sf-vector-sdk 0.4.0b21

Python SDK for the Vector Gateway service (embeddings and vector search)

Vector SDK for Python

A lightweight Python client for submitting embedding requests and vector search queries to the Vector Gateway service.

Overview

The Vector SDK provides a simple interface for generating embeddings via the centralized Vector Gateway service. The SDK communicates directly with Redis Streams, making it efficient and suitable for any Python service that can reach the shared Redis VM.

Key Features:

• Simple, Pythonic API with namespace-based organization
• Intuitive methods: client.embeddings, client.search, client.db
• Asynchronous request submission with optional waiting
• Full type hints and documentation
• Multiple embedding model support (Google Vertex AI and OpenAI)
• Client-side model validation before submission
• Minimal dependencies (just Redis)

Installation

From Source (Monorepo)

cd packages/py/vector-sdk
pip install -e .
# Or with uv
uv pip install -e .

From Package Registry (when published)

pip install sf-vector-sdk

Authentication

All SDK operations require a valid API key. Contact your administrator to obtain an API key.

from vector_sdk import VectorClient

client = VectorClient(
    redis_url="redis://your-redis-host:6379",
    http_url="http://localhost:8080",
    api_key="vsk_v1_your_api_key_here",  # Required
)

API Key Format: vsk_v1_{32_random_chars}

Unauthenticated Usage: Some utility functions work without VectorClient or API key:

from vector_sdk import compute_content_hash, extract_tool_text

# These work offline - no API key required
hash_val = compute_content_hash(
    "FlashCard",
    {"type": "BASIC", "term": "ATP", "definition": "Adenosine triphosphate"}
)

Quick Start

Basic Usage

from vector_sdk import VectorClient
import os

# Create client with API key
client = VectorClient(
    redis_url="redis://your-redis-host:6379",
    http_url="http://localhost:8080",
    api_key=os.environ["VECTOR_API_KEY"],  # Required
)

# Create embeddings
result = client.embeddings.create_and_wait(
    texts=[
        {"id": "doc1", "text": "Introduction to machine learning"},
        {"id": "doc2", "text": "Deep neural networks explained"},
    ],
    content_type="topic",
)
print(f"Processed: {result.processed_count}, Failed: {result.failed_count}")

# Vector search
search_result = client.search.query_and_wait(
    query_text="What is machine learning?",
    database="turbopuffer",
    namespace="topics",
    top_k=10,
)
for match in search_result.matches:
    print(f"{match.id}: {match.score}")

# Direct database lookup (no embedding)
docs = client.db.get_by_ids(
    ids=["doc1"],
    database="turbopuffer",
    namespace="topics",
)

client.close()

With Storage Configuration

from vector_sdk import VectorClient, StorageConfig, MongoDBStorage, TurboPufferStorage

client = VectorClient(redis_url="redis://your-redis-host:6379")

# Create embeddings with storage configuration
result = client.embeddings.create_and_wait(
    texts=[
        {
            "id": "tool123",
            "text": "Term: Photosynthesis. Definition: The process by which plants convert sunlight into energy.",
            "document": {
                "toolId": "tool123",
                "toolCollection": "FlashCard",
                "userId": "user456",
                "contentHash": "abc123",
            }
        }
    ],
    content_type="flashcard",
    priority="high",
    storage=StorageConfig(
        mongodb=MongoDBStorage(
            database="events_new",
            collection="tool_vectors",
            embedding_field="toolEmbedding",
            upsert_key="contentHash",
        ),
        turbopuffer=TurboPufferStorage(
            namespace="tool_vectors",
            id_field="_id",
            metadata=["toolId", "toolCollection", "userId"],
        ),
    ),
    metadata={"source": "my-service"},
)

client.close()

Context Manager

with VectorClient(redis_url="redis://localhost:6379") as client:
    result = client.embeddings.create_and_wait(
        texts=[{"id": "doc1", "text": "Hello world"}],
        content_type="document",
    )
# Connection automatically closed

API Reference

VectorClient

The main client class providing namespaced access to all SDK functionality.

Constructor

Standalone Redis:

client = VectorClient(
    redis_url="redis://localhost:6379",
    redis_password="your-password",        # Optional
    http_url="http://localhost:8080",      # Optional, required for db operations
    api_key="vsk_v1_your_api_key",         # Required
    redis_cluster_mode=False,              # Default
)

Redis Cluster:

client = VectorClient(
    redis_url="node1:6379,node2:6379,node3:6379",
    redis_password="your-password",
    redis_cluster_mode=True,               # Required for cluster
    http_url="http://localhost:8080",
    api_key="vsk_v1_your_api_key",
)

Parameters:

• redis_url (str, required): Redis connection URL or comma-separated cluster nodes
• http_url (str, optional): HTTP URL for db operations
• api_key (str, required): API key for authentication
• redis_password (str, optional): Redis password
• redis_cluster_mode (bool, optional): Enable cluster mode (default: False)

See REDIS-CONFIGURATION.md for Redis setup details.

Namespaces

client.embeddings

Embedding generation operations.

Method	Description
create(texts, content_type, ...)	Submit embedding request, return request ID
wait_for(request_id, timeout)	Wait for request completion
create_and_wait(texts, content_type, ...)	Submit and wait for result
get_queue_depth()	Get current queue depth for each priority

# Async: create and wait separately
request_id = client.embeddings.create(texts, content_type)
result = client.embeddings.wait_for(request_id)

# Sync: create and wait in one call
result = client.embeddings.create_and_wait(texts, content_type)

# Check queue depth
depths = client.embeddings.get_queue_depth()

client.search

Vector similarity search operations.

Method	Description
query(query_text, database, ...)	Submit search query, return request ID
wait_for(request_id, timeout)	Wait for query completion
query_and_wait(query_text, database, ...)	Submit and wait for result

# Vector search with semantic similarity
result = client.search.query_and_wait(
    query_text="What is machine learning?",
    database="turbopuffer",
    namespace="topics",
    top_k=10,
    include_metadata=True,
)

client.db

Direct database operations (no embedding required). Requires http_url.

Method	Description
get_by_ids(ids, database, ...)	Lookup documents by ID
find_by_metadata(filters, database, ...)	Search by metadata filters
clone(id, source_namespace, destination_namespace)	Clone document between namespaces
delete(id, namespace)	Delete document from namespace

client.structured_embeddings

Type-safe embedding for known tool types (FlashCard, TestQuestion, etc.) with automatic text extraction, content hash computation, and database routing.

Method	Description
embed_flashcard(data, metadata)	Embed a flashcard, return request ID
embed_flashcard_and_wait(data, metadata, timeout)	Embed and wait for result
embed_flashcard_batch(items)	Embed batch of flashcards, return request ID
embed_flashcard_batch_and_wait(items, timeout)	Embed batch and wait for result
embed_test_question(data, metadata)	Embed a test question, return request ID
embed_test_question_and_wait(data, metadata, timeout)	Embed and wait for result
embed_test_question_batch(items)	Embed batch of test questions, return request ID
embed_test_question_batch_and_wait(items, timeout)	Embed batch and wait for result
embed_spaced_test_question(data, metadata)	Embed a spaced test question, return request ID
embed_spaced_test_question_and_wait(data, metadata, timeout)	Embed and wait for result
embed_spaced_test_question_batch(items)	Embed batch of spaced test questions, return request ID
embed_spaced_test_question_batch_and_wait(items, timeout)	Embed batch and wait for result
embed_audio_recap(data, metadata)	Embed an audio recap section, return request ID
embed_audio_recap_and_wait(data, metadata, timeout)	Embed and wait for result
embed_audio_recap_batch(items)	Embed batch of audio recaps, return request ID
embed_audio_recap_batch_and_wait(items, timeout)	Embed batch and wait for result
embed_topic(data, metadata)	Embed a topic (uses TopicMetadata), return request ID
embed_topic_and_wait(data, metadata, timeout)	Embed and wait for result (uses TopicMetadata)
embed_topic_batch(items)	Embed batch of topics (uses TopicMetadata), return request ID
embed_topic_batch_and_wait(items, timeout)	Embed batch and wait for result (uses TopicMetadata)

Metadata Types:

• ToolMetadata - For tools (FlashCard, TestQuestion, etc.) - requires tool_id
• TopicMetadata - For topics only - all fields optional (user_id, topic_id)

from vector_sdk import VectorClient, ToolMetadata, TopicMetadata, TestQuestionInput

client = VectorClient(redis_url="redis://localhost:6379")

# Embed a flashcard - uses ToolMetadata (tool_id required)
result = client.structured_embeddings.embed_flashcard_and_wait(
    data={"type": "BASIC", "term": "Mitochondria", "definition": "The powerhouse of the cell"},
    metadata=ToolMetadata(tool_id="tool123", user_id="user456", topic_id="topic789"),
)

# Embed a test question - uses ToolMetadata (tool_id required)
result = client.structured_embeddings.embed_test_question_and_wait(
    data=TestQuestionInput(
        question="What is the capital?",
        answers=[...],
        question_type="multiplechoice",
    ),
    metadata=ToolMetadata(tool_id="tool456"),
)

# Embed a topic - uses TopicMetadata (all fields optional)
# Note: Topic data requires an "id" field which becomes the TurboPuffer document ID
result = client.structured_embeddings.embed_topic_and_wait(
    data={"id": "topic-123", "topic": "Photosynthesis", "description": "The process by which plants convert sunlight to energy"},
    metadata=TopicMetadata(user_id="user123", topic_id="topic456"),  # No tool_id needed
)

# Batch embedding - embed multiple topics in a single request
from vector_sdk import TopicBatchItem

batch_result = client.structured_embeddings.embed_topic_batch_and_wait(
    items=[
        TopicBatchItem(data={"id": "topic-1", "topic": "Topic 1", "description": "Description 1"}, metadata=TopicMetadata(user_id="user1")),
        TopicBatchItem(data={"id": "topic-2", "topic": "Topic 2", "description": "Description 2"}, metadata=TopicMetadata(topic_id="topic2")),
        TopicBatchItem(data={"id": "topic-3", "topic": "Topic 3", "description": "Description 3"}, metadata=TopicMetadata()),  # All optional
    ],
)

Database Routing:

Set the STRUCTURED_EMBEDDING_DATABASE_ROUTER environment variable:

Value	Behavior
dual	Write to both TurboPuffer AND Pinecone if both have enabled: True
turbopuffer	Only write to TurboPuffer
pinecone	Only write to Pinecone
undefined	Defaults to turbopuffer

# Lookup by IDs
result = client.db.get_by_ids(
    ids=["doc1", "doc2"],
    database="turbopuffer",
    namespace="topics",
)

# Find by metadata
result = client.db.find_by_metadata(
    filters={"userId": "user123"},
    database="mongodb",
    collection="vectors",
    database_name="mydb",
)

# Clone between namespaces
result = client.db.clone("doc1", "ns1", "ns2")

# Delete
result = client.db.delete("doc1", "ns1")

# Export entire namespace
export_result = client.db.get_vectors_in_namespace(
    namespace="tool_vectors",
    include_vectors=True,
)
print(f"Exported {len(export_result.documents)} documents")

Types

Result Types

@dataclass
class EmbeddingResult:
    request_id: str
    status: str  # "success", "partial", "failed"
    processed_count: int
    failed_count: int
    errors: list[EmbeddingError]
    timing: Optional[TimingBreakdown]
    completed_at: datetime

    @property
    def is_success(self) -> bool: ...
    @property
    def is_partial(self) -> bool: ...
    @property
    def is_failed(self) -> bool: ...

@dataclass
class QueryResult:
    request_id: str
    status: str  # "success", "failed"
    matches: list[VectorMatch]
    error: Optional[str]
    timing: Optional[QueryTiming]
    completed_at: datetime

@dataclass
class VectorMatch:
    id: str
    score: float  # Similarity score (0-1, higher is more similar)
    metadata: Optional[dict]
    vector: Optional[list[float]]

Priority Levels

Priority	Use Case	Description
critical	Real-time user requests	Reserved quota, processed first
high	New content embeddings	Standard processing priority
normal	Updates, re-embeddings	Default priority
low	Backfill, batch jobs	Processed when capacity available

result = client.embeddings.create_and_wait(texts, content_type="topic", priority="critical")

Embedding Models

Supported Models

Model	Provider	Dimensions	Custom Dims
gemini-embedding-001	Google	3072	No
text-embedding-004	Google	768	No
text-multilingual-embedding-002	Google	768	No
text-embedding-3-small	OpenAI	1536	Yes
text-embedding-3-large	OpenAI	3072	Yes

Using a Specific Model

result = client.embeddings.create_and_wait(
    texts=[{"id": "doc1", "text": "Hello world"}],
    content_type="document",
    embedding_model="text-embedding-3-small",
    embedding_dimensions=512,  # Custom dimensions (only for models that support it)
)

Content Hash

The SDK provides deterministic content hashing for learning tools.

from vector_sdk import compute_content_hash, extract_tool_text

# Compute hash for a FlashCard
hash = compute_content_hash(
    "FlashCard",
    {"type": "BASIC", "term": "Mitochondria", "definition": "The powerhouse of the cell"}
)

# Extract text for embedding
text = extract_tool_text(
    "FlashCard",
    {"type": "BASIC", "term": "Mitochondria", "definition": "The powerhouse of the cell"}
)

Migration from EmbeddingClient

The SDK now uses a namespace-based API with VectorClient. The old EmbeddingClient is preserved for backward compatibility.

Method Mapping

Old (EmbeddingClient)	New (VectorClient)
submit()	client.embeddings.create()
wait_for_result()	client.embeddings.wait_for()
submit_and_wait()	client.embeddings.create_and_wait()
get_queue_depth()	client.embeddings.get_queue_depth()
query()	client.search.query()
wait_for_query_result()	client.search.wait_for()
query_and_wait()	client.search.query_and_wait()
lookup_by_ids()	client.db.get_by_ids()
search_by_metadata()	client.db.find_by_metadata()
clone_from_namespace()	client.db.clone()
delete_from_namespace()	client.db.delete()

Migration Example

# Old API (still works, emits deprecation warnings)
from vector_sdk import EmbeddingClient

client = EmbeddingClient("redis://localhost:6379")
result = client.submit_and_wait(texts, content_type)
client.close()

# New API (recommended)
from vector_sdk import VectorClient

client = VectorClient(redis_url="redis://localhost:6379")
result = client.embeddings.create_and_wait(texts, content_type)
client.close()

Error Handling

from vector_sdk import VectorClient, ModelValidationError

try:
    with VectorClient(redis_url="redis://localhost:6379") as client:
        result = client.embeddings.create_and_wait(
            texts=[{"id": "doc1", "text": "Hello"}],
            content_type="test",
            embedding_model="text-embedding-3-small",
            timeout=30,
        )
        
        if result.is_success:
            print("Success!")
        elif result.is_partial:
            print("Partial success. Errors:")
            for err in result.errors:
                print(f"  - {err.id}: {err.error}")

except ModelValidationError as e:
    print(f"Model validation failed: {e}")
except TimeoutError as e:
    print(f"Request timed out: {e}")
except ValueError as e:
    print(f"Invalid input: {e}")

Best Practices

1. Use Appropriate Priority

# Use appropriate priority levels
client.embeddings.create(texts, content_type="backfill", priority="low")
client.embeddings.create(texts, content_type="userRequest", priority="critical")

2. Batch Your Requests

# Batch multiple texts per request for efficiency
texts = [{"id": doc.id, "text": doc.text} for doc in documents]
client.embeddings.create(texts, content_type)

3. Use Context Managers

with VectorClient(redis_url="redis://...") as client:
    # Client automatically closed on exit
    pass

4. Deduplication

The gateway automatically deduplicates embedding requests using the contentHash metadata field. If a vector with the same contentHash already exists in the target namespace, the embedding generation is skipped to reduce costs.

• Structured embeddings: Deduplication is enabled by default for all tool types except Topics (which always re-embed since content may change for the same ID).
• Raw embeddings: Pass allow_duplicates=True to skip deduplication when needed.

# Default: deduplication enabled (contentHash checked before embedding)
client.embeddings.create(texts, content_type="flashcard", storage=storage_config)

# Opt out of deduplication
client.embeddings.create(texts, content_type="topic", allow_duplicates=True)

The EmbeddingResult includes a skipped_count field showing how many items were deduplicated:

result = client.embeddings.create_and_wait(texts, content_type="flashcard")
print(f"Processed: {result.processed_count}, Skipped: {result.skipped_count}")

License

Proprietary - All rights reserved.