lexigram-ai-rag 0.1.1

Retrieval-Augmented Generation (RAG) pipeline for the Lexigram Framework

lexigram-ai-rag

Retrieval-Augmented Generation (RAG) pipeline for the Lexigram Framework

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Overview

RAG (Retrieval-Augmented Generation) pipeline for the Lexigram Framework. Provides a multi-stage, fully configurable pipeline covering ingestion, query processing, retrieval, context optimisation, synthesis, quality assurance, and post-processing — all wired through the DI container via RAGModule. Zero-config usage starts with sensible defaults.

Full documentation: docs.lexigram.dev

Install

uv add lexigram-ai-rag
# Optional extras
uv add "lexigram-ai-rag[pdf,web]"

Quick Start

from lexigram import Application
from lexigram.di.module import Module, module

from lexigram.ai.rag import RAGModule
from lexigram.ai.rag.config import RAGConfig

@module(imports=[
    RAGModule.configure(
        RAGConfig(
            vector_store_type="pgvector",
            collection_name="my_docs",
            top_k=5,
            enable_citations=True,
        )
    )
])
class AppModule(Module):
    pass

app = Application(modules=[AppModule])
if __name__ == "__main__":
    app.run()

Configuration

Zero-config usage: Call RAGModule.configure() with no arguments to use defaults.

Option 1 — YAML file

# application.yaml
ai_rag:
  vector_store_type: "pgvector"
  collection_name: "my_docs"
  top_k: 5
  embedding_model: "text-embedding-ada-002"
  enable_citations: true

Option 2 — Profiles + Environment Variables (recommended)

export LEX_AI_RAG__VECTOR_STORE_TYPE=pgvector
# Environment variables for each field

Option 3 — Python

from lexigram.ai.rag.config import RAGConfig
from lexigram.ai.rag import RAGModule

config = RAGConfig(
    vector_store_type="pgvector",
    collection_name="my_docs",
    top_k=5,
)
RAGModule.configure(config)

Config reference

Field	Default	Env var	Description
vector_store_type	pgvector	LEX_AI_RAG__VECTOR_STORE_TYPE	Backend: pgvector, chroma, qdrant, mock
collection_name	default	LEX_AI_RAG__COLLECTION_NAME	Collection / index name
vector_dimension	1536	LEX_AI_RAG__VECTOR_DIMENSION	Embedding dimension
top_k	5	LEX_AI_RAG__TOP_K	Documents to retrieve per query
similarity_threshold	0.7	LEX_AI_RAG__SIMILARITY_THRESHOLD	Minimum similarity score to include
use_hybrid_search	True	LEX_AI_RAG__USE_HYBRID_SEARCH	Combine semantic + keyword search
embedding_provider	openai	LEX_AI_RAG__EMBEDDING_PROVIDER	Embedding provider
embedding_model	None	LEX_AI_RAG__EMBEDDING_MODEL	Embedding model
chunking_strategy	recursive	LEX_AI_RAG__CHUNKING_STRATEGY	recursive, semantic, or token
chunk_size	512	LEX_AI_RAG__CHUNK_SIZE	Tokens per chunk
chunk_overlap	50	LEX_AI_RAG__CHUNK_OVERLAP	Token overlap between chunks
enable_citations	True	LEX_AI_RAG__ENABLE_CITATIONS	Include source citations in responses
citation_style	inline	LEX_AI_RAG__CITATION_STYLE	inline, footnote, or numbered
enable_query_expansion	True	LEX_AI_RAG__ENABLE_QUERY_EXPANSION	Expand queries before retrieval
enable_hyde	False	LEX_AI_RAG__ENABLE_HYDE	Hypothetical Document Embeddings
synthesis_strategy	hybrid	LEX_AI_RAG__SYNTHESIS_STRATEGY	direct, extractive, abstractive, hybrid
tenancy.enabled	False	—	Enable per-tenant pipeline isolation

Module Factory Methods

Method	Description
RAGModule.configure(config)	Production pipeline
RAGModule.stub()	In-memory / no-op pipeline for tests

Key Features

• Multi-stage pipeline: Ingestion, query processing, retrieval, context optimization, synthesis, quality assurance, post-processing
• Chunking strategies: recursive, semantic, token, fixed_size, sliding_window
• Retrieval: Vector search, BM25 keyword search, knowledge graph traversal
• Reranking: Cross-encoder and LLM-based rerankers
• Synthesis: Direct, extractive, abstractive, and hybrid synthesizers
• HyDE support: Hypothetical Document Embeddings for query expansion
• Citations: Inline, footnote, or numbered citation styles
• Quality assurance: Faithfulness check and hallucination detection

Multi-Tenancy

lexigram-ai-rag supports per-tenant pipeline isolation. When tenancy is enabled, the provider registers a TenantScopedRAGPipeline — a caching wrapper that builds a dedicated RAGPipelineProtocol per tenant, with a tenant-resolved collection_name.

Configuration

from lexigram.ai.rag.config import RAGConfig, RAGTenancyConfig, RAGModule

config = RAGConfig(
    tenancy=RAGTenancyConfig(enabled=True),
    collection_name="my_docs",
)
RAGModule.configure(config)

Per-Tenant Collection Name

Use RAGConfig.with_collection() to create configs scoped to different collection names — handy for dynamic per-tenant pipeline construction:

tenant_config = RAGConfig().with_collection("tenant_a_collection")

Components

Component	Role
RAGTenancyConfig	Dataclass with enabled flag
TenantScopedRAGPipeline	Caches per-tenant pipelines (LRU eviction)
TemplatedTenantCollectionResolver	Resolves logical → physical collection name per tenant

Testing

async with Application.boot(modules=[RAGModule.stub()]) as app:
    # your test code
    ...

Key Source Files

File	What it contains
src/lexigram/ai/rag/module.py	RAGModule.configure() and RAGModule.stub()
src/lexigram/ai/rag/config.py	RAGConfig, RAGTenancyConfig, PipelineConfig, all stage configs
src/lexigram/ai/rag/di/provider.py	RAGProvider — registers pipeline and supporting services
src/lexigram/ai/rag/pipeline/	Stage executor and pipeline runner
src/lexigram/ai/rag/tenancy/	TenantScopedRAGPipeline factory + resolver
src/lexigram/ai/rag/exceptions.py	Full exception hierarchy
src/lexigram/ai/rag/protocols.py	Package-local protocol extensions
src/lexigram/ai/rag/types.py	RAG-specific domain types