Pixeltable vs Pinecone

import pixeltable as pxt
from pixeltable.functions import openai, huggingface

documents = pxt.create_table('documents', {
    'document': pxt.Document,
    'title': pxt.String,
    'category': pxt.String
})

documents.add_computed_column(
    text_content=pxt.functions.extract_text(documents.document)
)

documents.add_embedding_index(
    'text_content',
    string_embed=huggingface.sentence_transformer.using(
        model_id='all-MiniLM-L6-v2'
    )
)

documents.insert([
    {'document': '/path/to/doc1.pdf', 'title': 'Research Paper', 'category': 'research'},
    {'document': '/path/to/doc2.pdf', 'title': 'Technical Manual', 'category': 'technical'}
])

results = documents.select(
    documents.title,
    documents.category,
    documents.text_content
).search('machine learning algorithms', limit=10)

# Automatic incremental updates — no separate orchestration

import pinecone
from sentence_transformers import SentenceTransformer

pinecone.init(api_key="your-api-key", environment="us-west1-gcp")
index = pinecone.Index("your-index")
model = SentenceTransformer('all-MiniLM-L6-v2')

def process_documents(docs):
    for doc in docs:
        text = extract_text(doc)
        embedding = model.encode(text)
        index.upsert([(doc['id'], embedding, doc)])

def search_docs(query, top_k=10):
    query_embedding = model.encode(query)
    return index.query(
        vector=query_embedding.tolist(),
        top_k=top_k,
        include_metadata=True
    )['matches']

# Separate orchestration needed for updates, versioning, etc.