Hybrid search (keyword + semantic) in LangChain - Mini Project: Build & Apply

Hybrid Search with Langchain: Keyword + Semantic

📖 Scenario: You are building a smart search feature for a small document collection. Users want to find documents by typing keywords or by meaning (semantic search). Combining both methods gives better results.

🎯 Goal: Create a hybrid search system using Langchain that first filters documents by keyword, then ranks them by semantic similarity.

📋 What You'll Learn

Create a list of documents with exact text content

Define a keyword to filter documents

Use Langchain's embedding model to get semantic vectors

Combine keyword filtering and semantic similarity ranking

Return the top matching documents

💡 Why This Matters

🌍 Real World

Hybrid search is used in apps like document search, customer support, and knowledge bases to find relevant info quickly by combining exact word matches and meaning.

💼 Career

Understanding hybrid search with Langchain is valuable for roles in AI, data science, and software development focused on search engines and natural language processing.

1

Create the document list

Create a list called documents with these exact strings: 'Langchain is a framework for building applications', 'Semantic search finds meaning', 'Keyword search matches exact words', 'Hybrid search combines both methods'.

LangChain

# Create the list of documents
# Your code here

Hint

Use a Python list with the exact strings given.

2

Set the keyword filter

Create a variable called keyword and set it to the string 'search' to filter documents containing this word.

LangChain

documents = [
    'Langchain is a framework for building applications',
    'Semantic search finds meaning',
    'Keyword search matches exact words',
    'Hybrid search combines both methods'
]

# Set the keyword to filter documents
# Your code here

Hint

Assign the exact string 'search' to the variable keyword.

3

Filter documents by keyword and embed

Import OpenAIEmbeddings from langchain.embeddings. Create an instance called embedding_model. Filter documents to keep only those containing keyword (case-insensitive) into filtered_docs. Then create a list embedded_docs by applying embedding_model.embed_query() to each document in filtered_docs.

LangChain

documents = [
    'Langchain is a framework for building applications',
    'Semantic search finds meaning',
    'Keyword search matches exact words',
    'Hybrid search combines both methods'
]

keyword = 'search'

# Import OpenAIEmbeddings and create embedding_model
# Filter documents by keyword
# Embed filtered documents
# Your code here

Hint

Use list comprehension to filter and embed documents. Remember to import the embedding class first.

4

Rank filtered documents by semantic similarity

Import cosine_similarity from sklearn.metrics.pairwise. Create a variable query_embedding by embedding the string 'hybrid search' using embedding_model.embed_query(). Compute a list similarities by calculating cosine similarity between query_embedding and each vector in embedded_docs. Create a list ranked_docs by sorting filtered_docs in descending order of similarity using similarities. The final code should combine keyword filtering and semantic ranking.

LangChain

from langchain.embeddings import OpenAIEmbeddings
from sklearn.metrics.pairwise import cosine_similarity

# Embed the query 'hybrid search'
# Calculate cosine similarities
# Sort filtered_docs by similarity descending
# Your code here

Hint

Use cosine similarity to rank documents by meaning. Sort documents by similarity descending.

Practice

1. What is the main advantage of using hybrid search (keyword + semantic) in Langchain?

easy

A. It combines exact word matching with understanding related meanings for better results.

B. It only uses keyword matching for faster searches.

C. It replaces semantic search completely with keyword search.

D. It ignores the meaning of words and focuses on spelling.

Solution

1. Step 1: Understand keyword search

   Keyword search finds exact matches of words typed by the user.

2. Step 2: Understand semantic search

   Semantic search finds results related in meaning, even if words differ.

3. Final Answer:

   It combines exact word matching with understanding related meanings for better results. -> Option A

4. Quick Check:

   Hybrid search = keyword + semantic [OK]

Hint: Remember hybrid means mixing two methods for best results [OK]

Common Mistakes:

• Thinking hybrid search only uses keywords
• Believing semantic search ignores meaning
• Confusing hybrid search with only semantic search

2. Which of the following is the correct way to initialize a hybrid search in Langchain combining keyword and semantic search?

easy

A. HybridSearch(semantic_searcher, keyword_searcher)

B. HybridSearch(keyword_searcher, semantic_searcher)

C. HybridSearch(keyword_searcher + semantic_searcher)

D. HybridSearch(keyword_searcher * semantic_searcher)

Solution

1. Step 1: Check constructor parameters

   HybridSearch expects two separate searcher objects as arguments.

2. Step 2: Confirm order and syntax

   The correct syntax is HybridSearch(keyword_searcher, semantic_searcher) with comma separation.

3. Final Answer:

   HybridSearch(keyword_searcher, semantic_searcher) -> Option B

4. Quick Check:

   Correct syntax uses comma-separated searchers [OK]

Hint: Use commas to separate searchers, not operators [OK]

Common Mistakes:

• Using + or * instead of comma to combine searchers
• Swapping the order of keyword and semantic searchers
• Passing a single combined object instead of two

3. Given the following code snippet in Langchain, what will be the output type of results?

hybrid_search = HybridSearch(keyword_searcher, semantic_searcher)
results = hybrid_search.search('apple')

medium

A. A single document matching the keyword exactly

B. A boolean indicating if 'apple' exists in the database

C. A list of documents ranked by combined keyword and semantic relevance

D. An error because HybridSearch has no search method

Solution

1. Step 1: Understand HybridSearch.search()

   The search method returns multiple documents ranked by relevance.

2. Step 2: Combine keyword and semantic results

   Hybrid search merges keyword and semantic scores to rank documents.

3. Final Answer:

   A list of documents ranked by combined keyword and semantic relevance -> Option C

4. Quick Check:

   Hybrid search returns ranked document list [OK]

Hint: Hybrid search returns ranked lists, not single items [OK]

Common Mistakes:

• Expecting a single document instead of a list
• Thinking it returns a boolean
• Assuming HybridSearch lacks a search method

4. You wrote this code but get an error:

hybrid_search = HybridSearch(keyword_searcher semantic_searcher)
results = hybrid_search.search('banana')

What is the error and how to fix it?

medium

A. Missing comma between keyword_searcher and semantic_searcher; add a comma.

B. search method does not exist; rename to find.

C. HybridSearch cannot take two arguments; combine them first.

D. The string 'banana' should be a list; change to ['banana'].

Solution

1. Step 1: Identify syntax error in constructor

   There is no comma between keyword_searcher and semantic_searcher, causing a syntax error.

2. Step 2: Fix by adding comma

   Insert a comma to separate the two arguments correctly.

3. Final Answer:

   Missing comma between keyword_searcher and semantic_searcher; add a comma. -> Option A

4. Quick Check:

   Arguments must be comma-separated [OK]

Hint: Check commas between arguments to avoid syntax errors [OK]

Common Mistakes:

• Forgetting commas between function arguments
• Assuming method name is wrong
• Changing input type unnecessarily

5. You want to improve search results by weighting keyword matches twice as much as semantic similarity in Langchain's hybrid search. Which approach correctly applies this weighting?

hard

A. Use only keyword_searcher to get stronger keyword matches.

B. Multiply semantic_searcher results by 2 before combining.

C. Swap keyword_searcher and semantic_searcher order in HybridSearch constructor.

D. Use HybridSearch(keyword_searcher, semantic_searcher, weights=[2,1]) to set weights.

Solution

1. Step 1: Understand weighting in hybrid search

   HybridSearch can accept weights to prioritize one search type over another.

2. Step 2: Apply weights parameter correctly

   Passing weights=[2,1] gives keyword search double importance compared to semantic search.

3. Final Answer:

   Use HybridSearch(keyword_searcher, semantic_searcher, weights=[2,1]) to set weights. -> Option D

4. Quick Check:

   Weights parameter controls search importance [OK]

Hint: Use weights parameter to prioritize keyword or semantic search [OK]

Common Mistakes:

• Trying to multiply results manually before combining
• Swapping searchers without weighting
• Using only one searcher loses hybrid benefits