Google Drive & Pathway RAG Integration Implementation Plan

Generated: 2025-01-12 UTC
Updated: 2025-08-12 UTC - Added comprehensive RAG evaluation framework and Pathway tuning guide
Purpose: Complete implementation plan for Google Drive mounting with Pathway RAG indexing, quality evaluation, and performance tuning
Status: Planning Phase (with Evaluation & Tuning Framework)

Executive Summary

This document outlines the implementation plan for integrating Google Drive with Sasha Studio using a dual approach:

1. rclone for actual file system mounting (making files accessible to Claude CLI)
2. Pathway for real-time RAG indexing and semantic search

The implementation starts with a simple Google Drive prototype and scales to support multiple cloud storage providers with advanced RAG capabilities.

Architecture Overview

Current State

• Sasha Studio has a "Cloud Storage Integration" UI in Settings (currently demo mode)
• Docker container runs with basic file system access
• Claude CLI works with local workspace files
• No cloud storage connectivity

Target Architecture

Sasha Studio Container
├── Express Server (Port 3005)
│   ├── OAuth Management
│   ├── Cloud Storage API
│   └── RAG Query Endpoints
├── rclone Service
│   ├── Google Drive Mount
│   ├── SharePoint Mount (future)
│   └── S3 Mount (future)
├── Pathway RAG Service (Port 8000)
│   ├── Real-time Indexing
│   ├── Vector Store
│   └── Semantic Search API
└── Mounted Volumes
    ├── /app/workspaces/local
    ├── /app/workspaces/google-drive
    └── /app/workspaces/[other-clouds]

Phase 1: Basic Google Drive Integration

1.1 OAuth2 Implementation

Goal: Enable users to connect their Google Drive account

Components:

• Google Cloud Console project setup
• OAuth 2.0 credentials configuration
• Token storage and refresh mechanism

API Endpoints:

POST   /api/cloud/google/auth        // Initiate OAuth flow
GET    /api/cloud/google/callback    // Handle OAuth callback
POST   /api/cloud/google/refresh     // Refresh access token
DELETE /api/cloud/google/disconnect  // Revoke access

1.2 Folder Selection UI

Goal: Let users choose which folders to mount

Features:

• Tree view of Google Drive structure
• Checkbox selection for folders
• Mount point customization
• Folder search/filter

UI Location: Settings > Tools > Shared Drives > Google Drive

1.3 rclone Mounting

Goal: Mount selected folders as local file system

Implementation:

# Mount configuration
rclone mount gdrive:Marketing /app/workspaces/google-drive/marketing \
  --daemon \
  --allow-other \
  --vfs-cache-mode writes \
  --vfs-cache-max-size 5G

Benefits:

• Files accessible to Claude CLI immediately
• Standard file operations work normally
• On-demand downloading (not full sync)

Phase 2: Basic Pathway RAG Integration

2.1 Document Monitoring

Goal: Index mounted Google Drive files

Pathway Configuration:

sources:
  - !pw.io.fs.read
    path: /app/workspaces/google-drive
    format: binary
    mode: streaming
    refresh_interval: 30

2.2 Simple RAG Pipeline

Goal: Enable semantic search across Google Drive

Features:

• Automatic document parsing (PDF, DOCX, TXT, MD)
• Basic embeddings with OpenAI
• In-memory vector index
• Simple keyword + semantic search

API Endpoints:

POST /api/rag/search      // Search across all content
GET  /api/rag/stats       // Index statistics
POST /api/rag/reindex     // Force reindexing

Phase 3: UI Integration

3.1 Transform Demo to Active Mode

Current: Demo placeholder in ToolsSettings.jsx
Target: Functional Google Drive connector

3.2 Connection Status Dashboard

Connected Services:
┌─────────────────────────────────┐
│ 🟢 Google Drive                 │
│ user@example.com                │
│ 3 folders mounted               │
│ 1,247 files indexed             │
│ Last sync: 2 seconds ago        │
└─────────────────────────────────┘

3.3 File Browser Enhancement

Show cloud folders with special icons:

• for Google Drive folders
• for SharePoint (future)
• 🪣 for S3 buckets (future)

Pathway's Off-the-Shelf Integration Capabilities

Data Source Connectors (Available Now)

1. File System & Cloud Storage

# Local file system
pw.io.fs.read(path="/data", format="csv", mode="streaming")

# AWS S3
pw.io.s3.read(
    bucket="my-bucket",
    region="us-east-1",
    access_key="xxx",
    secret_key="xxx",
    format="json"
)

# MinIO (S3-compatible)
pw.io.minio.read(
    endpoint="minio.example.com:9000",
    bucket="documents",
    access_key="xxx",
    secret_key="xxx"
)

# Google Drive (via mounted filesystem or API)
pw.io.gdrive.read(
    folder_id="xxx",
    credentials_file="service-account.json"
)

# SharePoint (with Scale/Enterprise license)
pw.xpacks.connectors.sharepoint.read(
    url="https://company.sharepoint.com",
    tenant="xxx",
    client_id="xxx",
    cert_path="cert.pem"
)

2. Databases

# PostgreSQL
pw.io.postgres.read(
    host="localhost",
    database="mydb",
    query="SELECT * FROM documents"
)

# MySQL
pw.io.mysql.read(
    host="localhost",
    database="mydb",
    table="documents"
)

# MongoDB
pw.io.mongodb.read(
    connection_string="mongodb://localhost:27017",
    database="mydb",
    collection="documents"
)

# SQLite
pw.io.sqlite.read(
    path="database.db",
    query="SELECT * FROM documents"
)

# Elasticsearch
pw.io.elasticsearch.read(
    hosts=["localhost:9200"],
    index="documents"
)

3. Streaming Platforms

# Apache Kafka
pw.io.kafka.read(
    servers=["localhost:9092"],
    topic="document-updates",
    format="json"
)

# Apache Pulsar
pw.io.pulsar.read(
    service_url="pulsar://localhost:6650",
    topic="persistent://public/default/documents"
)

# RabbitMQ
pw.io.rabbitmq.read(
    host="localhost",
    queue="documents",
    format="json"
)

# Redis Streams
pw.io.redis.read(
    host="localhost",
    stream="documents:stream"
)

# MQTT
pw.io.mqtt.read(
    broker="mqtt.broker.com",
    topic="sensors/+/data"
)

4. APIs & Web Sources

# REST APIs
pw.io.http.read(
    url="https://api.example.com/documents",
    method="GET",
    headers={"Authorization": "Bearer xxx"},
    interval=60  # Poll every 60 seconds
)

# GraphQL
pw.io.graphql.read(
    endpoint="https://api.example.com/graphql",
    query="""
        query { documents { id title content } }
    """,
    interval=30
)

# WebSocket
pw.io.websocket.read(
    url="wss://stream.example.com/documents",
    format="json"
)

# RSS/Atom Feeds
pw.io.rss.read(
    url="https://example.com/feed.xml",
    interval=300
)

5. Data Lakes & Warehouses

# Delta Lake
pw.io.deltalake.read(
    table_path="s3://bucket/delta-table",
    version=None  # Latest version
)

# Apache Iceberg
pw.io.iceberg.read(
    table="catalog.database.table",
    warehouse="s3://bucket/warehouse"
)

# Snowflake
pw.io.snowflake.read(
    account="xxx",
    warehouse="COMPUTE_WH",
    database="MYDB",
    schema="PUBLIC",
    table="DOCUMENTS"
)

# BigQuery
pw.io.bigquery.read(
    project="my-project",
    dataset="my_dataset",
    table="documents"
)

# Databricks
pw.io.databricks.read(
    server_hostname="xxx.databricks.com",
    http_path="/sql/1.0/warehouses/xxx",
    catalog="main",
    schema="default",
    table="documents"
)

6. Collaboration Tools

# Slack
pw.io.slack.read(
    token="xoxb-xxx",
    channel="general",
    history_limit=1000
)

# Microsoft Teams
pw.io.teams.read(
    tenant_id="xxx",
    client_id="xxx",
    client_secret="xxx",
    team_id="xxx",
    channel_id="xxx"
)

# Confluence
pw.io.confluence.read(
    url="https://company.atlassian.net",
    username="xxx",
    api_token="xxx",
    space="DOCS"
)

# Notion
pw.io.notion.read(
    token="secret_xxx",
    database_id="xxx"
)

# Discord
pw.io.discord.read(
    token="xxx",
    guild_id="xxx",
    channel_id="xxx"
)

7. CRM & Business Systems

# Salesforce
pw.io.salesforce.read(
    instance_url="https://xxx.salesforce.com",
    username="xxx",
    password="xxx",
    security_token="xxx",
    object="Account"
)

# HubSpot
pw.io.hubspot.read(
    api_key="xxx",
    object_type="contacts"
)

# Zendesk
pw.io.zendesk.read(
    subdomain="company",
    email="xxx",
    token="xxx",
    endpoint="tickets"
)

# Jira
pw.io.jira.read(
    url="https://company.atlassian.net",
    username="xxx",
    api_token="xxx",
    jql="project = PROJ"
)

8. Airbyte Integration (350+ connectors)

# Access ANY Airbyte connector
pw.io.airbyte.read(
    connector="source-google-sheets",
    config={
        "spreadsheet_id": "xxx",
        "credentials": {
            "auth_type": "Service",
            "service_account_info": "xxx"
        }
    }
)

# Examples of Airbyte sources:
# - Google Analytics
# - Facebook Ads
# - Stripe
# - Shopify
# - LinkedIn
# - Twitter
# - Instagram
# - YouTube Analytics
# - And 350+ more

Output Connectors (Write Capabilities)

# Write to various destinations
pw.io.csv.write(table, "output.csv")
pw.io.json.write(table, "output.json")
pw.io.parquet.write(table, "output.parquet")

# Database writes
pw.io.postgres.write(table, connection_params, table_name="results")
pw.io.elasticsearch.write(table, hosts=["localhost:9200"], index="results")

# Stream writes
pw.io.kafka.write(table, servers=["localhost:9092"], topic="results")

# API writes
pw.io.http.write(
    table,
    url="https://api.example.com/webhook",
    method="POST",
    headers={"Content-Type": "application/json"}
)

Future Extensions: Multi-Cloud Support

Tier 1: Priority Integrations (Native Pathway Support)

1. SharePoint - Built-in connector (Scale license required)
2. AWS S3 - Native support with pw.io.s3
3. Google Drive - Via API or mounted filesystem
4. PostgreSQL/MySQL - Direct database connection

Tier 2: Via Airbyte Connectors

• OneDrive
• Dropbox
• Box
• Google Cloud Storage
• Azure Blob Storage
• 350+ additional sources

🔮 Advanced Pathway Features (Future)

Near-Term Enhancements

1. Hybrid Search (BM25 + Semantic)

# Combine keyword and semantic search
results = pathway.search.hybrid(
    query="quarterly revenue projections",
    bm25_weight=0.3,
    semantic_weight=0.7
)

Benefit: Better accuracy for technical documents

2. Multi-Modal RAG

• Extract and index images from documents
• OCR for scanned PDFs
• Table extraction and understanding
• Chart/graph interpretation
• Audio transcription support
• Video frame analysis

Use Case: Financial reports with charts, architecture diagrams, recorded meetings

3. Incremental Updates

# Only process changed files
pathway.index.incremental_update(
    modified_since=last_sync_time
)

Benefit: Efficient for large document sets

4. LLM Integrations (Off-the-shelf)

# OpenAI
llm = pw.xpacks.llm.OpenAIChat(
    model="gpt-4",
    api_key=api_key
)

# Anthropic Claude
llm = pw.xpacks.llm.AnthropicChat(
    model="claude-3-opus",
    api_key=api_key
)

# Google Gemini
llm = pw.xpacks.llm.GeminiChat(
    model="gemini-pro",
    api_key=api_key
)

# Local Models (Ollama)
llm = pw.xpacks.llm.OllamaChat(
    model="llama2",
    host="localhost:11434"
)

# HuggingFace
llm = pw.xpacks.llm.HuggingFaceChat(
    model="meta-llama/Llama-2-70b-chat-hf",
    api_key=api_key
)

# Azure OpenAI
llm = pw.xpacks.llm.AzureOpenAIChat(
    deployment="gpt-4",
    api_base="https://xxx.openai.azure.com",
    api_key=api_key
)

Medium-Term Features

5. Advanced Document Processing

# Document parsers (all included)
parsers = {
    "pdf": pw.xpacks.llm.parsers.PDFParser(),
    "docx": pw.xpacks.llm.parsers.DocxParser(),
    "pptx": pw.xpacks.llm.parsers.PptxParser(),
    "xlsx": pw.xpacks.llm.parsers.ExcelParser(),
    "html": pw.xpacks.llm.parsers.HTMLParser(),
    "markdown": pw.xpacks.llm.parsers.MarkdownParser(),
    "csv": pw.xpacks.llm.parsers.CSVParser(),
    "json": pw.xpacks.llm.parsers.JSONParser(),
    "xml": pw.xpacks.llm.parsers.XMLParser(),
    "email": pw.xpacks.llm.parsers.EmailParser(),
    "audio": pw.xpacks.llm.parsers.AudioParser(),  # Transcription
    "video": pw.xpacks.llm.parsers.VideoParser(),  # Frame extraction
}

6. Embedding Models (Multiple Options)

# OpenAI Embeddings
embedder = pw.xpacks.llm.OpenAIEmbeddings(
    model="text-embedding-3-large"
)

# Sentence Transformers (local)
embedder = pw.xpacks.llm.SentenceTransformerEmbeddings(
    model="all-MiniLM-L6-v2"
)

# Cohere Embeddings
embedder = pw.xpacks.llm.CohereEmbeddings(
    model="embed-english-v3.0"
)

# Custom embeddings
embedder = pw.xpacks.llm.CustomEmbeddings(
    embed_fn=my_custom_embed_function
)

7. Vector Indices (Built-in Options)

# Default (USearc - very fast)
index = pw.xpacks.llm.VectorIndex(
    embedder=embedder,
    index_type="usearch"
)

# Hybrid search (BM25 + Vector)
index = pw.xpacks.llm.HybridIndex(
    embedder=embedder,
    bm25_index=True,
    vector_index=True
)

# Hierarchical index
index = pw.xpacks.llm.HierarchicalIndex(
    embedder=embedder,
    levels=3
)

Long-Term Vision

8. Pathway Agents & Tools

# Create agents with tools
tools = [
    pw.xpacks.llm.tools.GoogleSearch(),
    pw.xpacks.llm.tools.Calculator(),
    pw.xpacks.llm.tools.PythonREPL(),
    pw.xpacks.llm.tools.SQLDatabase(connection),
    pw.xpacks.llm.tools.FileSystem(root="/data"),
    pw.xpacks.llm.tools.WebBrowser(),
]

agent = pw.xpacks.llm.Agent(
    llm=llm,
    tools=tools,
    memory=pw.xpacks.llm.ConversationMemory()
)

9. Monitoring & Observability

# Built-in monitoring
pw.monitoring.enable(
    prometheus_port=9090,
    log_level="INFO"
)

# Trace queries
pw.debug.trace_computation(
    query="complex RAG pipeline",
    visualize=True
)

# Performance profiling
pw.profile.analyze(
    pipeline=my_pipeline,
    sample_data=test_data
)

10. Data Transformation Capabilities

# SQL on streaming data
result = pw.sql("""
    SELECT 
        document_type,
        COUNT(*) as count,
        AVG(processing_time) as avg_time
    FROM documents
    GROUP BY document_type
    WINDOW TUMBLING (SIZE 1 HOUR)
""")

# Machine Learning pipelines
from pathway.sklearn import StandardScaler, KMeans

scaled = StandardScaler().fit_transform(features)
clusters = KMeans(n_clusters=5).fit_predict(scaled)

# Time series analysis
from pathway.ts import rolling_mean, detect_anomalies

smoothed = rolling_mean(data, window="1h")
anomalies = detect_anomalies(data, method="isolation_forest")

Pathway-Specific Benefits

Why Pathway Over Traditional Solutions

Pathway Licensing Options

Open Source (Free)

• Core streaming engine
• Basic connectors
• Community support
• Perfect for prototypes

Scale License

• SharePoint connector
• Priority support
• Advanced monitoring
• Production ready

Enterprise License

• All connectors
• Distributed processing
• SLA guarantees
• Custom development

Pathway Instance Tuning Guide

Core Configuration Parameters

1. Streaming Engine Configuration

# Pathway streaming engine tuning
import pathway as pw

config = pw.Config(
    # Processing parameters
    max_batch_size=1000,          # Documents per batch
    batch_timeout_ms=100,         # Max wait time for batch
    parallelism=8,                # Number of parallel workers
    
    # Memory management
    memory_limit_gb=16,           # Total memory allocation
    cache_size_gb=4,              # Cache for frequently accessed data
    buffer_size_mb=256,           # Stream buffer size
    
    # Persistence
    persistence_mode="async",     # async, sync, or disabled
    checkpoint_interval_sec=60,   # Checkpoint frequency
    state_backend="rocksdb",      # rocksdb or memory
    
    # Network
    grpc_max_message_size=100_000_000,  # 100MB
    http_timeout_sec=30,
    retry_policy={
        "max_attempts": 3,
        "backoff_ms": 1000
    }
)

# Apply configuration
pw.set_global_config(config)

2. Index Optimization Parameters

# Vector index tuning for different scenarios
class PathwayIndexConfig:
    @staticmethod
    def high_accuracy_config():
        """Optimize for search quality"""
        return {
            "index_type": "usearch",
            "metric": "cosine",
            "connectivity": 32,        # Higher = better quality, more memory
            "expansion_add": 128,      # Higher = better quality, slower indexing
            "expansion_search": 256,   # Higher = better quality, slower search
            "quantization": None,      # No quantization for best quality
        }
    
    @staticmethod
    def balanced_config():
        """Balance between quality and performance"""
        return {
            "index_type": "usearch",
            "metric": "cosine",
            "connectivity": 16,
            "expansion_add": 64,
            "expansion_search": 128,
            "quantization": "scalar",  # Moderate compression
        }
    
    @staticmethod
    def high_performance_config():
        """Optimize for speed and scale"""
        return {
            "index_type": "usearch",
            "metric": "ip",            # Inner product (faster)
            "connectivity": 8,
            "expansion_add": 32,
            "expansion_search": 64,
            "quantization": "binary",  # Maximum compression
        }

Document Processing Tuning

1. Chunking Strategy Optimization

# Adaptive chunking based on document type
class AdaptiveChunker:
    def __init__(self):
        self.configs = {
            "technical_docs": {
                "chunk_size": 512,
                "chunk_overlap": 128,
                "split_method": "recursive",
                "separators": ["\n\n", "\n", ". ", " "]
            },
            "legal_docs": {
                "chunk_size": 1024,
                "chunk_overlap": 200,
                "split_method": "semantic",
                "preserve_sections": True
            },
            "conversational": {
                "chunk_size": 256,
                "chunk_overlap": 50,
                "split_method": "sentence",
                "min_chunk_size": 100
            },
            "code": {
                "chunk_size": 1500,
                "chunk_overlap": 300,
                "split_method": "ast_aware",  # Syntax tree aware
                "preserve_functions": True
            }
        }
    
    def get_config(self, doc_type):
        return self.configs.get(doc_type, self.configs["technical_docs"])

# Apply adaptive chunking in Pathway
def create_tuned_pipeline(doc_type="technical_docs"):
    chunker_config = AdaptiveChunker().get_config(doc_type)
    
    return pw.io.fs.read(
        path="/data",
        format="binary",
        mode="streaming"
    ).apply(
        pw.udf(
            chunk_document,
            chunk_size=chunker_config["chunk_size"],
            overlap=chunker_config["chunk_overlap"]
        )
    )

2. Embedding Model Tuning

# Optimize embedding generation
class EmbeddingOptimizer:
    def __init__(self, use_case="general"):
        self.configs = {
            "high_quality": {
                "model": "text-embedding-3-large",
                "dimensions": 3072,
                "batch_size": 10,
                "normalize": True,
                "instruction_prefix": "Represent this document for retrieval: "
            },
            "balanced": {
                "model": "text-embedding-3-small",
                "dimensions": 1536,
                "batch_size": 50,
                "normalize": True,
                "instruction_prefix": ""
            },
            "fast": {
                "model": "text-embedding-3-small",
                "dimensions": 512,  # Reduced dimensions
                "batch_size": 100,
                "normalize": True,
                "use_cache": True
            },
            "multilingual": {
                "model": "multilingual-e5-large",
                "dimensions": 1024,
                "batch_size": 25,
                "normalize": True,
                "instruction_prefix": "query: "
            }
        }
        self.config = self.configs[use_case]
    
    def create_embedder(self):
        return pw.xpacks.llm.OpenAIEmbeddings(
            model=self.config["model"],
            dimensions=self.config["dimensions"],
            batch_size=self.config["batch_size"]
        )

Performance Optimization Strategies

1. Caching Layer Configuration

# Multi-level caching for Pathway
class PathwayCacheConfig:
    def __init__(self):
        self.cache_layers = {
            "embedding_cache": {
                "type": "lru",
                "max_size": 10000,
                "ttl_seconds": 3600,
                "similarity_threshold": 0.99  # Cache near-duplicates
            },
            "query_cache": {
                "type": "redis",
                "host": "localhost",
                "port": 6379,
                "max_size_gb": 5,
                "ttl_seconds": 1800
            },
            "document_cache": {
                "type": "memory",
                "max_size_mb": 1024,
                "eviction_policy": "lfu"  # Least frequently used
            }
        }
    
    def apply_to_pipeline(self, pipeline):
        """Apply caching to Pathway pipeline"""
        return pipeline.with_cache(
            embedding_cache=self.cache_layers["embedding_cache"],
            query_cache=self.cache_layers["query_cache"],
            document_cache=self.cache_layers["document_cache"]
        )

2. Resource Allocation Tuning

# Dynamic resource allocation based on load
class ResourceManager:
    def __init__(self):
        self.profiles = {
            "minimal": {
                "workers": 2,
                "memory_gb": 4,
                "cpu_cores": 2,
                "gpu": False
            },
            "standard": {
                "workers": 4,
                "memory_gb": 16,
                "cpu_cores": 8,
                "gpu": False
            },
            "performance": {
                "workers": 8,
                "memory_gb": 32,
                "cpu_cores": 16,
                "gpu": True,
                "gpu_memory_gb": 8
            },
            "scale": {
                "workers": 16,
                "memory_gb": 64,
                "cpu_cores": 32,
                "gpu": True,
                "gpu_memory_gb": 16,
                "distributed": True
            }
        }
    
    def auto_scale(self, metrics):
        """Automatically adjust resources based on metrics"""
        if metrics["latency_p95"] > 1000:
            return self.profiles["performance"]
        elif metrics["qps"] > 100:
            return self.profiles["scale"]
        elif metrics["qps"] < 10:
            return self.profiles["minimal"]
        else:
            return self.profiles["standard"]

Query Optimization

1. Query Processing Pipeline Tuning

# Optimize query processing
class QueryOptimizer:
    def __init__(self):
        self.strategies = {
            "fast": {
                "top_k": 5,
                "rerank": False,
                "expand_query": False,
                "use_cache": True,
                "timeout_ms": 200
            },
            "accurate": {
                "top_k": 20,
                "rerank": True,
                "reranker": "cohere",
                "expand_query": True,
                "use_cache": False,
                "timeout_ms": 2000
            },
            "hybrid": {
                "top_k": 10,
                "rerank": True,
                "reranker": "cross-encoder",
                "bm25_weight": 0.3,
                "semantic_weight": 0.7,
                "use_cache": True,
                "timeout_ms": 500
            }
        }
    
    def optimize_query(self, query, strategy="hybrid"):
        config = self.strategies[strategy]
        
        # Query expansion
        if config.get("expand_query"):
            query = self.expand_with_synonyms(query)
        
        # Hybrid search setup
        if "bm25_weight" in config:
            return self.hybrid_search(
                query,
                bm25_weight=config["bm25_weight"],
                semantic_weight=config["semantic_weight"],
                top_k=config["top_k"]
            )
        
        return query

2. Retrieval Strategy Tuning

# Advanced retrieval tuning
class RetrievalTuner:
    def __init__(self, pathway_index):
        self.index = pathway_index
        
    def tune_for_use_case(self, use_case):
        """Tune retrieval for specific use cases"""
        configs = {
            "customer_support": {
                "min_relevance_score": 0.7,
                "max_results": 5,
                "include_metadata": ["category", "date", "author"],
                "boost_recent": True,
                "recency_weight": 0.2
            },
            "research": {
                "min_relevance_score": 0.6,
                "max_results": 20,
                "include_metadata": ["citations", "journal", "year"],
                "diversity_factor": 0.3,  # Ensure diverse results
                "academic_boost": True
            },
            "legal": {
                "min_relevance_score": 0.85,
                "max_results": 10,
                "include_metadata": ["case_id", "jurisdiction", "date"],
                "exact_match_boost": 2.0,
                "precedent_weight": 0.4
            }
        }
        
        config = configs.get(use_case, configs["customer_support"])
        self.index.update_config(config)
        return config

Scaling Strategies

1. Horizontal Scaling Configuration

# Distributed Pathway setup
class DistributedPathway:
    def __init__(self, num_nodes=3):
        self.config = {
            "cluster": {
                "nodes": num_nodes,
                "replication_factor": 2,
                "sharding_strategy": "consistent_hash",
                "load_balancing": "round_robin"
            },
            "coordinator": {
                "host": "pathway-coordinator",
                "port": 8080,
                "health_check_interval": 10
            },
            "workers": [
                {
                    "id": f"worker-{i}",
                    "host": f"pathway-worker-{i}",
                    "port": 8081 + i,
                    "resources": {
                        "cpu": 4,
                        "memory_gb": 16,
                        "gpu": i == 0  # GPU on first worker
                    }
                }
                for i in range(num_nodes)
            ]
        }
    
    def deploy(self):
        """Deploy distributed Pathway cluster"""
        return pw.cluster.deploy(
            config=self.config,
            orchestrator="kubernetes",  # or "docker-swarm"
            monitoring="prometheus"
        )

2. Auto-scaling Rules

# Auto-scaling configuration
auto_scaling_rules = {
    "scale_up_triggers": [
        {"metric": "cpu_usage", "threshold": 80, "duration": 60},
        {"metric": "memory_usage", "threshold": 85, "duration": 30},
        {"metric": "queue_depth", "threshold": 1000, "duration": 10},
        {"metric": "latency_p95", "threshold": 1000, "duration": 30}
    ],
    "scale_down_triggers": [
        {"metric": "cpu_usage", "threshold": 20, "duration": 300},
        {"metric": "memory_usage", "threshold": 30, "duration": 300},
        {"metric": "queue_depth", "threshold": 10, "duration": 300}
    ],
    "scaling_policy": {
        "min_instances": 1,
        "max_instances": 10,
        "scale_up_increment": 2,
        "scale_down_increment": 1,
        "cooldown_period": 180
    }
}

Monitoring and Profiling

1. Performance Monitoring Setup

# Comprehensive monitoring configuration
monitoring_config = {
    "metrics": {
        "system": ["cpu", "memory", "disk", "network"],
        "pathway": [
            "documents_processed",
            "embeddings_generated",
            "queries_per_second",
            "index_size",
            "cache_hit_rate"
        ],
        "quality": [
            "retrieval_precision",
            "generation_faithfulness",
            "user_satisfaction"
        ]
    },
    "dashboards": {
        "grafana": {
            "url": "http://grafana:3000",
            "dashboards": [
                "pathway-overview",
                "rag-quality-metrics",
                "system-resources"
            ]
        }
    },
    "alerts": {
        "critical": [
            {"metric": "error_rate", "threshold": 0.01},
            {"metric": "latency_p99", "threshold": 2000}
        ],
        "warning": [
            {"metric": "cache_hit_rate", "threshold": 0.5},
            {"metric": "memory_usage", "threshold": 0.8}
        ]
    }
}

2. Profiling and Optimization Tools

# Profile Pathway pipeline performance
class PathwayProfiler:
    def profile_pipeline(self, pipeline, sample_data):
        """Profile pipeline performance"""
        import cProfile
        import pstats
        
        profiler = cProfile.Profile()
        profiler.enable()
        
        # Run pipeline with sample data
        results = pipeline.process(sample_data)
        
        profiler.disable()
        stats = pstats.Stats(profiler)
        
        # Identify bottlenecks
        bottlenecks = {
            "slowest_operations": stats.sort_stats('cumtime').print_stats(10),
            "most_called": stats.sort_stats('calls').print_stats(10),
            "memory_intensive": self.profile_memory(pipeline, sample_data)
        }
        
        return self.generate_optimization_report(bottlenecks)
    
    def generate_optimization_report(self, bottlenecks):
        """Generate optimization recommendations"""
        recommendations = []
        
        # Analyze and recommend
        if "embedding" in str(bottlenecks["slowest_operations"]):
            recommendations.append("Consider batch processing or caching embeddings")
        
        if "search" in str(bottlenecks["slowest_operations"]):
            recommendations.append("Optimize index parameters or add more workers")
        
        return {
            "bottlenecks": bottlenecks,
            "recommendations": recommendations,
            "estimated_improvement": "20-40% with recommended changes"
        }

Cost Optimization

1. Resource-Cost Trade-offs

# Cost optimization configurations
cost_optimization_profiles = {
    "minimal_cost": {
        "description": "Lowest cost, acceptable performance",
        "embedding_model": "text-embedding-3-small",
        "embedding_dimensions": 512,
        "llm_model": "gpt-3.5-turbo",
        "cache_everything": True,
        "batch_size": 100,
        "estimated_cost_per_1k_queries": "$0.50"
    },
    "balanced_cost": {
        "description": "Good performance, reasonable cost",
        "embedding_model": "text-embedding-3-small",
        "embedding_dimensions": 1536,
        "llm_model": "gpt-4-turbo",
        "cache_strategy": "smart",
        "batch_size": 50,
        "estimated_cost_per_1k_queries": "$2.00"
    },
    "premium_performance": {
        "description": "Best performance, higher cost",
        "embedding_model": "text-embedding-3-large",
        "embedding_dimensions": 3072,
        "llm_model": "gpt-4",
        "cache_strategy": "minimal",
        "batch_size": 10,
        "estimated_cost_per_1k_queries": "$5.00"
    }
}

Tuning Decision Matrix

Practical Tuning Workflow

Step-by-Step Tuning Process

class PathwayTuningWorkflow:
    """
    Systematic approach to tuning Pathway instance
    """
    
    def __init__(self, baseline_config):
        self.baseline = baseline_config
        self.experiments = []
        self.best_config = baseline_config
        
    def run_tuning_workflow(self):
        """Execute complete tuning workflow"""
        
        # Step 1: Establish baseline
        print("Step 1: Establishing baseline performance...")
        baseline_metrics = self.measure_performance(self.baseline)
        
        # Step 2: Identify bottlenecks
        print("Step 2: Identifying bottlenecks...")
        bottlenecks = self.identify_bottlenecks(baseline_metrics)
        
        # Step 3: Generate tuning experiments
        print("Step 3: Generating tuning experiments...")
        experiments = self.generate_experiments(bottlenecks)
        
        # Step 4: Run experiments
        print("Step 4: Running experiments...")
        for exp in experiments:
            result = self.run_experiment(exp)
            if result["improvement"] > 0:
                self.best_config = exp["config"]
                print(f"  ✓ {exp['name']}: {result['improvement']:.1%} improvement")
            else:
                print(f"  ✗ {exp['name']}: No improvement")
        
        # Step 5: Validate final configuration
        print("Step 5: Validating final configuration...")
        self.validate_configuration(self.best_config)
        
        return self.best_config
    
    def identify_bottlenecks(self, metrics):
        """Identify what needs tuning"""
        bottlenecks = []
        
        if metrics["latency_p95"] > 500:
            bottlenecks.append("high_latency")
        if metrics["retrieval_precision"] < 0.85:
            bottlenecks.append("low_precision")
        if metrics["memory_usage"] > 0.8:
            bottlenecks.append("high_memory")
        if metrics["cost_per_query"] > 0.10:
            bottlenecks.append("high_cost")
            
        return bottlenecks
    
    def generate_experiments(self, bottlenecks):
        """Generate targeted experiments based on bottlenecks"""
        experiments = []
        
        if "high_latency" in bottlenecks:
            experiments.extend([
                {
                    "name": "Increase cache size",
                    "config": {"cache_size_gb": 8}
                },
                {
                    "name": "Reduce embedding dimensions",
                    "config": {"embedding_dimensions": 512}
                },
                {
                    "name": "Enable GPU acceleration",
                    "config": {"gpu": True}
                }
            ])
        
        if "low_precision" in bottlenecks:
            experiments.extend([
                {
                    "name": "Enable reranking",
                    "config": {"rerank": True, "reranker": "cohere"}
                },
                {
                    "name": "Increase chunk overlap",
                    "config": {"chunk_overlap": 200}
                },
                {
                    "name": "Use larger embedding model",
                    "config": {"embedding_model": "text-embedding-3-large"}
                }
            ])
        
        return experiments

Quick Tuning Templates

# Pre-configured tuning templates for common scenarios

tuning_templates = {
    "startup_mvp": {
        "description": "Minimal viable configuration for startups",
        "config": {
            "workers": 2,
            "memory_gb": 4,
            "embedding_model": "text-embedding-3-small",
            "embedding_dimensions": 512,
            "chunk_size": 512,
            "cache_strategy": "aggressive",
            "rerank": False
        },
        "expected_performance": {
            "latency_p95": "300ms",
            "precision": "0.75",
            "cost_per_1k_queries": "$0.50"
        }
    },
    
    "enterprise_standard": {
        "description": "Balanced configuration for enterprise deployment",
        "config": {
            "workers": 8,
            "memory_gb": 32,
            "embedding_model": "text-embedding-3-small",
            "embedding_dimensions": 1536,
            "chunk_size": 256,
            "chunk_overlap": 128,
            "cache_strategy": "smart",
            "rerank": True,
            "reranker": "cohere"
        },
        "expected_performance": {
            "latency_p95": "500ms",
            "precision": "0.85",
            "cost_per_1k_queries": "$2.00"
        }
    },
    
    "high_performance": {
        "description": "Maximum performance for critical applications",
        "config": {
            "workers": 16,
            "memory_gb": 64,
            "gpu": True,
            "embedding_model": "text-embedding-3-large",
            "embedding_dimensions": 3072,
            "chunk_size": 256,
            "chunk_overlap": 200,
            "cache_strategy": "minimal",
            "rerank": True,
            "reranker": "cross-encoder",
            "distributed": True
        },
        "expected_performance": {
            "latency_p95": "200ms",
            "precision": "0.95",
            "cost_per_1k_queries": "$5.00"
        }
    }
}

# Apply template
def apply_tuning_template(template_name):
    """Quick start with pre-tuned configuration"""
    template = tuning_templates[template_name]
    print(f"Applying template: {template['description']}")
    print(f"Expected performance:")
    for metric, value in template['expected_performance'].items():
        print(f"  - {metric}: {value}")
    return template['config']

Tuning Checklist

pathway_tuning_checklist:
  initial_setup:
    - [ ] Measure baseline performance
    - [ ] Identify primary use case (QA, search, analysis)
    - [ ] Define performance targets
    - [ ] Set budget constraints
  
  quick_wins:
    - [ ] Enable caching if not already
    - [ ] Optimize batch sizes
    - [ ] Adjust chunk size based on document type
    - [ ] Configure appropriate number of workers
  
  retrieval_optimization:
    - [ ] Test different embedding models
    - [ ] Experiment with chunk sizes (128, 256, 512, 1024)
    - [ ] Try different overlap percentages
    - [ ] Evaluate hybrid search vs pure semantic
    - [ ] Test reranking impact
  
  performance_tuning:
    - [ ] Profile pipeline to find bottlenecks
    - [ ] Optimize index parameters
    - [ ] Configure caching layers
    - [ ] Consider GPU acceleration
    - [ ] Evaluate distributed setup need
  
  quality_tuning:
    - [ ] Run RAGAS evaluation
    - [ ] A/B test configurations
    - [ ] Collect user feedback
    - [ ] Iterate based on failure analysis
  
  production_hardening:
    - [ ] Set up monitoring
    - [ ] Configure auto-scaling
    - [ ] Implement failover
    - [ ] Document final configuration

Implementation Timeline

Month 1: Foundation

• Week 1-2: Google OAuth + rclone mounting
• Week 3: Basic Pathway with file system connector
• Week 4: Search UI integration

Month 2: Enhancement

• Week 1: Add S3 connector
• Week 2: Implement hybrid search
• Week 3: Add PostgreSQL connector
• Week 4: Performance optimization

Month 3: Advanced Features

• Week 1: SharePoint integration (if licensed)
• Week 2: Kafka streaming connector
• Week 3: Multi-modal RAG
• Week 4: Agent capabilities

Key Differentiators

Our Unique Value Proposition

1. True File System Access: rclone mounts + Pathway indexing
2. Claude CLI Compatible: Full file system access for AI
3. 350+ Connectors: Via Airbyte integration
4. Real-time Everything: Streaming-first architecture
5. Business Friendly: No code/low code setup

Success Metrics

Technical Metrics

• Index 10,000 documents in < 5 minutes
• Search latency < 200ms for 1M documents
• Support 50+ concurrent users
• 99.9% uptime for mounted drives
• RAG Quality Score > 0.85 (see evaluation framework below)

Business Metrics

• 80% reduction in document search time
• 60% faster employee onboarding
• 90% user adoption within 3 months
• 5x ROI within first year
• 95% query satisfaction rate (measured via feedback)

RAG Quality Evaluation Framework

Why Evaluation Matters

Before deploying Pathway RAG to production, we need to validate that it:

1. Retrieves the right documents (high precision and recall)
2. Generates accurate answers (factually correct, no hallucinations)
3. Responds appropriately (relevant, complete, and helpful)
4. Performs consistently (across different query types and domains)

Core Evaluation Metrics

1. Retrieval Quality Metrics

# Retrieval metrics configuration
retrieval_metrics = {
    "context_precision": {
        "description": "Signal-to-noise ratio of retrieved chunks",
        "threshold_green": 0.85,
        "threshold_amber": 0.70,
        "threshold_red": 0.70
    },
    "context_recall": {
        "description": "Completeness of relevant information retrieval",
        "threshold_green": 0.90,
        "threshold_amber": 0.75,
        "threshold_red": 0.75
    },
    "mrr": {
        "description": "Mean Reciprocal Rank of first relevant result",
        "threshold_green": 0.80,
        "threshold_amber": 0.60,
        "threshold_red": 0.60
    },
    "ndcg_at_10": {
        "description": "Quality of top 10 results ranking",
        "threshold_green": 0.75,
        "threshold_amber": 0.60,
        "threshold_red": 0.60
    }
}

2. Generation Quality Metrics

# Generation metrics configuration
generation_metrics = {
    "faithfulness": {
        "description": "Factual accuracy vs retrieved context",
        "threshold_green": 0.90,
        "threshold_amber": 0.75,
        "threshold_red": 0.75
    },
    "answer_relevancy": {
        "description": "How directly the answer addresses the question",
        "threshold_green": 0.85,
        "threshold_amber": 0.70,
        "threshold_red": 0.70
    },
    "answer_completeness": {
        "description": "Coverage of all aspects of the question",
        "threshold_green": 0.80,
        "threshold_amber": 0.65,
        "threshold_red": 0.65
    },
    "hallucination_rate": {
        "description": "Percentage of unsupported claims",
        "threshold_green": 0.05,  # Less than 5% hallucination
        "threshold_amber": 0.10,
        "threshold_red": 0.10
    }
}

3. End-to-End Performance Metrics

# System performance metrics
performance_metrics = {
    "response_latency_ms": {
        "p50_threshold": 200,
        "p95_threshold": 500,
        "p99_threshold": 1000
    },
    "task_completion_rate": {
        "threshold": 0.95  # 95% of queries fully answered
    },
    "user_satisfaction_score": {
        "threshold": 4.0  # Out of 5.0
    },
    "system_availability": {
        "threshold": 0.999  # 99.9% uptime
    }
}

RAGAS Framework Integration

Installation and Setup

# Install RAGAS for automated evaluation
pip install ragas langchain pathway

# Configure RAGAS with Pathway
from ragas import evaluate
from ragas.metrics import (
    context_precision,
    context_recall,
    faithfulness,
    answer_relevancy
)
import pathway as pw

# Initialize RAGAS evaluator
def setup_ragas_evaluation():
    return {
        "metrics": [
            context_precision,
            context_recall,
            faithfulness,
            answer_relevancy
        ],
        "llm": pw.xpacks.llm.OpenAIChat(
            model="gpt-4",
            temperature=0  # Deterministic evaluation
        )
    }

Automated Evaluation Pipeline

# Pathway + RAGAS evaluation pipeline
class RAGEvaluationPipeline:
    def __init__(self, pathway_index, test_dataset):
        self.index = pathway_index
        self.test_data = test_dataset
        self.ragas_config = setup_ragas_evaluation()
    
    def evaluate_batch(self, queries):
        results = []
        for query in queries:
            # Get RAG response
            context = self.index.search(query.question)
            answer = self.index.generate(query.question, context)
            
            # Prepare for RAGAS evaluation
            eval_sample = {
                "question": query.question,
                "answer": answer,
                "contexts": context,
                "ground_truth": query.expected_answer  # Optional
            }
            
            # Run RAGAS evaluation
            scores = evaluate(
                [eval_sample],
                metrics=self.ragas_config["metrics"]
            )
            results.append(scores)
        
        return self.aggregate_scores(results)
    
    def aggregate_scores(self, results):
        # Calculate mean scores and determine RAG status
        aggregated = {}
        for metric in self.ragas_config["metrics"]:
            scores = [r[metric.name] for r in results]
            mean_score = sum(scores) / len(scores)
            
            # Determine RAG status (Red/Amber/Green)
            if mean_score >= retrieval_metrics[metric.name]["threshold_green"]:
                status = "🟢 GREEN"
            elif mean_score >= retrieval_metrics[metric.name]["threshold_amber"]:
                status = "🟡 AMBER"
            else:
                status = "🔴 RED"
            
            aggregated[metric.name] = {
                "score": mean_score,
                "status": status
            }
        
        return aggregated

Benchmark Datasets for Sasha Studio

1. Custom Business Document Benchmarks

# Create domain-specific test sets
sasha_benchmarks = {
    "company_knowledge": {
        "description": "Test understanding of company policies and procedures",
        "size": 200,
        "categories": [
            "HR policies",
            "Product documentation",
            "Sales methodologies",
            "Technical specifications"
        ]
    },
    "multi_doc_reasoning": {
        "description": "Questions requiring information from multiple documents",
        "size": 100,
        "example": "What is the total budget for Q3 marketing across all regions?"
    },
    "temporal_queries": {
        "description": "Time-sensitive information retrieval",
        "size": 50,
        "example": "What were the key decisions from last month's board meeting?"
    }
}

2. Standard Benchmark Adaptation

# Adapt standard benchmarks for business context
benchmark_config = {
    "hotpotqa_subset": {
        "size": 500,
        "focus": "Multi-hop reasoning for business documents",
        "adaptation": "Replace Wikipedia with company knowledge base"
    },
    "ms_marco_business": {
        "size": 1000,
        "focus": "Single-document question answering",
        "adaptation": "Business-specific queries and passages"
    },
    "custom_fiqa": {
        "size": 300,
        "focus": "Financial and analytical queries",
        "adaptation": "Company financial reports and analytics"
    }
}

Production Quality Gates

Deployment Readiness Checklist

# Minimum scores for production deployment
production_requirements:
  mandatory:
    - context_precision: >= 0.85
    - faithfulness: >= 0.90
    - answer_relevancy: >= 0.85
    - hallucination_rate: <= 0.05
    - response_latency_p95: <= 500ms
  
  recommended:
    - context_recall: >= 0.90
    - answer_completeness: >= 0.80
    - mrr: >= 0.80
    - user_satisfaction: >= 4.0/5.0
  
  monitoring:
    - daily_evaluation_runs: true
    - alert_on_degradation: true
    - human_review_sample: 5%  # Review 5% of queries manually

Continuous Monitoring Dashboard

# Real-time monitoring configuration
monitoring_config = {
    "metrics_endpoint": "/api/rag/metrics",
    "update_frequency": "5_minutes",
    "visualizations": [
        "time_series_quality_scores",
        "retrieval_precision_heatmap",
        "answer_quality_distribution",
        "latency_percentiles"
    ],
    "alerts": [
        {
            "metric": "faithfulness",
            "condition": "< 0.85",
            "severity": "critical",
            "notification": "slack_and_email"
        },
        {
            "metric": "response_latency_p99",
            "condition": "> 1000",
            "severity": "warning",
            "notification": "slack"
        }
    ]
}

Evaluation Best Practices

1. Test Data Management

# Structured test data organization
test_data_structure = {
    "golden_qa_pairs": "test_data/golden_qa.json",
    "edge_cases": "test_data/edge_cases.json",
    "adversarial": "test_data/adversarial.json",
    "multilingual": "test_data/multilingual.json",
    "update_frequency": "weekly",
    "version_control": "git",
    "human_validation": "required"
}

2. A/B Testing Framework

# Compare different RAG configurations
ab_test_config = {
    "variant_a": {
        "name": "baseline",
        "chunk_size": 512,
        "overlap": 50,
        "embedding_model": "text-embedding-3-small",
        "reranker": "none"
    },
    "variant_b": {
        "name": "optimized",
        "chunk_size": 256,
        "overlap": 100,
        "embedding_model": "text-embedding-3-large",
        "reranker": "cohere"
    },
    "traffic_split": "50/50",
    "minimum_sample_size": 1000,
    "success_metric": "answer_relevancy"
}

3. Failure Analysis

# Analyze and categorize failures
failure_categories = {
    "retrieval_failures": {
        "no_relevant_docs": "Query outside knowledge base",
        "wrong_docs": "Semantic similarity mismatch",
        "partial_retrieval": "Missing critical context"
    },
    "generation_failures": {
        "hallucination": "Claims not supported by context",
        "incomplete": "Partial answer provided",
        "irrelevant": "Answer doesn't address question"
    },
    "system_failures": {
        "timeout": "Response time exceeded",
        "error": "Processing exception",
        "rate_limit": "API quota exceeded"
    }
}

Implementation Timeline with Evaluation

Updated Phase 1: Google Drive MVP + Evaluation (Month 1)

• Week 1-2: Google OAuth + rclone mounting
• Week 3: Basic Pathway indexing + RAGAS setup
• Week 4: Search UI + Initial evaluation baseline

Updated Phase 2: Multi-Source + Quality Gates (Month 2)

• Week 1: Add S3 connector + Retrieval metrics
• Week 2: Hybrid search + Generation metrics
• Week 3: PostgreSQL connector + Custom benchmarks
• Week 4: Performance optimization + Production readiness assessment

Cost Considerations

Evaluation Infrastructure Costs

evaluation_costs:
  llm_evaluation:
    ragas_gpt4: "$0.03 per evaluation"
    daily_automated_tests: "$30/day (1000 queries)"
    monthly_estimate: "$900"
  
  human_evaluation:
    review_rate: "100 queries/day"
    cost_per_query: "$0.50"
    monthly_estimate: "$1,500"
  
  infrastructure:
    monitoring_tools: "$200/month"
    storage_test_data: "$50/month"
    compute_evaluation: "$100/month"
  
  total_monthly: "$2,750"
  roi_justification: "Prevents customer churn worth $50K+/month"

Troubleshooting Low RAG Scores

Diagnosis Decision Tree

Common Issues and Solutions

1. Low Context Precision (< 0.85)

Symptoms: Retrieved documents contain irrelevant information

Solutions:

# Solution 1: Implement reranking
from pathway import rerankers

reranker = rerankers.CohereReranker(
    model="rerank-english-v3.0",
    top_k=5
)

# Solution 2: Optimize chunk configuration
chunk_config = {
    "size": 256,  # Smaller chunks for precision
    "overlap": 128,  # 50% overlap
    "method": "semantic"  # Use semantic chunking
}

# Solution 3: Use hybrid search
hybrid_config = {
    "bm25_weight": 0.3,
    "semantic_weight": 0.7,
    "minimum_score": 0.75
}

2. Low Faithfulness (< 0.90)

Symptoms: Generated answers contain hallucinations

Solutions:

# Solution 1: Strict prompt engineering
system_prompt = """
You are a factual assistant. ONLY use information from the provided context.
If the context doesn't contain the answer, say "I don't have that information."
Never make assumptions or add information not explicitly stated in the context.
"""

# Solution 2: Add citation requirements
generation_config = {
    "require_citations": True,
    "max_claims_without_citation": 0,
    "citation_format": "[doc_id:chunk_id]"
}

# Solution 3: Implement fact-checking layer
def validate_claims(answer, context):
    claims = extract_claims(answer)
    for claim in claims:
        if not is_supported_by_context(claim, context):
            return False, f"Unsupported claim: {claim}"
    return True, "All claims validated"

3. Low Answer Relevancy (< 0.85)

Symptoms: Answers are correct but don't directly address the question

Solutions:

# Solution 1: Query understanding enhancement
def enhance_query(original_query):
    # Add intent classification
    intent = classify_intent(original_query)
    
    # Expand with synonyms
    expanded = expand_with_synonyms(original_query)
    
    # Add metadata filters
    filters = extract_filters(original_query)
    
    return {
        "original": original_query,
        "intent": intent,
        "expanded": expanded,
        "filters": filters
    }

# Solution 2: Answer validation loop
def validate_answer_relevance(question, answer):
    # Generate reverse question from answer
    reverse_question = generate_question_from_answer(answer)
    
    # Calculate similarity
    similarity = calculate_similarity(question, reverse_question)
    
    if similarity < 0.85:
        # Regenerate with more specific prompt
        return regenerate_with_focus(question, answer)
    
    return answer

4. High Response Latency (> 500ms)

Symptoms: Slow query responses affecting user experience

Solutions:

# Solution 1: Implement caching
cache_config = {
    "type": "redis",
    "ttl": 3600,  # 1 hour
    "max_size": "10GB",
    "similarity_threshold": 0.95  # Cache near-duplicate queries
}

# Solution 2: Optimize index structure
index_optimization = {
    "use_hierarchical_index": True,
    "enable_gpu_acceleration": True,
    "batch_size": 32,
    "prefetch_neighbors": 10
}

# Solution 3: Async processing
async def process_query_async(query):
    # Parallel retrieval from multiple sources
    tasks = [
        retrieve_from_vector_db(query),
        retrieve_from_keyword_index(query),
        retrieve_from_cache(query)
    ]
    results = await asyncio.gather(*tasks)
    return merge_results(results)

Performance Tuning Checklist

retrieval_tuning:
  embedding_model:
    - [ ] Test different models (OpenAI, Cohere, local)
    - [ ] Fine-tune on domain data if needed
    - [ ] Optimize model size vs accuracy trade-off
  
  chunking_strategy:
    - [ ] Experiment with chunk sizes (128, 256, 512, 1024)
    - [ ] Test overlap percentages (0%, 25%, 50%)
    - [ ] Try semantic vs fixed-size chunking
  
  index_optimization:
    - [ ] Choose appropriate index type (HNSW, IVF, LSH)
    - [ ] Tune index parameters (ef_construction, M)
    - [ ] Consider quantization for large datasets

generation_tuning:
  prompt_engineering:
    - [ ] Create domain-specific prompts
    - [ ] Add few-shot examples
    - [ ] Implement chain-of-thought reasoning
  
  model_selection:
    - [ ] Compare GPT-4 vs GPT-3.5 vs Claude
    - [ ] Test specialized models for your domain
    - [ ] Consider cost vs quality trade-offs
  
  post_processing:
    - [ ] Add answer validation
    - [ ] Implement citation extraction
    - [ ] Create feedback loops

Evaluation-Driven Development Workflow

# Continuous improvement pipeline
class EvaluationDrivenDevelopment:
    def __init__(self):
        self.baseline_scores = {}
        self.experiments = []
        
    def establish_baseline(self):
        """Run initial evaluation to set baseline"""
        self.baseline_scores = run_full_evaluation()
        log_baseline(self.baseline_scores)
        
    def test_improvement(self, change_description, config_changes):
        """Test a specific improvement"""
        # Apply configuration changes
        apply_config(config_changes)
        
        # Run evaluation
        new_scores = run_full_evaluation()
        
        # Compare with baseline
        improvement = calculate_improvement(
            self.baseline_scores, 
            new_scores
        )
        
        # Decision logic
        if improvement > 0.05:  # 5% improvement threshold
            self.baseline_scores = new_scores
            commit_changes(change_description, config_changes)
            return "ACCEPTED"
        else:
            rollback_changes()
            return "REJECTED"
    
    def run_experiments(self):
        """Run A/B tests on different configurations"""
        experiments = [
            ("Smaller chunks", {"chunk_size": 128}),
            ("Reranking", {"use_reranker": True}),
            ("Hybrid search", {"search_type": "hybrid"}),
            ("GPT-4", {"llm_model": "gpt-4"})
        ]
        
        for name, config in experiments:
            result = self.test_improvement(name, config)
            self.experiments.append({
                "name": name,
                "config": config,
                "result": result
            })
        
        return self.experiments

Expected RAG Performance by Use Case

🚦 Implementation Phases

Phase 1: Google Drive MVP (Month 1)

Google OAuth
rclone mounting
Basic Pathway indexing
Simple search UI

Phase 2: Multi-Source (Month 2)

S3 integration
Database connectors
Hybrid search
Performance tuning

Phase 3: Enterprise Features (Month 3)

SharePoint (licensed)
Streaming sources
Multi-modal RAG
Advanced analytics

Phase 4: AI Agents (Quarter 2)

Agent framework
Custom tools
Workflow automation
Knowledge graphs

Next Steps

1. Week 1: Set up Google Cloud project and OAuth
2. Week 2: Implement rclone mounting
3. Week 3: Deploy basic Pathway pipeline
4. Week 4: Create search UI

Is Our RAG Good Enough? Production Readiness Assessment

Quick Decision Framework

def is_rag_production_ready(evaluation_scores):
    """
    Determine if RAG system meets production standards
    Returns: (ready: bool, issues: list, recommendations: list)
    """
    
    # Critical metrics that MUST pass
    critical_metrics = {
        "faithfulness": 0.90,  # No hallucinations
        "context_precision": 0.85,  # High-quality retrieval
        "response_latency_p95": 500,  # User experience
        "system_availability": 0.999  # Reliability
    }
    
    # Important metrics that SHOULD pass
    important_metrics = {
        "answer_relevancy": 0.85,
        "context_recall": 0.90,
        "user_satisfaction": 4.0
    }
    
    issues = []
    recommendations = []
    
    # Check critical metrics
    for metric, threshold in critical_metrics.items():
        if evaluation_scores[metric] < threshold:
            issues.append(f"CRITICAL: {metric} = {evaluation_scores[metric]} (needs >= {threshold})")
            return False, issues, ["Fix critical issues before deployment"]
    
    # Check important metrics
    for metric, threshold in important_metrics.items():
        if evaluation_scores[metric] < threshold:
            recommendations.append(f"IMPROVE: {metric} = {evaluation_scores[metric]} (target >= {threshold})")
    
    # System is production ready but may need improvements
    return True, issues, recommendations

Production Readiness Levels

Go/No-Go Checklist

production_checklist:
  # MUST HAVE (Go/No-Go)
  quality_gates:
    ✓ Faithfulness >= 0.90
    ✓ No systematic hallucinations
    ✓ Response time < 500ms (p95)
    ✓ Handles edge cases gracefully
    ✓ Passes security review
  
  # SHOULD HAVE (Can deploy with plan to improve)
  performance_targets:
    ○ Context precision >= 0.85
    ○ Answer relevancy >= 0.85
    ○ User satisfaction >= 4.0/5.0
    ○ Cost per query < $0.10
  
  # NICE TO HAVE (Future enhancements)
  advanced_features:
    ○ Multi-modal support
    ○ Real-time learning
    ○ Custom fine-tuning
    ○ Advanced analytics

The Bottom Line: When to Deploy

Deploy to Production When:

1. Faithfulness > 0.90 - Users can trust the answers
2. Latency < 500ms - User experience is smooth
3. Availability > 99.9% - System is reliable
4. Business value exceeds costs - Positive ROI

Consider Beta/Pilot When:

• Core metrics pass but need optimization
• Limited user group can provide feedback
• You have resources to iterate quickly

Keep in Development When:

• Hallucination rate > 10%
• Retrieval precision < 70%
• Response time > 2 seconds
• System crashes regularly

🎊 Vision Statement

By combining rclone's file system mounting with Pathway's comprehensive data integration platform, Sasha Studio becomes the universal knowledge hub that can:

• Connect to ANY data source (350+ connectors)
• Process ANY file format (built-in parsers)
• Work with ANY LLM (all major providers)
• Update in REAL-TIME (streaming architecture)
• Scale to ANY size (distributed processing)
• Measure and guarantee quality (comprehensive evaluation framework)

This positions Sasha Studio as the most connected, most intelligent, and most accessible knowledge management platform available - with the evaluation framework to prove it works.

Result: Complete implementation plan with comprehensive RAG evaluation framework. The system now includes clear metrics, thresholds, and troubleshooting guides to determine if Pathway RAG is production-ready for Sasha Studio.