Langchain hub api. Creating an assistant Creating an assistant is easy.

# start by installing semanticscholar api. The Assistants API allows you to build AI assistants within your own applications. runnables. Download the file for your platform. LangServe helps developers deploy LangChain runnables and chains as a REST API. ChatZhipuAI. Install Chroma with: pip install langchain-chroma. This guide will continue from the hub quickstart, using the Python or TypeScript SDK to interact with the hub instead of the Playground UI. agents import AgentExecutor, create_openai_functions_agent. Use of LangChain is not necessary - LangSmith works on its own! 1. --dev/--no-dev: Toggles the development mode. Can be set using the LANGFLOW_LANGCHAIN_CACHE environment variable. This tool is a wrapper for the GitHub API, useful when you need to update the contents of a file in a GitHub repository. Each exists at its own URL and in a self-hosted environment are set via the LANGCHAIN_HUB_API_URL and LANGCHAIN_ENDPOINT environment variables, respectively, and have their own separate SDKs. By default, pulling from the repo loads the latest version of the prompt into memory. However, if you have complex security requirements - you may want to use Azure Active Directory. 众所周知 OpenAI 的 API 无法联网的，所以如果只使用自己的功能实现联网搜索并给出回答、总结 PDF 文档、基于某个 Youtube 视频进行问答等等的功能肯定是无法实现的。. RetrievalQA Chain: use prompts from the hub in an example RAG pipeline. You signed in with another tab or window. If your API requires authentication or other headers, you can pass the chain a headers property in the config object. While generating diverse samples, it infuses the unique personality of 'GitMaxd', a direct and casual communicator, making the data more engaging. Adapters are used to adapt LangChain models to other APIs. # RetrievalQA. Reuse trained models like BERT and Faster R-CNN with just a few lines of code. llms import Ollama. GLM-4 is a multi-lingual large language model aligned with human intent, featuring capabilities in Q&A, multi-turn dialogue, and code generation. agents import AgentExecutor, create_openai_functions_agent from langchain_community. Often in Q&A applications it's important to show users the sources that were used to generate the answer. HubRunnable implements the standard RunnableInterface. Introduction. 👉 Bring your own DB. Extraction Using OpenAI Functions: Extract information from text using OpenAI Function Calling. Access the hub through the login address. %pip install langchain. Request an API key and set it as an environment variable: export GROQ_API_KEY=<YOUR API KEY>. Jun 12, 2024 · The LangChain Hub API client. It’s not as complex as a chat model, and it’s used best with simple input–output Apr 22, 2024 · LangChain Hub is a versatile platform designed to streamline the management of model components. Customizing the prompt As shown above, we can load prompts (e. Tools can be just about anything — APIs, functions, databases, etc. llms import OpenAI llm = OpenAI (model_name = "text-davinci-003") # 告诉他我们生成的内容需要哪些字段，每个字段类型式啥 response_schemas = [ ResponseSchema (name = "bad_string LangChain Hub; JS/TS Docs; 💬. Baidu Qianfan. Navigate to the LangChain Hub section of the left-hand sidebar. Users can access the service through REST APIs, Python SDK, or a web Dec 1, 2023 · API Key; Azure Active Directory (AAD) Using the API key is the easiest way to get started. 3. cpp into a single file that can run on most computers without any additional dependencies. Let's build a simple chain using LangChain Expression Language ( LCEL) that combines a prompt, model and a parser and verify that streaming works. If the 6 days ago · To use, you should have the ``huggingface_hub`` python package installed, and the environment variable ``HUGGINGFACEHUB_API_TOKEN`` set with your API token, or pass it as a named parameter to the constructor. 👉 Give context to the chatbot using external datasources, chatGPT plugins and prompts. pip install -U langchain-community tavily-python. OpenAPI Agent. """ from __future__ import annotations import json from typing import TYPE_CHECKING, Any, Optional from langchain_core. The overall performance of the new generation base model GLM-4 has been significantly 介绍. Once you've downloaded the credentials. Install LangSmith. Example: . An Assistant has instructions and can leverage models, tools, and knowledge to respond to user queries. View the latest docs here. Langchain’s core mission is to shift control from We also can use the LangChain Prompt Hub to fetch and / or store prompts that are model specific. The downside of agents are that you have less control. This notebook shows how to load Hugging Face Hub datasets to LangChain. ts:1; Index Functions Explore a range of topics and insights on the Zhihu Column, a platform for sharing knowledge and ideas. Still, this is a great way to get started with LangChain - a lot of features can be built with just some prompting and an LLM call! We've implemented the assistant API in LangChain with some helpful abstractions. LangChain v0. We will use StrOutputParser to parse the output from the model. The overall performance of the new generation base model GLM-4 has been significantly GitHub is a developer platform that allows developers to create, store, manage and share their code. LangChain simplifies every stage of the LLM application lifecycle: Development: Build your applications using LangChain's open-source building blocks, components, and third-party integrations . **VERY IMPORTANT**: Your input to this tool MUST strictly follow these rules: - First you must specify which file to modify by passing a full file path (**IMPORTANT**: the path must not start with a slash) Amazon API Gateway is a fully managed service that makes it easy for developers to create, publish, maintain, monitor, and secure APIs at any >scale. This option is for development purposes only. batch: call the chain on a list of inputs. Groq. There are two primary ways to interface LLMs with external APIs: Functions: For example, OpenAI functions is one popular means of doing this. LangChain has a large ecosystem of integrations with various external resources like local and remote file systems, APIs and databases. The Hugging Face Hub is a platform with over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together. Overview. Sep 10, 2023 · 1. Since we are using GitHub to organize this Hub, adding artifacts can best be done in one of three ways: Create a fork and then open a PR against the repo. The default is SQLiteCache. Jul 13, 2024 · langchain. Creating an assistant Creating an assistant is easy. Tencent Hunyuan. 👉 Dedicated API endpoint for each Chatbot. llm, retriever=vectorstore. langgraph is an extension of langchain aimed at building robust and stateful multi-actor applications with LLMs by modeling steps as edges and nodes in a graph. Returning sources. Utilize the ChatHuggingFace class to enable any of these LLMs to interface with LangChain's Chat Messages abstraction. You can find more information on how to use AAD with Azure OpenAI here. --path: Specifies the path to the frontend directory containing build files. Support for async allows servers hosting the LCEL based programs to scale better for higher concurrent loads. prompts import BasePromptTemplate if TYPE_CHECKING: from langchainhub Overview. 0) langchain. Each time you push to a given prompt "repo", the new version is saved with a commit hash so you can track the prompt's lineage. Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining. OpenAI assistants. A JavaScript client is available in LangChain. Azure AI Search (formerly known as Azure Search and Azure Cognitive Search) is a cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale. May 14, 2024 · Source code for langchain. environ["TAVILY_API_KEY"] = getpass. The RunnableInterface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. 7¶ langchain_community. Oct 31, 2023 · LangChain provides a way to use language models in JavaScript to produce a text output based on a text input. Once this is done, we'll install the required libraries. However, if you want to load a specific version, you can do so by including the hash at the end of the prompt name. It uses Git software, providing the distributed version control of Git plus access control, bug tracking, software feature requests, task management, continuous integration, and wikis for every project. 2. You are currently within the LangChain Hub. Load prompt. What is LangChain Hub? 📄️ Developer Setup. llamafiles bundle model weights and a specially-compiled version of llama. Use the most basic and common components of LangChain: prompt templates, models, and output parsers. All parameters supported by SearchApi can be passed when executing the query. Get started with LangSmith. The SearchApi tool connects your agents and chains to the internet. 📄️ Quick Start. code-block:: python This is an example agent to deploy with LangGraph Cloud. ZHIPU AI. This example goes over how to use LangChain to interact with Together AI models. Please confirm that huggingfacehub_api_token is what you intended. A valid API key is needed to communicate with the API. llm = OpenAI() If you manually want to specify your OpenAI API key and/or organization ID, you can use the following: llm = OpenAI(openai_api_key="YOUR_API_KEY", openai_organization="YOUR_ORGANIZATION_ID") Remove the openai_organization parameter should it not apply to you. Preparing search index The search index is not available; LangChain. 1 docs. langchain-gemini-api is an AI-powered conversation API that integrates Google's Gemini API, designed to facilitate advanced text and image-based interactions. Each of the different types of artifacts (listed 1) Download a llamafile from HuggingFace 2) Make the file executable 3) Run the file. api_url ( Optional[str]) – The URL of the LangChain Hub API. 1. g. You signed out in another tab or window. dump import dumps from langchain_core. This notebook walks through connecting a LangChain email to the Gmail API. Baidu AI Cloud Qianfan Platform is a one-stop large model development and service operation platform for enterprise developers. 2. Use the createAssistant method and pass in a model ID, and optionally more parameters to further customize your assistant. Here's an example of calling a HugggingFaceInference model as an LLM: LangChain v0. This is a simple parser that extracts the content field from an AIMessageChunk, giving us the token returned by the model. Quickstart. LangChain is a framework for developing applications powered by large language models (LLMs). Explore the code, options, and API of this powerful chain. Action: api_controllerAction Input: 1. Batch operations allow for processing multiple inputs in parallel. [0m LangChain UI enables anyone to create and host chatbots using a no-code type of inteface. This notebook shows how to get started using Hugging Face LLM's as chat models. In this guide, we will go over the basic ways to create Chains and Agents that call Tools. GET /models to retrieve the list of available models2. chains import RetrievalQA. Assuming your organization's handle is "my The Hugging Face Hub is home to over 5,000 datasets in more than 100 languages that can be used for a broad range of tasks across NLP, Computer Vision, and Audio. Supports `text-generation`, `text2text-generation`, `conversational`, `translation`, and `summarization`. 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. SearchApi tool. © 2023, LangChain, Inc. Install the langchain-groq package if not already installed: pip install langchain-groq. Source Distribution TensorFlow Hub is a repository of trained machine learning models ready for fine-tuning and deployable anywhere. MistralAI. In this guide we'll go over those, and show how to use them to create powerful assistants. LangChain integrates with many model providers. Import the ChatGroq class and initialize it with a model: You can share prompts within a LangSmith organization by uploading them within a shared organization. [0m Observation: [33;1m [1;3mThe generated text is not a piece of advice on improving communication skills. The simplest way to do this is for the chain to return the Documents that were retrieved in each generation. 文档地址： https://python Jul 4, 2023 · The LangChain framework includes a retry mechanism for handling API errors, including timeouts. The upside is that they are more powerful, which allows you to use them on larger and more complex schemas. LLM-generated interface: Use an LLM with access to API documentation to create an interface. In addition, it provides a client that can be used to call into runnables deployed on a server. In particular, we will: Utilize the HuggingFaceEndpoint integrations to instantiate an LLM. %pip install --upgrade --quiet semanticscholar. Discover, share, and version control prompts in the Prompt Hub. Azure AI Search. Head to the API reference for detailed documentation of all attributes and methods. LangGraph exposes high level interfaces for creating common types of agents, as well as a low-level API for composing custom flows. hub . If you're not sure which to choose, learn more about installing packages. First we'll need to import the LangChain x Anthropic package. llm_chain = prompt | llm. We will pass the prompt in via the chain_type_kwargs argument. This addition complements the existing OpenAI API, offering advanced functionalities for chatbots and automated writing assistants. AzureAISearchRetriever is an integration module that returns documents from an unstructured query. Supports text-generation, text2text-generation, conversational, translation, and summarization. Parameters. from langchain import hub. llm = ChatOpenAI(temperature=0. import os. Defaults to the hosted API service from langchain import hub from langchain. LangChain Hub. Tool calling . If you already have LANGCHAIN_API_KEY set to your current workspace's api key from LangSmith, you can skip this step. invoke: call the chain on an input. js. You switched accounts on another tab or window. api_url (Optional[str]) – The URL of the LangChain Hub API. qa_chain = RetrievalQA. This project combines the capabilities of modern deep learning models with FastAPI for high performance and scalability, Langchain for sophisticated conversational workflows Huggingface Endpoints. This notebook covers how to get started with MistralAI chat models, via their API. 5-Turbo, and Embeddings model series. Deprecated since version 0. object (Any) – The LangChain to serialize and push to the hub. This application will translate text from English into another language. %pip install --upgrade --quiet arxiv. This mechanism uses exponential backoff, starting with a wait time of 4 seconds and increasing exponentially up to a maximum of 10 seconds between retries. Python. Chroma is licensed under Apache 2. Use cases Given an llm created from one of the models above, you can use it for many use cases. They used for a diverse range of tasks such as translation, automatic speech recognition, and image classification. Chroma runs in various modes. This notebook shows how to use ZHIPU AI API in LangChain with the langchain. Azure OpenAI Service provides REST API access to OpenAI's powerful language models including the GPT-4, GPT-3. repo_full_name (str) – The full name of the repo to push to in the format of owner/repo. Construct the chain by providing a question relevant to the provided API documentation. getpass() It's also helpful (but not needed) to set up LangSmith for best-in-class observability. The Assistants API currently supports three types of tools: Code Interpreter, Retrieval, and Function calling. tool-calling is extremely useful for building tool-using chains and agents, and for getting structured outputs from models more generally. Use LangGraph to build stateful agents with This notebook demos how to use the semantic scholar tool with an agent. owner_repo_commit ( str) – The full name of the repo to pull from in the format of owner/repo:commit_hash. Qianfan not only provides including the model of Wenxin Yiyan (ERNIE-Bot) and the third-party open-source models, but also provides various AI development tools and the whole set of development environment, which facilitates customers to use and develop We also need to install the tavily-python package itself. Download files. Obtain an API Key for establishing connections between the hub and other applications. First, create an API key for your organization, then set the variable in your development environment: export LANGCHAIN_HUB_API_KEY = "ls__. The maximum number of retries is set by the max_retries attribute, which defaults to 6. Agents. To use this toolkit, you will need to set up your credentials explained in the Gmail API docs. Runnable PromptTemplate: streamline the process of saving prompts to the hub from the playground and integrating them into runnable chains. Search API Reference: HuggingFaceEmbeddings. Create a new model by parsing and validating input data from keyword arguments. LangSmith is a platform for building production-grade LLM applications. This tool is handy when you need to answer questions about current events. We'll work off of the Q&A app we built over the LLM Powered Autonomous Agents blog post by Lilian Weng in the This notebook goes over how to use the arxiv tool with an agent. Let's load the TensorflowHub Embedding class. Agents are more complex, and involve multiple queries to the LLM to understand what to do. Create an issue on the repo with details of the artifact you would like to add. 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. os. utilities import WikipediaAPIWrapper from langchain_openai import ChatOpenAI api_wrapper = WikipediaAPIWrapper (top_k_results = 1, doc_content_chars_max = 100) 5 days ago · To use, you should have the huggingface_hub python package installed, and the environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. We also need to set our Tavily API key. After that, you can do: from langchain_community. Tencent's hybrid model API (Hunyuan API) implements dialogue communication, content generation, analysis and understanding, and can be widely used in various scenarios such as intelligent customer service, intelligent marketing, role playing, advertising copywriting, product description, script creation, resume generation, article writing, code generation, data analysis, and Chroma is a AI-native open-source vector database focused on developer productivity and happiness. The default is no-dev. Search API Reference: load_huggingface_tool; model_download_counter: This is a tool that returns the most downloaded model of a 2 days ago · To use, you should have the huggingface_hub python package installed, and the environment variable HUGGINGFACEHUB_API_TOKEN set with your API token, or pass it as a named parameter to the constructor. output_parsers import StructuredOutputParser, ResponseSchema from langchain. llm = Ollama ( model = "llama2") API Reference: Ollama. Prompt Versioning ensure deployment stability by selecting specific prompt versions over the 'latest'. 所以，我们来介绍一个非常强大的第三方开源库： LangChain 。. 3 days ago · langchain_community 0. In this quickstart we'll show you how to build a simple LLM application with LangChain. Last updated on Jun 28, 2024. chat_models. 0. To get started, you'll first need to install the langchain-groq package: %pip install -qU langchain-groq. The Hugging Face Hub also offers various endpoints to build ML applications. 5 days ago · Push an object to the hub and returns the URL it can be viewed at in a browser. None of PyTorch, TensorFlow >= 2. from langchain_openai import ChatOpenAI. . [0m [1m> Finished chain. pip install -U langsmith. The key to using models with tools is correctly prompting a model and parsing its response so that it chooses the right tools and provides the See full list on blog. This will work with your LangSmith API key. These integrations allow developers to create versatile applications that combine the power of LLMs with the ability to access, interact with and manipulate external resources. from langchain_community. pull ¶. Extraction Using Anthropic Functions: Extract information from text using a LangChain wrapper around the Anthropic endpoints intended to simulate function calling. It allows you to closely monitor and evaluate your application, so you can ship quickly and with confidence. In this quickstart we'll show you how to: Get setup with LangChain and LangSmith. Example LangSmith has two APIs: One for interacting with the LangChain Hub/prompts and one for interacting with the backend of the LangSmith application. API chains | 🦜️🔗 Langchain. Jan 29, 2023 · 「LangChain Hub」が公開されたので概要をまとめました。 前回 1. Alternatively, you may configure the API key when you initialize ChatGroq. API chains. For example, here is a prompt for RAG with LLaMA-specific tokens. I would need to retry the API call with a different prompt or model to get a more relevant response. Here you'll find all of the publicly listed prompts in the LangChain Hub. huggingfacehub_api_token was transferred to model_kwargs. from_chain_type(. The standard interface exposed includes: stream: stream back chunks of the response. LangChain Hub 「LangChain Hub」は、「LangChain」で利用できる「プロンプト」「チェーン」「エージェント」などのコレクションです。複雑なLLMアプリケーションを構築するための高品質な「プロンプト」「チェーン」「エージェント」を Let's load the Hugging Face Embedding class. Otherwise, get an API key for your workspace by navigating to Settings > API Keys > Create API Key in LangSmith. . js; langchain/hub; Module langchain/hub. APIs act as the "front door" for applications to access data, business logic, or functionality from your backend services. Prompt Hub. These models can be easily adapted to your specific task including but not limited to content generation, summarization, semantic search, and natural language to code translation. Configure environment variables . Azure AI Search (formerly known as Azure Cognitive Search) is a Microsoft cloud search service that gives developers infrastructure, APIs, and tools for information retrieval of vector, keyword, and hybrid queries at scale. as_retriever(), chain_type_kwargs={"prompt": prompt} Sep 5, 2023 · gitmaxd/synthetic-training-data. tools import WikipediaQueryRun from langchain_community. For detailed information, visit: LangChain Introduction. """Interface with the LangChain Hub. LangGraph is a library for building stateful, multi-actor applications with LLMs. Groq specializes in fast AI inference. load. Together AI offers an API to query 50+ leading open-source models in a couple lines of code. Choose a suitable model from the list3. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. Using API Gateway, you can create RESTful APIs and >WebSocket APIs that enable real-time two-way communication applications Together AI. Reload to refresh your session. LangChain now integrates with Multion API, enhancing its NLP application development capabilities. " query_result langgraph. adapters ¶. 🏃. 2 is out! You are currently viewing the old v0. These templates extract data in a structured format based upon a user-specified schema. 0, or Flax have been found. load import loads from langchain_core. Tools allow us to extend the capabilities of a model beyond just outputting text/messages. dev LangChain Hub; JS/TS Docs; 💬. An instance of a runnable stored in the LangChain Hub. Defined in langchain/src/hub. prompts import PromptTemplate from langchain. First, you need to install the arxiv python package. WARNING! huggingfacehub_api_token is not default parameter. The main use cases for LangGraph are conversational agents, and long-running, multi-step LLM applications or any LLM application that would benefit from built-in support for persistent checkpoints, cycles The Hugging Face Model Hub hosts over 120k models, 20k datasets, and 50k demo apps (Spaces), all open source and publicly available, in an online platform where people can easily collaborate and build ML together. import getpass. Interface: API reference for the base interface. , this RAG prompt) from the prompt hub. Chat with Langchain's AI using retrieval QA and vector store. Features: 👉 Create custom chatGPT like Chatbot. APIChain enables using LLMs to interact with APIs to retrieve relevant information. Defaults to the hosted API service if you have an api key set, or a localhost Apr 3, 2024 · Langchain is an innovative open-source orchestration framework for developing applications harnessing the power of Large Language Models (LLM). Feb 11, 2024 · This is a standard interface with a few different methods, which make it easy to define custom chains as well as making it possible to invoke them in a standard way. While LangChain has its own message and model APIs, LangChain has also made it as easy as possible to explore other models by exposing an adapter to adapt LangChain models to the other APIs, as to the OpenAI API. With the data added to the vectorstore, we can initialize the chain. It's all about blending technical prowess with a touch of personality. The prompt can also be easily customized: POST /completions with the chosen model as a parameter to generate a short piece of advice [0mThought: [32;1m [1;3mI have an updated plan, now I need to execute the API calls. hub. This prompt uses NLP and AI to convert seed content into Q/A training data for OpenAI LLMs. A wrapper around the Search API. In the core logic of the API, we establish the endpoint /agent/invoke. search = SearchApiAPIWrapper(engine="google_jobs") . HubRunnable ¶. First, follow these instructions to set up and run a local Ollama instance: Then, make sure the Ollama server is running. You can find your API key in the Azure portal under your Azure OpenAI resource. Here's an example of it in action: Jun 2, 2024 · To use a prompt from Langchain Hub, you just need a link for that prompt to pull, but to do that, you need to install Langchain Hub so everything else remains the same make a Langchain chain, connect your LLM to the prompt, and voila! ready to go! So first, start with installing the essentials. embeddings = HuggingFaceEmbeddings text = "This is a test document. embeddings import TensorflowHubEmbeddings. Create a RunnableBinding from a Runnable and Gmail. Security. These can be called from LangChain either through this local pipeline wrapper or by calling their hosted inference endpoints through 2 days ago · Programs created using LCEL and LangChain Runnables inherently support synchronous, asynchronous, batch, and streaming operations. Models won't be available and only tokenizers, configuration and file/data utilities can be used. agents import AgentExecutor, create_react_agent, load_tools. Initialize the chain. json file, you can start using the Gmail API. Pull an object from the hub and returns it as a LangChain object. Add an artifact with the appropriate Google form: Prompts. from langchain. You can search for prompts by name, handle, use cases, descriptions, or models. " Then, you can upload prompts to the organization. TypeScript. This library is integrated with FastAPI and uses pydantic for data validation. See a guide on RAG with locally-running models here. kh xh dm jw jo it eo sn ze tr