Tabular Data Retrieval

LangChain · Intermediate ·📊 Data Analytics & Business Intelligence ·2y ago

Key Takeaways

LangChain's tabular data retrieval using semantic layers, featuring discussions with founders of Cube and Delphi Labs

Original Description

Lots of data and information is stored as tabular data whether it be in relational databases, cloud data warehouses, query engines or data lakes. Accessing this data in LangChain can be made easier and more reliable by working with a semantic layer. We'll be joined by Artyom Keydunov, founder of Cube (semantic layer for data apps) and David Jayatillake, CEO & cofounder of Delphi Labs (an AI natural language interface for analytics, using semantic layers), and Francisco and Manuel (from Pampa Labs) for a discussion on the topic.
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 8 Benchmarking Question/Answering Over CSV Data
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 9 LangChain "RAG Evaluation" Webinar
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 10 Fine-tuning in Your Voice Webinar
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 ▶ Tabular Data Retrieval
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 12 Building an LLM Application with Audio by AssemblyAI
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 13 Superagent Deepdive Webinar
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 14 Lessons from Deploying LLMs with LangSmith
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 15 Shortwave Assistant Deepdive Webinar
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 16 Cognitive Architectures for Language Agents
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 17 Effectively Building with LLMs in the Browser with Jacob
Effectively Building with LLMs in the Browser with Jacob
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 18 Data Privacy for LLMs
Data Privacy for LLMs
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 19 "Theory of Mind" Webinar with Plastic Labs
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 20 LangChain Templates
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 21 Using Natural Language to Query Postgres with Jacob
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 22 Building a Research Assistant from Scratch
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 23 Benchmarking RAG over LangChain Docs
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 24 Skeleton-of-Thought: Building a New Template from Scratch
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 25 Benchmarking Methods for Semi-Structured RAG
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 26 LangSmith Highlights: Getting Started
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 27 LangSmith Highlights: Debugging
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 28 LangSmith Highlights: Datasets
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 29 LangSmith Highlights: Evaluation
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 30 LangSmith Highlights: Human Annotation
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 31 LangSmith Highlights: Monitoring
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 32 LangSmith Highlights: Hub
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 33 SQL Research Assistant
SQL Research Assistant
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 34 Getting Started with Multi-Modal LLMs
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 35 Build a Full Stack RAG App With TypeScript
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 36 Auto-Prompt Builder (with Hosted LangServe)
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 37 LangChain v0.1.0 Launch: Introduction
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 38 LangChain v0.1.0 Launch: Observability
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 39 LangChain v0.1.0 Launch: Integrations
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 40 LangChain v0.1.0 Launch: Composability
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 41 LangChain v0.1.0 Launch: Streaming
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 42 LangChain v0.1.0 Launch: Output Parsing
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 43 LangChain v0.1.0 Launch: Retrieval
LangChain v0.1.0 Launch: Retrieval
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 44 LangChain v0.1.0 Launch: Agents
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 45 Build and Deploy a RAG app with Pinecone Serverless
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 46 Hosted LangServe + LangChain Templates
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 47 LangGraph: Intro
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 48 LangGraph: Agent Executor
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 49 LangGraph: Chat Agent Executor
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 50 LangGraph: Human-in-the-Loop
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 51 LangGraph: Dynamically Returning a Tool Output Directly
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 52 LangGraph: Respond in a Specific Format
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 53 LangGraph: Managing Agent Steps
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 54 LangGraph: Force-Calling a Tool
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 55 LangGraph: Multi-Agent Workflows
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 56 Streaming Events: Introducing a new `stream_events` method
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 57 Building a web RAG chatbot: using LangChain, Exa (prev. Metaphor), LangSmith, and Hosted Langserve
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 58 OpenGPTs
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 59 Open Source RAG with Nomic's New Embedding Model (and ChromaDB and Ollama)
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 60 LangGraph: Persistence
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Learn how to access tabular data in LangChain using semantic layers, with expert discussions from Cube and Delphi Labs founders

Key Takeaways
  1. Understand the importance of semantic layers in data retrieval
  2. Explore the use of relational databases and cloud data warehouses
  3. Learn how to query data using query engines and data lakes
  4. Implement a semantic layer in LangChain for easier data access
💡 Working with a semantic layer can make accessing tabular data in LangChain easier and more reliable

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