Getting Started with LangChain

Offical Doc

https://python.langchain.com/docs/introduction/

Install

pip install langchain

LLM and Chat Model

from langchain.llms import OpenAI
llm = OpenAI(openai_api_key="YOUR_OPENAI_API_KEY")

response = llm.invoke("What is the elevation of Mount Kilimanjaro?")
print(response)

Prompt

from langchain.prompts import PromptTemplate

# Create a prompt with placeholders for values
template = PromptTemplate.from_template(
    "What is the capital city of {country}?"
)

# Provide the value for the placeholder to create the prompt
filled_prompt = template.format(country="Italy")
print(filled_prompt)

Output parsers

import openai
import langchain

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.output_parsers import CommaSeparatedListOutputParser
from langchain.chat_models import ChatOpenAI

# Initialize the Chat Model
chat_model = ChatOpenAI(openai_api_key="YOUR_OPENAI_API_KEY")

# Initialize the parser and get instructions on how the LLM output should be formatted
output_parser = CommaSeparatedListOutputParser()
format_instructions = output_parser.get_format_instructions()

# Use a prompt template to get a list of items
prompt = PromptTemplate(
    template="The user will pass in a category.  Your job is to return a comma-separated list of 10 values.\n{format_instructions}\n{query}\n",
    input_variables=["category"],
    partial_variables={"format_instructions": format_instructions},
)

# Define the category to pass to the model
category = "animals"

# Chain together the prompt and model, then invoke the model to get structured output
prompt_and_model = prompt | chat_model
output = prompt_and_model.invoke({"query": category})

# Invoke the parser to get the parsed output
parsed_result = output_parser.invoke(output)

# Print the parsed result
print(parsed_result)

Example 1: Basic LLM Interaction

from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain

# Initialize the LLM
llm = OpenAI(temperature=0.7)

# Create a prompt template
prompt = PromptTemplate(
    input_variables=["topic"],
    template="Write a short paragraph about {topic}."
)

# Create a chain that combines the LLM and the prompt
chain = LLMChain(llm=llm, prompt=prompt)

# Run the chain
result = chain.run(topic="artificial intelligence")
print(result)

Example 2: Document Question-Answering

from langchain.document_loaders import TextLoader
from langchain.indexes import VectorstoreIndexCreator

# Load document
loader = TextLoader("my_document.txt")

# Create an index from the document
index = VectorstoreIndexCreator().from_loaders([loader])

# Query the document
response = index.query("What is the main topic of this document?")
print(response)

Example 3: Building a Simple Agent

from langchain.agents import initialize_agent, load_tools
from langchain.llms import OpenAI

# Initialize the LLM
llm = OpenAI(temperature=0)

# Load some tools (calculator and search)
tools = load_tools(["serpapi", "llm-math"], llm=llm)

# Initialize an agent with the tools
agent = initialize_agent(tools, llm, agent="zero-shot-react-description", verbose=True)

# Run the agent
agent.run("What was the high temperature in SF yesterday? What is that number raised to the .023 power?")