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LCA LangSmith Professional

Course materials for LangSmith Professional, with Python and TypeScript implementations.

Project Structure

Both python/ and ts/ directories mirror each other with equivalent implementations.

β”œβ”€β”€ python/
β”‚   β”œβ”€β”€ officeflow-agent/              # OfficeFlow customer support agent (Emma)
β”‚   β”‚   β”œβ”€β”€ agent_v0.py                # Baseline agent (no tracing)
β”‚   β”‚   β”œβ”€β”€ agent_v1.py                # + LangSmith tracing
β”‚   β”‚   β”œβ”€β”€ agent_v2.py                # + Enhanced tool instructions
β”‚   β”‚   β”œβ”€β”€ agent_v3.py                # + Stock information policy
β”‚   β”‚   β”œβ”€β”€ agent_v4.py                # + No-chunking RAG
β”‚   β”‚   β”œβ”€β”€ agent_v5.py                # + Conciseness improvements
β”‚   β”‚   β”œβ”€β”€ inventory/                 # SQLite product database
β”‚   β”‚   └── knowledge_base/            # Company policy documents + embeddings
β”‚   β”‚
β”‚   β”œβ”€β”€ module-1/
β”‚   β”‚   └── lesson-2/                  # Tracing with LangSmith
β”‚   β”‚       β”œβ”€β”€ third_party_agent.py   # Weather agent with tool calling
β”‚   β”‚       └── thread_agent.py        # Conversational agent with threads
β”‚   β”‚
β”‚   β”œβ”€β”€ module-2/                      # Evaluation fundamentals
β”‚   β”‚   β”œβ”€β”€ lesson-3/                  # Running experiments
β”‚   β”‚   β”‚   └── run_experiment.py
β”‚   β”‚   β”œβ”€β”€ lesson-4/                  # Code-based evaluation
β”‚   β”‚   β”‚   β”œβ”€β”€ eval_schema_check.py   # Schema-before-query evaluator
β”‚   β”‚   β”‚   └── run_eval.py
β”‚   β”‚   β”œβ”€β”€ lesson-5/                  # LLM-as-judge
β”‚   β”‚   β”‚   └── run_experiment.py
β”‚   β”‚   └── lesson-6/                  # Pairwise evaluation
β”‚   β”‚       β”œβ”€β”€ run_agents.py
β”‚   β”‚       β”œβ”€β”€ eval_conciseness_pairwise.py
β”‚   β”‚       └── run_pairwise_experiment.py
β”‚   β”‚
β”‚   β”œβ”€β”€ module-3/                      # Production & scaling
β”‚   β”‚   └── lesson-2/                  # Trace upload
β”‚   β”‚       β”œβ”€β”€ generate_traces.py
β”‚   β”‚       └── upload_traces.py
β”‚   β”‚
β”‚   β”œβ”€β”€ pyproject.toml
β”‚   └── .env.example
β”‚
└── ts/
    β”œβ”€β”€ officeflow-agent/              # OfficeFlow customer support agent (Emma)
    β”‚   β”œβ”€β”€ agent_v0.ts                # Baseline agent (no tracing)
    β”‚   β”œβ”€β”€ agent_v1.ts                # + LangSmith tracing
    β”‚   β”œβ”€β”€ agent_v2.ts                # + Enhanced tool instructions
    β”‚   β”œβ”€β”€ agent_v3.ts                # + Stock information policy
    β”‚   β”œβ”€β”€ agent_v4.ts                # + No-chunking RAG
    β”‚   β”œβ”€β”€ agent_v5.ts                # + Conciseness improvements
    β”‚   β”œβ”€β”€ inventory/                 # SQLite product database
    β”‚   └── knowledge_base/            # Company policy documents + embeddings
    β”‚
    β”œβ”€β”€ module-1/
    β”‚   └── lesson-2/                  # Tracing with LangSmith
    β”‚       β”œβ”€β”€ third_party_agent.ts   # Weather agent with tool calling
    β”‚       └── thread_agent.ts        # Conversational agent with threads
    β”‚
    β”œβ”€β”€ module-2/                      # Evaluation fundamentals
    β”‚   β”œβ”€β”€ lesson-3/                  # Running experiments
    β”‚   β”‚   └── run_experiment.ts
    β”‚   β”œβ”€β”€ lesson-4/                  # Code-based evaluation
    β”‚   β”‚   β”œβ”€β”€ eval_schema_check.ts   # Schema-before-query evaluator
    β”‚   β”‚   └── run_eval.ts
    β”‚   β”œβ”€β”€ lesson-5/                  # LLM-as-judge
    β”‚   β”‚   └── run_experiment.ts
    β”‚   └── lesson-6/                  # Pairwise evaluation
    β”‚       β”œβ”€β”€ run_agents.ts
    β”‚       β”œβ”€β”€ eval_conciseness_pairwise.ts
    β”‚       └── run_pairwise_experiment.ts
    β”‚
    β”œβ”€β”€ module-3/                      # Production & scaling
    β”‚   └── lesson-2/                  # Trace upload
    β”‚       β”œβ”€β”€ generate_traces.ts
    β”‚       └── upload_traces.ts
    β”‚
    β”œβ”€β”€ package.json
    └── example.env

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