synthetic data""". Without any debugging, here's what we see: import { AgentExecutor, createOpenAIToolsAgent } from "langchain/agents"; import { pull } from "langchain/hub"; LangChain Hub. Prompty is an asset class and format for LLM prompts designed to enhance observability, understandability, and portability for developers. When we use load_summarize_chain with chain_type="stuff", we will use the StuffDocumentsChain. memory import ConversationBufferWindowMemory from langchain_community. ) as a constructor argument, e. for storing to a database) the final prompt text. There are also several useful primitives for working with runnables, which you can read about in this section. For more complex tool use it's very useful to add few-shot examples to the prompt. \n' + '\n' + '5. Agents and Memory : For applications involving decision-making and state persistence, the Agents and Memory modules provide frameworks for debugging. If you are having a hard time finding the recent run trace, you can see the URL using the read_run command, as shown below. Apr 11, 2024 · LangChain has a set_debug() method that will return more granular logs of the chain internals: Let’s see it with the above example. “generate” calls the agent’s LLM Chain one final time to generate. LangChainは、大規模な言語モデルを使用したアプリケーションの作成を簡素化するためのフレームワークです。. chains. Create a chat prompt template from a template string. If a LangChain application experiences performance issues or unexpected behavior, traces can help identify the exact step or component responsible. 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. > Finished chain. LangChain Expression Language Cheatsheet. stream/astream: Streams output from a single input as it’s produced. LangChain comes with a few built-in helpers for managing a list of messages. verbose = False and print the result for final output, also I have added handle_parsing_errors=True if you encounter any issue while parsing the output Create a connection. @tool. To add support for PromptLayer: Create a PromptLayer account here: https://promptlayer. There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. --dev/--no-dev: Toggles the development mode. a final answer based on the previous steps. This option is for development purposes only. We'll largely focus on methods for getting relevant database-specific information in your prompt. LLM-apps are powerful, but have peculiar characteristics. Sep 26, 2023 · The goal is more to get acquainted with the tools needed to build a service like this rather than actually deploy the application, but along the way we’ll learn a bit about prompt engineering, LLM orchestration with LangChain, using the Google Maps API to extract directions and displaying the results with leafmap and gradio. Next, we need to define Neo4j credentials. RunnableSequence is the most important composition operator in LangChain as it is used in virtually every chain. Aim tracks inputs and outputs of LLMs and tools, as well as actions of agents. 2 days ago · The RunnableInterface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. The default is SQLiteCache. g: arxiv (free) azure_cognitive_services If you need more complex prompts, you can use the Chain module to create a pipeline of LLMs. Besides basic tracing capability, prompt flow also provides several trace toolkits that can improve the tracing experience (e. Prompt engineering refers to the design and optimization of prompts to get the most accurate and relevant responses from a Can be set using the LANGFLOW_LANGCHAIN_CACHE environment variable. Observation: the result of the action. This is a quick reference for all the most important LCEL primitives. LANGCHAIN_TRACING_V2=true. 2. %pip install --upgrade --quiet langchain langchain-openai wikipedia. from langchain_openai import OpenAI. Python. prompt. from_template (template) llm = TextGen (model_url LangChain Expression Language, or LCEL, is a declarative way to chain LangChain components. LangSmith makes it easy to debug, test, and continuously improve your Dec 11, 2023 · Debugging LangChain applications. {user_input}. Final Answer: the final answer to the original With LCEL, it's easy to add custom functionality for managing the size of prompts within your chain or agent. lower(). prompts. prompts import ChatPromptTemplate from langchain_openai import ChatOpenAI llm = ChatOpenAI (model = "gpt-4") # First we need a prompt that we can pass into an LLM to generate this search query prompt = ChatPromptTemplate. from_template("Question: {question}\n{answer}") Jul 3, 2023 · The method to use for early stopping if the agent never returns AgentFinish. Here is the code that demonstrates this: set_debug(True) input = {"bullet_points": 3} echo_prompt_template = ChatPromptTemplate. There are a number of ways to enable printing at varying degrees of verbosity. 220) comes out of the box with a plethora of tools which allow you to connect to all kinds of paid and free services or interactions, like e. It also helps with the LLM observability to visualize requests, version prompts, and track usage. llms import OpenAI from langchain. Integrates with LlamaIndex, Langchain, OpenAI SDK PromptTemplates are a concept in LangChain designed to assist with this transformation. Use LangGraph to build stateful agents with Sep 12, 2023 · First, we'll create a helper function to compare the outputs of real data and synthetic data. from langchain_core. With LangSmith access: Full read and write permissions. This blog post will guide you through the process of creating enterprise-grade GenAI solutions using PromptFlow and LangChain, with a focus on observability, trackability, model monitoring, debugging, and autoscaling. agents import AgentExecutor, create_openai_tools_agent. Setup from langchain. pipe() method, which does the same thing. 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 (we’ve seen folks successfully run LCEL chains with 100s of steps in production). prompts import PromptTemplate question_prompt = PromptTemplate. Here you'll find all of the publicly listed prompts in the LangChain Hub. combine_documents. Invoke a runnable Runnable. LangSmith Hub. “force” returns a string saying that it stopped because it met a. Use LangGraph. Either ‘force’ or ‘generate’. We would like to show you a description here but the site won’t allow us. May 14, 2023 · Understanding the prompts in play increases transparency and explainability and is crucial in debugging. g. # Define a custom prompt to provide instructions and any additional context. Let's look at simple agent example that can search Wikipedia for information. It will take in two user variables: language: The language to translate text into; text: The text to translate Prompty makes it easy to create, manage, debug, and evaluate LLM prompts for your AI applications. classlangchain_core. For debugging or other traceability purposes it is sometimes useful to see the final prompt text as sent to the completion model. . From the prompt view in the Playground, you can select either "Chat 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. # Optional, use LangSmith for best-in-class observability. LangChain Prompts. However, what is passed in only question (as query) and NOT summaries. However, delivering LLM applications to production can be deceptively difficult. globals import set_debug. invoke: call the chain on an input. form['Body']. chains import RetrievalQA from langchain. In LangSmith, you can create prompts using the Playground. LangChain integrates with PromptLayer for logging and debugging prompts and responses. prompt = FewShotPromptTemplate (example_selector = example_selector, example_prompt = example_prompt, prefix = "You are a Neo4j expert. prompts import PromptTemplate from langchain_openai Set environment variables. Follow these installation steps to set up a Neo4j database. Setting up Langsmith and GPTBoost The platform for your LLM development lifecycle. If you don 't know the answer, just say that you don' t know, don't try to make up an answer. Dec 8, 2023 · I'm using Langchain 0. strip() print(llm(. ChatPromptTemplate. StructuredPromptTemplate. Jun 12, 2023 7 min read. LANGSMITH_API_KEY=your-api-key. First let's define our tools and model. Bases: StringPromptTemplate. Execute SQL query: Execute the query. Few-shot prompt templates. , trace UI for visualization). llms import TextGen from langchain_core. stuff import StuffDocumentsChain. com. query_template = f"{query} Execute all necessary queries, and always return results to the query, no explanations or To make it as easy as possible to create custom chains, we've implemented a "Runnable" protocol. ", Apr 24, 2023 · prompt object is defined as: PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"]) expecting two inputs summaries and question. llm=llm, verbose=True, memory=ConversationBufferMemory() 1. It will take in two user variables: language: The language to translate text into; text: The text to translate Jun 8, 2023 · During my debugging process, I want to view the raw prompts generated by the application that are sent to the OpenAI library and subsequently to the requests library. from langchain. LangChain is a framework for developing applications powered by language models. PromptTemplate[source] ¶. from_template("""pyth Use the following portion of a long document to see if any of the text is relevant to answer the . Example: Add trace for Autogen May 14, 2024 · A prompt template consists of a string template. A unit of work that can be invoked, batched, streamed, transformed and composed. Jun 28, 2023 · Prompt after formatting: Use the following pieces of context to answer the question at the end. globals import set_debug, set_verbose from langchain. I'm assuming that these prompts are generated by a method or function within the Langchain library, but I'm unsure how to access or print these prompts for review. Convert question to DSL query: Model converts user input to a SQL query. 4, have updated pip, and reinstalled langchain. from_messages ([ LangChain is an open-source framework designed to easily build applications using language models like GPT, LLaMA, Mistral, etc. The FileCallbackHandler is similar to the StdOutCallbackHandler, but instead of printing logs to standard output it writes logs to a file. However, all that is being done under the hood is constructing a chain with LCEL. Open the ChatPromptTemplate child run in LangSmith and select "Open in Playground". debug 和 langchain. By default, it uses a protectai/deberta-v3-base-prompt-injection-v2 model trained to identify prompt injections. 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. Prompt Editing: You can modify the prompt and re-run it to observe the resulting changes to the output as many times as needed using LangSmith's playground Dec 20, 2023 · Wanted to use debug logs to figure this out but it was the debug logs themselves that confused me. We will cover: How the dialect of the LangChain SQLDatabase impacts the prompt of the chain; How to format schema information into the prompt using SQLDatabase. I'm looking for a way to debug it. This notebook serves as a step-by-step guide on how to log, trace, and monitor Langchain LLM calls using Portkey in your Langchain app. combine_documents import create_stuff_documents_chain from langchain_core. Example: Add trace for LLM. PromptLayer. prompts import ChatPromptTemplate from langchain. Creates a chat template consisting of a single message assumed to be from the human. '4. I find viewing these makes it much easier to see what each chain is doing under the hood - and find new useful tools within the codebase. This formatter should be a PromptTemplate object. The non-determinism, coupled with unpredictable, natural language inputs, make for countless ways the system can fall short. chains import LLMChain from langchain. This Quickstart guide will walk you how to use Trace to visualize and debug calls to LangChain, LlamaIndex or your own LLM Chain or Pipeline: Langchain: Use the 1-line LangChain environment variable or context manager integration for automated logging. Note that querying data in CSVs can follow a similar approach. Navigate to the LangChain Hub section of the left-hand sidebar. You can fork prompts to your personal organization, view the prompt's details, and run the prompt in the playground. LangSmith Walkthrough. cpp into a single file that can run on most computers any additional dependencies. Jul 20, 2023 · import os from langchain. Security warning: Prefer using template_format=”f-string” instead of. Some examples of prompts from the LangChain codebase. get_context; How to build and select few-shot examples to assist the model. For more advanced usage see the LCEL how-to guides and the full API reference. LLMChain(verbose=True), and it is equivalent to passing a ConsoleCallbackHandler to the from langchain. > Finished chain Note: chain = prompt | chain is equivalent to chain = LLMChain(llm=llm, prompt=prompt) (check LangChain Expression Language (LCEL) documentation for more details) The verbose argument is available on most objects throughout the API (Chains, Models, Tools, Agents, etc. It would be good to have a mechanism that logged or otherwise surfaced (e. Configure a formatter that will format the few-shot examples into a string. Create a connection that securely stores your credentials, such as your LLM API KEY or other required credentials. A prompt template consists of a string template. globals import set_debug from langchain_community. Finally, you can use the Agent module to deploy your prompts and generate output at scale. The standard interface exposed includes: stream: stream back chunks of the response. Debugging chains. Action: the action to take, should be one of [{tool_names}] Action Input: the input to the action. こんにちは。 はい、こんにちは。 Question: こんにちは Helpful Answer: > Finished chain. Langchain Modules are composable but have a hierarchy of order, as seen here. Setting verbose to true will print out some internal states of the Chain object while running it. \n\nBelow are a number of examples of questions and their corresponding Cypher queries. chains. document_loaders import TextLoader I am met with the error: ModuleNotFoundError: No module named 'langchain' I have updated my Python to version 3. invoke() call is passed as input to the next runnable. I've built an agent, but it's behaving a bit differently than I expected. Nov 30, 2023 · I will show you how to monitor AI applications, track costs, debug prompts, and monitor the usage of the functions API with two solutions: Langsmith — a service built by the creators of Langchain, and GPTBoost — a solution working as a proxy for the OpenAI API. llms import OpaquePrompts from langchain_core. from langchain_openai import ChatOpenAI. Python Deep Learning Crash Course. 4 days ago · Sequence of Runnables, where the output of each is the input of the next. Quick Start Output parser. 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 . Then add this code: from langchain. base. llm = OpenAI(temperature=0) conversation = ConversationChain(. A RunnableSequence can be instantiated directly or more commonly by using the | operator where either the left or right operands (or both) must be a Runnable. Traditional engineering best practices need to be re-imagined for working with LLMs, and LangSmith supports all Apr 9, 2023 · Patrick Loeber · · · · · April 09, 2023 · 11 min read. You can also see some great examples of prompt engineering. In this case we'll use the trim_messages helper to reduce how many messages we're sending to the model LangChain includes a class called PipelinePromptTemplate, which can be useful when you want to reuse parts of prompts. format(. PromptLayer is a platform for prompt engineering. I have this code: from langchain. To understand it fully, one must seek with an open and curious mind. invoke/ainvoke: Transforms a single input into an output. Let’s create a PromptTemplate here. spellbook-forge: Make your LLM prompts executable and version controlled. Let's suppose we have a simple agent, and want to visualize the actions it takes and tool outputs it receives. Interfaceは以下のページが分かり易かったですが、要は Runnable 共通のメソッドを実装しているというのと、入出力の型はコンポーネント毎に異なる（chainを組む時に Prompts. Thought: you should always think about what to do. tools import tool. They take in raw user input and return data (a prompt) that is ready to pass into a language model. It accepts a set of parameters from the user that can be used to generate a prompt for a language model. Debugging: LangSmith offers tracing and debugging information at each step of an LLM sequence, making it easier to identify and root-cause issues when things go wrong. If you have found a interesting prompt, you can try it out using the LangSmith Playground using your OpenAI API-Key. 345. 1: Use from_messages classmethod instead. invoke() / Runnable. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. batch/abatch: Efficiently transforms multiple inputs into outputs. First, we'll need to install the main langchain package for the entrypoint to import the method: %pip install langchain. While PromptLayer does have LLMs that integrate directly with LangChain (e. output_parser import StrOutputParser from langchain. You can explore all existing prompts and upload your own by logging in and navigate to the Hub from your admin panel. 👍 41. Jul 11, 2023 · LangChain (v0. basketball_query_template. Jul 13, 2024 · However, building end-to-end GenAI applications that are robust, observable, and scalable can be challenging. Stuff. from_messages Aug 26, 2023 · You need to make slight change in you code, below code would work for you. Specifically: it seems to not remember past messages. Aim makes it super easy to visualize and debug LangChain executions. Given an input question, create a syntactically correct Cypher query to run. [Legacy] Chains constructed by subclassing from a legacy Chain class. ainvoke() Retrieval. Debugging: LangSmith helps in debugging LLMs, chains, and agents by providing a visualization of the exact inputs/outputs to all LLM calls, allowing you to understand them easily. "verbose": True, "prompt": prompt. question = request. Many LangChain components implement the Runnable protocol, including chat models, LLMs, output parsers, retrievers, prompt templates, and more. We support three types of prompt templates: StringPromptTemplate. We can do this by adding AIMessage s with ToolCall s and corresponding ToolMessage s to our prompt. prompts import PromptTemplate. 】 18 LangChain Chainsとは？【Simple・Sequential・Custom】 19 LangChain Memoryとは？【Chat Message History・Conversation Buffer Memory】 20 LangChain Agents Feb 28, 2024 · LCELとは. as_retriever(search_kwargs={"k":6}), verbose=True, chain_type_kwargs={. We see how to use the FileCallbackHandler in this example. You can search for prompts by name, handle, use cases, descriptions, or models. We go over all important features of this framework. The default is no-dev. schema. classmethod from_template(template: str, **kwargs: Any) → ChatPromptTemplate [source] ¶. \n\nHere is the schema information\n{schema}. Create a formatter for the few-shot examples. One of these new, powerful tools is an LLM framework called LangChain. It looks like it's missing some of my instructions that I included in the prompt. See our how-to guide on question-answering over CSV data for more detail. from_template( "You will act as an echo server. The output of the previous runnable's . LangChain has many features, including different prompting methods, keeping conversational context, and connecting to external tools. stdout import StdOutCallbackHandler from langchain. , include metadata # about the document from which the text was extracted. This notebook covers how to do routing in the LangChain Expression Language. chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT) query = "What did the LLMアプリケーション開発のためのLangChain 前編② プロンプトトテンプレート. Oct 16, 2023 · With LangChain Hub, you have the opportunity to tap into the collective wisdom of fellow developers, researchers and enthusiasts, gaining valuable insights and best practices to enhance your LangChain application. PromptLayerOpenAI ), using a callback is the recommended way to integrate PromptLayer with LangChain. Answer the question: Model responds to user input using the query results. 0. 言語モデル統合フレームワークとして、LangChainの使用ケースは、文書 最简单的调试方式：设置langchain. Finally, let's take a look at using this in a chain (setting verbose=True so we can see the prompt). LlamaIndex: Use the W&B callback from Sep 5, 2023 · LangChain Hub is built into LangSmith (more on that below) so there are 2 ways to start exploring LangChain Hub. The recent explosion of LLMs has brought a new set of tools onto the scene. Select Create and select a connection type to store your credentials. Runnable [source] ¶. Prompt flow tracing works not only for general LLM application, but also for more frameworks like autogen and langchain. debugオプションを有効にすれば、より詳しい動作を表示させることができます Apr 15, 2023 · I'm using LangChain to build prompts that are later sent to the OpenAI API. --path: Specifies the path to the frontend directory containing build files. 1 day ago · Deprecated since version langchain-core==0. (this Thought/Action/Action Input/Observation can repeat N times) Thought: I now know the final answer. LangChain makes it easy to prototype LLM applications and Agents. llamafiles bundle model weights and a specially-compiled version of llama. chat_models import ChatOpenAI from langchain. Routing allows you to create non-deterministic chains where the output of a previous step defines the next step. 11. The chain will take a list of documents, insert them all into a prompt, and pass that prompt to an LLM: from langchain. example_prompt = PromptTemplate. """ prompt = PromptTemplate. The template can be formatted using either f-strings (default) or jinja2 syntax. Save to the hub. By Yujian Tang. Auto Evaluator: Langchain auto Prompt types. js to build stateful agents with first-class Option 1. May 31, 2023 · Get the user's question via inbound text message, print out the answer by passing the question to the prompt template and formatting it, and then pass that to Twilio as the text message to send back to the user. prompts import PromptTemplate set_debug (True) template = """Question: {question} Answer: Let's think step by step. Oct 24, 2023 · You can see your complete prompt by setting the verbose parameter to TRUE as mentionned below. # 1) You can add examples into the prompt template to improve extraction quality # 2) Introduce additional parameters to take context into account (e. llm=llm_model, chain_type='stuff', retriever=vectorsdb. Retrieval is a common technique chatbots use to augment their responses with data outside a chat model's training data. ) prompt = ChatPromptTemplate. Hugging Face prompt injection identification. batch: call the chain on a list of inputs. It can be hard to debug a Chain object solely from its output as most Chain objects involve a fair amount of input prompt preprocessing and LLM output post-processing. Traces can highlight anomalous patterns or deviations in the workflow, which might not be apparent through code analysis or standard logging. Let's create a PromptTemplate here. Without LangSmith access: Read only permissions. Routing helps provide structure and consistency around interactions with LLMs. In this LangChain Crash Course you will learn how to build applications powered by large language models. from_messages( [ SystemMessagePromptTemplate. First, let's import Portkey, OpenAI, and Agent tools. Introduction. In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. Additionally we use the StdOutCallbackHandler to print logs to the standard output. A PipelinePrompt consists of two main parts: Final prompt: The final prompt that is returned; Pipeline prompts: A list of tuples, consisting of a string name and a prompt template. time or iteration limit. We’ll use OpenAI in this example: OPENAI_API_KEY=your-api-key. LangChain is a framework for developing applications powered by large language models (LLMs). Explain me like I am technical Prompts Quickstart. For detailed information about these templates, please refer to the LangChain documentation. Inputs to the prompts are represented by e. Jun 20, 2023 · LangChainのAgentですけど、OpenAI Function calling対応になって、ますます動作が複雑になってますよね。出力オプション verbose=True を有効にしてもイマイチ動作が追いきれない時もあると思います。 そんなときは、langchain. Prompt template for a language model. PromptTemplates are a concept in LangChain designed to assist with this transformation. chains import ConversationChain. globals import set_verbose set_verbose(True) prompt = ChatPromptTemplate The Model IO and Retrieval modules within Langchain offer detailed documentation on handling prompts, LLMs, and data sources, which is vital for troubleshooting issues related to data handling. runnables. from operator import itemgetter. def run_and_compare_queries(synthetic, real, query: str): """Compare outputs of Langchain Agents running on real vs. You will have to iterate on your prompts, chains, and other components to build a high-quality product. With Aim, you can easily debug and examine an individual execution: Additionally, you have the option to compare multiple executions side by side: Aim is fully open source, learn more about Aim on Oct 19, 2023 · A few different ways to debug LCEL chains: Use set_debug (True) Jun 12, 2023 · Prompting in LangChain. In this notebook, we will use the ONNX version of the model to speed up How to use few-shot prompting with tool calling. Langchain visualizer: visualization and debugging tool for LangChain workflows ; LLM Strategy: implementing the Strategy Pattern using LLMs ; datasetGPT: A command-line interface to generate textual and conversational datasets with LLMs. LangChainでコンポーネントをchain（連続呼出）する共通のInterfaceおよびその記法です。. Beta Testing and Production: LangSmith enables the addition of runs as examples to datasets, expanding test coverage on real-world scenarios. LangChain provides the FileCallbackHandler to write logs to a file. llms import NIBittensorLLM set_debug (True) # System parameter in NIBittensorLLM is optional but you can set whatever you want to perform with model llm_sys = NIBittensorLLM (system_prompt = "Your task is to determine response based on user prompt. 🪢 Open source LLM engineering platform: Observability, metrics, evals, prompt management, playground, datasets. callbacks. Using an example set We can do this by adding a simple step in front of the prompt that modifies the messages key appropriately, and then wrap that new chain in the Message History class. wskish changed the title mechanism to provide visibility into final prompt All you need to do is: 1) Download a llamafile from HuggingFace 2) Make the file executable 3) Run the file. I cannot get a verbose output of what's going on under the hood using the LCEL approach to chain building. As a result, analyzing and debugging LangChain benefits from stack traces. In this case, LangChain offers a higher-level constructor method. This section will cover how to implement retrieval in the context of chatbots, but it's worth noting that retrieval is a very subtle and deep topic - we encourage you to explore other parts of the documentation that go into greater depth! Nov 20, 2023 · from langchain. import os. This can be done using the pipe operator ( | ), or the more explicit . Explain me like I am technical Dynamically route logic based on input. One point about LangChain Expression Language is that any two runnables can be "chained" together into sequences. One of the most powerful features of LangChain is its support for advanced prompt engineering. Go to prompt flow in your workspace, then go to connections tab. Furthermore, thinking about education, carefully considering the prompts generated and used by the agent(s) is necessary to understand both the decision-making process and the interactions with various tools as well as the data involved. Try in a Colab Notebook here →. verbose LangChain官方推出了LangSmith：用于更方便的构建生产级别的LLM应用。 它允许您调试、测试、评估和监控基于任何LLM框架构建的链和智能代理，并与LangChain无缝集成 除了LangSmith，还有另外一个工具WanDB用于方便 16 LangChain Model I/Oとは？【Prompts・Language Models・Output Parsers】 17 LangChain Retrievalとは？【Document Loaders・Vector Stores・Indexing etc. 4 days ago · class langchain_core. Create an API token and pass it either as promptLayerApiKey argument in the PromptLayerOpenAI constructor or in the PROMPTLAYER_API_KEY environment variable. This notebook shows how to prevent prompt injection attacks using the text classification model from HuggingFace. nb nr uz zn ny is gi qe jz ng