PDF document loaders

To begin implementing Retrieval Augmented Generation (RAG), you'll first need to load the documents that the model will access. These documents can come from a variety of sources, and LangChain supports document loaders for many of them.

In this exercise, you'll use a document loader to load a PDF document containing the paper, RAG VS Fine-Tuning: Pipelines, Tradeoffs, and a Case Study on Agriculture by Balaguer et al. (2024).

Note: pypdf, a dependency for loading PDF documents in LangChain, has already been installed for you.

Deze oefening maakt deel uit van de cursus

Developing LLM Applications with LangChain

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Oefeninstructies

Import the appropriate class for loading PDF documents in LangChain.
Create a document loader for the 'rag_vs_fine_tuning.pdf' document, which is available in the current directory.
Load the document into memory to view the contents of the first document, or page.

Praktische interactieve oefening

Probeer deze oefening eens door deze voorbeeldcode in te vullen.

# Import library
from langchain_community.document_loaders import ____

# Create a document loader for rag_vs_fine_tuning.pdf
loader = ____

# Load the document
data = ____
print(data[0])

Code bewerken en uitvoeren

Deze oefening maakt deel uit van de cursus

Developing LLM Applications with LangChain

SkillTag.level.intermediateSkillTag.label

4.8+

Begin de cursus gratis

Welcome to the LangChain framework for building applications on LLMs! You'll learn about the main components of LangChain, including models, chains, agents, prompts, and parsers. You'll create chatbots using both open-source models from Hugging Face and proprietary models from OpenAI, create prompt templates, and integrate different chatbot memory strategies to manage context and resources during conversations.

Exercise 1: The LangChain ecosystem Exercise 2: OpenAI models in LangChain!Exercise 3: Hugging Face models in LangChain!Exercise 4: Prompt templates Exercise 5: Prompt templates and chaining Exercise 6: Chat prompt templates Exercise 7: Few-shot prompting Exercise 8: Creating the few-shot example set Exercise 9: Building the few-shot prompt template Exercise 10: Implementing few-shot prompting

Time to level up your LangChain chains! You'll learn to use the LangChain Expression Language (LCEL) for defining chains with greater flexibility. You'll create sequential chains, where inputs are passed between components to create more advanced applications. You'll also begin to integrate agents, which use LLMs for decision-making.

Exercise 1: Sequential chains Exercise 2: Building prompts for sequential chains Exercise 3: Sequential chains with LCEL Exercise 4: Introduction to LangChain agents Exercise 5: What's an agent?Exercise 6: ReAct agents Exercise 7: Custom tools for agents Exercise 8: Defining a function for tool use Exercise 9: Creating custom tools Exercise 10: Integrating custom tools with agents

One limitation of LLMs is that they have a knowledge cut-off due to being trained on data up to a certain point. In this chapter, you'll learn to create applications that use Retrieval Augmented Generation (RAG) to integrate external data with LLMs. The RAG workflow contains a few different processes, including splitting data, creating and storing the embeddings using a vector database, and retrieving the most relevant information for use in the application. You'll learn to master the entire workflow!

Exercise 1: Integrating document loaders Exercise 2: PDF document loaders

Huidige oefening

Exercise 3: CSV document loaders Exercise 4: HTML document loaders Exercise 5: Splitting external data for retrieval Exercise 6: Splitting by character Exercise 7: Recursively splitting by character Exercise 8: Splitting HTML Exercise 9: RAG storage and retrieval using vector databases Exercise 10: Preparing the documents and vector database Exercise 11: Building a retrieval prompt template Exercise 12: Creating a RAG chain Exercise 13: Wrap-up!