RAG in 3 Lines of Python
FrameworkFreeGot tired of wiring up vector stores, embedding models, and chunking logic every time I needed RAG. So I built piragi. from piragi import Ragi kb = Ragi(\["./docs", "./code/\*\*/\*.py", "https://api.example.com/docs"\]) answer =
Capabilities5 decomposed
minimal-abstraction rag pipeline initialization
Medium confidenceAbstracts the boilerplate of RAG setup (document loading, embedding, vector storage, retriever instantiation) into a single function call with sensible defaults, eliminating the need for explicit orchestration of embedding models, vector databases, and retrieval chains. Uses a fluent or decorator-based API that auto-wires components based on input document type and query intent, reducing typical 50+ lines of LangChain/LlamaIndex setup to 3 lines.
Reduces RAG setup from 50+ lines of explicit component wiring (LangChain/LlamaIndex pattern) to 3 lines by auto-detecting document type, embedding model, and vector storage backend, then composing them into a retrieval chain without user intervention
Faster time-to-first-working-RAG than LangChain or LlamaIndex for prototypes, at the cost of production flexibility and customization
automatic document ingestion and chunking
Medium confidenceAutomatically detects document format (PDF, TXT, Markdown, JSON, CSV) and applies format-appropriate parsing and chunking strategies without explicit configuration. Likely uses file-type detection and pluggable parsers that handle encoding, structure extraction, and semantic-aware splitting (e.g., sentence or paragraph boundaries for text, table-aware chunking for structured data).
Combines format detection, parsing, and chunking into a single auto-wired step that infers optimal splitting strategy from document type, eliminating the need for separate loaders and splitters as in LangChain
Simpler than LangChain's multi-step loader + splitter pattern; less flexible than custom parsing pipelines but faster to implement
embedded vector storage with semantic search
Medium confidenceProvides built-in or tightly integrated vector storage (likely in-memory or lightweight persistent store like SQLite with vector extensions, or integration with free-tier services like Pinecone/Weaviate) that automatically embeds documents using a default embedding model and enables semantic similarity search without explicit vector DB setup. Likely uses cosine similarity or dot-product ranking to retrieve top-k most relevant chunks for a query.
Bundles vector storage and semantic search into the RAG abstraction, eliminating the need to instantiate a separate vector DB client or manage embedding/indexing separately, as required in LangChain or LlamaIndex
Faster to prototype than external vector DB setup; less scalable and feature-rich than production vector databases like Pinecone or Weaviate
llm-agnostic query answering with context injection
Medium confidenceAutomatically retrieves relevant document chunks and injects them into an LLM prompt (via a default prompt template) to generate answers, with support for multiple LLM providers (OpenAI, Anthropic, local models via Ollama) without requiring provider-specific code. Uses a standard prompt template that formats retrieved context and user query, then routes to the appropriate LLM API or local inference engine based on configuration.
Abstracts LLM provider selection and prompt template management into a single function, auto-routing to OpenAI/Anthropic/Ollama based on environment variables or config, eliminating boilerplate provider-specific code
Simpler than LangChain's LLMChain + PromptTemplate pattern; less customizable than hand-written prompts but faster to prototype
zero-configuration rag pipeline composition
Medium confidenceProvides a high-level API (likely a single function or class) that composes document loading, embedding, retrieval, and LLM generation into a single callable unit with no explicit step-by-step configuration. Uses sensible defaults for all intermediate steps (chunking strategy, embedding model, vector storage backend, prompt template, LLM provider) and allows optional overrides via keyword arguments or config objects.
Reduces RAG to a single function call with auto-wired defaults, vs LangChain/LlamaIndex which require explicit instantiation of loaders, splitters, embeddings, vector stores, retrievers, and chains
Dramatically faster to prototype than LangChain; production use requires migration to more flexible frameworks
Capabilities are decomposed by AI analysis. Each maps to specific user intents and improves with match feedback.
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Best For
- ✓solo developers building rapid prototypes
- ✓non-technical founders testing RAG feasibility
- ✓students learning RAG concepts without infrastructure complexity
- ✓developers building document-agnostic RAG systems
- ✓teams with heterogeneous document sources (PDFs, web content, databases)
- ✓rapid prototyping where format handling overhead is undesirable
- ✓prototyping and small-scale RAG (< 100k documents)
- ✓developers avoiding external vector DB dependencies
Known Limitations
- ⚠Opinionated defaults may not suit production workloads requiring custom embedding models, vector DB tuning, or specialized retrieval strategies
- ⚠Limited control over chunking strategy, overlap, or chunk size — likely uses fixed defaults
- ⚠No built-in persistence or state management — requires external vector DB for production use
- ⚠Abstractions likely add latency overhead vs hand-optimized pipelines
- ⚠Limited control over chunk size, overlap, or splitting strategy — uses fixed heuristics
- ⚠May struggle with complex PDF layouts, scanned images, or non-standard encodings
Requirements
Input / Output
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