langchainpython

Large Language Models

• To run python program install code runner extension

• To add langchain into my simple project you can go to the folder of your project and run bash pip i langchain

• OR

• You can create virtual env by run

 virtualenv env

the name of env at the last which are our virtual env or you can run

python -m venv env

• to activate env

  env\scripts\activate

  or

  env\bin\activate
  ```    or 
  ```bash  
  venv\Scripts\activate.bat(for windows)```
  ```bash  
  env\scripts\activate```
  or 
  ```bash
    env\bin\activate

    or 
  

   venv\Scripts\activate.bat(for windows)

• pip freeze to check which package are installed

• pip install langchain

• When we used model in this we used hugging face access token

• to install all requirement.txt file run

pip freeze > requirements.txt

pip install -r requirements.txt

• To Create django Project start

django-admin startproject

• to start django app

python manage.py startapp chef 

• To Use Embbeding Model Free you can use HuggingFace + Ollama are free & unlimited once set up. Let me show you both step by step so you can pick the one that fits your machine.

🔹 1. HuggingFace Embeddings (CPU-only, simple setup)

👉 Best if you don’t want to install heavy models locally.

Install requirements: pip install sentence-transformers langchain-huggingface

Example code: from langchain_huggingface import HuggingFaceEmbeddings

Small, fast model

embeddings = HuggingFaceEmbeddings(model_name="sentence-transformers/all-MiniLM-L6-v2")

Embed text

vector = embeddings.embed_query("Hello Sonam") print(len(vector), vector[:10]) # length + preview of embedding

✅ This downloads the model once from HuggingFace Hub and caches it locally. ✅ Works offline after first download. ⚡ Runs on CPU, lightweight.

🔹 2. Ollama Embeddings (local LLM server)

👉 Best if you want to run models fully offline.

Step 1: Install Ollama

Download from 👉 https://ollama.com/download

Run Ollama server (it runs automatically in background after install).

Step 2: Pull an embedding model ollama pull mxbai-embed-large

(You can also use nomic-embed-text if you want smaller + faster.)

Step 3: Install LangChain Ollama pip install langchain-community

Step 4: Example code from langchain_community.embeddings import OllamaEmbeddings

Use pulled model

embeddings = OllamaEmbeddings(model="mxbai-embed-large")

Embed text

vector = embeddings.embed_query("Hello Sonam") print(len(vector), vector[:10])

✅ Runs fully local (no quota, no API key). ✅ Works offline. ⚡ Faster on GPU, but also works on CPU.

🔹 Recommendation

If you want easy + light → HuggingFace (all-MiniLM-L6-v2).

If you want offline + powerful → Ollama (mxbai-embed-large).

👉 Do you want me to update your embedding.py factory so you can just set .env like:

PROVIDER=hf PROVIDER=ollama

and it will automatically switch without changing your Python code?