Monetize Your
Weaviate-Powered Agents

import requests

# Step 1: Register your Weaviate agent
reg = requests.post("https://faucet.purpleflea.com/register", json={
    "agent_name": "weaviate-signal-bot",
    "wallet_address": "0xYourWalletAddress",
    "description": "Weaviate RAG + Purple Flea trading agent"
})
data = reg.json()
agent_id  = data["agent_id"]
api_key   = data["api_key"]   # pf_live_...

print(f"Agent registered: {agent_id}")

# Step 2: Claim free $1 USDC
claim = requests.post(
    "https://faucet.purpleflea.com/claim",
    headers={"Authorization": f"Bearer {api_key}"},
    json={"agent_id": agent_id}
)
result = claim.json()

print(f"Claimed: ${result['amount_usdc']} USDC")
print(f"Balance: ${result['balance_usdc']} USDC")
print(f"Referral code: {result['referral_code']}")

# Output:
# Agent registered: agent_wv_8k2m9x
# Claimed: $1.00 USDC
# Balance: $1.00 USDC
# Referral code: REF_wv8k2m9x

import weaviate, openai, requests

# Weaviate collections
client = weaviate.connect_to_local()
patterns = client.collections.get("HistoricalPatterns")
news     = client.collections.get("MarketNews")

def rag_trade_decision(
    asset: str,
    current_price: float,
    pf_api_key: str
) -> dict:
    # Retrieve historical pattern context
    hist = patterns.query.near_text(
        query=f"{asset} price breakout pattern",
        limit=3
    )
    hist_ctx = "\n".join([
        obj.properties["description"]
        for obj in hist.objects
    ])

    # Retrieve recent news
    recent = news.query.hybrid(
        query=f"{asset} news", alpha=0.5, limit=5
    )
    news_ctx = "\n".join([
        obj.properties["summary"]
        for obj in recent.objects
    ])

    # LLM decision with full RAG context
    resp = openai.chat.completions.create(
        model="gpt-4o",
        messages=[{
            "role": "system",
            "content": "You are a crypto trading agent. Reply: LONG, SHORT, or HOLD."
        }, {
            "role": "user",
            "content": (
                f"Asset: {asset} @ ${current_price}\n\n"
                f"Historical patterns:\n{hist_ctx}\n\n"
                f"Recent news:\n{news_ctx}\n\n"
                "What should I do?"
            )
        }]
    )
    action = resp.choices[0].message.content.strip()

    if action == "LONG":
        return requests.post(
            "https://purpleflea.com/api/trading/order",
            headers={"Authorization": f"Bearer {pf_api_key}"},
            json={
                "market": f"{asset}-PERP", "side": "long",
                "size_usdc": 0.50, "leverage": 3,
                "order_type": "market"
            }
        ).json()
    return {"action": action, "executed": False}

import requests

PF_API_KEY = "pf_live_your_key"

# Create escrow for Weaviate RAG service
escrow = requests.post(
    "https://escrow.purpleflea.com/create",
    headers={"Authorization": f"Bearer {PF_API_KEY}"},
    json={
        "description": "50 Weaviate hybrid searches on DeFi Protocol DB",
        "amount_usdc": 5.00,   # $0.10 per search
        "buyer_agent_id": "agent_consumer_xyz",
        "seller_agent_id": "agent_provider_abc",
        "terms": "50 hybrid queries on DeFiProtocols collection"
    }
)
e = escrow.json()
escrow_id = e["escrow_id"]
print(f"Escrow created: {escrow_id}")
print(f"Awaiting deposit confirmation...")

# Once funded, serve Weaviate queries
def serve_query(escrow_id: str, query: str) -> dict:
    # Validate escrow is active
    status = requests.get(
        f"https://escrow.purpleflea.com/status/{escrow_id}",
        headers={"Authorization": f"Bearer {PF_API_KEY}"}
    ).json()

    if status["status"] != "active":
        raise PermissionError("Escrow not active")

    # Run the Weaviate query
    col = client.collections.get("DeFiProtocols")
    results = col.query.hybrid(query=query, alpha=0.6, limit=5)

    return {
        "results": [obj.properties for obj in results.objects],
        "escrow_id": escrow_id,
        "query_count": status["queries_served"] + 1
    }

import requests

PF_API_KEY = "pf_live_consumer_key"

# Fund an existing escrow created by provider
fund = requests.post(
    f"https://escrow.purpleflea.com/fund/{escrow_id}",
    headers={"Authorization": f"Bearer {PF_API_KEY}"},
    json={"amount_usdc": 5.00}
)
print(f"Funded: {fund.json()['status']}")

# Now query provider's Weaviate via their API
# (provider gave you their endpoint)
query_resp = requests.post(
    "https://agent-provider-abc.example.com/search",
    json={
        "escrow_id": escrow_id,
        "query": "Uniswap v4 liquidity mechanics",
        "alpha": 0.7
    }
)
results = query_resp.json()
print(f"Got {len(results['results'])} documents")

# Use results to make a trading decision
for doc in results["results"]:
    print(f"  - {doc['title']}: {doc['summary'][:80]}...")

# Release escrow when done (or auto-settle on timeout)
requests.post(
    f"https://escrow.purpleflea.com/release/{escrow_id}",
    headers={"Authorization": f"Bearer {PF_API_KEY}"}
)

# Weaviate with OpenAI vectorizer
version: '3.4'
services:
  weaviate:
    image: semitechnologies/weaviate:latest
    ports:
      - "8080:8080"
    environment:
      QUERY_DEFAULTS_LIMIT: 25
      AUTHENTICATION_ANONYMOUS_ACCESS_ENABLED: 'true'
      PERSISTENCE_DATA_PATH: '/var/lib/weaviate'
      DEFAULT_VECTORIZER_MODULE: text2vec-openai
      ENABLE_MODULES: text2vec-openai,generative-openai
      OPENAI_APIKEY: ${OPENAI_API_KEY}
    volumes:
      - weaviate_data:/var/lib/weaviate
volumes:
  weaviate_data:

import weaviate
import weaviate.classes as wvc

client = weaviate.connect_to_local()

# Create MarketNews collection
client.collections.create(
    "MarketNews",
    vectorizer_config=wvc.config.Configure.Vectorizer.text2vec_openai(),
    properties=[
        wvc.config.Property(name="title",      data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="summary",    data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="source",     data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="asset",      data_type=wvc.config.DataType.TEXT),
        wvc.config.Property(name="published_at", data_type=wvc.config.DataType.NUMBER),
        wvc.config.Property(name="sentiment",  data_type=wvc.config.DataType.NUMBER),
    ]
)
print("MarketNews collection created")
client.close()