dameerahmed/langchain_practice

How dameerahmed/langchain_practice works

This repository is a collection of standalone LangChain learning scripts, not one integrated application. Its examples cover Gemini models and embeddings, prompts and structured output, runnable composition, document loading and splitting, vector stores and retrieval, YouTube transcript question-answering, and tool calling. Each script runs its own demonstration; there is no documented shared user workflow or orchestration across examples. Several ingestion, model, and tool examples are represented as capability nodes because their implementations were not excerpted.

Main entry points: 28_langchain_faiss_vdb_alternative.py FAISS vector store · 29_langchain_through_yt_transcript.py Transcript Q&A · 27_langchain_chroma_vdb.py Chroma vector store · 10_langchain_str_outputparser.py String parsing

Components 33

Models and prompts

Gemini model 1_langchain_gemini_model.py
Embedding model 02_langchain_gemini_emb_model.py
Document embeddings 03_langchain_gemini_emb_doc.py
Embedding search 04_langchain_gemini_emb_serach.py
Chat messages 05_langchain_messages.py
Prompt placeholders 06_langchain_message_placeholder.py
Structured outputs 07_langchain_with_structure_output_dic.py
Pydantic output 08_langchain_with_structure_output_pydantic.py
JSON schema output 09_langchain_with_structure_output_json_schema.py
String parsing 10_langchain_str_outputparser.py
JSON parsing 11_langchain_json_outputparser.py

Runnable patterns

Simple chains 12_langchain_simple_chain.py
Two-model chain 13_langchain_simple_2llm_chain.py
Runnable sequence 14_langchain_runnable_sequence.py
Parallel runnables 15_langchain_runnable_parallel.py
Passthrough runnable 16_langchain_runnable_passthrough.py
Lambda runnable 17_langchain_runnable_lambda.py
Conditional branch 18_langchain_runnable_branch.py

Document processing

Text loading 19_langchain_text_loader.py
PDF loading 20_langchain_pdf_loader.py
Directory loading 21_langchain_directory_loader.py
Web loading 22_langchain_webbase_loader.py
Length splitting 23_langchain_text_splitter_length_based.py
Structure splitting 24_langchain_text_splitter_structure_based.py
Code splitting 25_langchain_text_splitter_python_code.py
Markdown splitting 26_langchain_text_splitter_markdown.py

Vector retrieval

Chroma vector store 27_langchain_chroma_vdb.py
FAISS vector store 28_langchain_faiss_vdb_alternative.py

Applied examples

Transcript Q&A 29_langchain_through_yt_transcript.py
Tool calling 30_langchain_tools_call.py

Other components

Google AI
Groq
YouTube transcripts

Connections 21

String parsing → Gemini model: invokes model 10_langchain_str_outputparser.py
String parsing → Groq: invokes model
JSON parsing → Groq: invokes model 11_langchain_json_outputparser.py
Two-model chain → Groq: generates story 13_langchain_simple_2llm_chain.py
Two-model chain → Google AI: returns review 13_langchain_simple_2llm_chain.py
Runnable sequence → Groq: generates story 14_langchain_runnable_sequence.py
Runnable sequence → Google AI: returns review 14_langchain_runnable_sequence.py
Parallel runnables → Groq: generates joke 15_langchain_runnable_parallel.py
Parallel runnables → Google AI: generates joke 15_langchain_runnable_parallel.py
Passthrough runnable → Groq: generates story 16_langchain_runnable_passthrough.py
Passthrough runnable → Google AI: structures review 16_langchain_runnable_passthrough.py
Lambda runnable → Groq: generates story 17_langchain_runnable_lambda.py
Conditional branch → Groq: classifies feedback 18_langchain_runnable_branch.py
Conditional branch → Groq: writes response 18_langchain_runnable_branch.py
Chroma vector store → Google AI: embeds documents 27_langchain_chroma_vdb.py
Chroma vector store → Chroma vector store: stores and searches 27_langchain_chroma_vdb.py
FAISS vector store → Google AI: embeds documents 28_langchain_faiss_vdb_alternative.py
FAISS vector store → FAISS vector store: stores and retrieves 28_langchain_faiss_vdb_alternative.py
Transcript Q&A → YouTube transcripts: fetches transcript 29_langchain_through_yt_transcript.py
Transcript Q&A → FAISS vector store: indexes chunks 29_langchain_through_yt_transcript.py
Transcript Q&A → Google AI: embeds and answers 29_langchain_through_yt_transcript.py

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