gushwork-rag 0.2.4

Python SDK for the Gushwork Retrieval-Augmented Generation (RAG) API

Gushwork RAG Python SDK

A fully typed Python client for the Gushwork Retrieval-Augmented Generation (RAG) API. The SDK mirrors Pinecone’s ergonomics while exposing every Gushwork resource—namespaces, files, chat, assistants, and API keys—through Pythonic, well-documented clients.

Features

• 🔐 API key management – Create, list, and revoke keys with role-based access.
• 📁 File pipeline – Presigned uploads, status updates, and listing helpers (plus S3 bulk ingestion).
• 🗂️ Namespace management – CRUD helpers that map to server namespaces/assistants.
• 🤖 High-level Assistant API – Pinecone-style façade with retries, S3 folder sync, and cached metadata.
• 💬 Chat completions – Sync or streaming responses with retrieval controls (top_k, top_n, top_p) and typed message models.
• 📊 Structured output & enums – Strongly typed models for requests/responses, file states, and access levels.
• 🧰 Utilities & context manager – HTTP session lifecycle helpers, S3 download utility, and comprehensive error classes.

Requirements

• Python 3.10+
• Runtime deps (declared in pyproject.toml): requests>=2.25.0, boto3>=1.26.0, backoff>=2.2.0
• Development: uv package manager (recommended)

Installation

Stable release

pip install gushwork-rag

Development / local editing

The SDK uses pyproject.toml as the single source of truth for dependencies. We recommend using uv for dependency management:

# Install dependencies with uv (recommended)
uv sync

# Or install with dev dependencies
uv sync --extra dev

# Activate the virtual environment
source .venv/bin/activate  # Linux/macOS
# or
.venv\Scripts\activate  # Windows

Alternatively, you can still use pip:

pip install -e ".[dev]"

Quick Start

Basic operations

from gushwork_rag import GushworkRAG

# Initialize the client
client = GushworkRAG(
    api_key="your-api-key-here",
    base_url="http://localhost:8080"  # or your production URL
)

# Create a namespace
namespace = client.namespaces.create(
    name="my-documents",
    instructions="Answer questions based on the provided documents."
)

# Upload a file
file = client.files.upload(
    file_path="document.pdf",
    namespace="my-documents"
)

# Chat with your documents
response = client.chat.create(
    namespace="my-documents",
    messages=[
        {"role": "user", "content": "What is the main topic of the document?"}
    ],
    model="claude-sonnet-4-20250514"
)

print(response.content)

Assistant API (recommended)

The assistant façade mirrors Pinecone’s API while using namespaces under the hood. It automatically retries failed generations (via backoff if available) and ships an opinionated S3 ingestion helper.

from gushwork_rag import GushworkRAG

client = GushworkRAG(
    api_key="your-api-key-here",
    base_url="http://localhost:8080"
)

# Create an assistant
assistant = client.assistant.create_assistant(
    assistant_name="my-assistant",
    instructions="Answer questions based on the provided documents."
)

# Get an assistant
assistant = client.assistant("my-assistant")

# Generate a response
response = assistant.generate_response(
    prompt="What is this document about?",
    model="claude-sonnet-4-20250514"
)
print(response)

# List files in the assistant
files = assistant.list_files()
print(f"Files: {len(files.files)}")

# Upload files from S3 folder
assistant.upload_s3_folder(
    bucket_name="my-bucket",
    folder_path="documents/folder",
    exclude=None,  # Optional: list of filenames to exclude
    max_workers=10,  # Number of parallel uploads
    rate_limit_delay=5.0,  # Delay between uploads
)

# Delete the assistant
assistant.delete_assistant()

Usage Examples

Context manager (recommended)

from gushwork_rag import GushworkRAG

with GushworkRAG(api_key="your-api-key") as client:
    # Your code here
    health = client.health_check()
    print(health["status"])
# Client is automatically closed

Managing namespaces

# Create a namespace
namespace = client.namespaces.create(
    name="research-papers",
    instructions="Provide scientific and accurate answers based on research papers."
)

# List all namespaces
namespaces = client.namespaces.list()
for ns in namespaces:
    print(f"{ns.name}: {ns.instructions}")

# Get a specific namespace
namespace = client.namespaces.get(namespace_id="ns_123")

# Update a namespace
updated = client.namespaces.update(
    namespace_id="ns_123",
    instructions="New instructions here"
)

# Delete a namespace
client.namespaces.delete(namespace_id="ns_123")

File operations

# Upload a file
file = client.files.upload(
    file_path="path/to/document.pdf",
    namespace="my-documents",
    mime_type="application/pdf"  # Optional, auto-detected
)
print(f"Uploaded: {file.file_name}")

# List files in a namespace
files = client.files.list_by_namespace(
    namespace="my-documents",
    limit=50,
    skip=0
)
print(f"Total files: {files.total}")
for file in files.files:
    print(f"- {file.file_name} ({file.status})")

# Get file details
file = client.files.get(file_id="file_123")
print(f"Status: {file.status}")
print(f"Uploaded: {file.uploaded_at}")

# Update file status (typically for internal use)
from gushwork_rag import FileStatus

file = client.files.update_status(
    file_id="file_123",
    status=FileStatus.FILE_INDEXED,
    processed_at="2024-01-01T00:00:00Z"
)

# Delete a file
client.files.delete(file_id="file_123")

Chat completions

Simple chat

response = client.chat.create(
    namespace="my-documents",
    messages=[
        {"role": "user", "content": "What are the key findings?"}
    ],
    model="claude-sonnet-4-20250514"
)
print(response.content)

Multi-turn conversation

from gushwork_rag import Message

messages = [
    Message(role="user", content="What is the document about?"),
    Message(role="assistant", content="The document discusses AI technologies."),
    Message(role="user", content="What are the main benefits mentioned?"),
]

response = client.chat.create(
    namespace="my-documents",
    messages=messages,
    model="gpt-4"
)
print(response.content)

Streaming chat

# Stream responses in real-time
for chunk in client.chat.stream(
    namespace="my-documents",
    messages=[{"role": "user", "content": "Summarize the document"}],
    model="claude-sonnet-4-20250514"
):
    content = chunk.get("content", "")
    print(content, end="", flush=True)
print()  # New line at the end

Structured output

# Get responses in a specific JSON format
response = client.chat.create(
    namespace="my-documents",
    messages=[{"role": "user", "content": "Extract key information"}],
    model="gpt-4",
    response_format={
        "type": "json_schema",
        "json_schema": {
            "name": "document_summary",
            "schema": {
                "type": "object",
                "properties": {
                    "title": {"type": "string"},
                    "summary": {"type": "string"},
                    "key_points": {
                        "type": "array",
                        "items": {"type": "string"}
                    }
                },
                "required": ["title", "summary", "key_points"]
            }
        }
    }
)
print(response.content)  # Returns a dictionary matching the schema

Advanced retrieval options

from gushwork_rag import RetrievalType

response = client.chat.create(
    namespace="my-documents",
    messages=[{"role": "user", "content": "What are the conclusions?"}],
    model="claude-sonnet-4-20250514",
    retrieval_type=RetrievalType.GEMINI,  # or RetrievalType.SIMPLE
    top_k=10,  # Number of top results to retrieve
    top_n=5,   # Number of top chunks to return
    top_p=0.9  # Top-p sampling parameter
)

Assistant workflows

The assistant wrapper adds retries, cached namespace metadata, and an opinionated upload_s3_folder() helper that deduplicates against existing files before downloading from S3.

# Create an assistant using AssistantCreator
assistant = client.assistant

# Create a new assistant
assistant = assistant.create_assistant(
    assistant_name="my-assistant",
    instructions="Answer questions based on the provided documents."
)

# Get an assistant
assistant = client.assistant("my-assistant")

# Generate a response (with automatic retries)
response = assistant.generate_response(
    prompt="What is this document about?",
    model="claude-sonnet-4-20250514",
    max_retries=3  # Optional: number of retries on failure
)
print(response)

# List files in the assistant
files = assistant.list_files(limit=50, skip=0)
print(f"Total files: {files.total}")
for file in files.files:
    print(f"- {file.file_name}")

# Upload files from S3 folder (with deduplication)
assistant.upload_s3_folder(
    bucket_name="my-bucket",
    folder_path="documents/folder",
    exclude=["file1.pdf", "file2.pdf"],  # Optional: files to exclude
    max_workers=10,  # Parallel upload workers
    rate_limit_delay=5.0,  # Delay between uploads (seconds)
)

# Delete the assistant
assistant.delete_assistant()

API key management (requires ADMIN access)

from gushwork_rag import APIAccess

# Create a new API key
api_key = client.auth.create_api_key(
    key_name="production-key",
    access=APIAccess.READ_WRITE
)
print(f"New API Key: {api_key.api_key}")
# Save this key securely!

# List all API keys
keys = client.auth.list_api_keys()
for key in keys:
    print(f"{key.key_name}: {key.access} (Last used: {key.last_used})")

# Delete an API key
client.auth.delete_api_key(api_key_id="key_123")

API Reference

GushworkRAG

Main client class for interacting with the API.

Properties:

• namespaces - NamespacesClient for managing namespaces
• files - FilesClient for managing files
• chat - ChatClient for chat completions
• auth - AuthClient for API key management
• assistant_ - Assistant for creating assistants

Methods:

• health_check() - Check API health
• assistant(assistant_name) - Get an Assistant client for a specific assistant
• close() - Close the HTTP session

AssistantCreator

Create and manage assistants (namespaces).

Methods:

• create_assistant(assistant_name, instructions) - Create a new assistant

Assistant

Manage a specific assistant (namespace).

Methods:

• generate_response(prompt, model, max_retries) - Generate a response with automatic retries
• list_files(limit, skip) - List files in the assistant
• upload_s3_folder(bucket_name, folder_path, exclude, max_workers, rate_limit_delay) - Upload files from S3
• delete_assistant() - Delete the assistant
• name - Property: Get the assistant name
• namespace - Property: Get the namespace object

NamespacesClient

Manage document namespaces.

Methods:

• create(name, instructions) - Create a namespace
• list() - List all namespaces
• get(namespace_id) - Get a namespace by ID
• update(namespace_id, instructions) - Update a namespace
• delete(namespace_id) - Delete a namespace

FilesClient

Manage files and documents.

Methods:

• upload(file_path, namespace, mime_type) - Upload a file
• get(file_id) - Get file details
• list_by_namespace(namespace, limit, skip) - List files in a namespace
• update_status(file_id, status, ...) - Update file status
• delete(file_id) - Delete a file

ChatClient

Chat completions with RAG.

Methods:

• create(namespace, messages, model, **kwargs) - Get a chat completion
• stream(namespace, messages, model, **kwargs) - Stream a chat completion
• completions(namespace, messages, model, **kwargs) - Generic completion method

AuthClient

Manage API keys (requires ADMIN access).

Methods:

• create_api_key(key_name, access) - Create a new API key
• list_api_keys() - List all API keys
• delete_api_key(api_key_id) - Delete an API key

Architecture

Design Philosophy

The SDK follows these principles:

1. Pythonic API: Clean, intuitive interfaces following Python best practices
2. Type Safety: Full type hints for IDE integration and type checking
3. Resource-Based: Organized around resources (files, namespaces, etc.)
4. Modular: Separation of concerns with sub-clients
5. Error Handling: Clear, descriptive exceptions
6. Extensible: Easy to add new features

Architecture Overview

┌─────────────────────────────────────────────────┐
│              GushworkRAG (Main Client)          │
│  ┌───────────────────────────────────────────┐  │
│  │         HTTPClient (HTTP Layer)           │  │
│  └───────────────────────────────────────────┘  │
│                      │                          │
│       ┌──────────────┼──────────────┐           │
│       │              │              │           │
│  ┌─────────┐   ┌─────────┐   ┌─────────┐        │
│  │ Files   │   │  Chat   │   │  Auth   │ ...    │
│  │ Client  │   │ Client  │   │ Client  │        │
│  └─────────┘   └─────────┘   └─────────┘        │
└─────────────────────────────────────────────────┘
         │              │              │
         └──────────────┼──────────────┘
                        │
                   API Server

Core Components

1. Main Client (GushworkRAG)

The entry point for all SDK operations. Similar to Pinecone's main client.

Responsibilities:

• Initialize HTTP client
• Provide access to sub-clients
• Manage session lifecycle

Design Pattern: Facade Pattern

class GushworkRAG:
    def __init__(self, api_key, base_url):
        self._http = HTTPClient(api_key, base_url)
        self._files = FilesClient(self._http)
        self._chat = ChatClient(self._http)
        # ... more clients

    @property
    def files(self) -> FilesClient:
        return self._files

Comparison with Pinecone:

# Pinecone
client = Pinecone(api_key="...")
index = client.Index("index-name")

# Gushwork RAG
client = GushworkRAG(api_key="...")
files = client.files

2. HTTP Client (HTTPClient)

Handles all HTTP communication with the API.

Responsibilities:

• Make HTTP requests
• Handle authentication
• Error handling and retry logic
• Streaming support

Design Pattern: Adapter Pattern

class HTTPClient:
    def request(self, method, endpoint, data=None):
        # Handle request, errors, etc.
        pass

    def request_stream(self, method, endpoint, data=None):
        # Handle streaming responses
        pass

3. Sub-Clients

Resource-specific clients for different API operations.

FilesClient – Manages file operations: upload (with S3 presigned URLs), list, get, update status, delete.

ChatClient – Handles chat completions: create, stream, structured output, retrieval options.

NamespacesClient – Manages namespaces: create, list/get, update, delete.

AuthClient – API key management: create, list, delete.

4. Models (models.py)

Data classes representing API resources (DTOs): type hints, from_dict() parsing, enums for status, DateTime handling.

5. Exceptions (exceptions.py)

Custom exception hierarchy:

GushworkError (Base)
├── AuthenticationError (401)
├── ForbiddenError (403)
├── NotFoundError (404)
├── BadRequestError (400)
└── ServerError (500)

Request Flow

1. User calls method on client → 2. Sub-client prepares request → 3. HTTPClient makes request → 4. Response parsed into model → 5. User receives typed object.

Design Decisions

• Sub-clients: Properties (client.files) for intuitive access, consistent with Pinecone.
• Models: Dataclasses (built-in, no extra deps) with manual from_dict().
• Concurrency: Synchronous for now; async can be added later.
• Errors: Custom exception hierarchy for specific handling and status codes.
• Streaming: Iterator pattern for memory efficiency and natural for loops.

Extensibility

To add new endpoints: (1) add a model and from_dict() if needed, (2) add a resource client that uses HTTPClient, (3) register it on GushworkRAG as a property. Future directions: batch operations, async client, caching, retries, webhooks, metrics.

Testing Strategy

• Unit: Mock HTTPClient; test clients, model parsing, and error handling.
• Integration: Test against real API in dev; cover workflows and error cases.

Performance and Security

• Performance: requests.Session for connection pooling, streaming iterators, lazy client creation.
• Security: Never log API keys; use HTTPS; validate inputs; prefer environment variables for secrets.

Future Enhancements

Async support, retry with backoff, rate limiting, response caching, batch operations, webhooks, metrics, plugin system.

Development

Version Bumping

We use uv for automated version management. To bump the version:

# Bump patch version (0.2.2 -> 0.2.3)
uv version --bump patch

# Bump minor version (0.2.2 -> 0.3.0)
uv version --bump minor

# Bump major version (0.2.2 -> 1.0.0)
uv version --bump major

# Create a prerelease version (0.2.2 -> 0.2.3a1)
uv version --bump prerelease

The uv version --bump command automatically:

• Updates the version in pyproject.toml
• Updates uv.lock to reflect the new version

Publishing

Publishing is automated via GitHub Actions:

1. Go to Actions → "Publish Python SDK to PyPI"
2. Click "Run workflow"