from qdrant_client import QdrantClient, models

client = QdrantClient(url="https://cluster.qdrant.io", api_key="...")

# Optimise for high-recall production search
client.create_collection(
    collection_name="production_docs",
    vectors_config=models.VectorParams(
        size=1536,
        distance=models.Distance.COSINE,
        hnsw_config=models.HnswConfigDiff(
            m=32,             # Higher m = better recall, more RAM (default 16)
            ef_construct=200, # Higher = better index quality (default 100)
            full_scan_threshold=10000  # Use full scan below this size
        )
    ),
    optimizers_config=models.OptimizersConfigDiff(
        indexing_threshold=50000,  # Build HNSW after 50K vectors
        memmap_threshold=50000     # Memory-map vectors above this threshold
    ),
    quantization_config=models.ScalarQuantization(
        scalar=models.ScalarQuantizationConfig(
            type=models.ScalarType.INT8,  # 4x storage reduction, small accuracy loss
            quantile=0.99,
            always_ram=True
        )
    )
)

# Always create payload indexes for your filter fields!
for field in ["tenant_id", "doc_type", "created_at"]:
    client.create_payload_index(
        collection_name="production_docs",
        field_name=field,
        field_schema=models.PayloadSchemaType.KEYWORD
    )

import re, asyncpg
from langchain_community.utilities import SQLDatabase
from langchain.chains import create_sql_query_chain

# ALWAYS connect to a read-only replica for agent queries
db = SQLDatabase.from_uri(
    "postgresql://readonly_user:pass@replica.db.internal/prod",
    include_tables=["orders", "customers", "products", "invoices"],
    # Only expose tables agents should see — never all tables
)

def validate_sql(sql: str) -> str:
    """Safety layer — reject any non-SELECT queries"""
    sql_clean = sql.strip().upper()
    dangerous = ["DROP", "DELETE", "UPDATE", "INSERT", "ALTER", "TRUNCATE", "CREATE"]
    for keyword in dangerous:
        if re.search(rf'\b{keyword}\b', sql_clean):
            raise ValueError(f"Unsafe SQL operation detected: {keyword}")
    if not sql_clean.startswith("SELECT"):
        raise ValueError("Only SELECT queries are permitted")
    # Enforce row limit
    if "LIMIT" not in sql_clean:
        sql += " LIMIT 1000"
    return sql

chain = create_sql_query_chain(llm, db)

def ask_database(question: str) -> str:
    raw_sql  = chain.invoke({"question": question})
    safe_sql = validate_sql(raw_sql)
    result   = db.run(safe_sql)
    # Let LLM format the result naturally
    return llm.invoke(f"Question: {question}\nSQL result: {result}\nAnswer naturally:").content

from pymongo import MongoClient, DESCENDING
from datetime import datetime, timedelta
import uuid

client = MongoClient("mongodb+srv://...")
db = client["agent_platform"]

# Store agent conversations with full context
def save_conversation(user_id: str, session_id: str, messages: list, outcome: str):
    db.conversations.insert_one({
        "_id": session_id,
        "user_id": user_id,
        "messages": messages,         # Full message history
        "outcome": outcome,
        "created_at": datetime.utcnow(),
        "tools_used": list({m["tool"] for m in messages if "tool" in m}),
        "token_count": sum(m.get("tokens", 0) for m in messages)
    })

# Retrieve recent conversations for a user
def get_recent_context(user_id: str, days: int = 30) -> list:
    return list(db.conversations.find(
        {"user_id": user_id, "created_at": {"$gte": datetime.utcnow() - timedelta(days=days)}},
        {"messages": 0}  # Exclude full messages for efficiency — get summaries
    ).sort("created_at", DESCENDING).limit(10))

# MongoDB Atlas Vector Search — semantic search over agent history
def semantic_memory_search(user_id: str, query: str, limit: int = 5) -> list:
    query_embedding = embed(query)
    pipeline = [
        {"$vectorSearch": {
            "index": "conversation_vector_index",
            "path": "embedding",
            "queryVector": query_embedding,
            "numCandidates": 100,
            "limit": limit,
            "filter": {"user_id": user_id}  # Pre-filter by user
        }},
        {"$project": {"outcome": 1, "created_at": 1, "score": {"$meta": "vectorSearchScore"}}}
    ]
    return list(db.conversations.aggregate(pipeline))

import redis, json
from datetime import timedelta

r = redis.Redis(host="localhost", port=6379, decode_responses=True)

# 1. Rate limiting — prevent runaway tool calls
def is_rate_limited(user_id: str, tool: str, max_per_hour: int = 100) -> bool:
    key = f"ratelimit:{user_id}:{tool}"
    count = r.incr(key)
    if count == 1: r.expire(key, 3600)  # 1 hour window
    return count > max_per_hour

# 2. Tool result caching — same search query = cached result
def cached_search(query: str, ttl: int = 3600) -> dict:
    key = f"search_cache:{hash(query)}"
    cached = r.get(key)
    if cached: return json.loads(cached)
    result = actual_search(query)
    r.setex(key, ttl, json.dumps(result))
    return result

# 3. Agent session state (fast short-term memory)
def save_session_state(session_id: str, state: dict, ttl: int = 86400):
    r.setex(f"session:{session_id}", ttl, json.dumps(state))

def load_session_state(session_id: str) -> dict:
    raw = r.get(f"session:{session_id}")
    return json.loads(raw) if raw else {}

# 4. Pub/sub for multi-agent coordination
def publish_task_complete(task_id: str, result: dict):
    r.publish(f"task_results", json.dumps({"task_id": task_id, "result": result}))

from neo4j import AsyncGraphDatabase

driver = AsyncGraphDatabase.driver("bolt://localhost:7687", auth=("neo4j", "password"))

# Agent tool: natural language → Cypher → graph query → answer
async def query_knowledge_graph(question: str) -> str:
    # 1. LLM generates Cypher query from natural language
    cypher = await llm.ainvoke(f"""
    Convert to Neo4j Cypher query for a company knowledge graph.
    Schema:
      (Company)-[:COMPETES_WITH]->(Company)
      (Company)-[:FOUNDED_BY]->(Person)
      (Company)-[:ACQUIRED]->(Company)
      (Person)-[:WORKS_AT {{role: 'CEO'}}]->(Company)
      (Company)-[:BACKED_BY]->(Investor)
      (Investor)-[:INVESTED_IN]->(Company)

    Only generate MATCH...RETURN queries (no writes).
    
    Question: {question}
    Cypher:""")

    # 2. Validate it's read-only
    if any(kw in cypher.upper() for kw in ["CREATE", "DELETE", "SET", "MERGE"]):
        return "Error: Only read queries are permitted"

    # 3. Execute on graph database
    async with driver.session() as session:
        result = await session.run(cypher)
        records = [dict(r) async for r in result]

    # 4. LLM formats the graph result as natural language
    return await llm.ainvoke(
        f"Question: {question}\nGraph data: {records}\nAnswer naturally:"
    )

# Example uses:
# "Who are all the investors who backed both Stripe and Adyen?"
# "Which companies did Patrick Collison found?"
# "What companies compete with Zetwerk in the B2B manufacturing space?"

from rank_bm25 import BM25Okapi
import nltk

def build_bm25_index(documents: list[str]) -> tuple:
    tokenised = [nltk.word_tokenize(doc.lower()) for doc in documents]
    return BM25Okapi(tokenised), tokenised

bm25, _ = build_bm25_index(your_documents)

def bm25_search(query: str, top_k: int = 10) -> list[str]:
    tokens = nltk.word_tokenize(query.lower())
    scores = bm25.get_scores(tokens)
    top_indices = sorted(range(len(scores)), key=lambda i: scores[i], reverse=True)[:top_k]
    return [your_documents[i] for i in top_indices]

# Great for: code search, API docs, exact terminology
# Poor for:  synonyms ("automobile" won't match "car"), paraphrases

-- Create a GIN index for full-text search (very fast)
CREATE INDEX docs_fts_idx ON documents USING gin(to_tsvector('english', content));

-- Full-text search query
SELECT id, content,
       ts_rank(to_tsvector('english', content), query) AS relevance
FROM documents, plainto_tsquery('english', 'rate limiting middleware') query
WHERE to_tsvector('english', content) @@ query
ORDER BY relevance DESC
LIMIT 10;

-- Phrase search (exact phrase)
SELECT * FROM documents
WHERE to_tsvector('english', content) @@ phraseto_tsquery('english', 'connection pool exhausted');

-- Weighted search (title matters more than body)
SELECT id,
  ts_rank(
    setweight(to_tsvector('english', title), 'A') ||
    setweight(to_tsvector('english', content), 'B'),
    plainto_tsquery('english', $1)
  ) AS rank
FROM documents
ORDER BY rank DESC LIMIT 20;

from elasticsearch import AsyncElasticsearch

es = AsyncElasticsearch("https://my-cluster.es.io", api_key="...")

async def search_logs(query: str, index: str, time_range: str = "1d") -> dict:
    """Search production logs — agent uses this to diagnose incidents"""
    return await es.search(
        index=index,
        body={
            "query": {
                "bool": {
                    "must": [{"match": {"message": query}}],
                    "filter": [{"range": {"@timestamp": {"gte": f"now-{time_range}"}}}]
                }
            },
            "aggs": {
                "error_count_over_time": {
                    "date_histogram": {"field": "@timestamp", "fixed_interval": "5m"}
                }
            },
            "size": 50,
            "sort": [{"@timestamp": "desc"}]
        }
    )