LangChain Basics

Duration: 5 min

This module introduces the fundamentals of LangChain, a framework designed to develop applications with language models. We will cover key concepts such as vector databases, embeddings, chunking, reranking, and hybrid search. Understanding these basics is crucial for leveraging LangChain effectively in various applications.

Vector Databases and Embeddings

Vector databases store data in a multi-dimensional space, allowing for efficient similarity searches. Embeddings are vector representations of data, typically text, that capture semantic meaning. By converting text into embeddings, we can perform operations like semantic search and clustering. LangChain utilizes these embeddings to enhance the capabilities of language models.

import numpy as np

# Example of creating embeddings using a simple function
def create_embedding(text):
    # In practice, use a pre-trained model like BERT or Word2Vec
    return np.random.rand(1, 100)  # Placeholder for a 100-dimensional vector

text = "Hello, world!"
embedding = create_embedding(text)
print(embedding)

Try it in Google Colab:

[[0.5488135  0.71518937 0.60276338... 0.2605042  0.77423369 0.4236548 ]]

Chunking and Reranking

Chunking involves breaking down large documents into smaller, manageable pieces called chunks. This makes it easier to process and analyze the text. Reranking is the process of reordering search results based on relevance. LangChain can be used to implement chunking and reranking, improving the accuracy and efficiency of text-based applications.

import random

# Example of chunking and reranking
def chunk_text(text, chunk_size=100):
    return [text[i:i+chunk_size] for i in range(0, len(text), chunk_size)]

def rerank_results(results):
    # Placeholder for a more complex reranking algorithm
    return sorted(results, key=lambda x: random.random())

text = "This is a long document that needs to be chunked and reranked."
chunks = chunk_text(text)
reranked_chunks = rerank_results(chunks)
print(reranked_chunks)

💡 Tip: When chunking text, ensure that the chunk size is appropriate for the context to maintain semantic coherence.

Key Concepts

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