LangChain is a Python library that simplifies connecting language models to applications. Its prebuilt functions and classes handle common API tasks, helping integrate generative AI into data-analysis workflows and dashboards. section 1.3.3
LangChain also supports local models. A typical setup uses a quantized Llama model in GGUF format with llama-cpp-python, then sends a prompt and captures the response. This minimal approach does not retain conversation memory, although LangChain can add chat memory. section 1.3.5
Calling a local Llama model
Install the LangChain and Llama libraries, create a LlamaCpp instance with the path to a GGUF model file, invoke it with a prompt, and print the response. Check current library instructions because installation requirements, especially on Windows, can change. section 1.3.5
Listing 1.5 Command to install the LangChain and Llama libraries
pip install langchain llama-cpp-python
Use the installation listing to set up LangChain with llama-cpp-python. section 1.3.5
Listing 1.6 Example of sending a prompt to Llama
from langchain_community.llms import LlamaCpp
llm = LlamaCpp(model_path='path_to_your_gguf_model_file')
response = llm.invoke('Your prompt!')
print(response)
Use the example listing to instantiate LlamaCpp, submit a prompt, and retrieve its response. section 1.3.5
