is there any way to put more weightage to metadata in Faiss store while querying?

[aradhanachaturvedi]

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Example Code

from langchain_community.vectorstores import FAISS

query = "tell us more about the market and its industry"
results = faiss_store.similarity_search(query,k=6,metadata_weight=2.0)

Description

I have a document with metadata which I indexed into Faiss vector storage.Is there any way by which we can query against metadata too , and give more weightage to it compared to page_content?

System Info

langchain==0.1.20
langchain-community==0.0.38
langchain-core==0.1.52
langchain-experimental==0.0.57
langchain-openai==0.1.3
langchain-text-splitters==0.0.1

[dosubot[bot]]

Yes, you can give more weightage to metadata in Faiss store while querying by using the max_marginal_relevance_search_with_score_by_vector method with the lambda_mult parameter. This parameter allows you to control the trade-off between relevance and diversity, effectively giving more weight to metadata.

Here is an example:

from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document

# Example documents with metadata
texts = ["foo", "foo", "fou", "foy"]
metadatas = [{"page": i} for i in range(len(texts))]
docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)

# Query vector
query_vec = FakeEmbeddings().embed_query(text="foo")

# Perform search with more weightage to metadata
output = docsearch.max_marginal_relevance_search_with_score_by_vector(
    query_vec, k=10, lambda_mult=0.1
)

# Output results
for doc, score in output:
    print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}")

In this example, the lambda_mult parameter is set to 0.1, which adjusts the weightage given to the metadata during the search. Adjusting this parameter allows you to control the influence of metadata in the search results ^[1].

Additionally, you can use the similarity_search_with_score method with metadata filtering:

results_with_scores = docsearch.similarity_search_with_score("foo", filter={"page": 1})
for doc, score in results_with_scores:
    print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}")

This method allows you to filter search results based on metadata values ^[2][1].

To continue talking to Dosu, mention @dosu.

[aradhanachaturvedi]

@dosu This is not working as expected , My document is something like this Document(page_content=, metadata={'1':'Project','2':'Market opportunity''4': 'Table of Contents', '5': '1 Presentation'}),

Now I want to retrieve particular chunk based based on giving more weightage to metadata, For e.g. if query is looking for Market opportunity it should first search over metadata, I cant apply filter , because this particular text can be present anywhere in metadata , and some where it may mention as opportunity only. So my goal is to give more weightage to metadata

[dosubot[bot]]

To retrieve a particular chunk from a Faiss vector store by giving more weightage to metadata, especially when the query text can appear anywhere in the metadata, you can use the amax_marginal_relevance_search_with_score_by_vector method with a filter on the metadata. Here is an example of how to do this:

from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain.embeddings import FakeEmbeddings

# Example documents with metadata
texts = ["Document content 1", "Document content 2", "Document content 3"]
metadatas = [
    {'1': 'Project', '2': 'Market opportunity', '4': 'Table of Contents', '5': '1 Presentation'},
    {'1': 'Project', '2': 'Opportunity', '4': 'Table of Contents', '5': '2 Presentation'},
    {'1': 'Project', '2': 'Market', '4': 'Table of Contents', '5': '3 Presentation'}
]
docsearch = FAISS.from_texts(texts, FakeEmbeddings(), metadatas=metadatas)

# Query vector
query_vec = FakeEmbeddings().embed_query(text="Market opportunity")

# Perform search with more weightage to metadata
results = await docsearch.amax_marginal_relevance_search_with_score_by_vector(
    query_vec,
    k=10,
    lambda_mult=0.1,
    filter=lambda metadata: any("Market opportunity" in value for value in metadata.values())
)

# Output results
for doc, score in results:
    print(f"Content: {doc.page_content}, Metadata: {doc.metadata}, Score: {score}")

In this example:

• Replace "Market opportunity" with your actual query text.
• The filter lambda function checks if the query text appears in any of the metadata values.
• The lambda_mult parameter controls the trade-off between relevance and diversity in the results.

This approach ensures that the search gives more weightage to metadata while retrieving the relevant chunks ^[1][2][3][4].

To continue talking to Dosu, mention @dosu.