LangChain Agents : A VERY SIMPLE & Quick Tutorial!
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🚀 In this tutorial video, I present a very simple and quick tutorial on LangChain Agents. Specifically, I introduce you the concept of agents and tools in LangChain, and show you how you can use them with simple sample codes.
🔖LangChain is an open source framework that allows AI developers to combine LLMs like GPT-4 with external sources of computation and data. Specifically, LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). It enables developers to build context-aware and reasoning LLM applications by combining a large language model prompt with various external resources. LangChain is an open-source library that provides developers with tools to build applications powered by LLMs, and it is built around LLMs, allowing developers to create chains of different prompts interactively. Additionally, LangChain is a powerful, open-source framework that helps developers develop applications powered by a language model, particularly an LLM, and it is designed to streamline AI application development, focusing on real-time data processing and integration with LLMs.
🔖LangChain agents and tools are components of the LangChain framework that enable the use of large language models (LLMs) to perform various tasks. Agents are programs that use a language model to choose a sequence of actions to take, while tools are interfaces that an agent can use to interact with the world.
🔖The benefits of using LangChain agents and tools include:
1. Autonomy: LangChain agents can act autonomously, allowing them to perform tasks independently without the need for human intervention.
2. Customization: LangChain provides an extensive library of off-the-shelf tools and an intuitive framework for customizing your own tools, allowing developers to tailor the agents to their specific needs.
3. Improved accuracy and relevancy: LangChain tools and abstractions can improve the customization, accuracy, and relevancy of the information generated by LLMs.
4. Access to new data sets: LangChain components allow LLMs to access new data sets without retraining, expanding their capabilities and potential uses.
5. Simplified development: LangChain's agent framework and toolkits simplify the development process for developers, allowing them to focus on building powerful agents rather than building everything from scratch.
6. Increased versatility: LangChain agents and tools offer a wide range of possibilities for interacting with data, performing searches, and executing tasks, making them versatile and useful in various applications.
7. Enhanced collaboration: LangChain's open-source library and community-driven development enable collaboration and knowledge-sharing among developers, promoting innovation and progress in the field of LLMs and AI.
8. Improved user experience: LangChain's focus on user experience and ease of use makes it easier for developers to create agents that can interact with users in a more natural and intuitive way.
🔖Overall, LangChain agents and tools offer a powerful and flexible way to utilize LLMs in various applications, providing numerous benefits for developers, users, and the AI community as a whole.
Here is the python code of this video which you can run it in google colab as well:
!pip install langchain
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0, openai_api_key="YOUR_OPENAI_API_KEY")
tool_names = ["serpapi"]
tools = load_tools(tool_names)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
agent.run("What is the current temperature in New York?")
⭐️HashTags ⭐️
#langchain #chain #vectorstore #prompt #largelanguagemodels #llm #ai #agents #schemas #index #retriever #database #memory #openai #chatgpt #gpt #gpt3 #gpt4 #generativeai #text #documents #chroma #FAISS #pinecone #sql #rag #tutorial #agents #tools #python
🔖LangChain is an open source framework that allows AI developers to combine LLMs like GPT-4 with external sources of computation and data. Specifically, LangChain is a framework designed to simplify the creation of applications using large language models (LLMs). It enables developers to build context-aware and reasoning LLM applications by combining a large language model prompt with various external resources. LangChain is an open-source library that provides developers with tools to build applications powered by LLMs, and it is built around LLMs, allowing developers to create chains of different prompts interactively. Additionally, LangChain is a powerful, open-source framework that helps developers develop applications powered by a language model, particularly an LLM, and it is designed to streamline AI application development, focusing on real-time data processing and integration with LLMs.
🔖LangChain agents and tools are components of the LangChain framework that enable the use of large language models (LLMs) to perform various tasks. Agents are programs that use a language model to choose a sequence of actions to take, while tools are interfaces that an agent can use to interact with the world.
🔖The benefits of using LangChain agents and tools include:
1. Autonomy: LangChain agents can act autonomously, allowing them to perform tasks independently without the need for human intervention.
2. Customization: LangChain provides an extensive library of off-the-shelf tools and an intuitive framework for customizing your own tools, allowing developers to tailor the agents to their specific needs.
3. Improved accuracy and relevancy: LangChain tools and abstractions can improve the customization, accuracy, and relevancy of the information generated by LLMs.
4. Access to new data sets: LangChain components allow LLMs to access new data sets without retraining, expanding their capabilities and potential uses.
5. Simplified development: LangChain's agent framework and toolkits simplify the development process for developers, allowing them to focus on building powerful agents rather than building everything from scratch.
6. Increased versatility: LangChain agents and tools offer a wide range of possibilities for interacting with data, performing searches, and executing tasks, making them versatile and useful in various applications.
7. Enhanced collaboration: LangChain's open-source library and community-driven development enable collaboration and knowledge-sharing among developers, promoting innovation and progress in the field of LLMs and AI.
8. Improved user experience: LangChain's focus on user experience and ease of use makes it easier for developers to create agents that can interact with users in a more natural and intuitive way.
🔖Overall, LangChain agents and tools offer a powerful and flexible way to utilize LLMs in various applications, providing numerous benefits for developers, users, and the AI community as a whole.
Here is the python code of this video which you can run it in google colab as well:
!pip install langchain
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.llms import OpenAI
llm = OpenAI(temperature=0, openai_api_key="YOUR_OPENAI_API_KEY")
tool_names = ["serpapi"]
tools = load_tools(tool_names)
agent = initialize_agent(tools, llm, agent="zero-shot-react-description")
agent.run("What is the current temperature in New York?")
⭐️HashTags ⭐️
#langchain #chain #vectorstore #prompt #largelanguagemodels #llm #ai #agents #schemas #index #retriever #database #memory #openai #chatgpt #gpt #gpt3 #gpt4 #generativeai #text #documents #chroma #FAISS #pinecone #sql #rag #tutorial #agents #tools #python
