vectra-rag-py 0.9.11

A production-ready, provider-agnostic Python SDK for End-to-End RAG pipelines.

Vectra (Python)

Vectra is a production-grade, provider-agnostic Python SDK for building end-to-end Retrieval-Augmented Generation (RAG) systems. It is designed for teams that need correctness, extensibility, async performance, and observability across embeddings, vector databases, retrieval strategies, and LLM providers.

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Table of Contents

• 1. Overview
• 2. Design Goals & Philosophy
• 3. Feature Matrix
• 4. Installation
• 5. Quick Start
• 6. Core Concepts
  • Providers
  • Vector Stores
  • Chunking
  • Retrieval
  • Reranking
  • Metadata Enrichment
  • Query Planning & Grounding
  • Conversation Memory
• 7. Configuration Reference (Usage-Driven)
• 8. Ingestion Pipeline
• 9. Querying & Streaming
• 10. Conversation Memory
• 11. Evaluation & Quality Measurement
• 12. CLI
  • Ingest & Query
  • WebConfig (Config Generator UI)
  • Observability Dashboard
• 13. Observability & Callbacks
• 14. Telemetry
• 15. Database Schemas & Indexing
• 16. Extending Vectra
• 17. Architecture Overview
• 18. Development & Contribution Guide
• 19. Production Best Practices

---

1. Overview

Vectra implements a fully modular RAG pipeline:

Load → Chunk → Embed → Store → Retrieve → Rerank → Plan → Ground → Generate → Stream

Vectra SDK – End-to-End RAG Architecture

All stages are explicitly configured, async-first, and observable.

Key Characteristics

• Async-first API (asyncio)
• Provider-agnostic embeddings & LLMs
• Multiple vector backends (Postgres, Chroma, Qdrant, Milvus)
• Advanced retrieval (HyDE, Multi-Query, Hybrid RRF, MMR)
• Unified streaming interface
• Built-in evaluation and observability
• CLI + SDK parity

---

2. Design Goals & Philosophy

Explicitness over Magic

Vectra avoids hidden defaults. Chunking, retrieval, grounding, memory, and generation behavior are always explicit and validated.

Production-First

Index helpers, rate limiting, embedding cache, observability, and evaluation are first-class features.

Provider Neutrality

Switching providers (OpenAI ↔ Gemini ↔ Anthropic ↔ Ollama) requires no application code changes.

Extensibility

All major subsystems are interface-driven and designed to be extended safely.

---

3. Feature Matrix

Providers

• Embeddings: OpenAI, Gemini, Ollama, HuggingFace
• Generation: OpenAI, Gemini, Anthropic, Ollama, OpenRouter, HuggingFace
• Streaming: Async generators with normalized output

Vector Stores

• PostgreSQL (Prisma + pgvector)
• ChromaDB
• Qdrant
• Milvus

Retrieval Strategies

• Naive cosine similarity
• HyDE (Hypothetical Document Embeddings)
• Multi-Query expansion (RRF)
• Hybrid semantic + lexical (RRF)
• MMR diversification

---

4. Installation

Library

pip install vectra-py
# or
uv pip install vectra-py

Backends

# Prisma Client Python – https://prisma.brendonovich.dev
pip install prisma-client-py
# ChromaDB – https://docs.trychroma.com
pip install chromadb
# Qdrant Python Client – https://qdrant.tech/documentation
pip install qdrant-client
# Milvus Python SDK – https://milvus.io/docs
pip install pymilvus

CLI

vectra --help
# alternative
python -m vectra.cli --help

Requirements

Vectra depends on: pydantic, asyncio, prisma-client-py, chromadb, openai, google-generativeai, anthropic, pypdf, mammoth, openpyxl

---

5. Quick Start

import asyncpg
from vectra import VectraClient, VectraConfig, ProviderType

pool = await asyncpg.create_pool(os.getenv('DATABASE_URL'))

config = VectraConfig(
    embedding={
        'provider': ProviderType.OPENAI,
        'api_key': os.getenv('OPENAI_API_KEY'),
        'model_name': 'text-embedding-3-small'
    },
    llm={
        'provider': ProviderType.GEMINI,
        'api_key': os.getenv('GOOGLE_API_KEY'),
        'model_name': 'gemini-2.5-flash'
    },
    database={
        'type': 'postgres',
        'client_instance': pool,
        'table_name': 'document',
        'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'vector' }
    }
)

client = VectraClient(config)
await client.ingest_documents('./docs')
result = await client.query_rag('What is the vacation policy?')
print(result['answer'])

---

6. Core Concepts

Providers

Providers implement embeddings, generation, or both. Vectra normalizes responses and streaming across providers.

Vector Stores

Vector stores persist embeddings and metadata. Backends are swappable via configuration.

Chunking

• Recursive: Token-aware, separator-aware splitting
• Agentic: LLM-driven semantic propositions

Retrieval

Configurable strategies to balance recall, precision, and latency.

Reranking

Optional LLM-based reordering of candidate chunks.

Metadata Enrichment

Optional per-chunk summaries, keywords, and hypothetical questions generated during ingestion.

Query Planning & Grounding

Controls context assembly and factual grounding constraints.

Conversation Memory

Persist multi-turn chat history across sessions.

---

7. Configuration Reference (Usage-Driven)

All configuration is validated using Pydantic at runtime.

Embedding

embedding={
  'provider': ProviderType.OPENAI,
  'api_key': os.getenv('OPENAI_API_KEY'),
  'model_name': 'text-embedding-3-small',
  'dimensions': 1536
}

Use dimensions when using pgvector to avoid runtime mismatches.

---

LLM

llm={
  'provider': ProviderType.GEMINI,
  'api_key': os.getenv('GOOGLE_API_KEY'),
  'model_name': 'gemini-2.5-flash',
  'temperature': 0.3,
  'max_tokens': 1024
}

Used for generation

---

Database

Supports Prisma, Chroma, Qdrant, Milvus.

# PostgreSQL (native asyncpg)
database={
  'type': 'postgres',
  'client_instance': pg_pool,  # asyncpg.Pool or Connection
  'table_name': 'document',
  'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'vector' }
}

# Prisma (Postgres via prisma-client-py)
database={
  'type': 'prisma',
  'client_instance': prisma,
  'table_name': 'Document',
  'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'embedding' }
}

# ChromaDB
database={
  'type': 'chroma',
  'client_instance': chroma_client,  # chromadb.Client or PersistentClient
  'table_name': 'rag_collection',
  'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'embedding' }
}

# Qdrant
database={
  'type': 'qdrant',
  'client_instance': qdrant_client,  # qdrant_client.QdrantClient
  'table_name': 'rag_collection',
  'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'embedding' }
}

# Milvus
database={
  'type': 'milvus',
  'client_instance': milvus_client,  # pymilvus client
  'table_name': 'rag_collection',
  'column_map': { 'content': 'content', 'metadata': 'metadata', 'vector': 'embedding' }
}

---

Chunking

chunking={
  'strategy': ChunkingStrategy.RECURSIVE,
  'chunk_size': 1000,
  'chunk_overlap': 200
}

Agentic:

chunking={
  'strategy': ChunkingStrategy.AGENTIC,
  'agentic_llm': {
    'provider': ProviderType.OPENAI,
    'api_key': os.getenv('OPENAI_API_KEY'),
    'model_name': 'gpt-4o-mini'
  }
}

---

Retrieval

retrieval={ 'strategy': RetrievalStrategy.HYBRID }

Hybrid is recommended for production workloads.

---

Reranking

reranking={
  'enabled': True,
  'window_size': 20,
  'top_n': 5
}

---

Memory

memory={ 'enabled': True, 'type': 'in-memory', 'max_messages': 20 }

Redis and Postgres are supported.

# Redis
memory={
  'enabled': True,
  'type': 'redis',
  'max_messages': 20,
  'redis': {
    'client_instance': redis_client,
    'key_prefix': 'vectra:chat:'
  }
}

# Postgres
memory={
  'enabled': True,
  'type': 'postgres',
  'max_messages': 20,
  'postgres': {
    'client_instance': pg_pool,  # asyncpg.Pool or Connection
    'table_name': 'ChatMessage',
    'column_map': {
      'sessionId': 'sessionId',
      'role': 'role',
      'content': 'content',
      'createdAt': 'createdAt'
    }
  }
}

---

Observability

observability={
  'enabled': True,
  'sqlite_path': 'vectra-observability.db'
}

---

8. Ingestion Pipeline

await client.ingest_documents('./documents')

• Files or directories supported
• Recursive traversal
• Embedding cache via SHA256
• Optional rate limiting

Supported formats: PDF, DOCX, XLSX, TXT, Markdown

---

9. Querying & Streaming

Standard:

res = await client.query_rag('Refund policy?')

Streaming:

stream = await client.query_rag('Draft email', stream=True)
async for chunk in stream:
    print(chunk.get('delta', ''), end='')

---

10. Conversation Memory

Pass a session_id to preserve multi-turn context.

---

11. Evaluation & Quality Measurement

await client.evaluate([
  { 'question': 'Capital of France?', 'expected_ground_truth': 'Paris' }
])

Metrics: Faithfulness, Relevance

---

12. CLI

Ingest & Query

vectra ingest ./docs --config=./config.json
vectra query "What are the payment terms?" --config=./config.json --stream

---

WebConfig (Config Generator UI)

vectra webconfig

Launches a local web UI to interactively generate and validate vectra.config.json.

---

Observability Dashboard

vectra dashboard

Launches a local dashboard for metrics, traces, and session analysis.

---

13. Observability & Callbacks

Tracks metrics, traces, and chat sessions when enabled.

Callbacks allow hooking into ingestion, retrieval, reranking, and generation stages.

---

14. Telemetry

Vectra collects anonymous usage data to help us improve the SDK, prioritize features, and detect broken versions.

What we track

• Identity: A random UUID (distinct_id) stored locally in ~/.vectra/telemetry.json. No PII, emails, IPs, or hostnames.
• Events:
  • sdk_initialized: Config shape (providers used), OS/Runtime version, session type (api/cli/chat).
  • ingest_started/completed: Source type, chunking strategy, duration bucket, chunk count bucket.
  • query_executed: Retrieval strategy, query mode (rag), result count, latency bucket.
  • feature_used: WebConfig/Dashboard usage.
  • evaluation_run: Dataset size bucket.
  • error_occurred: Error type and stage (no stack traces).
  • cli_command_used: Command name and flags.

Why we track it

• Detect broken versions: Spikes in error_occurred help us find bugs.
• Measure adoption: Helps us understand which providers (OpenAI vs Gemini) and vector stores are most popular.
• Drop support safely: We can see if anyone is still using Python 3.8 before dropping it.

How to opt-out

Telemetry is enabled by default. To disable it:

Option 1: Config

client = VectraClient(
    VectraConfig(
        # ...
        telemetry={'enabled': False}
    )
)

Option 2: Environment Variable Set VECTRA_TELEMETRY_DISABLED=1 or DO_NOT_TRACK=1.

---

15. Database Schemas & Indexing

model Document {
  id        String   @id @default(uuid())
  content   String
  metadata  Json
  embedding Unsupported("vector")?
  createdAt DateTime @default(now())
}

---

16. Extending Vectra

Implement custom vector stores by extending VectorStore.

---

17. Architecture Overview

Vectra follows a modular, provider-agnostic RAG architecture with clear separation of ingestion, retrieval, and generation pipelines.

---

18. Development & Contribution Guide

• Python 3.8+
• Async-first (asyncio)
• Pydantic-based configuration

---

19. Production Best Practices

• Match embedding dimensions to pgvector
• Prefer Hybrid retrieval
• Enable observability in staging
• Evaluate before changing chunk sizes

---

Vectra (Python) scales cleanly from local prototypes to production-grade RAG platforms.