Uploaded September 2025 | Updated September 2026, 2 weeks ago
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
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Welcome to Episode 2 of the LangChain v1 Agents Series! In this episode, you'll learn how to create LangChain agents with explicit model instances and full control over configuration parameters.
What You'll Learn:
- Creating explicit ChatOllama model instances
- Understanding critical model parameters (temperature, num_predict, num_ctx)
- Configuring agents for different use cases (deterministic vs creative)
- Optimizing model performance for production environments
- Working with calculator tools for mathematical reasoning
- Comparing different configuration strategies
Tech Stack:
- LangChain v1 (latest prerelease)
- ChatOllama (explicit model configuration)
- Ollama (local LLM runtime)
- Qwen 3 model
- Custom calculator tools
- Python 3.x
Key Parameters Covered:
- temperature: Controls randomness and creativity
- num_predict: Maximum tokens to generate
- num_ctx: Context window size
- top_k and top_p: Token selection strategies
- repeat_penalty: Control output repetition
- seed: Reproducible results
- num_thread: Multi-core optimization
Prerequisites:
- Completed Episode 1 (Basic Agents)
- Basic Python knowledge
- Ollama installed and running
- Understanding of LLM concepts
Code Repository:
github.com/laxmimerit/Langchain-v1-Agents
Full Playlist
youtube.com/playlist?list=PLc2rvfiptPSR2fnZQLmq5TB36E0S0hdW4
Langchain v1 Projects Playlist
youtube.com/watch?v=xOE1yDFGePA&list=PLc2rvfiptPSQzrdPe5apNAq1bbBROUXKh&pp=0gcJCaIEOCosWNin
📚 Learn More with My Udemy Courses
🔥 MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
🚀 Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
🧠 LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
⚡ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
🔧 Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
📖 NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
🌐 Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
📌 Support the Channel
👍 Like the video if it helps you
💬 Comment your doubts & feedback
🔔 Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT
🚀 The LangChain 10 Days FREE Bootcamp is live: 10 lessons, free AI models only, from your first API call to a production grade RAG agent. Start with Day 0 for the roadmap and setup.
📺 Full playlist: youtube.com/watch?v=KJ3_NExk7-Q&list=PLW4pPr9JCovI&index=1
----------
Welcome to Episode 2 of the LangChain v1 Agents Series! In this episode, you'll learn how to create LangChain agents with explicit model instances and full control over configuration parameters.
What You'll Learn:
- Creating explicit ChatOllama model instances
- Understanding critical model parameters (temperature, num_predict, num_ctx)
- Configuring agents for different use cases (deterministic vs creative)
- Optimizing model performance for production environments
- Working with calculator tools for mathematical reasoning
- Comparing different configuration strategies
Tech Stack:
- LangChain v1 (latest prerelease)
- ChatOllama (explicit model configuration)
- Ollama (local LLM runtime)
- Qwen 3 model
- Custom calculator tools
- Python 3.x
Key Parameters Covered:
- temperature: Controls randomness and creativity
- num_predict: Maximum tokens to generate
- num_ctx: Context window size
- top_k and top_p: Token selection strategies
- repeat_penalty: Control output repetition
- seed: Reproducible results
- num_thread: Multi-core optimization
Prerequisites:
- Completed Episode 1 (Basic Agents)
- Basic Python knowledge
- Ollama installed and running
- Understanding of LLM concepts
Code Repository:
github.com/laxmimerit/Langchain-v1-Agents
Full Playlist
youtube.com/playlist?list=PLc2rvfiptPSR2fnZQLmq5TB36E0S0hdW4
Langchain v1 Projects Playlist
youtube.com/watch?v=xOE1yDFGePA&list=PLc2rvfiptPSQzrdPe5apNAq1bbBROUXKh&pp=0gcJCaIEOCosWNin
📚 Learn More with My Udemy Courses
🔥 MCP Mastery: Build AI Apps with Claude, LangChain and Ollama
udemy.com/course/mcp-mastery-build-ai-apps-with-claude-langchain-and-ollama/?referralCode=31C17C306A59601B8689
🚀 Agentic RAG with LangChain & LangGraph
udemy.com/course/agentic-rag-with-langchain-and-langgraph/?referralCode=C0BCC208F53AF2C98AC5
🧠 LangGraph with Ollama
udemy.com/course/langgraph-with-ollama/?referralCode=B646DCB44A189BEBC20C
⚡ Ollama and LangChain
udemy.com/course/ollama-and-langchain/?referralCode=7F4C0C7B8CF223BA9327
🔧 Fine-Tuning LLM with Hugging Face Transformers
udemy.com/course/fine-tuning-llm-with-hugging-face-transformers/?referralCode=6DEB3BE17C2644422D8E
📖 NLP with BERT in Python
udemy.com/course/nlp-with-bert-in-python/?referralCode=063516494616C76907CD
🌐 Connect with Me
Website & Blogs: kgptalkie.com
LinkedIn: linkedin.com/in/laxmimerit
GitHub: github.com/laxmimerit
Twitter (X): twitter.com/laxmimerit
📌 Support the Channel
👍 Like the video if it helps you
💬 Comment your doubts & feedback
🔔 Subscribe for free weekly AI & Data Science content
#DataScience #MachineLearning #LangChain #LangGraph #Ollama #Python #AI #DeepLearning #NLP #GenerativeAI #LLM #HuggingFace #BERT
