arbazkhan-cs/genai

How arbazkhan-cs/genai works

This repository is a collection of hands-on LangChain tutorials, not one integrated application. Its clearest end-to-end product example is the Streamlit web Q&A app: a user submits a URL and question, the app loads page text, passes it with the question through a prompt and chat model, then displays the parsed answer. Other tutorials cover model providers, prompt and chain composition, retrieval, embeddings, and structured output as separate examples. The graph preserves those documented branches; most tutorial files are standalone, so no shared pipeline is implied.

Main entry points: 6_app.py Web Q&A App · EmbeddingModels Embedding Models · 3_mmr_retriever.py MMR Retrieval · 4_multi_query_retriever.py Multi-Query Retrieval

Components 21

Web Q&A

Web Q&A App RAG/DocumentLoader/6_app.py
Web Page Loader RAG/DocumentLoader/6_app.py
Answer Prompt RAG/DocumentLoader/6_app.py

Prompt Composition

Sequential Chain Chains/1_sequential_chain.py
Parallel Chain Chains/2_parallel_chain.py
Conditional Chain Chains/3_conditional_chain.py
Runnable Patterns Runnables
Prompt Templates Prompts

Retrieval Tutorials

Embedding Models EmbeddingModels
Document Loaders RAG/DocumentLoader
Text Splitters RAG/TextSplitter
Vector Retrieval RAG/Retrievers/2_vector_store_retriever.py
MMR Retrieval RAG/Retrievers/3_mmr_retriever.py
Multi-Query Retrieval RAG/Retrievers/4_multi_query_retriever.py
Contextual Compression RAG/Retrievers/5_contextual_compression_retriever.py

Model Providers

Groq Chat Model RAG/DocumentLoader/6_app.py
Chat Model Examples ChatModels

Output Formats

String Parser RAG/DocumentLoader/6_app.py
Output Parser Examples OutputParsers
Structured Output StructuredOutput

Other components

Questioner

Connections 12

Questioner → Web Q&A App: submits URL and question RAG/DocumentLoader/6_app.py
Web Q&A App → Web Page Loader: loads page text RAG/DocumentLoader/6_app.py
Web Q&A App → Answer Prompt: creates prompt RAG/DocumentLoader/6_app.py
Web Q&A App → Groq Chat Model: invokes model RAG/DocumentLoader/6_app.py
Web Q&A App → String Parser: parses answer RAG/DocumentLoader/6_app.py
Web Q&A App → Runnable Patterns: composes processing chain RAG/DocumentLoader/6_app.py
MMR Retrieval → Embedding Models: embeds documents RAG/Retrievers/3_mmr_retriever.py
MMR Retrieval → Vector Retrieval: builds vector store RAG/Retrievers/3_mmr_retriever.py
Multi-Query Retrieval → Embedding Models: embeds documents RAG/Retrievers/4_multi_query_retriever.py
Multi-Query Retrieval → Groq Chat Model: uses query model RAG/Retrievers/4_multi_query_retriever.py
Contextual Compression → Embedding Models: embeds documents RAG/Retrievers/5_contextual_compression_retriever.py
Contextual Compression → Groq Chat Model: uses compressor model RAG/Retrievers/5_contextual_compression_retriever.py

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