Metadata filtering in vector stores in LangChain - Interactive Code Practice

Practice - 5 Tasks

1fill in blank

easy

Complete the code to create a filter dictionary for metadata with key 'category' and value 'science'.

LangChain

filter = [1]

Drag options to blanks, or click blank then click option'

A'category:science'

B['category', 'science']

C{'category': 'science'}

D('category', 'science')

Attempts:

3 left

💡 Hint

Common Mistakes

Using a list or tuple instead of a dictionary.

Using a string instead of a dictionary.

The filter must be a dictionary with the key and value to match metadata entries.

2fill in blank

medium

Complete the code to pass the filter when calling the vector store's search method.

LangChain

results = vector_store.search(query, filter=[1])

Drag options to blanks, or click blank then click option'

A{'category': 'science'}

B['category', 'science']

C'category=science'

D('category', 'science')

Attempts:

3 left

💡 Hint

Common Mistakes

Passing a list or tuple instead of a dictionary.

Passing a string instead of a dictionary.

The filter argument expects a dictionary specifying metadata keys and values to filter results.

3fill in blank

hard

Fix the error in the filter dictionary to correctly filter metadata where 'year' is 2023.

LangChain

filter = [1]

Drag options to blanks, or click blank then click option'

A{year: '2023'}

B{'year': 2023}

C{'year': 'twenty twenty three'}

D['year', 2023]

Attempts:

3 left

💡 Hint

Common Mistakes

Using unquoted keys.

Using string instead of integer for year.

Using a list instead of a dictionary.

The filter must be a dictionary with the key as a string and the value as the integer 2023.

4fill in blank

hard

Fill both blanks to create a filter that matches documents with 'author' equal to 'Alice' and 'published' equal to True.

LangChain

filter = [1]

Drag options to blanks, or click blank then click option'

A{'author': 'Alice', 'published': True}

B{'author': 'Alice'}

C{'published': True}

D{'author': 'Bob', 'published': False}

Attempts:

3 left

💡 Hint

Common Mistakes

Including only one key in the filter.

Using incorrect values for keys.

The filter dictionary must include both keys with their correct values to match the metadata.

5fill in blank

hard

Fill all three blanks to create a filter dictionary that matches documents with 'category' 'tech', 'year' 2024, and 'verified' True.

LangChain

filter = [1]

Drag options to blanks, or click blank then click option'

A{'year': 2024}

B{'category': 'tech'}

C{'verified': True}

D{'category': 'tech', 'year': 2024, 'verified': True}

Attempts:

3 left

💡 Hint

Common Mistakes

Using separate dictionaries instead of one combined dictionary.

Missing one or more keys in the filter.

The filter dictionary must include all three keys with their correct values to filter properly.

Practice

1. What is the main purpose of metadata filtering in vector stores?

easy

A. To delete irrelevant data from the vector store

B. To increase the size of the vector store

C. To speed up vector calculations without filtering

D. To narrow search results using extra information about data

Solution

1. Step 1: Understand metadata filtering concept

   Metadata filtering uses extra information (metadata) to limit search results to relevant items.

2. Step 2: Identify the main purpose

   The main goal is to narrow down results, not to delete data or speed up calculations without filtering.

3. Final Answer:

   To narrow search results using extra information about data -> Option D

4. Quick Check:

   Metadata filtering = Narrow search results [OK]

Hint: Metadata filters results by extra info, not by size or speed [OK]

Common Mistakes:

• Confusing filtering with deleting data
• Thinking metadata speeds up vector math alone
• Believing filtering increases store size

2. Which of the following is the correct way to apply metadata filtering in a Langchain vector store search?

easy

A. search(query, filter=[('category', 'news')])

B. search(query, filter='category=news')

C. search(query, filter={'category': 'news'})

D. search(query, filter='category:news')

Solution

1. Step 1: Recall correct filter syntax

   Langchain expects a dictionary for metadata filtering, e.g., {'key': 'value'}.

2. Step 2: Match syntax to options

   Only search(query, filter={'category': 'news'}) uses a dictionary correctly; others use strings or lists incorrectly.

3. Final Answer:

   search(query, filter={'category': 'news'}) -> Option C

4. Quick Check:

   Filter syntax = dictionary format [OK]

Hint: Use dictionary for filters: filter={'key': 'value'} [OK]

Common Mistakes:

• Using string instead of dictionary for filter
• Passing list of tuples instead of dict
• Using colon syntax inside string

3. Given this code snippet using Langchain vector store:

results = vector_store.search('climate change', filter={'source': 'news', 'year': 2023})
print(len(results))

If the vector store has 10 documents with source 'news' and year 2023, and 5 with source 'news' but year 2022, what will be printed?

medium

A. 15

B. 10

C. 5

D. 0

Solution

1. Step 1: Understand filter effect

   The filter requires both 'source' = 'news' and 'year' = 2023 to match.

2. Step 2: Count matching documents

   Only 10 documents match both conditions; 5 with year 2022 do not match.

3. Final Answer:

   10 -> Option B

4. Quick Check:

   Filter matches 10 docs with both conditions [OK]

Hint: Filter matches all conditions exactly, not partial [OK]

Common Mistakes:

• Counting documents matching only one filter key
• Ignoring year condition
• Assuming filter returns all 'news' documents

4. You wrote this code but get an error:

results = vector_store.search('AI', filter=['category', 'tech'])

What is the main problem causing the error?

medium

A. Filter should be a dictionary, not a list

B. Search query must be a dictionary

C. Filter keys must be integers

D. Filter cannot be used with string queries

Solution

1. Step 1: Check filter type

   The filter parameter expects a dictionary with key-value pairs.

2. Step 2: Identify incorrect usage

   The code uses a list instead of a dictionary, causing a type error.

3. Final Answer:

   Filter should be a dictionary, not a list -> Option A

4. Quick Check:

   Filter type must be dict, not list [OK]

Hint: Always use dict for filter, not list or other types [OK]

Common Mistakes:

• Passing list instead of dict for filter
• Thinking query must be dict
• Believing filter keys must be numbers

5. You want to find documents in a Langchain vector store about 'machine learning' but only from the year 2022 or later, and with the tag 'tutorial'. Which filter dictionary correctly applies this condition?

hard

A. {'tag': 'tutorial', 'year': {'$gte': 2022}}

B. {'tag': 'tutorial', 'year': 2022}

C. {'tag': ['tutorial'], 'year': 2022}

D. {'tag': 'tutorial', 'year': [2022, 2023, 2024]}

Solution

1. Step 1: Understand filtering for range conditions

   To filter for year 2022 or later, a comparison operator like '$gte' (greater than or equal) is needed.

2. Step 2: Analyze options for correct syntax

   {'tag': 'tutorial', 'year': {'$gte': 2022}} uses {'year': {'$gte': 2022}} which correctly expresses the condition; others do not support range filtering properly.

3. Final Answer:

   {'tag': 'tutorial', 'year': {'$gte': 2022}} -> Option A

4. Quick Check:

   Use comparison operators for range filters [OK]

Hint: Use {'key': {'$gte': value}} for filtering ranges [OK]

Common Mistakes:

• Using exact value instead of range operator
• Passing list instead of operator for year
• Misunderstanding how to filter numeric ranges