waveflowdb-client 0.0.1

VectorLake SDK — Deterministic backend engine powering agent workflows

WaveflowDB SDK Starter

A lightweight launcher script for interacting with WaveflowDB and performing WaveQL (VQL) brace-based semantic retrieval.

This starter project demonstrates how to:

• Configure and initialize a Vector Lake client\
• Ingest documents (direct or path-based)\
• Refresh documents\
• Run semantic chat (static + dynamic)\
• Retrieve matching documents\
• Query namespaces\
• Use WaveQL-style logical filtering for agentic retrieval

---

📌 Overview

Vector Lake is an unstructured semantic data platform enabling:

• Natural-language structured filtering through WaveQL (VQL)
• Hybrid ranking (Filter + Semantic)
• Zero-schema ingestion (no JSON schemas required)
• SQL-like logical joins on raw text
• Automatic semantic fallback when filters fail

The included starter.py file provides ready-to-run function wrappers to interact with the Vector Lake API.

---

🚀 Getting Started

1. Install Dependencies

pip install waveflowdb-client

2. Configure API Credentials

Edit the top section of starter.py:

API_KEY = "<<>>"                 
HOST = "https://waveflow-analytics.com"
VECTOR_LAKE_PATH = "<<>>"        
USER_ID = ""                     
NAMESPACE = ""                   

---

🧠 Using WaveQL (VQL) Queries

WaveQL enables natural language filtering using brace-based logical groups:

{clinical trials or observational studies} {type 2 diabetes} {India}

Key Rules

✔ Each {} is a logical filter group
✔ Groups combine with implicit AND
✔ Use AND, OR, () inside braces
✔ Multi-word phrases must use parentheses when operators are used

Examples:

---

Correct Incorrect

---

{(machine learning) or (deep learning)} {machine learning or deep learning}

{(product manager) or (data scientist)} {product manager or Delhi}

WaveQL supports three-tier hybrid ranking:

1. Tier 1 -- Filter + Semantic match (best)\
2. Tier 2 -- Filter-only match\
3. Tier 3 -- Semantic-only fallback

---

🧪 Using the Starter Script

The script exposes multiple ready-to-run functions.

Run Health Check

run_health()

Add Documents

run_add_direct()
run_add_path()

Refresh Documents

run_refresh_direct()
run_refresh_path()

Chat With Documents

run_chat_static("your question")
run_chat_dynamic("summarize this")

Retrieve Matching Documents

run_match_static("your query")
run_match_dynamic("your query")
run_match_with_data("your query")

Namespace & Document Inspection

run_namespace_details()
run_docs_info()

---

🧩 Example WaveQL Queries

• {diabetes} {(clinical trial)} {India}
• {(product manager)} {Python} {Delhi}
• {genomics} {cancer}
• {(supply chain)} {pharma}

📝 Tips & Best Practices

Do:

• Use 1--2 keywords per brace\
• Wrap multi-word phrases in () when using OR/AND\
• Keep groups domain-consistent

Don't:

• Use long multi-word phrases\
• Mix unrelated domains\
• Forget parentheses for multi-word logic

---

📧 Support

For API or platform support, visit:

https://db.agentanalytics.ai