wetro 0.1.12

Wetrocloud's Official SDK

Wetrocloud SDK

A powerful Python SDK for interacting with Wetrocloud's suite of AI and data processing services.

Table of Contents

• Installation
• Authentication
• Core Concepts
• Modules
  • Wetrocloud Client
  • RAG Module
  • Tools Module
• Examples
  • RAG Examples
  • Tools Examples
• Error Handling
• Advanced Usage
• API Reference
• Troubleshooting

Installation

pip install wetro

Authentication

Authentication with the Wetrocloud API requires an API key. You can provide this key when initializing any client.

from wetro import Wetrocloud

# Initialize the main client and access modules
client = Wetrocloud(api_key="your_api_key")
rag_client = client.rag
tools_client = client.tools

Core Concepts

The Wetrocloud SDK is organized into specialized modules that focus on different functionalities:

1. RAG (Retrieval-Augmented Generation): Manage collections of documents and query them using natural language.
2. Tools: Access utility functions including text generation, image processing, web extraction, and content categorization.

Each module can be used independently or together through the unified Wetrocloud client.

Modules

Wetrocloud Client

The main entry point that provides access to all functionality in the SDK.

from wetro import Wetrocloud

client = Wetrocloud(api_key="your_api_key")

# Access modules
rag_client = client.rag
tools_client = client.tools

RAG Module

The RAG (Retrieval-Augmented Generation) module allows you to create, manage, and query collections of documents.

Key Features

• Create and manage document collections
• Insert documents from various sources (web, text, files)
• Query collections using natural language
• Chat with context from your collections
• Structured output formatting with JSON schemas

Basic Usage

from wetro import WetroRAG

# Initialize RAG client
rag_client = WetroRAG(api_key="your_api_key")

# Get or create a collection
rag_client.collection.get_or_create_collection_id(collection_id="my_unique_collection_id")

# Create a collection
rag_client.collection.create_collection_id(collection_id="my_unique_collection_id")

# Get all collections
all_collection_resp = rag_client.collection.get_all()

# Insert a web resource
insert_response = rag_client.collection.insert(collection_id="my_unique_collection_id",resource="https://example.com/article", type="web")
print("Insert response: %s", insert_response)

# Query the collection
query_response = rag_client.collection.query(collection_id="my_unique_collection_id",request_query= "What are the key points of the article?")
print("Query response: %s", query_response)

Tools Module

The Tools module provides access to various AI-powered utilities.

Key Features

• Text generation with different models
• Content categorization
• Image-to-text conversion (OCR)
• Web extraction with structured output

Basic Usage

from wetro import WetroTools

# Initialize Tools client
tools_client = WetroTools(api_key="your_api_key")

# Generate text
generate_response = tools_client.generate_text(
    messages=[{"role": "user", "content": "What is a large language model?"}],
    model="gpt-4"
)
print("Generate text response: %s", generate_response)

Examples

RAG Examples

Working with Collections

# Create or access a collection
rag_client.collection.create_collection_id(collection_id="research_papers")

# Insert documents from different sources
rag_client.collection.insert(collection_id="research_papers",resource="https://medium.com/@wetrocloud/why-legal-tech-needs-wetrocloud-ai-rag-and-the-future-of-legal-practice-66fb38c4df09", type="web")
rag_client.collection.insert(resource ="This is a sample text document about AI.", type ="text")

Basic Querying

# Simple query
response = rag_client.collection.query(collection_id="research_papers",request_query="What are the main findings in the research?")
print(response)

Specify Model

# Define Model to get response from the specific model
response = rag_client.collection.query(
    collection_id="research_papers",
    request_query="Give me a detailed summary of the article",
    model="gpt-3.5-turbo"
)

print(response)

Structured Output with JSON Schema

# Define a JSON schema for structured output
json_schema = [{"point_number": "<int>", "point": "<str>"}]

# Add processing rules
rules = ["Only 5 points", "Strictly return JSON only"]

# Query with structured output requirements
response = rag_client.collection.query(
    collection_id="research_papers",
    request_query= "What are the key points of the article?",
    json_schema = json_schema,
    json_schema_rules = rules
)
print(response)

Streaming Responses

# Stream responses for long-form content
streaming_response = rag_client.collection.query(
    collection_id="research_papers",
    request_query = "Give me a detailed summary of the article",
    stream=True
)

# Process streaming response
for chunk in streaming_response:
    print(chunk.response, end="")

Note: Streaming is not supported with Structured Output with JSON Schema, it's one or the other

Conversational Context

# Create a chat history
chat_history = [
    {"role": "user", "content": "What is this collection about?"}, 
    {"role": "system", "content": "It stores research papers on AI technology."}
]

# Continue the conversation with context
chat_response = rag_client.collection.chat(
    collection_id="research_papers",
    message = "Can you explain the latest paper's methodology?",
    chat_history = chat_history
)
print(chat_response)

Tools Examples

Text Generation

# Generate text with a specific model
response = tools_client.generate_text(
    messages=[
        {"role": "system", "content": "You are a helpful assistant."}, 
        {"role": "user", "content": "Write a short poem about technology."}
    ],
    model="gpt-4"
)
print(response)

Content Categorization

# Categorize content
categorize_response = tools_client.categorize(
    resource="match review: John Cena vs. The Rock.",
    type="text",
    json_schema='{"label": "string"}',
    categories=["wrestling", "entertainment", "sports", "news"]
)
print(categorize_response)

Image to Text (OCR)

# Extract text from an image and answer questions about it
ocr_response = tools_client.image_to_text(
    image_url = "https://images.squarespace-cdn.com/content/v1/54822a56e4b0b30bd821480c/45ed8ecf-0bb2-4e34-8fcf-624db47c43c8/Golden+Retrievers+dans+pet+care.jpeg",
    request_query="What animal is in this image?"
)
print(ocr_response)

Web Extraction

# Extract structured data from a website
extract_response = tools_client.extract(
    website="https://www.forbes.com/real-time-billionaires/",
    json_schema='[{"name": "<name>", "networth": "<networth>"}]'
)
print(extract_response)

Error Handling

The SDK uses standardized error handling. All API calls may raise exceptions derived from WetrocloudError.

from wetro import Wetrocloud, WetrocloudError

client = Wetrocloud(api_key="your_api_key")

try:
    response = client.rag.collection.query("What is this article about?")
except WetrocloudError as e:
    print(f"Error: {e.status_code} - {e.message}")
    # Handle specific error cases
    if e.status_code == 401:
        print("Authentication failed. Check your API key.")
    elif e.status_code == 404:
        print("Collection not found. Create a collection first.")

Advanced Usage

Custom Domain

client = Wetrocloud(
    api_key="your_api_key",
    base_url="custom_url"
)

Configuring Request Timeouts

client = Wetrocloud(
    api_key="your_api_key",
    timeout=30  # 30 seconds timeout
)

Using with Async Frameworks (Coming Soon)

import asyncio
from wetro import AsyncWetrocloud

async def main():
    client = AsyncWetrocloud(api_key="your_api_key")
    response = await client.rag.collection.query("What are the key insights?")
    return response

result = asyncio.run(main())

API Reference

WetroRAG Methods

collection.get_or_create_collection_id(collection_id)

Sets the current collection ID or creates a new collection.

collection.create_collection_id(collection_id)

Creates a new collection.

collection.get_all()

Gets all existing colllections

collection.insert(collection_id, resource, type)

Inserts a document into the collection.

• collection_id: Collection ID
• resource: URL, text content, or file path
• type: "web", "text", "json", or "file"

collection.query(collection_id, request_query, model=None, json_schema=None, json_schema_rules=None, stream=False)

Queries the collection.

• collection_id: Collection ID
• request_query: Natural language query
• model: Optional model name (e.g., "gpt-3.5-turbo", "gpt-4")
• json_schema: Optional JSON schema for structured output
• json_schema_rules: Optional list of processing rules
• stream: Boolean to enable response streaming

collection.chat(collection_id, message, chat_history=None, model=None, stream=False)

Chat with context from the collection.

• collection_id: Collection ID
• message: Current user message
• chat_history: List of previous message dictionaries
• model: Optional model name
• stream: Boolean to enable response streaming

collection.delete_resource(collection_id, resource_id)

Delete a resource from the collection.

• collection_id: Collection ID
• resource_id: Current user message

collection.delete(collection_id)

Delete the collection.

• collection_id: Collection ID

WetroTools Methods

generate_text(messages, model=None)

Generates text using a specified model.

• messages: List of message dictionaries
• model: Model name (e.g., "gpt-3.5-turbo", "gpt-4")

categorize(resource, type, json_schema, categories, prompt)

Categorizes content according to provided categories.

• resource: Content to categorize
• type: "text", "url", etc.
• json_schema: Schema for structured output
• categories: List of category options
• prompt: Prompt to instruction or inform the LLM on what and how to perfom the categorization

image_to_text(image_url, request_query=None)

Extracts text from images and optionally answers questions about the content.

• image_url: URL of the image
• request_query: Optional question about the image content

extract(website, json_schema)

Extracts structured data from websites.

• website: URL to extract from
• json_schema: Schema defining the data structure to extract

Troubleshooting

Common Issues

1. Authentication Errors

   Error: 401 - Invalid API key
   

   Solution: Verify your API key is correct and has the necessary permissions.

2. Collection Not Found

   Error: 404 - Collection not found
   

   Solution: Use get_or_create_collection_id() before querying.

3. Rate Limiting

   Error: 429 - Too many requests
   

   Solution: Implement backoff and retry logic for high-volume operations.

Support

For additional support, please contact [e-mail] or visit our documentation at https://docs.wetrocloud.com.