Embedding with Metadata RAG

2025-01-29 11:11 UTC gpt-4o Open in ChatGPT ↗

No, you don’t necessarily need two separate text files for the text and metadata when doing embeddings for Retrieval-Augmented Generation (RAG). However, the approach depends on your use case and the specific embedding storage and retrieval system you’re using. Here are your options:

1. Store Text and Metadata Together

You can keep the text and metadata together in a structured format (e.g., JSON, CSV, or a database).

Each record/document will have fields like:

{
  "text": "This is the main content for embedding.",
  "metadata": {
    "source": "document1.pdf",
    "author": "John Doe",
    "date": "2024-01-29"
  }
}

Many vector databases (e.g., Pinecone, Weaviate, Chroma, FAISS) allow storing metadata alongside embeddings.

2. Store Text and Metadata Separately

If your storage system requires separate files, you can:
- Store the raw text in one file (e.g., text.txt).
- Store the metadata in another file (e.g., metadata.json or metadata.csv).
The two files should be linked via unique identifiers to match text with metadata.

Best Practice

Most RAG implementations keep text and metadata together, storing everything in a vector database or a structured format (JSON/CSV) for easy retrieval.

Would you like help with structuring your files or integrating metadata into a vector database? 🚀

Yes, you can store both text and metadata in a CSV file, and it’s a common practice for structured storage before embedding. Here’s how you can structure your CSV:

CSV Structure Example

id	text	source	author	date
1	”This is the first document text.”	doc1.pdf	John Doe	2024-01-29
2	”Another document with different info.”	doc2.txt	Jane Doe	2024-01-30

Key Points:

✅ Each row represents a separate document or chunk of text.
✅ Columns include text and metadata such as source, author, and date.
✅ You can add more metadata like category, tags, or embeddings later.
✅ Works well with pandas in Python (pd.read_csv() makes it easy to process).

Using CSV with Embeddings in RAG

Read CSV in Python:

import pandas as pd

df = pd.read_csv("documents.csv")
print(df.head())

Generate and Store Embeddings:
- Use an embedding model (e.g., OpenAI, Hugging Face, etc.) to generate vector embeddings for the text column.
- Store the embeddings separately in a vector database (like Pinecone, FAISS, Weaviate) while keeping metadata in the database.

Alternative: Storing Embeddings in CSV (Not Ideal)

While you can store embeddings in a CSV, it’s not ideal because embeddings are high-dimensional vectors. If necessary:

Store them as a JSON string in a column:
```
"[0.12, 0.85, -0.33, 0.44, ...]"
```
Use json.loads() to convert them back to a list in Python.

Recommendation

🔹 Best approach: Store text and metadata in a CSV and keep embeddings in a vector database.
🔹 CSV alone works for basic use cases but is not efficient for large-scale vector searches.

Would you like help with a Python script to generate embeddings and store them efficiently? 🚀

Yes, you can store all your entries in a single JSON file, and it’s a great option for structuring text and metadata together.

JSON Structure for RAG

A typical JSON format would be a list of dictionaries, each representing a document with both text and metadata:

[
    {
        "id": 1,
        "text": "This is the first document text.",
        "metadata": {
            "source": "doc1.pdf",
            "author": "John Doe",
            "date": "2024-01-29"
        }
    },
    {
        "id": 2,
        "text": "Another document with different info.",
        "metadata": {
            "source": "doc2.txt",
            "author": "Jane Doe",
            "date": "2024-01-30"
        }
    }
]

Advantages of Using JSON

✅ Easy to process: JSON is natively supported in Python (json module) and many databases.
✅ Supports nested metadata: Unlike CSV, JSON allows storing hierarchical data (e.g., multiple tags, categories).
✅ Efficient for APIs: Many vector DBs (like Pinecone, Weaviate, ChromaDB) accept JSON directly.
✅ Can include embeddings later: You can add an embedding field to store vectors.

Working with JSON in Python

Read JSON File

import json

with open("documents.json", "r") as file:
    data = json.load(file)

print(data[0])  # Print first entry

Generate Embeddings & Update JSON

from sentence_transformers import SentenceTransformer

model = SentenceTransformer("all-MiniLM-L6-v2")

for entry in data:
    entry["embedding"] = model.encode(entry["text"]).tolist()  # Convert to list

# Save back to JSON
with open("documents_with_embeddings.json", "w") as file:
    json.dump(data, file, indent=4)

When to Use JSON vs CSV

Feature	JSON	CSV
Metadata Support	✅ Nested metadata is easy	❌ Flat structure only
Readability	✅ Human-readable & flexible	✅ Easier for spreadsheets
Embeddings	✅ Can store lists easily	❌ Harder to store vectors
Scalability	✅ Good for NoSQL, APIs, vector DBs	✅ Better for tabular data

Best Practice

Use JSON if you need structured metadata and may later integrate with vector databases.
Use CSV if you only need flat metadata (like text, date, and author) without complex nesting.

Do You Need Help?

Would you like a Python script to automatically process documents into JSON format and store embeddings? 🚀