Large language models (LLMs) are revolutionizing the way we interact with technology.
They play a crucial role in various application areas such as chatbots, data analytics and more.
An advanced framework that simplifies the development of such applications is LangChain.
What is LangChain?
LangChain is a comprehensive framework for developing applications that use large language models (LLMs).
It is designed to reduce the complexity of working with LLMs and provides a variety of tools and components that support the entire lifecycle of LLM applications.
The aim is to provide developers with a powerful and flexible platform that facilitates the development, implementation and maintenance of LLM applications.
Needs and challenges that LangChain addresses
Complex prompting: LangChain enables the creation of detailed and specific prompts to control the behavior of language models.
One example of this is automated customer service chatbots that generate specific responses to customer queries. Data integration: LangChain combines data from different sources for a unified view.
One example is the integration of CRM data and social media feeds to create comprehensive customer profiles. Memory management: LangChain manages the state and context of conversations over time.
This is particularly useful for virtual assistants who need a long-term conversation history. Modularity: LangChain enables easy integration of LLMs and external services through a modular architecture.
One example is the exchange of LLMs without changes to the underlying code. Example of a LangChain agent:
from dotenv import load_dotenv
from langchain import hub
from langchain.agents import AgentExecutor, create_openai_functions_agent, load_tools
from langchain.tools.tavily_search import TavilySearchResults
from langchain.utilities.tavily_search import TavilySearchAPIWrapper
from langchain_openai import ChatOpenAI
load_dotenv()
def get_function_tools():
search = TavilySearchAPIWrapper()
tavily_tool = TavilySearchResults(api_wrapper=search)
tools = [tavily_tool]
tools.extend(load_tools(['wikipedia']))
return tools
def init_action():
llm = ChatOpenAI(model="gpt-4", temperature=0.1)
prompt = hub.pull("hwchase17/openai-functions-agent")
tools = get_function_tools()
agent = create_openai_functions_agent(llm, tools, prompt)
agent_executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
agent_executor.invoke({"input": "Who is the owner of Tesla company? Let me know details about the owner."})
init_action()In this code snippet:
- The
load_dotenv()function loads environment variables from an .env file. - The
get_function_tools()function creates tools for the LangChain agent, including a Tavily search tool and a Wikipedia tool. - The
init_action()function initializes the LangChain agent with an OpenAI model, tools and a prompt. - The
AgentExecutorclass is used to execute the agent and transfer an input request to it. - The agent is called with an input request that asks for the owner of Tesla.
Further details and explanations can be found in the complete LangChain agent tutorial and documentation:
Foundation and development of LangChain
LangChain was co-founded by Harrison Chase at the end of 2022.
Harrison Chase, a Harvard graduate, recognized the challenges and potential in developing applications that use large language models (LLMs).
With his expertise and vision, he founded LangChain to reduce the complexity of working with LLMs and provide developers with a powerful platform.
Since its inception, LangChain has achieved several important milestones.
The company has continuously introduced new features and improvements to meet the needs of the developer community.
Some of the notable milestones include:
- End of 2022: Foundation of LangChain by Harrison Chase.
- 2023: Release of the first stable version of LangChain, which offers a wide range of tools and components to support LLM development.
- May 2024: Release of LangChain v0.2, which provides enhanced data integration and storage management capabilities and improves stability and security
LangChain has received significant financial backing to drive its development and expansion.
The company has received a total of 35 million US dollars in funding, including:
- April 2023: A $10 million seed round led by Benchmark.
- February 2024: A Series A round of 25 million US dollars, led by Sequoia Capital.
This funding has enabled LangChain to further develop its technology, integrate new features and expand its reach.
The investments reflect the investors’ confidence in LangChain’s vision and potential.
LangChain is now an active and dynamic project with a growing community of developers and supporters.
The community plays a crucial role in the further development and improvement of the framework.
With continuous improvements and a strong community, the framework is well positioned to continue its leading role as the most widely used framework for developing generative AI applications.
The support of major investors and the growing acceptance in the developer community underline LangChain’s potential to change the way we work with large language models.
Advantages of LangChain compared to other solutions
Abstraction of complexity: LangChain reduces the complexity of integrating LLMs and external services.
Modularity and flexibility: LangChain enables easy customization and integration of specific modules.
Memory management: LangChain supports the management of conversational memory and complex workflows.
Flexibility: LangChain offers a standardized interface for accessing different LLM providers.
Why LangChain instead of OpenAI directly?
Flexibility and avoidance of vendor lock-in: LangChain enables the connection of various LLMs, including open source models such as Llama 3. This prevents vendor lock-in and offers more control over data protection and data management.
Data protection and control: The ability to run open source models locally means that LangChain offers significant advantages in terms of data protection and data control.
Support for different LLMs: LangChain offers flexibility in selecting the best models for specific requirements.
The 10 most important LangChain integrations
- OpenAI: Integration of OpenAI models such as GPT-3 and GPT-4.
- Hugging Face: Support for models from the Hugging Face library.
- Google Cloud: Integration of Google Cloud services.
- AWS: Support for Amazon Web Services.
- Azure: Integration of Microsoft Azure services.
- Llama 3: Support for the open source model Llama 3.
- Wikipedia: Integration of Wikipedia tools for advanced search functions.
- Tavily Search: Use of Tavily search tools.
- CRM systems: Integration of CRM data for comprehensive customer profiles.
- Social media feeds: Combination of data from various social media platforms.
Application examples and use cases
Summarization: Automated summaries of texts, calls, articles, books, academic papers, legal documents, user histories, spreadsheets or financial documents.
Example: Automated summaries of customer calls for customer service. Chatbots: Development of intelligent chatbots for various industries.
Example: A chatbot for e-commerce that helps customers with product selection. Data extraction: Extraction of relevant information from unstructured data.
Example: Extraction of key information from legal documents. Query analysis: Analyzing and answering complex queries.
Example: Answering customer queries in natural language. Tool use and agents: Use of tools and autonomous agents to automate tasks.
Example: An agent that automatically schedules appointments based on email conversations.