DEV Community

Abel Peter
Abel Peter

Posted on • Updated on

Introduction to LangChain.

What is LangChain?

LangChain is a software development framework designed to simplify the creation of applications using large language models (LLMs). As a language model integration framework, LangChain's use-cases largely overlap with those of language models in general, including document analysis and summarization, chatbots, and code analysis.

What are the benefits of using LangChain?

The main benefits of using LangChain, include:

Ease of use: LangChain is very easy to use, even for developers who are not familiar with LLMs.
Flexibility: LangChain is very flexible, and can be used to create a wide variety of applications.
Scalability: LangChain is scalable, and can be used to create applications that can handle large amounts of data.

How to get started with LangChain

To get started with LangChain, you will need to:

  1. Install the LangChain library.
  2. Create a new LangChain project.
  3. Add an LLM to your project.
  4. Write your application code.

Here is an example of how to use LangChain to create a chatbot:

import langchain

# Create a new LangChain chatbot.
chatbot = langchain.Chatbot()

# Add an LLM to the chatbot.
chatbot.add_model("gpt-3")

# Write the chatbot's code.
@chatbot.on_message
def handle_message(message):
  # Get the user's message.
  user_message = message.text

  # Respond to the user's message.
  chatbot.reply(user_message)

# Run the chatbot.
chatbot.run()

Enter fullscreen mode Exit fullscreen mode

Use cases

LangChain can be used for a wide variety of applications, including:

  • Document analysis and summarization: LangChain can be used to analyze documents and summarize their contents. This can be useful for tasks such as research, customer support, and marketing.
  • Chatbots: LangChain can be used to create chatbots that can interact with users in natural language. This can be useful for tasks such as customer service, sales, and education.
  • Code analysis: LangChain can be used to analyze code and identify potential errors. This can be useful for tasks such as software development, quality assurance, and security.
  • Other applications: LangChain can also be used for a variety of other applications, such as translation, creative writing, and gaming.

Autonomous Agents

LangChain can be used to create autonomous agents that can make decisions and take actions in the real world. For example, LangChain could be used to create a robot that can navigate a warehouse and pick up items, or a self-driving car that can safely navigate traffic.

Agent Simulations

LangChain can also be used to create simulations of autonomous agents. This can be useful for testing and evaluating new agent algorithms, or for training agents in a safe environment.

Question Answering over Docs

LangChain can be used to answer questions about documents. For example, LangChain could be used to answer questions about a research paper, a product manual, or a legal document.
example.

import langchain

# Create a new LangChain document analyzer.
analyzer = langchain.DocumentAnalyzer()

# Analyze the document.
analyzer.analyze("This is a document about the use of LangChain.")

# Get the summary of the document.
summary = analyzer.summary()

# Print the summary.
print(summary)

Enter fullscreen mode Exit fullscreen mode

Querying Tabular Data

LangChain can be used to query tabular data. For example, LangChain could be used to query a database of customer records, a spreadsheet of financial data, or a table of scientific data.

example.

import langchain

# Create a new LangChain tabular data query.
query = langchain.TabularDataQuery()

# Set the data source.
query.set_data_source("https://www.example.com/data.csv")

# Set the query.
query.set_query("SELECT * FROM data WHERE age > 18")

# Get the results of the query.
results = query.results()

# Print the results.
for result in results:
  print(result)


Enter fullscreen mode Exit fullscreen mode

Interacting with APIs

LangChain can be used to interact with APIs. For example, LangChain could be used to get weather data from an API, get stock quotes from an API, or get directions from an API.
example.

import langchain

# Create a new LangChain API client.
client = langchain.APIClient()

# Set the API endpoint.
client.set_endpoint("https://www.example.com/api")

# Set the API key.
client.set_api_key("YOUR_API_KEY")

# Make a request.
response = client.get("/users")

# Print the response.
print(response)

Enter fullscreen mode Exit fullscreen mode

Extraction

LangChain can be used to extract information from text. For example, LangChain could be used to extract the names of people from a document, the dates of events from a document, or the prices of products from a document.
example.

import langchain

# Create a new LangChain text extractor.
extractor = langchain.TextExtractor()

# Set the document.
extractor.set_document("This is a document about the use of LangChain.")

# Extract the names of people.
names = extractor.extract_names()

# Print the names.
for name in names:
  print(name)

Enter fullscreen mode Exit fullscreen mode

Summarization

LangChain can be used to summarize text. For example, LangChain could be used to summarize a research paper, a product manual, or a legal document.
example.

import langchain

# Create a new LangChain text summarizer.
summarizer = langchain.TextSummarizer()

# Set the document.
summarizer.set_document("This is a document about the use of LangChain.")

# Get the summary.
summary = summarizer.summary()

# Print the summary.
print(summary)

Enter fullscreen mode Exit fullscreen mode

Evaluation

LangChain can be used to evaluate the performance of LLMs. For example, LangChain could be used to evaluate the accuracy of an LLM's answers to questions, the fluency of an LLM's generated text, or the correctness of an LLM's code.
example.

import langchain

# Create a new LangChain LLM evaluator.
evaluator = langchain.LLMEvaluator()

# Set the LLM.
evaluator.set_llm("gpt-3")

# Evaluate the LLM.
results = evaluator.evaluate()

# Print the results.
for result in results:
  print(result)

Enter fullscreen mode Exit fullscreen mode

LangChain is a powerful framework that can be used to create a wide variety of applications using LLMs. If you are looking for a way to simplify the development of LLM-powered applications, then LangChain is a great option. To learn more and engage the community , here is a link to their documentation.(https://python.langchain.com/en/latest/index.html)

Top comments (3)

Collapse
 
tohodo profile image
Tommy

Good introduction. Have you played around with OpenAI's new function calling superpowers? IMHO it's going to make a lot of what we'd use LangChain for redundant, especially as we'll be able to leverage ChatGPT plugins programmatically. I never used the memory modules because I had more control building my own context summarization logic using the messages array to minimize token consumption (or at least reach a good balance between depth & cost).

Collapse
 
peterabel profile image
Abel Peter

Hi Tommy, Function calling is really cool and does the job as required. LangChain continues to roll out more use cases for llms frequently but at the moment, its really hard to predict what will work or won't work, as for now, understanding the depth and breadth of using these tools to interact with the models will be of significance later on as the market and community converge towards value use cases.

Collapse
 
tohodo profile image
Tommy

Agreed. The AI scene feels like the wild wild west right now and that's exciting 🤠