openqueryagent 1.0.0

Open-source, database-agnostic query agent that translates natural language into vector database operations

OpenQueryAgent

Open-source, database-agnostic query agent for vector databases.

Translate natural language into precise vector database operations across multiple backends — with a single unified API.

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✨ Features

• 🔌 8 Vector Database Adapters — Qdrant, Milvus, pgvector, Weaviate, Pinecone, Chroma, Elasticsearch, AWS S3 Vectors
• 🧠 LLM-Powered Query Planning — Decomposes complex queries into optimized sub-queries with automatic fallback
• 🔍 Universal Filter DSL — Write filters once, compile to any backend's native format
• 📡 Streaming Responses — Token-by-token answer generation with citation extraction
• 🔄 Multi-DB Federation — Query across multiple databases in a single request
• 🏗️ Pluggable Pipeline — Swap any component: planner, reranker, synthesizer, LLM, embedding model
• 💬 Conversation Memory — Multi-turn dialogue with automatic token budget management
• 🔒 Security Hardened — Input validation, SQL injection prevention, credential redaction, prompt injection protection
• ✅ Fully Typed — Strict mypy with py.typed marker for downstream consumers

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🚀 Quick Start

Installation

# Core + Qdrant adapter + OpenAI LLM & embeddings
pip install openqueryagent[qdrant,openai]

# Or with Milvus
pip install openqueryagent[milvus,openai]

# Or with pgvector
pip install openqueryagent[pgvector,openai]

# Or with Elasticsearch
pip install openqueryagent[elasticsearch,openai]

# Everything
pip install openqueryagent[all]

Basic Usage

import asyncio
from openqueryagent.core.agent import QueryAgent
from openqueryagent.adapters.qdrant import QdrantAdapter
from openqueryagent.llm.openai import OpenAIProvider
from openqueryagent.embeddings.openai import OpenAIEmbedding

async def main():
    # Create components
    adapter = QdrantAdapter(url="localhost", port=6333)
    await adapter.connect()

    llm = OpenAIProvider(model="gpt-4o-mini")
    embedding = OpenAIEmbedding(model="text-embedding-3-small")

    # Create agent
    agent = QueryAgent(
        adapters={"qdrant": adapter},
        llm=llm,
        embedding=embedding,
    )
    await agent.initialize()

    # Ask a question — gets synthesized answer with citations
    response = await agent.ask("What are the best products under $50?")
    print(response.answer)
    for citation in response.citations:
        print(f"  [{citation.document_id}]: {citation.text_snippet}")

    # Search — returns ranked documents without synthesis
    results = await agent.search("machine learning papers", limit=5)
    for doc in results.documents:
        print(f"  {doc.document.id}: {doc.score:.3f}")

asyncio.run(main())

Without LLM (Zero-Cost Search)

from openqueryagent.core.agent import QueryAgent
from openqueryagent.adapters.qdrant import QdrantAdapter
from openqueryagent.core.planner import SimpleQueryPlanner

agent = QueryAgent(
    adapters={"qdrant": adapter},
    planner=SimpleQueryPlanner(default_collection="products"),
)
# Uses SimpleQueryPlanner — no LLM calls, just vector search
results = await agent.search("wireless headphones")

---

🏗️ Architecture

User Query
    │
    ▼
┌──────────────────────────────────────────────────────┐
│                  QueryAgent                           │
│                                                       │
│  ┌──────────┐   ┌────────┐   ┌──────────┐            │
│  │  Schema   │──▶│ Query  │──▶│  Router  │            │
│  │ Inspector │   │Planner │   │          │            │
│  └──────────┘   └────────┘   └────┬─────┘            │
│                                    │                  │
│                               ┌────▼─────┐            │
│                               │ Executor │ (parallel)  │
│                               └────┬─────┘            │
│                                    │                  │
│                               ┌────▼─────┐            │
│                               │ Reranker │ (RRF)      │
│                               └────┬─────┘            │
│                                    │                  │
│                               ┌────▼──────┐           │
│                               │Synthesizer│ (LLM)     │
│                               └───────────┘           │
└──────────────────────────────────────────────────────┘
    │
    ▼
AskResponse { answer, citations, query_plan }

Pipeline

Stage	Component	Purpose
Plan	LLMQueryPlanner / SimpleQueryPlanner / RuleBasedPlanner	Decompose query into sub-queries with intent detection
Route	QueryRouter	Resolve collections, compile filters to native format
Execute	QueryExecutor	Parallel execution with timeouts and dependency ordering
Rerank	RRFReranker / NoopReranker	Reciprocal Rank Fusion for multi-source results
Synthesize	LLMSynthesizer	Generate answer with [N] citation extraction

---

🔌 Supported Backends

Vector Databases

Backend	Extra	Search Types	Aggregation
Qdrant	openqueryagent[qdrant]	Vector, Keyword, Hybrid (prefetch)	Client-side scroll
Milvus	openqueryagent[milvus]	Vector, Keyword, Hybrid	Client-side query
pgvector	openqueryagent[pgvector]	Vector (⟺), Keyword (tsquery), Hybrid (CTE RRF)	Native SQL
Weaviate	openqueryagent[weaviate]	Vector, Keyword (BM25), Hybrid	Client-side
Pinecone	openqueryagent[pinecone]	Vector, Keyword, Hybrid	Client-side
Chroma	openqueryagent[chroma]	Vector, Keyword	Client-side
Elasticsearch	openqueryagent[elasticsearch]	Vector (kNN), Keyword (BM25), Hybrid	Native agg framework
AWS S3 Vectors	openqueryagent[s3vectors]	Vector	Client-side

LLM Providers

Provider	Extra	Features
OpenAI	openqueryagent[openai]	GPT-4o, JSON mode, streaming, Azure support
Anthropic	openqueryagent[anthropic]	Claude, JSON extraction, streaming
Ollama	Built-in	Local models (Llama 3, Mistral, Mixtral), streaming
AWS Bedrock	openqueryagent[bedrock]	Claude, Titan, Llama via Bedrock

Embedding Providers

Provider	Models
OpenAI	text-embedding-3-small, text-embedding-3-large, text-embedding-ada-002
Cohere	embed-english-v3.0, embed-multilingual-v3.0
HuggingFace	Any sentence-transformers model
AWS Bedrock	Titan Embed, Cohere Embed via Bedrock

---

🔍 Universal Filter DSL

Write filters once, compile to any backend:

from openqueryagent.core.filters import F

# Build a filter
f = (F.price < 50) & (F.category == "electronics") & ~(F.status == "discontinued")

# The adapter's FilterCompiler translates to native format automatically
results = await agent.search("wireless headphones", filters=f)

Supported Operators

Category	Operators
Comparison	==, !=, <, <=, >, >=
Collection	in_, not_in, between
Text	contains, not_contains, starts_with, ends_with, regex
Geo	geo_radius
Existence	exists
Boolean	& (AND), `

---

💬 Conversation Memory

Multi-turn dialogue with automatic context management:

response1 = await agent.ask("What products do you have in electronics?")
# Agent remembers context
response2 = await agent.ask("Which of those are under $30?")
# Uses previous context for follow-up

# Access memory directly
agent.memory.get_messages()
agent.memory.clear()

---

🛠️ Development

Setup

git clone https://github.com/thirukguru/openqueryagent.git
cd openqueryagent
python -m venv .venv
source .venv/bin/activate
pip install -e ".[dev,qdrant,milvus,pgvector,openai,anthropic]"

Quality Checks

# Linting
ruff check openqueryagent/ tests/

# Type checking (strict)
mypy openqueryagent/

# Tests (337 passing)
pytest tests/ -v

Project Structure

openqueryagent/
├── core/
│   ├── agent.py          # QueryAgent — main orchestrator
│   ├── planner.py        # LLM + Simple query planners
│   ├── rule_planner.py   # Rule-based query planner
│   ├── router.py         # Collection resolution + filter compilation
│   ├── executor.py       # Parallel execution with timeouts
│   ├── reranker.py       # NoopReranker + RRFReranker
│   ├── synthesizer.py    # LLM answer generation with citations
│   ├── memory.py         # Token-budgeted conversation history
│   ├── schema.py         # Schema inspector + caching
│   ├── filters.py        # Universal filter DSL (F proxy)
│   ├── types.py          # Pydantic v2 models + enums
│   ├── config.py         # Agent/executor configuration
│   └── exceptions.py     # Error hierarchy
├── adapters/
│   ├── base.py               # VectorStoreAdapter protocol
│   ├── qdrant.py             # Qdrant adapter
│   ├── milvus.py             # Milvus adapter
│   ├── pgvector.py           # pgvector adapter
│   ├── weaviate.py           # Weaviate adapter
│   ├── pinecone.py           # Pinecone adapter
│   ├── chroma.py             # Chroma adapter
│   ├── elasticsearch.py      # Elasticsearch adapter
│   ├── s3vectors.py          # AWS S3 Vectors adapter
│   └── *_filters.py          # Per-adapter filter compilers
├── llm/
│   ├── base.py           # LLMProvider protocol
│   ├── openai.py         # OpenAI provider
│   ├── anthropic.py      # Anthropic provider
│   ├── ollama.py         # Ollama provider (local)
│   └── bedrock.py        # AWS Bedrock provider
├── embeddings/
│   ├── base.py           # EmbeddingProvider protocol
│   ├── openai.py         # OpenAI embeddings
│   ├── cohere.py         # Cohere embeddings
│   ├── huggingface.py    # HuggingFace embeddings
│   └── bedrock.py        # AWS Bedrock embeddings
└── py.typed              # PEP 561 marker

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📋 Roadmap

Phase 3 — Server Layer (Remaining)

####Sprint 9: gRPC Server

• .proto definitions for all service methods (Ask, Search, Aggregate)
• Generate Python stubs with grpcio-tools
• Implement gRPC server in server/grpc/
• Server-streaming RPC for AskStream

TypeScript & Go SDKs

• TypeScript SDK (sdks/typescript/)
  • OpenQueryAgent client class with ask(), search(), aggregate()
  • askStream() with WebSocket/SSE
  • Filter builder, full TypeScript types
  • Published to npm
• Go SDK (sdks/go/)
  • gRPC client with Ask(), Search(), Aggregate()
  • AskStream() with gRPC streaming
  • Published as Go module

Phase 5 — Enterprise

Multi-Tenancy

• Namespace isolation per tenant
• Per-tenant configuration and rate limits
• Tenant-scoped API keys

RBAC & Audit

• Role-based collection access control
• API key scoping (read-only, admin, per-collection)
• Audit logging and usage analytics

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📄 License

Apache 2.0 — see LICENSE for details.