GitHub
github.com › alphasecio › langchain-examples
GitHub - alphasecio/langchain-examples: A collection of apps powered by the LangChain LLM framework. · GitHub
A multi-page Streamlit application showcasing generative AI uses cases using LangChain, OpenAI, and others.
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Languages Python 67.7% | Jupyter Notebook 32.3%
Langchain
docs.langchain.com › oss › python › langchain › quickstart
Quickstart - Docs by LangChain
In the following example you will build a research agent that can answer questions about text files.
Videos
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LangChain is AMAZING | Quick Python Tutorial - YouTube
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LangChain Master Class For Beginners 2024 [+20 Examples, LangChain ...
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LangChain Full Crash Course - AI Agents in Python - YouTube
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Building Deep Agents Tutorial With Langchain- Part 1 - YouTube
LangChain Explained in 10 Minutes (Components Breakdown + Build ...
Google Cloud
cloud.google.com › use-cases › langchain
What Is LangChain? Examples and definition | Google Cloud
The flexibility and modularity of LangChain make it suitable for building a wide array of LLM-powered applications across various domains. Some common applications and examples include:
Published August 29, 2023
GitHub
github.com › langchain-ai › langchain
GitHub - langchain-ai/langchain: The agent engineering platform. Available in TypeScript! · GitHub
2 weeks ago - Just getting started? Check out Deep Agents — a higher-level package built on LangChain for agents that have built-in capabilites for common usage patterns such as planning, subagents, file system usage, and more.
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Languages Python 99.3% | Makefile 0.5% | Shell 0.1% | XSLT 0.1% | HTML 0.0% | Dockerfile 0.0%
LangChain
langchain.com
LangChain: Observe, Evaluate, and Deploy Reliable AI Agents
Join us May 13th & May 14th at Interrupt, the Agent Conference by LangChain
GitHub
github.com › djsquircle › LangChain_Examples
GitHub - djsquircle/LangChain_Examples: A collection of LangChain examples in Python · GitHub
A collection of working code examples using LangChain for natural language processing tasks. This repository provides implementations of various tutorials found online.
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Nanonets
nanonets.com › blog › langchain
LangChain: A Complete Guide & Tutorial
January 11, 2025 - Each loader caters to different requirements and uses different underlying libraries. Below are detailed examples for each loader. PyPDFLoader is used for basic PDF parsing. from langchain.document_loaders import PyPDFLoader loader = PyPDFLoader("example_data/layout-parser-paper.pdf") pages = loader.load_and_split()
Medium
medium.com › @dvasquez.422 › building-a-simple-ai-agent-1e2f2b369b25
Building a Simple AI Agent With Python and Langchain | by David Vasquez | Medium
August 7, 2025 - Now, this project will be built using Python. The way we will use this is by implementing Langchain for our AI Agent, then apply Langchain tools which our model will use to generate its response. You can use any model you like, for this example, I will be using Google’s Gemini 2.0 Flash.
Top answer 1 of 2
1
As mentioned in the comments, the documentation assumes that the code is being written in a Jupyter notebook. The return type of the invoke method is a BaseMessage. If you want to see the response object, first assign the response of the invoke function to a variable:
response = llm.invoke("how can langsmith help with testing?")
and then print its value:
print(response)
If you're only interested in the text of the response, use this instead:
print(response.content)
2 of 2
1
OPENAI_API_KEY = os.getenv("OPENAI_API_KEY")
from langchain_openai import ChatOpenAI
def ask_gpt(prompt, temperature, max_tokens):
"""
Sends a prompt to the GPT-3.5 Turbo model and returns the AI response.
Parameters:
prompt (str): The input prompt to send to the GPT-3.5 Turbo model.
temperature (float): The temperature parameter controls the randomness of the output. Higher values (e.g., 0.8) make the output more random, while lower values (e.g., 0.2) make it more focused and deterministic.
max_tokens (int): The maximum number of tokens in the response. This parameter can be used to limit the length of the generated text.
Returns:
str: The AI response generated by the GPT-3.5 Turbo model.
"""
llm = ChatOpenAI(api_key=OPENAI_API_KEY, temperature=temperature, max_tokens=max_tokens, model="gpt-3.5-turbo")
AI_Response = llm.invoke(prompt)
return AI_Response.content
SitePoint
sitepoint.com › blog › python › a complete guide to langchain in python
A Complete Guide to LangChain in Python — SitePoint
November 7, 2024 - This code takes two variables into its prompt and formulates a creative answer (temperature=0.9). In this example, we’ve asked it to come up with a good title for a horror movie about math. The output after running this code was “The Calculating Curse”, but this doesn’t really show the full power of chains. ... from langchain.chat_models import ChatOpenAI from langchain.prompts import PromptTemplate from typing import Optional from langchain.chains.openai_functions import ( create_openai_fn_chain, create_structured_output_chain, ) import os os.environ["OPENAI_API_KEY"] = "YOUR_KEY" llm