Introduction LangChain Cheat Sheet Overview: Your LLM Development Toolkit
This LangChain cheat sheet serves as your go-to resource for building sophisticated language model (LLM) applications. We'll cover essential components like conversation agents, tool integrations, memory management, and asynchronous API usage. This guide is designed for both beginners and experienced developers looking to streamline their workflows and optimize LLM performance.
Whether you're creating chatbots, data analysis tools, or complex automated workflows, mastering LangChain is key. This cheat sheet will provide code snippets, best practices, and clear explanations to get you up and running quickly.
Agents Conversation : Building Conversational AI
Conversation agents are designed to have conversations with users, leveraging tools and memory to respond to user inputs. They are optimized for dynamic interactions.
Key components include: importing necessary libraries, defining tools (e.g., OpenAPI, Python code execution), initializing memory, setting up the agent (model, agent type, memory), and running the agent. Below are detailed examples:
OpenAPI Agent Deep Dive
The OpenAPI Agent is designed to interact with API documentation and perform the correct API requests based on the specifications. This allows agents to interface with a wide range of services.
Steps for implementation: Import libraries and load the OpenAPI spec, configure the agent components, and then initiate the agent for interaction with the OpenAPI spec.
Python Agent : Executing Python Code within Agents
The Python Agent allows the agent to write and run Python code to answer questions. This unlocks the ability to perform computations, manipulate data, and integrate with external libraries.
This example showcases creating an agent that computes the 10th Fibonacci number, illustrating basic Python integration. Another example shows the training of a single neuron neural network in PyTorch using a Python Agent.
Pinecone LangChain and Integration
This section provides guidance on connecting to Pinecone for vector storage, and then using it with LangChain for semantic search and question answering.
Key steps include: creating a Pinecone service, embedding model, and vectorstore, initializing the LLM and loading/splitting documents. Documents are uploaded, and a RetrievalQA chain is set up. Agents may optionally leverage this chain. A tool to upload files from a URL to Pinecone is also discussed.
Tools LangChain : Customization and Prioritization
Creating custom tools with the tool decorator enables you to extend the capabilities of your LangChain agents. You can define custom functions and easily integrate them.
Modifying existing tools and prioritizing them based on the task at hand also provides valuable control over agent behavior. Use the tool's description to specify priorities.
“LangChain empowers developers to quickly prototype, experiment, and build LLM-powered applications.
LangChain Team
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Code Snippets
Copy and paste code examples directly into your project.
Tool Integration Guide
Step-by-step instructions for integrating your tools.
Advanced Chains
Discover chains with various inputs and outputs.
Async LangChain API for Agent Concurrency
For improved performance, use asyncio for concurrent operation of multiple agents. This involves utilizing an aiohttp.ClientSession and CallbackManager with a custom LangChainTracer.
Make sure to close the aiohttp.ClientSession after execution to free up resources.
Self Ask & Max Iterations Advanced Agent Techniques
Explore the Self Ask With Search chain to enable agents to search for information, greatly improving their ability to answer complex questions. Also, manage the potential risk of infinite loops by setting the max number of agent iterations.
The Max Iterations example offers a way to limit the number of steps an agent can take to prevent errors or wasted resources.
LLMChain & Sequential Chains Chains: Orchestrating LLMs
Chains allow you to combine multiple LLM calls or other utilities into a sequence. This section covers using LLMChain, and building more complex sequential chains, for example incorporating memory with SimpleMemory.
Create LLMChain instances to perform simple tasks. Create SequentialChain instances, with multiple inputs and outputs, and use SimpleMemory to maintain context.
Coming Soon Future Updates
More details and code samples are coming soon. Keep checking back for updates and new LangChain features.