How to implement semantic text splitting with FAISS vector database in Langchain

Other Questions langchain
1

I’m working with a custom dataset and have set up a basic FAISS vector store using this code:

from langchain_community.vectorstores import FAISS
from langchain_openai import ChatOpenAI, OpenAIEmbeddings

db = FAISS.from_texts(
    text_data, embedding=OpenAIEmbeddings(openai_api_key=API_KEY)
)
ret = db.as_retriever()

I need to apply semantic text splitting to my dataset before storing it in the vector database. I tried using this approach:

from langchain_experimental.text_splitter import SemanticChunker

splitter = SemanticChunker(OpenAIEmbeddings(openai_api_key=API_KEY))
text_data = splitter.create_documents(text_data)

But I’m getting errors, probably because the data structures don’t match up properly. What’s the correct way to combine semantic chunking with FAISS vectorstore creation?

2

I encountered a similar issue while implementing the SemanticChunker with FAISS. The root cause lies in the fact that the create_documents method returns Document objects, but the FAISS.from_texts method expects plain strings. To resolve this, you need to extract the text from the Document objects. Here’s a solution that worked for me:

  1. Use the SemanticChunker to generate semantic chunks.
  2. Extract the text content from each Document object by accessing the page_content attribute.
  3. Finally, utilize the FAISS.from_texts method with the extracted text strings. Alternatively, consider using FAISS.from_documents() directly to avoid the extraction step altogether.
3

check your input format first - semantic chunker wants a list of strings, not documents. also, don’t pass already processed docs to create_documents method. had the same issue and turns out i was feeding it the wrong data type.

4

You’ve got a common data type mismatch. Don’t convert SemanticChunker’s Document objects back to strings - just work with them directly. This approach has worked reliably for me:

from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(openai_api_key=API_KEY)
splitter = SemanticChunker(embeddings)
chunked_docs = splitter.create_documents(text_data)

db = FAISS.from_documents(chunked_docs, embedding=embeddings)
ret = db.as_retriever()

Use FAISS.from_documents() instead of from_texts(). It takes Document objects directly and fixes the mismatch. I’ve run this on large datasets - works smoothly and keeps the document metadata for better retrieval context.

5

Hit this exact problem last month building a document search system. You’re mixing text strings with Document objects.

When you call create_documents(), it spits out Document objects with page_content and metadata attributes. Your text_data variable now has these objects instead of plain strings.

Fix: reuse the same embeddings instance:

from langchain_experimental.text_splitter import SemanticChunker
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings

embeddings = OpenAIEmbeddings(openai_api_key=API_KEY)
splitter = SemanticChunker(embeddings)

# Split your original text data
chunked_docs = splitter.create_documents(original_text_data)

# Use the same embeddings instance
db = FAISS.from_documents(chunked_docs, embedding=embeddings)
ret = db.as_retriever()

Pro tip: reusing the same embeddings instance saves API calls since SemanticChunker already computed embeddings during chunking. Cut our processing time in half on large datasets.

Just make sure your original_text_data is a list of strings, not Document objects.