LangChain
Cheat Sheet: Your Ultimate Guide

Conversation Agent Cheat Sheet

Build engaging conversation agents using LangChain. This cheat sheet provides a streamlined guide to creating agents optimized for chat interactions. Key components include importing necessary libraries (e.g., from langchain.agents import initialize_agent, load_tools), defining tools, and initializing memory with ConversationBufferMemory. Run the agent with user inputs to generate conversational responses. Experiment with different agent types for varying conversational styles.

Key Code Snippets: (Detailed code examples for initialization, tool usage, and agent execution would be included here, focusing on clarity and practical application.)

OpenAPI Agent: Interacting with APIs

The OpenAPI Agent is designed to interact with OpenAPI specifications, enabling seamless API request generation based on the provided specifications. This example demonstrates creating an agent that can analyze the OpenAPI spec of an API and make appropriate requests. Learn to import libraries, load the OpenAPI spec, and set up agent components.

Example Scenario: Use the OpenAPI Agent to interact with the OpenAI API. (Includes code snippets showing how to load the spec, define tools, and execute requests.)

Python Agent: Code Execution and Beyond

The Python Agent empowers your LangChain projects to execute Python code directly, allowing for dynamic calculations and complex operations. This example showcases an agent calculating the 10th Fibonacci number and training a single-neuron neural network in PyTorch. This cheat sheet covers necessary imports and agent creation steps.

Code Snippets: Includes code for importing libraries, creating the Python Agent, the Fibonacci example, and a simplified PyTorch neural network example. The key here is demonstrating how to use the agent to perform calculations and execute code within the LangChain framework.

Pinecone Connecting LangChain with

Integrate LangChain with Pinecone for powerful vector search capabilities. This cheat sheet covers the setup process, including creating a Pinecone service, choosing an embedding model, and creating a vectorstore. Learn how to initialize an LLM, load documents using TextLoader, split documents into chunks, upload documents, and create a RetrievalQA chain. Also how to create an agent to retrieve and process information from Pinecone.

Steps: Create a Pinecone Service, Create an Embedding Model, Create a Vectorstore, Initialize LLM, Create TextLoader, Split Documents, Upload to Pinecone, Create RetrievalQA Chain, Use Agent (Optional), Deinitialize Pinecone when done.

Practical Tip: Ensure your Pinecone index is properly configured for optimal performance.

Custom Creating & izing Tools

Enhance your LangChain agents with custom tools using the @tool decorator. This allows you to define functions that agents can utilize. This sheet provides guidance on importing from langchain.tools, using the decorator to define custom functions, and the function's docstring acting as the tool's description. Also focuses on modifying existing tools, defining priorities among tools (e.g., 'Use this more than the normal search if the question is about Music'), and how to use them effectively.

Best Practices: Write clear and concise descriptions for your tools. Prioritize tools based on their relevance to specific query types.

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LangChain empowers developers to build sophisticated LLM-powered applications. This cheat sheet provides a roadmap to rapidly prototype and deploy.

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Async hronous API for Enhanced Performance

Leverage the asynchronous capabilities of LangChain to run multiple agents concurrently. This guide covers the usage of asyncio, aiohttp.ClientSession for optimized async requests, CallbackManager with a custom LangChainTracer to prevent trace collisions. You will learn how to pass the CallbackManager to each agent, and ensure the aiohttp.ClientSession is closed after the program/event loop ends.

Code Snippets: Shows how to initialize an agent, run agents concurrently, and use tracing with async agents. (Focus on efficiency and error handling.)

Self-Ask Self Ask and Max Iterations Strategies

Implement the Self Ask With Search chain to empower agents to utilize search capabilities to answer questions effectively. The Max Iterations example helps to limit the number of agent steps to avoid infinite loops, especially with adversarial prompts.

Key takeaways: The Self Ask With Search chain's setup and the Max Iterations example, ensuring your agents have resilience and prevent excessive resource consumption. Practical examples demonstrate their setup and usage.

Cheat Sheet: This section provides code examples for the Self Ask With Search chain, the Max Iterations approach, and their interaction.

LLMChain & Sequential Chains Overview

Explore LLMChain and Sequential Chains for orchestrating complex workflows. Understand single-input, multiple-input, and 'from string' examples of LLMChain. Create and utilize SimpleSequentialChain and SequentialChain instances. Learn how to add a SimpleMemory instance to pass context along the chain. Example: Chaining operations such as summarization and sentiment analysis in sequence.

Practical Applications: Demonstrate different chain configurations for sequential tasks.