Plimver Python SDK

Official Python SDK for the Plimver AI Platform - Unified LLM, RAG, and Memory API.

Installation

pip install plimver

Quick Start

from plimver import Plimver

client = Plimver(
    api_key="pk_live_your_api_key",
    workspace_id="ws_your_workspace_id"
)

response = client.chat("What is machine learning?")
print(response.text)

Features

• ✅ Type hints - Full type annotations with dataclasses
• ✅ Sync & Async - Both synchronous and async clients
• ✅ Streaming - Generator-based streaming responses
• ✅ Multimodal - Images, audio, and video support
• ✅ RAG - Document upload and vector search
• ✅ Memory - Conversation history management
• ✅ Retries - Built-in retry with exponential backoff
• ✅ 22+ LLM Providers - OpenAI, Anthropic, Google, Groq, and more

Usage Examples

Basic Chat

# Simple message
response = client.chat("Hello!")
print(response.text)

# With options
response = client.chat(
    "Explain quantum computing",
    mode="chat_and_rag",  # Use RAG for context
    model="gpt-4o",       # Specific model
    temperature=0.5,
    user_id="user-123"    # Isolate conversation
)

Conversation History

from plimver import Message

response = client.chat_with_messages([
    Message(role="user", content="My name is Alice"),
    Message(role="assistant", content="Nice to meet you, Alice!"),
    Message(role="user", content="What is my name?"),
])

print(response.text)  # "Your name is Alice"

Streaming

# Stream response token by token
for chunk in client.stream("Tell me a story"):
    print(chunk.content or "", end="", flush=True)

# With options
for chunk in client.stream("Write a poem", mode="chat_only"):
    if chunk.done:
        print(f"\n\nDone!")
    else:
        print(chunk.content or "", end="", flush=True)

Async Client

from plimver import AsyncPlimver
import asyncio

async def main():
    async with AsyncPlimver(
        api_key="pk_live_...",
        workspace_id="ws_..."
    ) as client:
        response = await client.chat("Hello!")
        print(response.text)
        
        # Async streaming
        async for chunk in client.stream("Tell me a joke"):
            print(chunk.content or "", end="", flush=True)

asyncio.run(main())

Vision (Images)

# Analyze an image
response = client.vision(
    "What do you see in this image?",
    "https://example.com/photo.jpg"
)

# With base64
response = client.vision(
    "Describe this diagram",
    "data:image/png;base64,iVBORw0KGgo..."
)

Audio

# Transcribe or analyze audio (requires Gemini 1.5 Pro+)
response = client.audio(
    "Transcribe this audio and summarize it",
    "https://example.com/audio.mp3",
    model="gemini-1.5-pro"
)

Video

# Analyze video (requires Gemini 1.5 Pro+)
response = client.video(
    "What happens in this video?",
    "https://example.com/video.mp4",
    model="gemini-1.5-pro"
)

RAG Documents

# Upload a document
doc = client.documents.upload(
    "Plimver is an AI platform that provides unified LLM access...",
    source="about-plimver.txt"
)

# Upload a file
doc = client.documents.upload_file("document.pdf")

# List documents
docs = client.documents.list()
print(f"{len(docs)} documents indexed")

# Search documents
results = client.documents.search("what is plimver", limit=5)
for r in results:
    print(f"{r.source}: {r.score:.2f}")

# Delete a document
client.documents.delete("about-plimver.txt")

Memory Management

# Get conversation history for a user
messages = client.memory.get("user-123", limit=50)

# Clear user's history
client.memory.clear("user-123")

# Clear all history (danger!)
client.memory.clear_all()

Routing Modes

# Auto (smart routing based on query)
client.chat("Hello", mode="auto")

# Chat only (no RAG, no memory)
client.chat("Hello", mode="chat_only")

# RAG only (search documents, no memory)
client.chat("What is in my docs?", mode="rag_only")

# Chat + RAG (full context)
client.chat("Summarize my documents", mode="chat_and_rag")

Error Handling

from plimver import Plimver, PlimverError

try:
    response = client.chat("Hello")
except PlimverError as e:
    print(f"API Error {e.status}: {e.message}")

Context Manager

# Automatically closes connection
with Plimver(api_key="...", workspace_id="...") as client:
    response = client.chat("Hello!")
    print(response.text)

Configuration

client = Plimver(
    # Required
    api_key="pk_live_...",
    workspace_id="ws_...",
    
    # Optional
    base_url="https://api.plimvr.tech",  # Custom API URL
    timeout=30.0,                         # Request timeout (seconds)
    max_retries=2,                        # Retry on failure
)

Response Objects

ChatResponse

@dataclass
class ChatResponse:
    text: str           # AI response
    model: str          # Model used
    provider: str       # Provider used
    mode: str           # Routing mode
    usage: Usage        # Token usage
    sources: List[Source]  # RAG sources
    metadata: dict      # Extra metadata

Usage

@dataclass
class Usage:
    prompt_tokens: int
    completion_tokens: int
    total_tokens: int

Supported Models

Provider	Models
OpenAI	gpt-4o, gpt-4o-mini, gpt-4-turbo
Anthropic	claude-3-5-sonnet, claude-3-opus
Google	gemini-2.0-flash, gemini-1.5-pro
Groq	llama-3.1-70b, mixtral-8x7b
DeepSeek	deepseek-chat, deepseek-coder
xAI	grok-beta
+ 15 more...

License

MIT © Plimver Team