LangChain comes with a few built-in helpers for managing a list of messages. 1 day ago · class langchain_core. Here’s an example: Let’s build a basic chain — create a prompt and get a prediction prompt = FewShotPromptTemplate (example_selector = example_selector, example_prompt = example_prompt, prefix = "You are a Neo4j expert. Jan 30, 2024 · Prompt engineering is the process of influencing the model's continuous responses by meticulous crafting of prompts. It can work with either language model Output parsers are classes that help structure language model responses. However, all that is being done under the hood is constructing a chain with LCEL. This chain first does a retrieval step to fetch relevant documents, then passes those documents into an LLM to generate a response. By sequentially feeding prompts to the language model, developers can create a contextually aware interaction. In agents, a language model is used as a reasoning engine to determine which actions to take and in which order. chains import LangChain is a framework for developing applications powered by large language models (LLMs). Ollama bundles model weights, configuration, and data into a single package, defined by a Modelfile. chains import SimpleSequentialChain from langchain. js to build stateful agents with first-class Prompt + LLM. Jul 15, 2024 · A PipelinePrompt consists of two main parts: - final_prompt: This is the final prompt that is returned - pipeline_prompts: This is a list of tuples, consisting of a string (`name`) and a Prompt Template. chains import LLMChain from langchain. "), Mar 12, 2023 · 使い方まとめ(1)で説明したLangChainの各モジュールはこれを解決するためのものでした。 Prompt Templates: プロンプトの管理; LLMs: 言語モデルのラッパー(OpenAI::GPT-3やGPT-Jなど) Document Loaders: PDFなどのファイルの下処理; Utils: 検索APIのラッパーなど便利関数保管庫 from langchain. 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. router. It optimizes setup and configuration details, including GPU usage. In this case we'll use the trim_messages helper to reduce how many messages we're sending to the model Ensuring reliability usually boils down to some combination of application design, testing & evaluation, and runtime checks. chat import ChatPromptTemplate, SystemMessagePromptTemplate. The RunnableInterface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. ›. pull ( "rlm/rag-prompt") Agents. Those variables are then passed into the prompt to produce a formatted string. A key feature of chatbots is their ability to use content of previous conversation turns as context. Next, we need to define Neo4j credentials. Chain-of-thought prompting enables large language models to address complex tasks like common sense 2 days ago · langchain_core. param output_parser: Optional [BaseOutputParser] = None ¶ How to parse the output of calling an LLM on this formatted prompt. Feb 11, 2023 · Blog Microblog About A Look Under the Hood: Using PromptLayer to Analyze LangChain Prompts February 11, 2023. [2]: from langchain. In this notebook, we will use the ONNX version of the model to speed up The below quickstart will cover the basics of using LangChain's Model I/O components. ) prompt = ChatPromptTemplate. MultiPromptChain: This chain routes input between multiple prompts. Not all prompts use these components, but a good prompt often uses two or more. pydantic_v1 import BaseModel, Field. This is a relatively simple LLM application - it's just a single LLM call plus some prompting. Oct 24, 2023 · Another 2 options to print out the full chain, including prompt. LangChain strives to create model agnostic templates to The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. When working with string prompts, each template is joined together. llms import OpenAI from langchain. from langchain_core. PromptTemplate ¶. chain = load_qa_with_sources_chain(OpenAI(temperature=0), chain_type="stuff", prompt=PROMPT) query = "What did the May 14, 2023 · You would do something like this: from langchain. This prompt is run on each individual post and is used to extract a set of “topics” local to that post. \n\nBelow are a number of examples of questions and their corresponding Cypher queries. Sep 11, 2023 · Please note that the load_summarize_chain function requires a BaseLanguageModel instance as the first argument, a chain_type as the second argument, and a refine_prompt as the third argument. We'll use the with_structured_output method supported by OpenAI models: %pip install --upgrade --quiet langchain langchain-openai. In this tutorial, we'll learn how to create a prompt template that uses few-shot examples. Use Case In this tutorial, we'll configure few-shot examples for self-ask with search. Use the most basic and common components of LangChain: prompt templates, models, and output parsers. LANGCHAIN_TRACING_V2=true. """ from __future__ import annotations from typing import Any, Dict, List, Optional from langchain_core. Pydantic parser. The most basic type of chain simply takes your input, formats it with a prompt template, and sends it to an LLM for processing. Use LangChain Expression Language, the protocol that LangChain is built on and which facilitates component chaining. input (Dict) – Dict, input Jul 6, 2024 · LangChain is a Python library that provides various functionality for building and chaining prompts. The core idea of agents is to use a language model to choose a sequence of actions to take. 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. 3 days ago · Source code for langchain. chains import LLMChain from langchain. Create a formatter for the few-shot examples. 🔗 Chains: Chains go beyond a single LLM call and involve Memory management. A multi-route chain that uses an LLM router chain to choose amongst prompts. Language models in LangChain come in two Llama. Let's look at simple agent example that can search Wikipedia for information. from langchain_openai import OpenAI. Parameters. In this quickstart we'll show you how to: Get setup with LangChain, LangSmith and LangServe. Aug 10, 2023 · The key point is that you're calling gpt_model. from_template("You have access to {tools}. In your case, the template Apr 24, 2024 · Many of the applications you build with LangChain will contain multiple steps with multiple invocations of LLM calls. environ["AZURE_OPENAI_API_KEY"] = getpass. Note that querying data in CSVs can follow a similar approach. 3 days ago · langchain_core. It will then cover how to use Prompt Templates to format the inputs to these models, and how to use Output Parsers to work with the outputs. classlangchain. These prompts can incorporate elements such as instructions, context, input, output instructions, and techniques like few-shot prompting and retrieval augmented generation (RAG). Given an input question, create a syntactically correct Cypher query to run. Save to the hub. from langchain. 4 days ago · First, this pulls information from the document from two sources: This takes the information from the document. At the moment I’m writing this post, the langchain documentation is a bit lacking in providing simple examples of how to pass custom prompts to some of the LangChain Expression Language . [docs] class PromptTemplate(StringPromptTemplate): """Prompt template for a language model. Jul 24, 2023 · The possibilities are limitless when it comes to chaining together different AI agents and models, to create even more powerful and useful applications!" FAQ. Quick reference. api import open_meteo_docs. prompts import PromptTemplate template = """Assistant is a very smart {branch} professor. Plain strings are intepreted as Human messages. A big use case for LangChain is creating agents . Each PromptTemplate will be formatted and then passed to future prompt templates as a variable with the same name as `name` """ final_prompt Here we demonstrate how to use prompt templates to format multimodal inputs to models. Prompt templates are predefined recipes for generating prompts for language models. In chains, a sequence of actions is hardcoded (in code). The most basic and common use case is chaining a prompt template and a model together. getpass("Enter your AzureOpenAI API key: ") . OpenAI. prompt = (. Jul 3, 2023 · The RunnableInterface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. Let's walk through an example of that in the example below. SystemMessagePromptTemplate. Since we're working with OpenAI function-calling, we'll need to do a bit of extra structuring to send example inputs and outputs to the model. map_reduce. Several examples include: Sequentially combining multiple LLMs by using the output of the first LLM as input for the second LLM (refer to this section) Partial prompt templates. \n\nHere is the schema information\n{schema}. llm_chain = prompt | llm. import os. 1. I have also provided some examples and code snippets to help you get started. Apr 4, 2023 · Prompt chaining in essence is a chain of thought application. You can fork prompts to your personal organization, view the prompt's details, and run the prompt in the playground. Nov 24, 2023 · if topic: result = chain. 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 . We would need to be careful with how we format the input into the next chain. This state management can take several forms, including: Simply stuffing previous messages into a chat model prompt. """Use a single chain to route an input to one of multiple llm chains. However, what is passed in only question (as query) and NOT summaries. bind_tools () With OllamaFunctions. Under the hood these are converted to a tool definition schemas, which looks like: from langchain_core. Aug 21, 2023 · Answer - The context and question placeholders inside the prompt template are meant to be filled in with actual values when you generate a prompt using the template. # 1) You can add examples into the prompt template to improve extraction quality # 2) Introduce additional parameters to take context into account (e. This application will translate text from English into another language. Here it is in Architecture. os. First, follow these instructions to set up and run a local Ollama instance: Download and install Ollama onto the available supported platforms (including Windows Subsystem for Linux) Fetch available LLM model via ollama pull <name-of-model>. Quickstart. Inference can be established via chain-of-thought prompting. write(result['story']) We simply embed the title value in the next prompt template and use RunnablePassthrough. Bases: BaseCombineDocumentsChain. Combining LLMs and Prompts in Multi-Step Workflows. 6, openai_api_key = openai_key) ##### Chain 1 - Restaurant Name prompt May 10, 2023 · In this post, I have shown you how to use LangChain Prompts to program language models and chat models for various use cases. Quickstart Basic example: prompt + model + output parser. Apr 4, 2024 · Basic chain — Prompt Template > LLM > Response. LangChain has a number of components designed to help build Q&A applications, and RAG applications more generally. While LLMs can effectively help prototype single ML functionalities, many real-world applications involve complex tasks that cannot be easily handled via a single 2 days ago · Source code for langchain_core. MultiPromptChain[source] ¶. Tongshuang Wu, Ellen Jiang, Aaron Donsbach, Jeff Gray, Alejandra Molina, Michael Terry, Carrie J Cai. chains. llm = PromptLayerChatOpenAI(model=gpt_model,pl_tags=["InstagramClassifier"]) map_template = """The following is a set of There are two types of off-the-shelf chains that LangChain supports: Chains that are built with LCEL. load_prompt (path: Union [str, Path], encoding: Optional [str] = None) → BasePromptTemplate [source] ¶ Unified method for loading a prompt from LangChainHub or local fs. _DEFAULT_TEMPLATE = """Given an input question, first create a syntactically correct {dialect} query to run, then look at the results of the query and return the answer. SQL_PROMPTS_MAP from langchain/chains/sql_db Table definitions and example rows In basically any SQL chain, we'll need to feed the model at least part of the database schema. page_content and assigns it to a variable named page_content. The process of bringing the appropriate information and inserting it into the model prompt is known as Retrieval Augmented Generation (RAG). Prompt template for a language model. This happens to be the same format the next prompt template expects. from_messages ([ LangChain Prompts. Custom agent. Chaining within the LangChain context refers to the act of integrating LLMs with other elements to build an application. Bases: LLMChain. LANGSMITH_API_KEY=your-api-key. We have covered the main features of LangChain Prompts, including Prompt Templates, Example Selectors, and Output Parsers. Few-shot prompt templates. Execute SQL query: Execute the query. API Reference: ChatPromptTemplate | ChatOpenAI. This notebook goes over how to run llama-cpp-python within LangChain. output_parsers import PydanticOutputParser from langchain_core. run ("gaming laptop")) Output: Based on this we get the name of a company called “GamerTech Laptops”. Navigate to the LangChain Hub section of the left-hand sidebar. StringPromptTemplate [source] ¶. Ollama allows you to run open-source large language models, such as Llama 2, locally. Let's see a very straightforward example of how we can use OpenAI tool calling for tagging in LangChain. This notebook goes through how to create your own custom agent. A prompt template consists of a string template. bind_tools, we can easily pass in Pydantic classes, dict schemas, LangChain tools, or even functions as tools to the model. env file: # import dotenv. Bases: BasePromptTemplate, ABC String prompt that exposes the format method, returning a prompt. This formatter should be a PromptTemplate object. Combining documents by mapping a chain over them, then combining results. 2. This class is deprecated. Let’s define them more precisely. The image depicts a sunny day with a beautiful blue sky filled with scattered white clouds. Moderation chains are useful for detecting text that could be hateful, violent, etc. We can also build our own interface to external APIs using the APIChain and provided API documentation. "Parse": A method which takes in a string (assumed to be the response 2. header(result['title']) st. pipeline_prompts: This is a list of tuples, consisting of a string ( name) and a Prompt Template. In the OpenAI family, DaVinci can do reliably but Curie 4 days ago · Prompt template for composing multiple prompt templates together. Now Apr 24, 2023 · prompt object is defined as: PROMPT = PromptTemplate(template=template, input_variables=["summaries", "question"]) expecting two inputs summaries and question. globals import set_debug. 4 days ago · A list of the names of the variables that are optional in the prompt. This can be useful to apply on both user input, but also on the output of a Language Model. multi_prompt. A PromptValue is a wrapper around a completed prompt that can be passed to either an LLM (which takes a string as input) or ChatModel (which takes a sequence of messages as input). # Optional, use LangSmith for best-in-class observability. Almost all other chains you build will use this building block. Here you'll find all of the publicly listed prompts in the LangChain Hub. prompt = hub. After executing actions, the results can be fed back into the LLM to determine whether more actions are needed, or whether it is okay to finish. llm. The template can be formatted using either f-strings LangChain is a technique that takes advantage of the OpenAI API to chain multiple prompts together, simulating a conversation or dialogue. Prompt prompt is a BasePromptTemplate, which means it takes in an object of template variables and produces a PromptValue. Before diving into Langchain’s PromptTemplate, we need to better understand prompts and the discipline of prompt engineering. One of the most foundational Expression Language compositions is taking: PromptTemplate / ChatPromptTemplate -> LLM / ChatModel -> OutputParser. 🏃. This notebook walks through examples of how to use a moderation chain, and several common ways for doing so. # Set env var OPENAI_API_KEY or load from a . 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. This is generally the most reliable way to create agents. [ Deprecated] Chain to run queries against LLMs. We will first create it WITHOUT memory, but we will then show how to add memory in. MultiRetrievalQAChain: Retriever 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. MapReduceDocumentsChain [source] ¶. append({"input": question, "tool_calls": [query]}) Now we need to update our prompt template and chain so that the examples are included in each prompt. View a list of available models via the model library and pull to use locally with the command Nov 8, 2023 · The following example will show routing chains used in a MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question and then answers the question using that prompt. Few-shot prompting involves providing the LLM with You can also chain arbitrary chat prompt templates or message prompt templates together. Note: new versions of llama-cpp-python use GGUF model files (see here ). Credentials. Memory is needed to enable conversation. prompts import PromptTemplate from pydantic import BaseModel, Field # Output parser will split the LLM result into a list of queries class LineList (BaseModel): # "lines" is the key (attribute name) of the parsed output MistralAI. template = ChainedPromptTemplate([. To see how this works, let's create a chain that takes a topic and generates a joke: %pip install --upgrade --quiet langchain-core langchain-community langchain-openai. Moderation chain. Jan 1, 2024 · from langchain. Like other methods, it can make sense to "partial" a prompt template - eg pass in a subset of the required values, as to create a new prompt template which expects only the remaining subset of values. PromptTemplate implements the standard RunnableInterface. chains import SequentialChain openai_key = "" # Sequential chain llm = OpenAI(temperature=0. We consider the integration of LangChain and prompt flow as a powerful combination that can help you to build and test your custom language models with ease, especially in the case where you may want to use LangChain modules to initially build your flow and then use our prompt Flow to easily scale the experiments for bulk testing, evaluating Jul 8, 2024 · This means the chain can dynamically process and generate responses tailored to this specific product input. Answer the question: Model responds to user input using the query results. Apr 21, 2023 · Generic — A single LLM is the simplest chain. A formatted string. Prompt Engineering. prompts. language_models import BaseLanguageModel from langchain_core. 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! # Define a custom prompt to provide instructions and any additional context. combine_documents. Each PromptTemplate will be formatted and then passed to future prompt templates as a variable with the same name as name. In this example, we will use OpenAI Tool Calling to create this agent. chains 2 days ago · Async format the prompt with the inputs. string. llama-cpp-python is a Python binding for llama. The following prompt is used to develop the “map” step of the MapReduce chain. LangChain Expression Language, or LCEL, is a declarative way to easily compose chains together. Head to the Azure docs to create your deployment and generate an API key. This notebook shows how to prevent prompt injection attacks using the text classification model from HuggingFace. Open the ChatPromptTemplate child run in LangSmith and select "Open in Playground". LangChain supports this in two ways: Partial formatting with string values. These are, in increasing order of complexity: 📃 Models and Prompts: This includes prompt management, prompt optimization, a generic interface for all LLMs, and common utilities for working with chat models and LLMs. Use LangGraph. The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. With LCEL, it's easy to add custom functionality for managing the size of prompts within your chain or agent. chains import LLMChain chain = LLMChain (llm=llm, prompt=prompt, verbose=True) print (chain. Head to the API reference for detailed documentation of all attributes and methods. from_llm_and_api_docs(. The above, but trimming old messages to reduce the amount of distracting information the model has to deal with. You can search for prompts by name, handle, use cases, descriptions, or models. . class langchain. invoke({"topic": topic}) st. A valid API key is needed to communicate with the API. Configure a formatter that will format the few-shot examples into a string. prompt. You can also see some great examples of prompt engineering. Follow these installation steps to set up a Neo4j database. The sky has varying shades of blue, ranging from a deeper hue Setup. Note that if you change this, you should also change the prompt used in the chain to reflect this naming change. LLMChain [source] ¶. Bases: Chain. A template may include instructions, few-shot examples, and specific context and questions appropriate for a given task. prompt import PromptTemplate. from_template("Question: {question}\n{answer}") 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. class GetWeather(BaseModel): Hugging Face prompt injection identification. Some examples of prompts from the LangChain codebase. , include metadata # about the document from which the text was extracted. conversation. This notebook demonstrates how to use the RouterChain paradigm to create a chain that dynamically selects the prompt to use for a given input. We’ll use OpenAI in this example: OPENAI_API_KEY=your-api-key. Bases: MultiRouteChain. What does chain_type_kwargs={"prompt": QA_CHAIN_PROMPT} actually accomplish? Answer - chain_type_kwargs is used to pass additional keyword argument to RetrievalQA. llm = OpenAI(temperature=0) chain = APIChain. prompts import PromptTemplate from langchain. metadata and assigns it to variables of the same name. Features LangChain allows for easy provider switching, standardizes interactions with LLMs, and provides agents for things like symbolic reasoning and MRKL architectures 1 . PromptValue. Please note that this approach will require you to manage the conversation history manually if you want the second prompt to be aware of the context from the first prompt's output. Interactive tutorial. We also collate Depending on what tools are being used and how they're being called, the agent prompt can easily grow larger than the model context window. Inputs to the prompts are represented by e. ", Oct 2, 2023 · Creating the map prompt and chain. Prompts. Jul 3, 2023 · The Runnable Interface has additional methods that are available on runnables, such as with_types, with_retry, assign, bind, get_graph, and more. globals import set_verbose, set_debug set_debug(True) set_verbose(True) examples. chains import LLMChain. ConversationChain [source] ¶. Once you've done this set the AZURE_OPENAI_API_KEY and AZURE_OPENAI_ENDPOINT environment variables: import getpass. First, we'll need to install the main langchain package for the entrypoint to import the method: %pip install langchain. LangChain Hub. Agents are systems that use LLMs as reasoning engines to determine which actions to take and the inputs to pass them. llm=llm, verbose=True, memory=ConversationBufferMemory() OllamaFunctions. e. 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. Create a new model by parsing and validating input data from keyword arguments. You can work with either prompts directly or strings (the first element in the list needs to be a prompt). String prompt composition. A few-shot prompt template can be constructed from either a set of examples, or from an Example Selector object. It takes an input prompt and the name of the LLM and then uses the LLM for text generation (i. This helps standardize the structure and content of prompts. base. For a complete list of supported models and model variants, see the Ollama model Mar 13, 2022 · PromptChainer: Chaining Large Language Model Prompts through Visual Programming. Oct 25, 2022 · There are five main areas that LangChain is designed to help with. Nov 7, 2023 · from langchain. I hope this helps! Nov 20, 2023 · Custom prompts for langchain chains. In principle chain-of-thought prompting allows for the decomposition of multi-step requests into intermediate steps. This takes information from document. output for the prompt). The best way to do this is with LangSmith. This notebook covers how to get started with MistralAI chat models, via their API. By default, it uses a protectai/deberta-v3-base-prompt-injection-v2 model trained to identify prompt injections. Then add this code: from langchain. [Legacy] Chains constructed by subclassing from a legacy Chain class. Set environment variables. Use this when you have multiple potential prompts you could use to respond and want to route to just one. 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). cpp. 3. In this quickstart we'll show you how to build a simple LLM application with LangChain. Note: Here we focus on Q&A for unstructured data. Using an example set ChatOllama. async ainvoke (input: Dict, config: Optional [RunnableConfig] = None, ** kwargs: Any) → PromptValue ¶ Async invoke the prompt. dynamically updating the classifier chain’s prompt as we go along. loading. In this case, LangChain offers a higher-level constructor method. {user_input}. In the below example, the dict in the chain is automatically parsed and converted into a RunnableParallel, which runs all of its values in parallel and returns a dict with the results. It accepts a set of parameters from the user that can be used to generate a prompt for a language model. In this example we will ask a model to describe an image. kwargs (Any) – Any arguments to be passed to the prompt template. The refine_prompt should be an instance of PromptTemplate, which requires a template string and a list of input variables. LangChain provides tooling to create and work with prompt templates. [ Deprecated] Chain to have a conversation and load context from memory. # set the LANGCHAIN_API_KEY environment variable (create key in settings) from langchain import hub. This can be useful when you want to reuse parts of prompts. Returns. Evaluation and testing are both critical when thinking about deploying LLM applications, since By default, this is set to "AI", but you can set this to be anything you want. example_prompt = PromptTemplate. There are two main methods an output parser must implement: "Get format instructions": A method which returns a string containing instructions for how the output of a language model should be formatted. The guides in this section review the APIs and functionality LangChain provides to help you better evaluate your applications. To understand it fully, one must seek with an open and curious mind. router import MultiPromptChain from langchain. prompt . param partial_variables: Mapping [str, Any] [Optional] ¶ A dictionary of the partial variables the prompt template carries. Return type. g. At a high-level, the steps of these systems are: Convert question to DSL query: Model converts user input to a SQL query. Prompt templates provide us with a reusable way to generate prompts using a base prompt structure. Specifically we show how to use the MultiPromptChain to create a question-answering chain that selects the prompt which is most relevant for a given question, and then answers the question using that prompt. Enable verbose and debug; from langchain. This output parser allows users to specify an arbitrary Pydantic Model and query LLMs for outputs that conform to that schema. prompts import PromptTemplate. generate (or whatever method you use to call GPT) separately for each formatted prompt. What is LangChain? LangChain is a Python library that allows you to create and chain together different AI models, agents, and prompts in a structured way. chains import APIChain. It will introduce the two different types of models - LLMs and Chat Models. This is a breaking change. If you are interested for RAG over 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. 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. In my last post, I shared an example of using LangChain, the OpenAI Embeddings API, FAISS vector search, and GPT-3 to create a question-answering AI agent, that answers questions based on the information it has learned from a collection of Cloudflare's markdown documentation. In LangChain, we can use the PromptTemplate () function and the from_template () function defined in the PromptTemplate module to generate prompt templates. A prompt is typically composed of multiple parts: A typical prompt structure. It supports inference for many LLMs models, which can be accessed on Hugging Face. vy wl mq ne zy jp cw vq fx hn