{"id":8041,"date":"2023-10-30T14:22:25","date_gmt":"2023-10-30T22:22:25","guid":{"rendered":"https:\/\/live-cometml.pantheonsite.io\/?p=8041"},"modified":"2025-04-24T17:05:07","modified_gmt":"2025-04-24T17:05:07","slug":"working-with-language-models-in-langchain","status":"publish","type":"post","link":"https:\/\/www.comet.com\/site\/blog\/working-with-language-models-in-langchain\/","title":{"rendered":"Working with Language Models in LangChain"},"content":{"rendered":"\n<section class=\"section section--body\">\n<div class=\"section-divider\"><\/div>\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<h4 class=\"graf graf--h4\">A straightforward API for all the language models<\/h4>\n<figure class=\"graf graf--figure\"><img decoding=\"async\" class=\"graf-image\" src=\"https:\/\/cdn-images-1.medium.com\/max\/1600\/0*LgjgWdR1dxyfjAY1\" data-image-id=\"0*LgjgWdR1dxyfjAY1\" data-width=\"6016\" data-height=\"4016\" data-unsplash-photo-id=\"EmrBdJ4G0CE\" data-is-featured=\"true\"><figcaption class=\"imageCaption\">Photo by <a class=\"markup--anchor markup--figure-anchor\" href=\"https:\/\/unsplash.com\/@davidclode?utm_source=medium&amp;utm_medium=referral\" target=\"_blank\" rel=\"photo-creator noopener\" data-href=\"https:\/\/unsplash.com\/@davidclode?utm_source=medium&amp;utm_medium=referral\">David Clode<\/a> on&nbsp;<a class=\"markup--anchor markup--figure-anchor\" href=\"https:\/\/unsplash.com?utm_source=medium&amp;utm_medium=referral\" target=\"_blank\" rel=\"photo-source noopener\" data-href=\"https:\/\/unsplash.com?utm_source=medium&amp;utm_medium=referral\">Unsplash<\/a><\/figcaption><\/figure>\n<h3 class=\"graf graf--h3\">Introduction to Language Models in LangChain<\/h3>\n<p class=\"graf graf--p\">In today\u2019s digital age, language models have established their significance in various applications, from chatbots to content generation, and enhancing user experiences across platforms. Imagine harnessing the power of multiple state-of-the-art language models through one unified interface. This is precisely what LangChain offers\u200a\u2014\u200aa single API to bridge the gap between different language models, ensuring seamless integration and interaction.<\/p>\n<p class=\"graf graf--p\">The beauty of LangChain is its inherent adaptability. Whether you\u2019re keen on using the capabilities of OpenAI, Cohere, or HuggingFace, LangChain ensures that the transition between these models is as smooth as possible. Its Model I\/O module provides a structured approach to interact with these models, ensuring that developers can focus on building applications rather than grappling with API-specific nuances.<\/p>\n<p class=\"graf graf--p\">This guide will walk you through the essentials of working with LangChain, detailing the components that make it tick, and demonstrating its versatility. Whether aiming to generate text or engage in intricate dialogues with the model, LangChain has got you covered.<\/p>\n<p class=\"graf graf--p\">Dive in, and let\u2019s explore the world of language models through the lens of LangChain.<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">%%capture\n!pip install langchain openai cohere transformers\n\n<span class=\"hljs-keyword\">import<\/span> getpass\n<span class=\"hljs-keyword\">import<\/span> os\n\nos.environ[<span class=\"hljs-string\">\"OPENAI_API_KEY\"<\/span>] = getpass.getpass(<span class=\"hljs-string\">\"Open AI API Key:\"<\/span>)\n\nos.environ[<span class=\"hljs-string\">\"COHERE_API_KEY\"<\/span>] = getpass.getpass(<span class=\"hljs-string\">\"Cohere API Key:\"<\/span>)\n\nos.environ[<span class=\"hljs-string\">\"HUGGINGFACEHUB_API_TOKEN\"<\/span>] = getpass.getpass(<span class=\"hljs-string\">\"HuggingFace API Key:\"<\/span>)<\/span><\/pre>\n<h3 class=\"graf graf--h3\">If you remove language models from the picture, \ud83e\udd9c\ud83d\udd17 LangChain is pretty much&nbsp;useless.<\/h3>\n<p class=\"graf graf--p\">That\u2019s why the most essential module of \ud83e\udd9c\ud83d\udd17 LangChain is Model I\/O, which gives you the building blocks for interacting with a language model.<\/p>\n<p class=\"graf graf--p\">There are three components to this module:<\/p>\n<p class=\"graf graf--p\">1) <strong class=\"markup--strong markup--p-strong\">Prompts<\/strong>, which provide templates that allow you to parametrize and reuse prompts.<\/p>\n<p class=\"graf graf--p\">2) <strong class=\"markup--strong markup--p-strong\">Language models<\/strong>, which allow you to interface with:<\/p>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">LLMs, which take a string as input and return a string as output<\/li>\n<li class=\"graf graf--li\">Chat models, which take a list of chat messages as input and return<\/li>\n<\/ul>\n<p class=\"graf graf--p\">3) <strong class=\"markup--strong markup--p-strong\">Output Parsers<\/strong> allow you to extract and structure the text output from a language model in the way you want. These are useful for tasks like QA, where you must parse an answer.<\/p>\n<p class=\"graf graf--p\">The typical workflow is as follows:<\/p>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">Choose either an LLM or a Chat model; this depends on your use case<\/li>\n<li class=\"graf graf--li\">Construct a prompt that you customize as the inputs<\/li>\n<li class=\"graf graf--li\">Send the input to the LLM or Chat model<\/li>\n<li class=\"graf graf--li\">Parse outputs with output parsers, if needed<\/li>\n<\/ul>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n<section class=\"section section--body\">\n<div class=\"section-divider\">\n<hr class=\"section-divider\">\n<\/div>\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<blockquote class=\"graf graf--pullquote\"><p>Want to build real-world applications with LLMs? <a class=\"markup--anchor markup--pullquote-anchor\" href=\"https:\/\/www.comet.com\/production\/site\/llm-course\/\" target=\"_blank\" rel=\"noopener\" data-href=\"https:\/\/www.comet.com\/production\/site\/llm-course\/?utm_source=Heartbeat&amp;utm_medium=referral&amp;utm_content=Medium\">Try this free LLMOps course<\/a> from industry-expert Elvis Saravia of&nbsp;DAIR.AI!<\/p><\/blockquote>\n<\/div>\n<\/div>\n<\/section>\n\n\n\n<section class=\"section section--body\">\n<div class=\"section-divider\">\n<hr class=\"section-divider\">\n<\/div>\n<div class=\"section-content\">\n<div class=\"section-inner sectionLayout--insetColumn\">\n<h3 class=\"graf graf--h3\">\ud83d\uddb1\ufe0f Let\u2019s double-click on the language models component.<\/h3>\n<p class=\"graf graf--p\">In general, all Chat models are LLMs. But, not all LLMs are Chat models.<\/p>\n<p class=\"graf graf--p\">But both implement the same Base Language Model interface, making it easy to swap between them.<\/p>\n<h3 class=\"graf graf--h3\">\ud83d\udde3\ufe0f LLMs:<\/h3>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">Take a string as input and return a string as output<\/li>\n<li class=\"graf graf--li\">Implemented for models optimized for text completion<\/li>\n<li class=\"graf graf--li\">Expose a <code class=\"markup--code markup--li-code\">predict<\/code> method that takes a string prompt and returns a string completion<\/li>\n<\/ul>\n<h3 class=\"graf graf--h3\">\ud83d\udcac Chat&nbsp;models:<\/h3>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">Take a list of <code class=\"markup--code markup--li-code\">ChatMessages<\/code> as input and return a <code class=\"markup--code markup--li-code\">ChatMessage<\/code> as output<\/li>\n<li class=\"graf graf--li\">Implemented for models that are tuned for dialogue<\/li>\n<li class=\"graf graf--li\">Expose a <code class=\"markup--code markup--li-code\">predict_messages<\/code> method that takes a list of ChatMessages and returns a single ChatMessage<\/li>\n<li class=\"graf graf--li\">ChatMessages contain a content field with the message text and a role field indicating the speaker<\/li>\n<\/ul>\n<h3 class=\"graf graf--h3\">\ud83d\udd11 The key differences:<\/h3>\n<ul class=\"postList\">\n<li class=\"graf graf--li\">Input\/output types: strings vs. ChatMessages<\/li>\n<li class=\"graf graf--li\">Methods: <code class=\"markup--code markup--li-code\">predict<\/code> vs. <code class=\"markup--code markup--li-code\">predict_messages<\/code><\/li>\n<li class=\"graf graf--li\">Optimization: text completion vs. dialog<\/li>\n<\/ul>\n<h3 class=\"graf graf--h3\">Working LLMs<\/h3>\n<p class=\"graf graf--p\">The LLM class is designed to be a standard interface to an LLM provider. This class abstracts away provider-specific APIs and exposes common methods like <code class=\"markup--code markup--p-code\">predict<\/code> and <code class=\"markup--code markup--p-code\">generate<\/code>.<\/p>\n<p class=\"graf graf--p\">\u2022 <code class=\"markup--code markup--p-code\">predict<\/code> is optimized for text completion. It allows you to format and pass a prompt template to the language model. The output is just plain text.<\/p>\n<p class=\"graf graf--p\">\u2022 <code class=\"markup--code markup--p-code\">generate<\/code> takes a list of prompts and returns detailed <code class=\"markup--code markup--p-code\">LLMResult<\/code> objects with completions and metadata.<\/p>\n<p class=\"graf graf--p\">Let\u2019s instantiate LLMs from Cohere, OpenAI, and HuggingFace and look at the difference between the generate and predict methods.<\/p>\n<h3 class=\"graf graf--h3\">Predict method<\/h3>\n<p class=\"graf graf--p\">Notice that it\u2019s the same API for each model, which is nice.<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\"><span class=\"hljs-keyword\">from<\/span> langchain.llms <span class=\"hljs-keyword\">import<\/span> OpenAI, Cohere, HuggingFacePipeline, HuggingFaceHub\n\nopenai_llm = OpenAI()\n\ncohere_llm = Cohere()\n\nhuggingface_llm = HuggingFaceHub(repo_id=<span class=\"hljs-string\">\"tiiuae\/falcon-7b\"<\/span>, model_kwargs={<span class=\"hljs-string\">\"max_length\"<\/span>: <span class=\"hljs-number\">1000<\/span>})\n\nprompt = <span class=\"hljs-string\">\"How do I become an AI Engineer?\"<\/span>\n\nopenai_llm.predict(prompt)<\/span><\/pre>\n<p class=\"graf graf--p\">And the model will give the following response:<\/p>\n<p class=\"graf graf--p\">1. Earn a Bachelor\u2019s Degree: To become an AI engineer, you will need at least a bachelor\u2019s degree in computer science, mathematics, or a related field.<\/p>\n<p class=\"graf graf--p\">2. Gain Experience: It is important to gain experience in the field of AI engineering. This can be done through internships, research projects, and taking courses in AI-related topics.<\/p>\n<p class=\"graf graf--p\">3. Get Certified: AI engineers can become certified in various areas of AI technology, such as natural language processing, robotics, machine learning, and more.<\/p>\n<p class=\"graf graf--p\">4. Develop Your Skills: AI engineers must continually develop their skills to stay up-to-date with the latest technologies and trends. This can be done through attending conferences, reading books, and taking courses.<\/p>\n<p class=\"graf graf--p\">5. Stay Informed: To stay ahead of the game, AI engineers must stay informed of the latest trends and technologies in the field. This can be done through reading industry blogs, attending conferences, and networking with others in the field.<\/p>\n<h4 class=\"graf graf--h4\">Getting output from&nbsp;Cohere<\/h4>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">cohere_llm.predict(prompt)<\/span><\/pre>\n<p class=\"graf graf--p\">Which produces the following output:<\/p>\n<p class=\"graf graf--p\">There is no one answer to this question, as the path to becoming an AI Engineer will vary depending on your background and experience. However, some tips on how to become an AI Engineer include:<\/p>\n<p class=\"graf graf--p\">1. Getting a degree in computer science or a related field.<br>\n2. Learning the basics of machine learning and artificial intelligence.<br>\n3. Working on projects related to machine learning and artificial intelligence.<br>\n4. Networking with other AI Engineers and professionals.<br>\n5. Stay up to date on the latest developments in the field.<\/p>\n<p class=\"graf graf--p\">If you have any questions or need any help along the way, feel free to ask me!<\/p>\n<h4 class=\"graf graf--h4\">Getting output from a HuggingFace model<\/h4>\n<p class=\"graf graf--p\">We left the generation parameters alone, but you can change those.<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">huggingface_llm.predict(prompt)<\/span><\/pre>\n<p class=\"graf graf--p\">The AI Engineer is a new role that is emerging in the tech industry. It is a combination<\/p>\n<h3 class=\"graf graf--h3\">Generate<\/h3>\n<p class=\"graf graf--p\">And as you can see below, the generate method gives more details:<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\">LLMResult(generations=[[Generation(text=<span class=\"hljs-string\">\" To become an AI engineer, you will need to complete a bachelor's or master's degree in a relevant field such as computer science, machine learning, or data science. You will also need to have strong programming skills and experience with machine learning algorithms.\\n\\nAfter completing your education, you will need to find a job in the AI field. This can be done by applying to companies that are hiring AI engineers or by working on your own projects.\\n\\nTo be successful as an AI engineer, you will need to be able to work well in a team, have strong communication skills, and be able to think creatively. You will also need to be able to keep up with the latest developments in the field and be willing to learn new skills.\"<\/span>, generation_info=<span class=\"hljs-literal\">None<\/span>)]], llm_output=<span class=\"hljs-literal\">None<\/span>, run=[RunInfo(run_id=UUID(<span class=\"hljs-string\">'79920107-3a34-4d61-b95e-280420ad3899'<\/span>))])<\/span><\/pre>\n<h3 class=\"graf graf--h3\">Working with Chat&nbsp;models<\/h3>\n<p class=\"graf graf--p\">We\u2019ll stick to the OpenAI chat models for this section.<\/p>\n<p class=\"graf graf--p\">The chat model interface is based around messages rather than raw text.<\/p>\n<p class=\"graf graf--p\">The types of messages currently supported in LangChain are <code class=\"markup--code markup--p-code\">AIMessage<\/code>, <code class=\"markup--code markup--p-code\">HumanMessage<\/code>, <code class=\"markup--code markup--p-code\">SystemMessage<\/code>.<\/p>\n<pre class=\"graf graf--pre graf--preV2\" spellcheck=\"false\" data-code-block-mode=\"2\" data-code-block-lang=\"python\"><span class=\"pre--content\"><span class=\"hljs-keyword\">from<\/span> langchain.chat_models <span class=\"hljs-keyword\">import<\/span> ChatOpenAI\n\n<span class=\"hljs-keyword\">from<\/span> langchain.schema <span class=\"hljs-keyword\">import<\/span> (\n    AIMessage,\n    HumanMessage,\n    SystemMessage\n)\n\nllm = OpenAI(model_name=<span class=\"hljs-string\">\"gpt-3.5-turbo\"<\/span>)\nchat = ChatOpenAI()\n\nmessages = [\n    SystemMessage(content=<span class=\"hljs-string\">\"You are a tough love career coach who gets to the point and pushes your mentees to be their best.\"<\/span>),\n    HumanMessage(content=<span class=\"hljs-string\">\"How do I become an AI engineer?\"<\/span>)\n]\n\nchat(messages)<\/span><\/pre>\n<p class=\"graf graf--p\">There you have it.<\/p>\n<p class=\"graf graf--p\">It\u2019s the basics, and we\u2019ll build on it from here.<\/p>\n<h3 class=\"graf graf--h3\">Conclusion: Navigating the Future with LangChain<\/h3>\n<p class=\"graf graf--p\">As we\u2019ve journeyed through the intricacies of LangChain, it\u2019s evident that the world of language models is not just about individual capabilities but also about integration and accessibility. LangChain stands out as a beacon of innovation in this context, offering a unified interface to tap into the vast potential of various leading language models. Its adaptability, structured approach, and ease of use make it an indispensable tool for developers and businesses.<\/p>\n<p class=\"graf graf--p\">The future of digital communication and AI-driven applications is deeply intertwined with language models. As these models evolve, so will our need for platforms like LangChain that simplify complexities and empower us to create transformative solutions. Whether you\u2019re an AI enthusiast, a developer, or a business leader, embracing tools like LangChain will undeniably position you at the forefront of this AI revolution.<\/p>\n<p class=\"graf graf--p\">Here\u2019s to harnessing the combined might of the world\u2019s best language models and to the countless innovations that await us. With LangChain by our side, the horizon looks promising and limitless.<\/p>\n<\/div>\n<\/div>\n<\/section>\n","protected":false},"excerpt":{"rendered":"<p>A straightforward API for all the language models Photo by David Clode on&nbsp;Unsplash Introduction to Language Models in LangChain In today\u2019s digital age, language models have established their significance in various applications, from chatbots to content generation, and enhancing user experiences across platforms. Imagine harnessing the power of multiple state-of-the-art language models through one unified [&hellip;]<\/p>\n","protected":false},"author":68,"featured_media":0,"comment_status":"closed","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"customer_name":"","customer_description":"","customer_industry":"","customer_technologies":"","customer_logo":"","footnotes":""},"categories":[65,7],"tags":[70,71,31,33,34],"coauthors":[166],"class_list":["post-8041","post","type-post","status-publish","format-standard","hentry","category-llmops","category-tutorials","tag-langchain","tag-language-models","tag-llmops","tag-openai","tag-prompt-engineering"],"yoast_head":"<!-- This site is optimized with the Yoast SEO Premium plugin v25.9 (Yoast SEO v25.9) - https:\/\/yoast.com\/wordpress\/plugins\/seo\/ -->\n<title>Working with Language Models in LangChain - Comet<\/title>\n<meta name=\"description\" content=\"This guide will walk you through the essentials of working with LangChain and all its components and demonstrating its versatility.\" \/>\n<meta name=\"robots\" content=\"index, follow, max-snippet:-1, max-image-preview:large, max-video-preview:-1\" \/>\n<link rel=\"canonical\" href=\"https:\/\/www.comet.com\/site\/blog\/working-with-language-models-in-langchain\/\" \/>\n<meta property=\"og:locale\" content=\"en_US\" \/>\n<meta property=\"og:type\" content=\"article\" \/>\n<meta property=\"og:title\" content=\"Working with Language Models in LangChain\" \/>\n<meta property=\"og:description\" content=\"This guide will walk you through the essentials of working with LangChain and all its components and demonstrating its versatility.\" \/>\n<meta property=\"og:url\" content=\"https:\/\/www.comet.com\/site\/blog\/working-with-language-models-in-langchain\/\" \/>\n<meta property=\"og:site_name\" content=\"Comet\" \/>\n<meta property=\"article:publisher\" content=\"https:\/\/www.facebook.com\/cometdotml\" \/>\n<meta property=\"article:published_time\" content=\"2023-10-30T22:22:25+00:00\" \/>\n<meta property=\"article:modified_time\" content=\"2025-04-24T17:05:07+00:00\" \/>\n<meta property=\"og:image\" content=\"https:\/\/cdn-images-1.medium.com\/max\/1600\/0*LgjgWdR1dxyfjAY1\" \/>\n<meta name=\"author\" content=\"Harpreet Sahota\" \/>\n<meta name=\"twitter:card\" content=\"summary_large_image\" \/>\n<meta name=\"twitter:creator\" content=\"@Cometml\" \/>\n<meta name=\"twitter:site\" content=\"@Cometml\" \/>\n<meta name=\"twitter:label1\" content=\"Written by\" \/>\n\t<meta name=\"twitter:data1\" content=\"Harpreet Sahota\" \/>\n\t<meta name=\"twitter:label2\" content=\"Est. reading time\" \/>\n\t<meta name=\"twitter:data2\" content=\"7 minutes\" \/>\n<!-- \/ Yoast SEO Premium plugin. -->","yoast_head_json":{"title":"Working with Language Models in LangChain - 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