LangChain is a powerful framework that simplifies the process of building advanced language model applications. As the popularity of large models continues to rise, LangChain has also won the favor of more and more developers.
Hello! Let me introduce you to LangChain, an awesome library that enables developers to leverage large language models (LLMs) and other computational resources to build powerful applications. In this guide, I'll give a quick overview of how LangChain works and explore some cool use cases such as question answering systems, chatbots, and intelligent agents. I'll also walk you through a quick start guide to help you get started. let's start!
What is LangChain?
LangChain is a powerful framework designed to help developers build end-to-end applications using language models. It provides a set of tools, components, and interfaces that simplify the process of creating applications powered by large language models (LLMs) and chat models. LangChain can easily manage interactions with language models, link multiple components together, and integrate additional resources such as APIs and databases.
LangChain has many core concepts:
1. Components and Chains
In LangChain, Components are modular building blocks that can be combined to create powerful applications. A Chain is a series of Components (or other Chains) grouped together to accomplish a specific task. For example, a Chain might include a prompt template, a language model, and an output parser that work together to process user input, generate a response, and process the output.
2. Prompt Templates and Values
The Prompt Template is responsible for creating the PromptValue, which is what is ultimately passed to the language model. Prompt Template helps convert user input and other dynamic information into a format suitable for language models. PromptValues are classes with methods that convert to the exact type of input each model type expects (such as text or chat messages).
3. Example Selectors
Example Selectors are useful when you want to dynamically include examples in Prompts. They take user input and return a list of examples to use in the prompt, making them more powerful and context-specific.
4. Output Parsers
Output Parsers are responsible for building language model responses into a more useful format. They implement two main methods: one for providing formatting instructions and one for parsing the language model's response into a structured format. This makes it easier to process the output data in your application.
5. Indexes and Retrievers
Index is a way of organizing documents to make it easier for language models to interact with them. A retriever is an interface for taking relevant documents and combining them with a language model. LangChain provides tools and functions for working with different types of indexes and retrievers, such as vector databases and text splitters.
6. Chat Message History
LangChain mainly interacts with the language model through the chat interface. The ChatMessageHistory class is responsible for remembering all previous chat interaction data, which can then be passed back to the model, aggregated, or otherwise combined. This helps maintain context and improves the model's understanding of the conversation.
7. Agents and Toolkits
Agents are entities that drive decision-making in LangChain. They have access to a suite of tools and can decide which tool to invoke based on user input. Tookits are a set of tools that, when used together, accomplish a specific task. The agent executor is responsible for running the agent with the appropriate tools.
By understanding and leveraging these core concepts, you can leverage the power of LangChain to build advanced language model applications that are adaptable, efficient, and able to handle complex use cases.
What is a LangChain Agent?
LangChain Agent is the entity in the framework that drives decision making. It has access to a set of tools and can decide which tool to invoke based on user input. Proxies help build complex applications that require adaptive and context-specific responses. They are especially useful when there are unknown interaction chains that depend on user input and other factors.
How to use LangChain?
To use LangChain, developers first import necessary components and tools, such as LLMs, chat models, agents, chains, memory functions. These components combine to create an application that understands, processes, and responds to user input.
LangChain provides various components for specific use cases, such as personal assistant, document Q&A, chatbot, querying form data, interacting with API, extracting, evaluating and summarizing.
What's a LangChain model?
The LangChain model is an abstraction representing the different types of models used in the framework. The models in LangChain are mainly divided into three categories:
1. LLM (Large Language Model): These models take text strings as input and return text strings as output. They are the backbone of many language model applications.
2. Chat Model: The chat model is supported by the language model, but with a more structured API. They take a list of chat messages as input and return chat messages. This makes it easy to manage conversation history and maintain context.
3. Text Embedding Models: These models take text as input and return a list of floats representing the text embedding. These embeddings can be used for tasks such as document retrieval, clustering, and similarity comparison.
Developers can choose the appropriate LangChain model for their use case and leverage the provided components to build their applications.
Main features of LangChain
LangChain aims to support developers in six main areas:
1. LLMs and Prompts: LangChain makes it easy to manage prompts, optimize them, and create a common interface for all LLMs. Additionally, it includes some handy utilities for working with LLMs.
2. Chain: These are sequences of calls to LLM or other utilities. LangChain provides a standard interface for the chain, integrates with various tools, and provides an end-to-end chain for popular applications.
3. Data augmentation generation: LangChain enables the chain to interact with external data sources to collect data for generation steps. For example, it can help summarize long texts or answer questions using a specific data source.
4. Agents: Agents let the LLM make decisions about actions, take those actions, check the results, and move on until the job is done. LangChain provides a standard interface for proxies, a variety of proxies to choose from, and end-to-end proxy examples.
5. Memory: LangChain has a standard memory interface that helps maintain state between chain or proxy calls. It also provides a range of memory implementations and examples of chains or agents that use memory.
6. Evaluation: It is difficult to evaluate generative models with traditional metrics. That's why LangChain provides hints and chains to help developers evaluate their models using LLM themselves.
Example of use
LangChain supports a large number of use cases, such as:
• Document-specific Q&A: Answer questions given documents, using information in those documents to create answers.
• Chatbots: Build chatbots that can leverage the power of LLMs to generate text.
•Agents: Develop agents that can decide on actions, take those actions, observe the results, and continue execution to completion.
Quick Start Guide: Building an End-to-End Language Model Application Using LangChain
•Install
First, install LangChain. Just run the following command:
pip install langchain
•Environment settings
Now, since LangChain often needs to integrate with model providers, data stores, APIs, etc., we will set up our environment. In this example, we'll be using OpenAI's API, so we'll need to install their SDK:
pip install openai
Next, let's set the environment variable in the terminal:
export OPENAI_API_KEY = "..."
Alternatively, if you prefer to work in a Jupyter notebook or Python script, you can set the environment variable like this:
import os
os.environ[ "OPENAI_API_KEY" ] = "..."
• Build language model applications: LLM
With LangChain installed and the environment set up, we can start building our language model application. LangChain provides a bunch of modules that you can use to create language model applications. You can combine these modules for more complex applications, or use them individually for simpler applications.
• Building language model applications: Chat Model
In addition to LLM, you can also use chat models. These are variants of language models that use a language model under the hood but have a different interface. The chat model uses chat messages as input and output instead of the "text in, text out" API. The use of the chat model API is relatively new, so everyone is still figuring out the best abstraction to use. To complete a chat, you need to pass one or more messages to the chat model. LangChain currently supports AIMessage, HumanMessage, SystemMessage and ChatMessage types. You will primarily work with HumanMessage, AIMessage, and SystemMessage.
Here's an example using the chat model:
from langchain.chat_models import ChatOpenAI
from langchain.schema import (
AIMessage,
Human Message,
SystemMessage
)
chat = ChatOpenAI(temperature=0)
You can do it by passing a message:
chat([HumanMessage(content="Translate this sentence from English to French. I love programming.")])
# -> AIMessage(content="J'aime programmer.", additional_kwargs={})
Or pass multiple messages to OpenAI's gpt-3.5-turbo and gpt-4 models:
messages = [
SystemMessage(content="You are a helpful assistant that translates English to Chinese."),
HumanMessage(content="Translate this sentence from English to Chinese. I love programming.")
]
chat(messages)
# -> AIMessage(content="I like programming.(I like programming.)", additional_kwargs={})
You can also use generate to generate completions for groups of messages. This will return a
LLMResult:
batch_messages = [
[
SystemMessage(content="You are a helpful assistant that translates English to Chinese."),
HumanMessage(content="Translate this sentence from English to Chinese. I love programming.")
],
[
SystemMessage(content="You are a helpful assistant that translates English to Chinese."),
HumanMessage(content="Translate this sentence from English to Chinese. I love artificial intelligence.")
],
]
result = chat. generate(batch_messages)
result
# -> LLMResult(generations=[[ChatGeneration(text="I like programming. (I like programming.)", generation_info=None, message=AIMessage(content="I like programming. (I like programming.)", additional_kwargs ={}))], [ChatGeneration(text="I love artificial intelligence. (I love artificial intelligence.)", generation_info=None, message=AIMessage(content="I love artificial intelligence. (I love artificial intelligence.) ", additional_kwargs={}))]], llm_output={'token_usage': {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}})
You can also extract information such as token usage from the LLMResult:
result.llm_output['token_usage']
# -> {'prompt_tokens': 71, 'completion_tokens': 18, 'total_tokens': 89}
For chat models, you can also use templates by using MessagePromptTemplate. You can create a ChatPromptTemplate from one or more MessagePromptTemplates. The method format_prompt of the ChatPromptTemplate returns a PromptValue that you can convert to a string or a Message object, depending on whether you want to use the formatted value as input to the LLM or Chat model.
Here is an example:
from langchain.chat_models import ChatOpenAI
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
# get a chat completion from the formatted messages
chat(chat_prompt.format_prompt(input_language="English", output_language="Chinese", text="I love programming.").to_messages())
# -> AIMessage(content="i like programming。(i like programming.)", additional_kwargs={})
You can also use LLMChain with Chat Model:
from langchain.chat_models import ChatOpenAI
from langchain import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate,
)
chat = ChatOpenAI(temperature=0)
template="You are a helpful assistant that translates {input_language} to {output_language}."
system_message_prompt = SystemMessagePromptTemplate.from_template(template)
human_template="{text}"
human_message_prompt = HumanMessagePromptTemplate.from_template(human_template)
chat_prompt = ChatPromptTemplate.from_messages([system_message_prompt, human_message_prompt])
chain = LLMChain(llm=chat, prompt=chat_prompt)
chain.run(input_language="English", output_language="Chinese", text="I love programming.")
# -> "i like programming。(i like programming.)"
You can also use agents with chat models. Initialize Agent with AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION as agent type
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain.chat_models import ChatOpenAI
from langchain.llms import OpenAI
# First, let's load the language model we're going to use to control the agent.
chat = ChatOpenAI(temperature=0)
# Next, let's load some tools to use. Note that the `llm-math` tool uses an LLM, so we need to pass that in.
llm = OpenAI(temperature=0)
tools = load_tools(["serpapi", "llm-math"], llm=llm)
# Finally, let's initialize an agent with the tools, the language model, and the type of agent we want to use.
agent = initialize_agent(tools, chat, agent=AgentType.CHAT_ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
# Now let's test it out!
agent.run("Who is Olivia Wilde's boyfriend? What is his current age raised to the 0.23 power?")
In this example, the agent will interactively perform searches and calculations to provide the final answer.
Finally, let's explore using memory with chains and brokers initialized with the chat model. The main difference between this and Memory for LLMs is that we can keep previous messages as their own unique memory objects instead of compressing them into a string.
Here is an example ConversationChain using a:
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain.chains import ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
prompt = ChatPromptTemplate.from_messages([
SystemMessagePromptTemplate.from_template("The following is a friendly conversation between a human and an AI. The AI is talkative and provides lots of specific details from its context. If the AI does not know the answer to a question, it truthfully says it does not know."),
MessagesPlaceholder(variable_name="history"),
HumanMessagePromptTemplate.from_template("{input}")
])
llm = ChatOpenAI(temperature=0)
memory = ConversationBufferMemory(return_messages=True)
conversation = ConversationChain(memory=memory, prompt=prompt, llm=llm)
conversation.predict(input="Hi there!")
# -> 'Hello! How can I assist you today?'
conversation.predict(input="I'm doing well! Just having a conversation with an AI.")
# -> "That sounds like fun! I'm happy to chat with you. Is there anything specific you'd like to talk about?"
conversation.predict(input="Tell me about yourself.")
# -> "Sure! I am an AI language model created by OpenAI. I was trained on a large dataset of text from the internet, which allows me to understand and generate human-like language. I can answer questions, provide information, and even have conversations like this one. Is there anything else you'd like to know about me?"
In this example, we use aConversationChain to maintain a conversation context across multiple interactions with the AI.
That's it! Now you have a solid understanding of how to use LangChain to build an end-to-end language model application. By following these examples, you can develop powerful language model applications using LLM, chat models, agents, chains, and memory capabilities.
in conclusion
In summary, LangChain is a powerful framework that simplifies the process of building high-level language model applications by providing a modular and flexible approach. By understanding core concepts such as components, chains, prompt templates, output parsers, indexes, retrievers, chat message history, and proxies, you can create custom solutions that fit your specific needs. LangChain's adaptability and ease of use make it an invaluable tool for developers, enabling them to unleash the full potential of language models and create intelligent, context-aware applications across a wide range of use cases.
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