datapizza-ai
Build reliable Gen AI solutions without overhead 🍕
Stars: 2126
Datapizza AI is a Python-based GenAI framework designed for speed, providing an API-first design with multi-provider support, tool integration, memory management, and observability features. It offers less abstraction, more control, and is vendor-agnostic, allowing easy model swapping and clear interfaces. The framework enables building sophisticated AI systems, document processing, RAG pipelines, and more, with a focus on observability and performance monitoring. Datapizza AI is trusted by engineers for its predictability, fast debugging, and reliable code in production.
README:
Build reliable Gen AI solutions without overhead
Written in Python. Designed for speed. A no-fluff GenAI framework that gets your agents from dev to prod, fast
🏠Homepage • 🚀 Quick Start • 📖 Documentation • 🎯 Examples • 🤝 Community
A framework that keeps your agents predictable, your debugging fast, and your code trusted in production. Built by Engineers, trusted by Engineers.
pip install datapizza-aifrom datapizza.clients.openai import OpenAIClient
client = OpenAIClient(api_key="YOUR_API_KEY")
result = client.invoke("Hi, how are u?")
print(result.text)
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# Core framework
pip install datapizza-ai
# With specific providers (optional)
pip install datapizza-ai-clients-openai
pip install datapizza-ai-clients-google
pip install datapizza-ai-clients-anthropicfrom datapizza.agents import Agent
from datapizza.clients.openai import OpenAIClient
from datapizza.tools import tool
@tool
def get_weather(city: str) -> str:
return f"The weather in {city} is sunny"
client = OpenAIClient(api_key="YOUR_API_KEY")
agent = Agent(name="assistant", client=client, tools = [get_weather])
response = agent.run("What is the weather in Rome?")
# output: The weather in Rome is sunnyA key requirement for principled development of LLM applications over your data (RAG systems, agents) is being able to observe and debug.
Datapizza-ai provides built-in observability with OpenTelemetry tracing to help you monitor performance and understand execution flow.
pip install datapizza-ai-tools-duckduckgofrom datapizza.agents import Agent
from datapizza.clients.openai import OpenAIClient
from datapizza.tools.duckduckgo import DuckDuckGoSearchTool
from datapizza.tracing import ContextTracing
client = OpenAIClient(api_key="OPENAI_API_KEY")
agent = Agent(name="assistant", client=client, tools = [DuckDuckGoSearchTool()])
with ContextTracing().trace("my_ai_operation"):
response = agent.run("Tell me some news about Bitcoin")
# Output shows:
# ╭─ Trace Summary of my_ai_operation ──────────────────────────────────╮
# │ Total Spans: 3 │
# │ Duration: 2.45s │
# │ ┏━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓ |
# │ ┃ Model ┃ Prompt Tokens ┃ Completion Tokens ┃ Cached Tokens ┃ |
# │ ┡━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩ |
# │ │ gpt-4o-mini │ 31 │ 27 │ 0 │ |
# │ └─────────────┴───────────────┴───────────────────┴───────────────┘ |
# ╰─────────────────────────────────────────────────────────────────────╯Build sophisticated AI systems where multiple specialized agents collaborate to solve complex tasks. This example shows how to create a trip planning system with dedicated agents for weather information, web search, and planning coordination.
# Install DuckDuckGo tool
pip install datapizza-ai-tools-duckduckgofrom datapizza.agents.agent import Agent
from datapizza.clients.openai import OpenAIClient
from datapizza.tools import tool
from datapizza.tools.duckduckgo import DuckDuckGoSearchTool
client = OpenAIClient(api_key="YOUR_API_KEY", model="gpt-4.1")
@tool
def get_weather(city: str) -> str:
return f""" it's sunny all the week in {city}"""
weather_agent = Agent(
name="weather_expert",
client=client,
system_prompt="You are a weather expert. Provide detailed weather information and forecasts.",
tools=[get_weather]
)
web_search_agent = Agent(
name="web_search_expert",
client=client,
system_prompt="You are a web search expert. You can search the web for information.",
tools=[DuckDuckGoSearchTool()]
)
planner_agent = Agent(
name="planner",
client=client,
system_prompt="You are a trip planner. You should provide a plan for the user. Make sure to provide a detailed plan with the best places to visit and the best time to visit them."
)
planner_agent.can_call([weather_agent, web_search_agent])
response = planner_agent.run(
"I need to plan a hiking trip in Seattle next week. I want to see some waterfalls and a forest."
)
print(response.text)Process and index documents for retrieval-augmented generation (RAG). This pipeline automatically parses PDFs, splits them into chunks, generates embeddings, and stores them in a vector database for efficient similarity search.
pip install datapizza-ai-parsers-doclingfrom datapizza.core.vectorstore import VectorConfig
from datapizza.embedders import ChunkEmbedder
from datapizza.embedders.openai import OpenAIEmbedder
from datapizza.modules.parsers.docling import DoclingParser
from datapizza.modules.splitters import NodeSplitter
from datapizza.pipeline import IngestionPipeline
from datapizza.vectorstores.qdrant import QdrantVectorstore
vectorstore = QdrantVectorstore(location=":memory:")
embedder = ChunkEmbedder(client=OpenAIEmbedder(api_key="YOUR_API_KEY", model_name="text-embedding-3-small"))
vectorstore.create_collection("my_documents",vector_config=[VectorConfig(name="embedding", dimensions=1536)])
pipeline = IngestionPipeline(
modules=[
DoclingParser(),
NodeSplitter(max_char=1024),
embedder,
],
vector_store=vectorstore,
collection_name="my_documents"
)
pipeline.run("sample.pdf")
results = vectorstore.search(query_vector = [0.0] * 1536, collection_name="my_documents", k=5)
print(results)Create a complete RAG pipeline that enhances AI responses with relevant document context. This example demonstrates query rewriting, embedding generation, document retrieval, and response generation in a connected workflow.
from datapizza.clients.openai import OpenAIClient
from datapizza.embedders.openai import OpenAIEmbedder
from datapizza.modules.prompt import ChatPromptTemplate
from datapizza.modules.rewriters import ToolRewriter
from datapizza.pipeline import DagPipeline
from datapizza.vectorstores.qdrant import QdrantVectorstore
openai_client = OpenAIClient(
model="gpt-4o-mini",
api_key="YOUR_API_KEY"
)
dag_pipeline = DagPipeline()
dag_pipeline.add_module("rewriter", ToolRewriter(client=openai_client, system_prompt="Rewrite user queries to improve retrieval accuracy."))
dag_pipeline.add_module("embedder", OpenAIEmbedder(api_key= "YOUR_API_KEY", model_name="text-embedding-3-small"))
dag_pipeline.add_module("retriever", QdrantVectorstore(host="localhost", port=6333).as_retriever(collection_name="my_documents", k=5))
dag_pipeline.add_module("prompt", ChatPromptTemplate(user_prompt_template="User question: {{user_prompt}}\n:", retrieval_prompt_template="Retrieved content:\n{% for chunk in chunks %}{{ chunk.text }}\n{% endfor %}"))
dag_pipeline.add_module("generator", openai_client)
dag_pipeline.connect("rewriter", "embedder", target_key="text")
dag_pipeline.connect("embedder", "retriever", target_key="query_vector")
dag_pipeline.connect("retriever", "prompt", target_key="chunks")
dag_pipeline.connect("prompt", "generator", target_key="memory")
query = "tell me something about this document"
result = dag_pipeline.run({
"rewriter": {"user_prompt": query},
"prompt": {"user_prompt": query},
"retriever": {"collection_name": "my_documents", "k": 3},
"generator":{"input": query}
})
print(f"Generated response: {result['generator']}")
 OpenAI |
Google Gemini |
Anthropic |
Mistral |
Azure OpenAI |
| Category | Components |
|---|---|
| 📄 Document Parsers | Azure AI Document Intelligence, Docling |
| 🔍 Vector Stores | Qdrant |
| 🎯 Rerankers | Cohere, Together AI |
| 🌐 Tools | DuckDuckGo Search, Custom Tools |
| 💾 Caching | Redis integration for performance optimization |
| 📊 Embedders | OpenAI, Google, Cohere, FastEmbed |
- 📖 Complete Documentation - Comprehensive guides and API reference
- 🎯 RAG Tutorial - Build production RAG systems
- 🤖 Agent Examples - Real-world agent implementations
We love contributions! Whether it's:
- 🐛 Bug Reports - Help us improve
- 💡 Feature Requests - Share your ideas
- 📝 Documentation - Make it better for everyone
- 🔧 Code Contributions - Build the future together
Check out our Contributing Guide to get started.
This project is licensed under the MIT License - see the LICENSE file for details.
Built by Datapizza, the AI native company
A framework made to be easy to learn, easy to maintain and ready for production 🍕
⭐ Star us on GitHub • 🚀 Get Started • 💬 Join Discord
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