Unleash the Potential of LangChain in Modern AI Application Development

from langchain_openai import ChatOpenAI
from langchain_core.prompts import ChatPromptTemplate
 
model = ChatOpenAI(model="gpt-4.1")
 
prompt = ChatPromptTemplate.from_messages([
    ("system", "You are a helpful assistant."),
    ("user", "{question}")
])
 
chain = prompt | model
chain.invoke({"question": "Explain LangChain LCEL in one paragraph."})

pip install langchain langchain-openai langgraph faiss-cpu

pip install langgraph

from langchain_openai import ChatOpenAI
 
llm = ChatOpenAI(model="gpt-4.1", temperature=0)

from langchain_core.prompts import ChatPromptTemplate
 
prompt = ChatPromptTemplate.from_template(
    "Summarize the following text:\n\n{text}"
)

chain = prompt | llm

from langchain_openai import ChatOpenAI
 
llm = ChatOpenAI(
    model="gpt-4.1",
    temperature=0,
    streaming=False
)

from langchain_core.tools import tool
 
@tool
def search_wiki(query: str):
    """Search Wikipedia topics."""
    import wikipedia
    return wikipedia.summary(query)
 
tools = [search_wiki]

result = llm.invoke({
    "messages": [
        {"role": "user", "content": "Find me information about LangChain."}
    ],
    "tools": tools
})

from langchain.memory import ConversationBufferMemory
memory = ConversationBufferMemory()
 
memory.save_context(
    {"input": "Hello"},
    {"output": "Hi, how can I help?"}
)
 
memory.load_memory_variables({})

from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
 
loader = TextLoader("./state_of_the_union.txt")
docs = loader.load()
 
splitter = RecursiveCharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(docs)
 
emb = OpenAIEmbeddings()
db = FAISS.from_documents(chunks, emb)
 
query = "What policies did the president highlight?"
results = db.similarity_search(query)

from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_core.prompts import ChatPromptTemplate
from langchain_text_splitters import RecursiveCharacterTextSplitter
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import FAISS
 
# 1. Load LLM
llm = ChatOpenAI(model="gpt-4.1", temperature=0)
 
# 2. Load + split documents
loader = TextLoader("./state_of_the_union.txt")
docs = loader.load()
splitter = RecursiveCharacterTextSplitter(chunk_size=800)
chunks = splitter.split_documents(docs)
 
# 3. Build vectorstore
db = FAISS.from_documents(chunks, OpenAIEmbeddings())
 
# 4. Retrieval chain
prompt = ChatPromptTemplate.from_messages([
    ("system", "Answer based on the provided context."),
    ("user", "{question}\n\nContext: {context}")
])
 
def retriever(q):
    results = db.similarity_search(q, k=3)
    return "\n".join([r.page_content for r in results])
 
chain = (
    {
        "question": lambda x: x,
        "context": lambda x: retriever(x)
    }
    | prompt
    | llm
)
 
# 5. Run end-to-end
response = chain.invoke("What economic topics were mentioned?")
print(response.content)