How can I preserve table semantics when embedding PDFs into Qdrant?

Below is a recommended approach to ensure that table semantics are maintained when inserting PDFs with tables into a vector database like Qdrant (or even Supabase with vector support):

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1. Preprocess the PDF

The key is to extract the PDF content along with its table structure before creating embeddings. Instead of using a generic PDF text extractor, use a tool designed for table extraction (e.g., Camelot or Tabula). This lets you capture table rows, columns, and possibly headers.

Example with Camelot:

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import camelot

# Read tables from a PDF
tables = camelot.read_pdf("your_file.pdf", pages='all')

# Iterate through extracted tables and process them into a format maintaining semantics
for i, table in enumerate(tables):
    # Here, table.df is a pandas DataFrame containing the table data
    print(f"Table {i+1}:")
    print(table.df)

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2. Preserve Table Structure in Your Chunking Pipeline

Once you have extracted tables, you can combine the table data with the surrounding text into a single document or process them separately. It’s often beneficial to:

• Store table data as structured payloads (e.g., as JSON) alongside text.
• Include markers and headers that clearly delineate table boundaries within the text.

If you’re using a tool like LangChain for orchestration:

• Write a custom loader or preprocessor that treats tables as distinct document chunks.
• Append a metadata field that indicates the content is a table.

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3. Generate Embeddings

After preprocessing, generate embeddings for both the text and the tables. Make sure:

• The parser or embedding API you’re using supports the additional context provided by the table structure.
• You may have to experiment with different chunk sizes. Sometimes large block text from tables might need to be broken down further.

For example:

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from your_embedding_library import generate_embedding

def generate_chunk_embedding(content, metadata=None):
    # Custom function that wraps your embedding process
    embedding = generate_embedding(content)
    return embedding, metadata

# Process regular text and table data chunks similarly

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4. Insert into Qdrant

After processing, insert the embeddings along with their metadata into Qdrant. Your payload should include information about which chunks represent tables. When using integrated pipelines (for example, with dsRAG), you need to ensure that:

• Each chunk’s payload includes context (e.g., table headers, column names).
• The insertion operation preserves this additional metadata.

A typical insertion example might look like:

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from dsrag.database.vector import QdrantVectorDB
import numpy as np

# Initialize Qdrant instance
db = QdrantVectorDB(kb_id="your_kb_id", url="http://localhost:6334", prefer_grpc=True)

# Example vector and payload
vectors = [np.array([0.1, 0.9])]  # Replace with your computed embedding
documents = [{
    "doc_id": "pdf_1_table", 
    "chunk_index": 0, 
    "chunk_header": "Table: Sales Data", 
    "chunk_text": "Column1: Date, Column2: Sales, ...",  # Your processed table text
    "metadata": {"type": "table", "source": "pdf", "additional_info": "Keep table semantics"}
}]

db.add_vectors(vectors, documents)

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Summary

1. Extract with Table-Aware Tools: Use tools like Camelot or Tabula to capture table structures.
2. Custom Preprocessing: Incorporate the table data as structured information in your document chunks.
3. Generate Context-Aware Embeddings: Create embeddings that consider the additional semantics.
4. Use Metadata during Insertion: In your insertion process (via LangChain/dsRAG), make sure to tag table chunks properly.

By preprocessing the PDF to extract tables accurately and preserving these semantics in the metadata and chunk boundaries, your queries should improve in relevance when searching through the table contents.

This approach provides you with better control over how table content is interpreted and queried later in Qdrant or even in a Supabase vector setup.

Below is an approach that has worked well in practice:

1. Use a dedicated PDF extraction tool to parse tables separately.
      • Libraries like Camelot or Tabula-py can extract table structures into dataframes.
      • Alternatively, use advanced PDF loaders (for example in Langchain) but note that the built‐in loaders often don’t preserve complex table semantics.

2. Once you’ve extracted the tables, reassemble your document so that the table data isn’t lost in the chunking process.
      • For example, combine the extracted table text (or even better, structure it as JSON/metadata) with the rest of the page content.
      • This way, when you create your Document objects (or similar objects for insertion), you can include the table information either in the text or as metadata for later retrieval.

3. Customize your chunking pipeline:
      • Standard chunking algorithms (e.g., splitting on punctuation or fixed-length segments) might cut across table boundaries.
      • Instead, use a custom chunking function that treats table-extracted sections as distinct blocks. For instance, designate section markers or combine rows together, ensuring that these blocks remain contextually intact.

4. Insert into your vector store (be it Qdrant, Supabase, or Pinecone) by using the enriched Document objects.
      • In a Langchain Qdrant example, this is as simple as instantiating the Qdrant vector store with your documents that now contain metadata reflecting the table structure.
      • You can refer to the Qdrant retrieval and insertion examples in the Langchain docs to ensure compatibility.

Here’s a simplified Python illustration integrating these ideas:

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from langchain_community.vectorstores import Qdrant
from langchain_core.documents import Document
from langchain_openai.embeddings import OpenAIEmbeddings

# Step 1: Extract and process PDF
# (For the PDF file, use your chosen library, e.g., camelot or pdfplumber)
# Assume you have two pieces: regular text and table_text

regular_text = "Regular text extracted from the PDF..."
table_text = "Table rows combined in a structured format..."

# Optionally, if you have table data as a dict or JSON:
table_metadata = {
    "table_data": [
        {"column1": "value1", "column2": "value2"},
        {"column1": "value3", "column2": "value4"},
    ]
}

# Step 2: Create Document objects that preserve table semantics
documents = [
    Document(
        page_content=regular_text,
        metadata={"source": "document.pdf"}
    ),
    Document(
        page_content=table_text,  
        metadata={"source": "document.pdf", "table": table_metadata}
    )
]

# Step 3: Initialize the vector store and insert documents
embeddings = OpenAIEmbeddings()
vectordb = Qdrant.from_documents(
    documents,
    embeddings,
    location=":memory:",
    collection_name="your_collection"
)

print("Documents inserted with preserved table semantics.")

Summary:
• First, extract table content separately using the right tools.
• Then, merge or annotate your extracted tables into your Document objects.
• Finally, use a custom chunking approach (if necessary) so that tables remain whole.

This workflow gives your downstream retrieval operations the best chance at understanding and using the inherent table semantics in your PDFs.