Langchain api example json

Langchain api example json. Follow these installation steps to set up a Neo4j database. Document. It is parameterized by a list of characters. LCEL was designed from day 1 to support putting prototypes in production, with no code changes, from the simplest “prompt + LLM” chain to the most complex chains. from langchain_core. For a complete list of supported models and model variants, see the Ollama model library. npm install pdf-parse We're going to load a short bio of Elon Musk and extract the information we've previously generated. You can generate a free Unstructured API key here. String text. LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. It tries to split on them in order until the chunks are small enough. This will result in an AgentAction being returned. Generate a JSON representation of the model, include and exclude arguments as per dict(). , source, relationships to other documents, etc. LANGCHAIN_TRACING_V2=true. [docs] class JSONLoader(BaseLoader): """Load a `JSON` file using LangGraph is a library for building stateful, multi-actor applications with LLMs, built on top of (and intended to be used with) LangChain. cpp. In the OpenAI family, DaVinci can do reliably but Curie's ability already ChatOllama. Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated 4 days ago · Create k-shot example selector using example list and embeddings. documents. encoder is an optional function to supply as default to json. Custom tool agent In the above tutorial on agents, we used pre-existing tools with In this example, we first define a function schema and instantiate the ChatOpenAI class. json_loader. 5 days ago · Only use the information returned by the below tools to construct your final answer. Do not make up any information that is not contained in the JSON. JSON-based Agents With Ollama & LangChain was originally published in Neo4j Developer Blog on Medium, where people are continuing the conversation by highlighting and responding to this story. , Python) RAG Architecture A typical RAG application has two main components: Mar 29, 2024 · PowerShell. embeddings import OllamaEmbeddings ollama_emb = OllamaEmbeddings( model="llama:7b", ) r1 = ollama_emb. The indexing API lets you load and keep in sync documents from any source into a vector store. chains. This evaluator checks if a given JSON prediction conforms to the provided JSON schema. This notebook showcases an agent designed to interact with large JSON/dict objects. This will result in an AgentAction being May 8, 2023 · To load and extract data from files using LangChain, you can follow these steps. If the output signals that an action should be taken, should be in the below format. Agents select and use Tools and Toolkits for actions. ', 'experiment_design Nov 2, 2023 · For other API servers, acce via REST API usually, you can converse with the API via chat and tell it what the problems are until it gets it right. LANGSMITH_API_KEY=your-api-key. Step 5: Deploy the LangChain Agent. embed_documents( [ "Alpha is the first letter of Greek alphabet", "Beta is the second letter of Greek alphabet Aug 14, 2023 · Click “+ Create Service Account” and fill in the fields. json import parse_json_markdown from langchain. Usage Create a JsonSpec from a file. This is a breaking change. Examples using BaseTool¶ Function calling. Agent is a class that uses an LLM to choose a sequence of actions to take. LangChain provides a standard interface for chains, lots of integrations with other tools, and end-to-end chains for common applications. Additionally, you will need to set the LANGCHAIN_API_KEY environment variable to your API key (see Setup for more from typing import Any, Union from langchain_core. Note: new versions of llama-cpp-python use GGUF model files (see here ). js . # Optional, use LangSmith for best-in-class observability. Next, we need to define Neo4j credentials. import json from pathlib import Path from typing import Any, Callable, Dict, Iterator, Optional, Union from langchain_core. This notebook goes over how to run llama-cpp-python within LangChain. Parses tool invocations and final answers in JSON format. The value at the given path in the JSON object, as a string. It converts input schema into an OpenAI function, then forces OpenAI to call that function to return a response in the correct format. JSONFormer is a library that wraps local Hugging Face pipeline models for structured decoding of a subset of the JSON Schema. We will use StrOutputParser to parse the output from the model. OpenAIEmbeddings(). Note that it doesn't work with --public-api. 5-turbo” model API using LangChain’s ChatOpenAI() function and creates a q&a chain for answering our query. The agent is able to iteratively explore the blob to find what it needs to answer the user’s question. This output parser allows users to specify an arbitrary JSON schema and query LLMs for outputs that conform to that schema. examples (List[dict]) – List of examples to use in the prompt. The JSON loader uses JSON pointer to Let's build a simple chain using LangChain Expression Language ( LCEL) that combines a prompt, model and a parser and verify that streaming works. Once you’ve created the new service account click on it and go to “KEYS”. Oct 13, 2023 · To do so, you must follow these steps: Create a class that inherits the Chain class from the langchain. 5-turbo-1106"etc) and got the error: Mar 6, 2024 · I have a json file that has many nested json/dicts within it. Two RAG use cases which we cover elsewhere are: Q&A over SQL data; Q&A over code (e. schema import StringEvaluator [docs] class JsonSchemaEvaluator ( StringEvaluator ): """An evaluator that validates a JSON prediction against a JSON schema reference. Class for storing a piece of text and associated metadata. loads to illustrate; retrieve_from_db = json. It simplifies prompt engineering, data input and output, and tool interaction, so we can focus on core logic. parsing. It also contains supporting code for evaluation and parameter tuning. dumps (), other arguments as per json. To use an API key for authentication, add --api-key yourkey. Conveniently, LangChain has utilities just for this purpose. This text splitter is the recommended one for generic text. Unstructured API. Specifically, it helps: Avoid writing duplicated content into the vector store; Avoid re-writing unchanged content; Avoid re-computing embeddings over unchanged content The LANGCHAIN_TRACING_V2 environment variable must be set to 'true' in order for traces to be logged to LangSmith, even when using @traceable or traceable. chat_with_csv_verbose. loader = DirectoryLoader(DRIVE_FOLDER, glob='**/*. “action”: “search”, “action_input”: “2+2”. embeddings – An initialized embedding API interface, e. Arbitrary metadata about the page content (e. The input_keys property stores the input to the custom chain, while the output_keys stores the output of your custom chain. This allows ChatGPT to automatically select the correct method and populate the correct parameters for the a API call in the spec for a given user input. First we generate a user ID for ourselves. Step 4: Build a Graph RAG Chatbot in LangChain. pem. evaluation. JSON files - ️ Langchain Redirecting I leveraged a sample dataset of the Sales Performance DQLab Store from Kaggle to chat with data to figure out valuable insight. First, follow these instructions to set up and run a local Ollama instance: Then, make sure the Ollama server is running. 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. Reshuffles examples dynamically based on query similarity. dumps (). include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – langchain. It contains algorithms that search in sets of vectors of any size, up to ones that possibly do not fit in RAM. base. Agents. 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. When we invoke the runnable with an input, the response is already parsed thanks to the output parser. To listen on your local network, add the --listen flag. 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). 3 days ago · Source code for langchain_community. documents import Document from langchain_community. Ollama allows you to run open-source large language models, such as Llama 2, locally. It parses an input OpenAPI spec into JSON Schema that the OpenAI functions API can handle. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Feb 28, 2024 · The examples in LangChain documentation ( JSON agent, HuggingFace example) are using tools with a single string input. for more detailed information on code, you can Apr 2, 2023 · The langchain docs include this example for configuring and invoking a PydanticOutputParser # Define your desired data structure. . Creates a new model setting __dict__ and __fields_set__ from trusted or pre-validated 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. agent chatgpt json langchain llm mixtral Neo4j ollama. To change the port, which is 5000 by default, use --api-port 1234 (change 1234 to your desired port number). utils. It extends the LangChain Expression Language with the ability to coordinate multiple chains (or actors) across multiple steps of computation in a cyclic manner. This mode facilitates a more organized and efficient way to handle data, especially when dealing with complex information or integrating LLMs into larger systems. It provides a suite of components for crafting prompt templates, connecting to diverse data sources, and interacting seamlessly with various tools. z. output_parsers. Below are two sample curl requests to demonstrate how to use the API. All parameters supported by SearchApi can be passed when executing the query. This allows you to toggle tracing on and off without changing your code. If the value is a large dictionary or exceeds the maximum length, a message is returned instead Langchain Decorators: a layer on the top of LangChain that provides syntactic sugar 🍭 for writing custom langchain prompts and chains ; FastAPI + Chroma: An Example Plugin for ChatGPT, Utilizing FastAPI, LangChain and Chroma; AilingBot: Quickly integrate applications built on Langchain into IM such as Slack, WeChat Work, Feishu, DingTalk. Depending on your use case, you might want to adjust this parameter to control the variability of your model's output. %pip install --upgrade --quiet jsonformer > /dev/null. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. The code is available as a Langchain template and as a Jupyter notebook . Generally, this approach is the easiest to work with and is expected to yield good results. base import BaseLoader. Check these out to see the specific function arguments and simple examples of how to use the graph + checkpointing APIs or to see some of the higher-level prebuilt components. date() is not allowed. LangGraph's API has a few important classes and methods that are all covered in the Reference Documents. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. Let's build a simple chain using LangChain Expression Language ( LCEL) that combines a prompt, model and a parser and verify that streaming works. Jul 3, 2023 · Bases: Chain. Not required. In chains, a sequence of actions is hardcoded (in code). To use SSL, add --ssl-keyfile key. JsonSchemaEvaluator (** kwargs: Any) [source] ¶ An evaluator that validates a JSON prediction against a JSON schema reference. It supports inference for many LLMs models, which can be accessed on Hugging Face. schema. return_only_outputs ( bool) – Whether to return only outputs in the response. Each json differs drastically. For a complete list of supported models and model variants, see the Ollama model 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). OpenAI has a tool calling (we use "tool calling" and "function calling" interchangeably here) API that lets you describe tools and their arguments, and have the model return a JSON object with a tool to invoke and the inputs to that tool. Serve the Agent With FastAPI. 1 day ago · langchain_core. This example shows how to leverage OpenAI functions to output objects that match a given format for any given input. Create a Neo4j Cypher Chain. Example. This is a simple parser that extracts the content field from an AIMessageChunk, giving us the token returned by the model. This output parser can be also be used when you want to define the output schema using Zod, a TypeScript validation library. base . 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. At a high level, the following design principles are applied to serialization: Both JSON and YAML are supported. dumps(ingest_to_db)) transform the retrieved serialized object back to List[langchain. Sep 11, 2023 · LangChain is a framework designed to speed up the development of AI-driven applications. import streamlit as st from langchain. Facebook AI Similarity Search (Faiss) is a library for efficient similarity search and clustering of dense vectors. dumps(), other arguments as per json. Create Wait Time Functions. In Agents, a language model is used as a reasoning engine to determine which actions to take and in which order. json_schema. 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. LangChain provides a large collection of common utils to use in your application. from_template( "Return a JSON object with `birthdate` and `birthplace` key that answers the following question: {question}" ) # Initialize the JSON parser json_parser = SimpleJsonOutputParser() # Create a chain Ollama allows you to run open-source large language models, such as Llama 2, locally. Document ¶. Faiss documentation. Nov 15, 2023 · Here's an example: from langchain. memory import ConversationBufferMemory. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. streamEvents() and streamLog(): these provide a way to Parameters. There are two ways to achieve this: 1. Tool calling . LangChain Expression Language (LCEL) LCEL is the foundation of many of LangChain's components, and is a declarative way to compose chains. It optimizes setup and configuration details, including GPU usage. agents ¶. To use, follow the instructions at https://ollama. llms import Ollama. JsonSchemaEvaluator¶ class langchain. SearchApi wrapper can be customized to use different engines like Google News, Google Jobs, Google Scholar, or others which can be found in SearchApi documentation. You should only use keys that you May 31, 2023 · langchain, a framework for working with LLM models. Is there a solution? Structured Output Parser with Zod Schema. llms import OpenAI Next, display the app's title "🦜🔗 Quickstart App" using the st. When used in streaming mode, it will yield partial JSON objects containing all the keys that have been returned so far. That Jan 6, 2024 · Jupyter notebook showing various ways to extracting an output. 3 days ago · Parse the output of an LLM call to a JSON object. In this article, I have shown you how to use LangChain, a powerful and easy-to-use framework, to get JSON responses from ChatGPT, a 4 days ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). from langchain_openai import ChatOpenAI. from langchain. Create a Chat UI With Streamlit. Mar 1, 2024 · Its API facilitates seamless integration with existing applications, making it a powerful tool for enhancing user experiences and unlocking valuable insights. The create_json_agent function you're using to create your JSON agent takes a verbose parameter. dumps(). Mar 6, 2024 · Query the Hospital System Graph. Define input_keys and output_keys properties. Should contain all inputs specified in Chain. This example goes over how to use LangChain to interact with an Ollama-run Llama Custom parameters. Directly set up the key in the relevant class. 2 days ago · For example, {“openai_api_key”: “OPENAI_API_KEY”} property output_schema: Type [BaseModel] ¶ The type of output this runnable produces specified as a pydantic model. Apr 29, 2024 · LangChain Agents #4: JSON Chat Agent. I have scoured various forums and they are either implementing streaming with Python or their solution is not relevant to this problem. JSON Lines is a file format where each line is a valid JSON value. 5 days ago · Source code for langchain. chains import LLMMathChain from langchain_community. llama-cpp-python is a Python binding for llama. Recursively split by character. In streaming, if diff is set to True, yields JSONPatch operations describing the difference between the previous and the current object. from_llm(OpenAI()) Create a new model by parsing and validating input data from keyword arguments. loads(json. These only provide minimal examples of how to use the API, see the documentation for more information about the API and the extraction use-case documentation for more information about how to extract information using LangChain. Nov 26, 2023 · The JSON toolkit used in this example uses davinci:003, which is soon-to-be-deprecated and costs a whopping $0. First we'll need to import the LangChain x Anthropic package. output_parsers import StrOutputParser. Note: Here we focus on Q&A for unstructured data. Including guidance to the model that it should produce JSON as part of the messages conversation is required. Dec 13, 2023 · You've set this parameter to 0. Building a JSON-based Agent with Apr 8, 2023 · perform db operations to write to and read from database of your choice, I'll just use json. Uncomment the below to use LangSmith. g. I tried to replace llm=OpenAI with llm=ChatOpenAI(model_name="gpt-3. In this example, we're going to load the PDF file. The model is supposed to follow instruction from system chat message more closely. May 13, 2024 · Bases: AgentOutputParser. The JSON Chat Agent leverages JSON formatting for its outputs, making it suitable for applications that require structured response data. There are two key factors that need to be present to successfully use JSON mode: response_format={ "type": "json_object" } We told the model to output JSON as part of the system message. We need one extra dependency. If you want to get up and running with less set up, you can simply run pip install unstructured and use UnstructuredAPIFileLoader or UnstructuredAPIFileIOLoader. import { z } from "zod"; 3 days ago · Generate a JSON representation of the model, include and exclude arguments as per dict(). Return the keys of the dict at the given path. search = SearchApiAPIWrapper(engine="google_jobs") search. dumps and json. A lot of the data is not necessary, and this holds true for other jsons from the same source. Overview: LCEL and its benefits. Class hierarchy: JSON Agent #. The nests can get very complicated so manually creating schema/functions is not an option. This agent is ideal for chat models that excel in processing and generating JSON structures. 2 days ago · langchain. Parameters. That will process your document using the hosted Unstructured API. Important LangChain primitives like LLMs, parsers, prompts, retrievers, and agents implement the LangChain Runnable Interface. Aug 20, 2023 · This tutorial explains how you can run the Langchain framework without using a paid API and just a local LLM. This interface provides two general approaches to stream content: . [docs] class JSONAgentOutputParser(AgentOutputParser): """Parses tool invocations and final answers in JSON format. Create the Chatbot Agent. document_loaders. The Zod schema passed in needs be parseable from a JSON string, so eg. For example, Anthropic's models work best with XML while OpenAI's work best with JSON. Chains: Chains go beyond just a single LLM call, and are sequences of calls (whether to an LLM or a different utility). agents. 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. from dotenv import load_dotenv. Expects output to be in one of two formats. Copy. LangChain provides several prompt templates to make constructing and working with prompts easily. json import SimpleJsonOutputParser # Create a JSON prompt json_prompt = PromptTemplate. You can use it where you would use a chain with a StructuredOutputParser, but it doesn't Apr 8, 2024 · to stream the final output you can use a RunnableGenerator: from openai import OpenAI. An example library that does this is TypeChat; If you are running your own LLM, you can use decoder libraries such as lm-format-enforcer which has langchain integration, jsonformer, guidance and For example, in OpenAI Chat Completion API, a chat message can be associated with an AI, human or system role. inputs ( Union[Dict[str, Any], Any]) – Dictionary of inputs, or single input if chain expects only one param. V. Below is an example of a json. OPENAI_API_KEY="" If you'd prefer not to set an environment variable, you can pass the key in directly via the openai_api_key named parameter when initiating the OpenAI LLM class: 2. We then make the actual API call, and return the result. We want to support serialization methods that are human readable on disk, and YAML and JSON LangChain JSON Mode is a powerful feature designed to enhance the interaction with Large Language Models (LLMs) by structuring input and output in JSON format. pem --ssl-certfile cert. You should keep this in mind when designing your apps. from langchain_community. ai/. These LLMs can structure output according to a given schema. JSON. JSON Mode: Some LLMs are can be forced to Faiss. If you want to read the whole file, you can use loader_cls params: from langchain. import streamlit as st. #. JSON Agent. HumanMessage|AIMessage] retrieved_messages = messages_from_dict(retrieve_from_db) ChatOllama. title('🦜🔗 Quickstart App') The app takes in the OpenAI API key from the user, which it then uses togenerate the responsen. It is inspired by Pregel and Apache Beam . Nov 7, 2023 · The above code, calls the “gpt-3. 02/1K tokens. The core idea of agents is to use a language model to choose a sequence of actions to take. title() method: st. Warning - this module is still experimental. Click “ADD KEY”->”Create new key”->JSON. input_keys except for inputs that will be set by the chain’s memory. stream(): a default implementation of streaming that streams the final output from the chain. Create a Neo4j Vector Chain. base module. ). json', show_progress=True, loader_cls=TextLoader) also, you can use JSONLoader with schema params like: Aug 9, 2023 · -----Parsed/Processed output of langchain in a dictionary format/JSON: {'research_topic': 'Targeted Distillation with Mission-Focused Instruction Tuning', 'problem_statement': 'LLMs have demonstrated remarkable generalizability, yet student models still trail the original LLMs by large margins in downstream applications. We’ll use OpenAI in this example: OPENAI_API_KEY=your-api-key. After that, you can do: from langchain_community. `` ` {. langchain_core. llm = Ollama ( model = "llama2") API Reference: Ollama. Generate a JSON representation of the model, include and exclude arguments as per dict (). json. 2 days ago · Ollama locally runs large language models. So to summarize, I can successfully pull the response from OpenAI via the LangChain ConversationChain() API call, but I can’t stream the response. It simplifies the process of programming and integration with external data sources and software workflows. For this getting started guide, we will use chat models and will provide a few options: using an API like Anthropic or OpenAI, or using a local open source model via Ollama. run("AI Engineer Apr 21, 2023 · This notebook covers how to do that in LangChain, walking through all the different types of prompts and the different serialization options. Chain that interprets a prompt and executes python code to do math. ipynb <-- Example of using LangChain to interact with CSV data via chat, containing a verbose switch to show the LLM thinking process. Setting up key as an environment variable. We then create a runnable by binding the function to the model and piping the output through the JsonOutputFunctionsParser. ¶. document_loaders import DirectoryLoader, TextLoader. Llama. JSONFormer. Since the tools in the semantic layer use slightly more complex inputs, I had Set environment variables. include (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – exclude (Optional[Union[AbstractSetIntStr, MappingIntStrAny]]) – Here, we will look at a basic indexing workflow using the LangChain indexing API. Sample Code: May 17, 2023 · 14. Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. In Chains, a sequence of actions is hardcoded. It works by filling in the structure tokens and then sampling the content tokens from the model. 0, which will make the model's output completely deterministic. There are 3 broad approaches for information extraction using LLMs: Tool/Function Calling Mode: Some LLMs support a tool or function calling mode. Import things that are needed generically. llms import OpenAI llm_math = LLMMathChain. px yx yw ta uo gq uo rj if kl