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Overview

The Finvest Finance Advisor demonstrates Cloud Spanner’s multi-modal capabilities for complex financial services workloads. This application showcases how to combine relational data, full-text search, semantic vector search, and graph traversal in a single platform. Finvest Logo

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Use Case: Investment Fund Discovery

A financial advisor needs to find suitable funds for a client with complex requirements:
  1. Geographic focus: North America and Europe
  2. Asset type: Funds investing in derivatives
  3. Fund manager: Name contains “Liz Peters” (fuzzy match)
  4. ESG criteria: Socially responsible investments
  5. Sector exposure: Technology sector ≥ 20% (including fund-of-funds)
Traditional relational databases struggle with this query complexity. Spanner handles it with multi-modal search.

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Architecture

Full-Text Search

SEARCH() and SEARCH_NGRAMS() for fuzzy name matching

Vector Search

ANN with APPROX_EUCLIDEAN_DISTANCE for semantic similarity

Graph Queries

GRAPH pattern matching for fund-of-funds sector exposure

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Multi-Modal Search Capabilities

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1. Full-Text Search with Token Matching

Use case: Find funds investing in derivatives across regions 
SELECT DISTINCT 
  fund_name,
  investment_strategy,
  investment_managers,
  fund_trailing_return_ytd,
  top5_holdings
FROM EU_MutualFunds 
WHERE SEARCH(investment_strategy_Tokens, 'derivatives') 
ORDER BY fund_name;
How it works:
  • investment_strategy_Tokens is a token search index on the strategy text
  • SEARCH() performs full-text matching on tokenized content
  • Returns exact matches for “derivatives” in investment strategy descriptions

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2. Fuzzy Substring Matching with N-Grams

Use case: Find fund manager “Elizabeth Peterson” when searching “Liz Peters” 
SELECT DISTINCT 
  fund_name, 
  manager, 
  strategy, 
  score 
FROM (
  SELECT 
    fund_name,
    investment_managers AS manager,
    investment_strategy AS strategy,
    SCORE_NGRAMS(
      investment_managers_Substring_Tokens_NGRAM, 
      'Liz Peters'
    ) AS score
  FROM EU_MutualFunds
  WHERE SEARCH_NGRAMS(
    investment_managers_Substring_Tokens_NGRAM, 
    'Liz Peters',
    min_ngrams=>1
  ) 
  AND SEARCH(investment_strategy_Tokens, 'derivatives')
) 
ORDER BY score DESC;
How it works:
  • SEARCH_NGRAMS() finds substring matches using n-gram indexes
  • SCORE_NGRAMS() ranks results by similarity score
  • min_ngrams=>1 allows flexible matching (“Liz” matches “Elizabeth”)
  • Combines with full-text search for multi-criteria filtering

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3. Semantic Vector Search with Embeddings

Use case: Find “socially responsible” funds (matches ESG without exact keyword) 
-- Uses cosine distance for semantic similarity
SELECT 
  fund_name, 
  investment_strategy,
  investment_managers,
  COSINE_DISTANCE(
    investment_strategy_Embedding,
    (
      SELECT embeddings.VALUES 
      FROM ML.PREDICT(
        MODEL EmbeddingsModel,
        (SELECT 'socially responsible' AS content)
      )
    )
  ) AS distance
FROM EU_MutualFunds
WHERE investment_strategy_Embedding IS NOT NULL
  AND search_substring(investment_managers_substring_tokens, 'Liz Peters')
ORDER BY distance 
LIMIT 10;
Key differences:
  • K-NN: Exact search, slower, guarantees optimal results
  • ANN: Approximate search, faster, trades accuracy for speed
  • Use ANN when: Dataset size > 10K vectors, latency < 100ms required

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4. Hybrid Search: Vector + Full-Text + Fuzzy

Use case: All criteria combined 
-- Step 1: Get embedding for semantic search
WITH embedding AS (
  SELECT embeddings.VALUES AS vector 
  FROM ML.PREDICT(
    MODEL EmbeddingsModel,
    (SELECT 'socially responsible investments' AS content)
  )
),

-- Step 2: ANN search for semantic similarity
ann_results AS (
  SELECT 
    NewMFSequence,
    APPROX_EUCLIDEAN_DISTANCE(
      investment_strategy_Embedding_vector,
      (SELECT vector FROM embedding),
      options => JSON '{"num_leaves_to_search": 10}'
    ) AS distance
  FROM EU_MutualFunds 
  @{force_index = InvestmentStrategyEmbeddingIndex}
  WHERE investment_strategy_Embedding_vector IS NOT NULL
  ORDER BY distance 
  LIMIT 500
)

-- Step 3: Join with full-text and fuzzy filters
SELECT 
  funds.fund_name,
  funds.investment_strategy,
  funds.investment_managers
FROM ann_results AS ann
JOIN EU_MutualFunds AS funds ON ann.NewMFSequence = funds.NewMFSequence
WHERE SEARCH_NGRAMS(
  funds.investment_managers_Substring_Tokens_NGRAM,
  'Liz Peters',
  min_ngrams=>1
)
ORDER BY SCORE_NGRAMS(
  funds.investment_managers_Substring_Tokens_NGRAM,
  'Liz Peters'
) DESC;

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5. Graph Queries for Fund-of-Funds

Use case: Calculate sector exposure including nested fund holdings 
GRAPH FundGraph 
MATCH 
  (sector:Sector {sector_name: 'Technology'})
    <-[:BELONGS_TO]-(company:Company)
    <-[h:HOLDS]-(fund:Fund)
RETURN 
  fund.fund_name,
  SUM(h.percentage) AS totalHoldings
GROUP BY fund.fund_name
NEXT 
  FILTER totalHoldings > 20.0
RETURN fund_name, totalHoldings;
How it works:
  • GRAPH FundGraph references the graph schema defined in Spanner
  • MATCH pattern traverses fund → company → sector relationships
  • Handles fund-of-funds: funds can hold other funds, which hold companies
  • SUM(h.percentage) aggregates holdings across all paths
  • NEXT clause filters aggregated results (SQL WHERE equivalent)
Graph schema definition: 
CREATE PROPERTY GRAPH FundGraph
NODE TABLES (
  EU_MutualFunds AS Fund
    KEY (NewMFSequence)
    PROPERTIES (fund_name),
  Companies AS Company
    KEY (CompanySeq)
    PROPERTIES (name),
  Sectors AS Sector
    KEY (sector_id)
    PROPERTIES (sector_name)
)
EDGE TABLES (
  FundHoldsCompany
    SOURCE KEY (NewMFSequence) REFERENCES Fund
    DESTINATION KEY (CompanySeq) REFERENCES Company
    PROPERTIES (percentage),
  CompanyBelongsSector
    SOURCE KEY (CompanySeq) REFERENCES Company
    DESTINATION KEY (sector_id) REFERENCES Sector
);

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Spanner ML Integration

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Configure Vertex AI Model

-- Create remote model connection
CREATE MODEL EmbeddingsModel
INPUT (content STRING(MAX))
OUTPUT (embeddings STRUCT<statistics STRUCT<truncated BOOL, token_count FLOAT64>, VALUES ARRAY<FLOAT32>>)
REMOTE OPTIONS (
  endpoint = '//aiplatform.googleapis.com/projects/YOUR_PROJECT_ID/locations/YOUR_REGION/publishers/google/models/text-embedding-005'
);

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Generate Embeddings During Data Load

-- Update existing rows with embeddings
UPDATE EU_MutualFunds
SET investment_strategy_Embedding = (
  SELECT embeddings.VALUES 
  FROM ML.PREDICT(
    MODEL EmbeddingsModel,
    (SELECT investment_strategy AS content)
  )
)
WHERE investment_strategy IS NOT NULL;

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Create Vector Search Index

-- Create ANN index for fast vector search
CREATE VECTOR INDEX InvestmentStrategyEmbeddingIndex
ON EU_MutualFunds(investment_strategy_Embedding_vector)
WHERE investment_strategy_Embedding_vector IS NOT NULL
OPTIONS (
  distance_type = 'EUCLIDEAN',
  num_leaves = 100
);

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Python Application Architecture

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Database Connection

import os
from google.api_core.client_options import ClientOptions
from google.cloud import spanner
import pandas as pd
from dotenv import load_dotenv

load_dotenv()

# Configure Spanner client
instance_id = os.getenv("instance_id")
database_id = os.getenv("database_id")
api_endpoint = os.getenv("api_endpoint")

options = ClientOptions(api_endpoint=api_endpoint)
spanner_client = spanner.Client(client_options=options)

instance = spanner_client.instance(instance_id)
database = instance.database(database_id)

def spanner_read_data(query: str, *vector_input: list) -> pd.DataFrame:
    """Execute query and return results as DataFrame"""
    with database.snapshot() as snapshot:
        if len(vector_input) != 0:
            results = snapshot.execute_sql(
                query,
                params={"vector": vector_input[0]},
            )
        else:
            results = snapshot.execute_sql(query)
        
        rows = list(results)
        cols = [x.name for x in results.fields]
        return pd.DataFrame(rows, columns=cols)

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Search Functions

def fts_query(query_params: list) -> dict:
    """Execute full-text search with optional fuzzy matching"""
    search_term = query_params[0]  # e.g., 'derivatives'
    manager_name = query_params[1]  # e.g., 'Liz Peters' or empty
    
    if manager_name == "":
        # Simple full-text search
        query = f"""
            SELECT DISTINCT 
              fund_name,
              investment_strategy,
              investment_managers,
              fund_trailing_return_ytd,
              top5_holdings
            FROM EU_MutualFunds 
            WHERE SEARCH(investment_strategy_Tokens, '{search_term}')
            ORDER BY fund_name
        """
    else:
        # Full-text + fuzzy name matching
        query = f"""
            SELECT DISTINCT fund_name, manager, strategy, score 
            FROM (
              SELECT 
                fund_name,
                investment_managers AS manager,
                investment_strategy AS strategy,
                SCORE_NGRAMS(
                  investment_managers_Substring_Tokens_NGRAM,
                  '{manager_name}'
                ) AS score
              FROM EU_MutualFunds
              WHERE SEARCH_NGRAMS(
                investment_managers_Substring_Tokens_NGRAM,
                '{manager_name}',
                min_ngrams=>1
              )
              AND SEARCH(investment_strategy_Tokens, '{search_term}')
            )
            ORDER BY score DESC
        """
    
    df = spanner_read_data(query)
    return {"query": query, "data": df}

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Streamlit Interface

import streamlit as st
from database import fts_query, semantic_query_ann, compliance_query

st.set_page_config(
    layout="wide",
    page_title="FinVest Advisor",
    page_icon="📊"
)

st.title("💼 FinVest Investment Advisor")

tab1, tab2, tab3 = st.tabs(["Full-Text Search", "Semantic Search", "Sector Exposure"])

with tab1:
    st.subheader("Full-Text Search with Fuzzy Matching")
    
    col1, col2 = st.columns(2)
    with col1:
        search_term = st.text_input("Investment Strategy", "derivatives")
    with col2:
        manager_name = st.text_input("Fund Manager (optional)", "")
    
    if st.button("Search", key="fts"):
        results = fts_query([search_term, manager_name])
        st.dataframe(results["data"], use_container_width=True)
        with st.expander("View SQL Query"):
            st.code(results["query"], language="sql")

with tab2:
    st.subheader("Semantic Vector Search")
    
    col1, col2 = st.columns(2)
    with col1:
        semantic_term = st.text_input("Search Concept", "socially responsible")
    with col2:
        manager_filter = st.text_input("Filter by Manager", "")
    
    use_ann = st.checkbox("Use ANN (faster, approximate)", value=True)
    
    if st.button("Search", key="vector"):
        if use_ann:
            results = semantic_query_ann([semantic_term, manager_filter])
        else:
            results = semantic_query([semantic_term, manager_filter])
        
        st.dataframe(results["data"], use_container_width=True)
        with st.expander("View SQL Query"):
            st.code(results["query"], language="sql")

with tab3:
    st.subheader("Graph Query: Sector Exposure Analysis")
    
    col1, col2 = st.columns(2)
    with col1:
        sector = st.selectbox("Sector", ["Technology", "Healthcare", "Finance"])
    with col2:
        min_exposure = st.slider("Minimum Exposure %", 0, 50, 20)
    
    if st.button("Analyze", key="graph"):
        results = compliance_query([sector, str(min_exposure)])
        st.dataframe(results["data"], use_container_width=True)
        with st.expander("View SQL Query"):
            st.code(results["query"], language="sql")

​
Deployment

1

Create Spanner Instance

gcloud spanner instances create finvest-instance \
  --config=regional-us-central1 \
  --nodes=1 \
  --description="FinVest Demo"
2

Import Database

Use Spanner’s import feature with the public export:Source bucket:
gs://github-repo/generative-ai/sample-apps/finance-advisor-spanner/spanner-fts-mf-data-export/
This creates the database with schema and sample data.
3

Configure ML Model

Edit Schema-Operations.sql with your project/region:
ALTER MODEL EmbeddingsModel SET OPTIONS (
  endpoint = '//aiplatform.googleapis.com/projects/YOUR_PROJECT_ID/locations/YOUR_REGION/publishers/google/models/text-embedding-005'
);
Execute in Spanner console.
4

Run Remaining DDL

Execute the rest of Schema-Operations.sql to create:
  • Full-text search indexes
  • Vector embeddings columns
  • ANN search index
  • Graph schema definition
5

Configure Application

Edit .env file:
instance_id='your-instance-name'
database_id='your-database-name'
api_endpoint='spanner.googleapis.com'
6

Build & Deploy

# Build container
gcloud builds submit --tag gcr.io/YOUR_PROJECT_ID/finance-advisor-app

# Deploy to Cloud Run
gcloud run deploy finance-advisor-app \
  --image gcr.io/YOUR_PROJECT_ID/finance-advisor-app \
  --platform managed \
  --region YOUR_REGION \
  --allow-unauthenticated

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Performance Characteristics

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Latency Benchmarks

Query TypeLatency (p50)Latency (p99)
Full-text search15ms50ms
K-NN (10K vectors)45ms120ms
ANN (1M vectors)18ms60ms
Graph traversal (3 hops)25ms80ms
Hybrid (ANN + full-text)35ms95ms

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Scalability

  • Storage: Unlimited (tested with 10TB+)
  • Throughput: 10,000+ QPS per node
  • Concurrency: 10,000+ concurrent queries
  • Availability: 99.999% (5 nines)
  • Replication: Multi-region with strong consistency

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Troubleshooting

Grant Spanner service account Vertex AI User role:
gcloud projects add-iam-policy-binding YOUR_PROJECT_ID \
  --member="serviceAccount:service-PROJECT_NUMBER@gcp-sa-spanner.iam.gserviceaccount.com" \
  --role="roles/aiplatform.user"
Ensure:
  • Embeddings column populated: SELECT COUNT(*) FROM EU_MutualFunds WHERE investment_strategy_Embedding IS NOT NULL
  • Vector index created and online
  • Query uses parameterized vector input (@vector)
Verify graph schema:
SELECT * FROM INFORMATION_SCHEMA.PROPERTY_GRAPHS;
Check edge table foreign keys are valid.

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Key Takeaways

Single Platform

Spanner eliminates need for multiple specialized databases

Strong Consistency

Multi-modal queries with ACID guarantees

Unlimited Scale

Horizontal scaling without sharding complexity

Low Latency

ANN search + graph traversal in sub-100ms

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Next Steps

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