interacting with an OpenAPI API. We will pass the prompt in via the chain_type_kwargs argument. LangChain provides integrations for over 25 different embedding methods, as well as for over 50 different vector storesLangChain is a tool for building applications using large language models (LLMs) like chatbots and virtual agents. `` ` {. "} Initialize the chain. In this example, we asked the agent to recommend a good comedy. There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. 3 days ago · config – A config to use when invoking the runnable. 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 best way to do this is with LangSmith. ConversationalChatAgent¶ class langchain. It enables applications that: Are context-aware: connect a language model to sources of context (prompt instructions, few shot examples, content to ground its response in, etc. Agents. Do not make up any information that is not contained in the JSON. LangSmith. llms import OpenAI. converters for formatting various types of objects to the Only use the information returned by the below tools to construct your final answer. For example, this toolkit can be used to send emails on behalf of the associated account. code-block:: python from langchain_core. encoder is an optional function to supply as default to json. There are quite a few agents that LangChain supports — see here for the complete list, but quite frankly the most common one I came across in tutorials and YT videos was zero-shot-react-description. Each example should be a dictionary with the keys being the input variables and the values being the values for those input variables. json file, you can start using the Gmail API. prompts import ChatPromptTemplate, MessagesPlaceholder system = '''Assistant is a large language model trained by OpenAI. """ **Agent** is a class that uses an LLM to choose a sequence of actions to take. 4 days ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). In the OpenAI family, DaVinci can do reliably but Curie’s Dec 1, 2023 · To use AAD in Python with LangChain, install the azure-identity package. AgentAction. The default way to split is based on percentile. ¶. llm=ChatOpenAI(temperature=0, model="gpt-4-1106-preview"), tool=PythonREPLTool(), verbose=True, Mar 13, 2024 · Generate a JSON representation of the model, include and exclude arguments as per dict(). import langchain. 02/1K tokens. JSON Lines is a file format where each line is a valid JSON value. Examples: from langchain import hub from langchain_community. Many agents will only work with tools that have a single string input. gp sd ae ii xa mp bz fp ck et