How can I combine keyword filtering with semantic search on my large JSON dataset?

You have a couple of options for combining keyword and semantic searches on your JSON data. Here are two main approaches:

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1. Enrich Your Data with Metadata and Use a Vector Database

Rather than relying solely on the semantic (embedding) search, enrich your JSON rows by parsing the inner JSON (the content field) so that each category (e.g., Name, Category, City, etc.) is stored as a separate metadata field. Then:

• Create Embeddings: Use OpenAI's embedding API to generate embeddings for the textual parts (or even the whole object if that suits your use case).
• Index with Metadata: Store these embeddings in a vector database (like Pinecone, Weaviate, Milvus, or even Elasticsearch with dense vector support) along with the separate metadata fields.
• Hybrid Search: When querying, you can first do a semantic search based on the embedding query and then filter or further rank results by applying keyword-based filtering on the metadata fields.

For example, using Pinecone and Python:

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import openai
import pinecone

# Initialize Pinecone and OpenAI
pinecone.init(api_key="YOUR_PINECONE_API_KEY", environment="YOUR_ENVIRONMENT")
index = pinecone.Index("your-index-name")

def get_embedding(text):
    response = openai.Embedding.create(
        input=text,
        engine="text-embedding-ada-002"
    )
    return response["data"][0]["embedding"]

# Example row from your JSON
row = {
    "content": "{'Name': 'Place A', 'Category': 'Restaurant', 'Sub-Category': 'Italian', 'City': 'New York', 'Latitude': '40.7128', 'Longitude': '-74.0060', 'Address': '123 Main St', 'filename': ['image1.jpg'], 'text': 'Great authentic pasta and pizza.'}"
    # ... vector field if already present
}

# Parse the inner JSON from the content
import ast
parsed_content = ast.literal_eval(row["content"])

# Create embedding for the combined text fields (could join multiple fields)
combined_text = f"{parsed_content['Name']} {parsed_content['Category']} {parsed_content['Sub-Category']} {parsed_content['City']} {parsed_content['Address']} {parsed_content['text']}"
embedding = get_embedding(combined_text)

# Upsert the item into the vector store with metadata
index.upsert([{
    "id": "unique-id-1",
    "values": embedding,
    "metadata": parsed_content
}])

When querying, you can build a semantic query, then filter by metadata. For instance:

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def semantic_query(query_text, metadata_filter=None):
    query_embedding = get_embedding(query_text)
    # The filter structure depends on your vector database.
    # Here, we assume a simple metadata filter for the 'Category'
    response = index.query(
        vector=query_embedding,
        filter=metadata_filter,  # e.g., {"Category": {"$eq": "Restaurant"}}
        top_k=10,
        include_metadata=True
    )
    return response

# Example query filtering for 'Restaurant' category
results = semantic_query("Best Italian food", metadata_filter={"Category": {"$eq": "Restaurant"}})

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2. Combine Keyword Search with Semantic Search in a Two-Step Approach

If you prefer not to restructure your data significantly, you can perform a two-step search:

1. Keyword Search: Use conventional text search (e.g., Elasticsearch or even a simple in-memory filter) to narrow down rows by specific keywords or category values.
2. Semantic Search: Then, generate embeddings for those results (or the query) and perform a semantic similarity search or ranking.

This approach might look like:

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# Assume rows are loaded into a list called `rows`
def keyword_filter(rows, keyword, field):
    return [row for row in rows if keyword.lower() in row['content'].lower()]

filtered_rows = keyword_filter(all_rows, "Italian", "Category")

# Now, perform semantic search on the filtered rows
embedding = get_embedding("Best Italian restaurant")
# Compute similarity scores (this could be cosine similarity between the query embedding and each row's precomputed embedding)
# ... your similarity computation here

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Summary

• Hybrid Search: Enrich your JSON data with metadata fields and index with a vector database that supports filtering.
• Two-Step: First filter using keywords, then re-rank or search semantically within the filtered set.

Using a vector database with metadata filtering is typically more scalable and accurate, as it allows you to combine the strengths of semantic similarity (capturing context) with precise control over specific fields through keyword filters.

Choose the method that best fits your data size, query requirements, and infrastructure capabilities.