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Rag Pipeline/Developer Tutorials/change_model.py

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+# ---
+# jupyter:
+#   jupytext:
+#     text_representation:
+#       extension: .py
+#       format_name: light
+#       format_version: '1.5'
+#       jupytext_version: 1.16.3
+#   kernelspec:
+#     display_name: openml
+#     language: python
+#     name: python3
+# ---
+
+# # Tutorial on changing models
+# - How would you use a different embedding and llm model?
+
+from __future__ import annotations
+from langchain_community.cache import SQLiteCache
+import os
+import sys
+import chromadb
+
+from backend.modules.utils import load_config_and_device
+from backend.modules.rag_llm import QASetup
+
+# ## Initial config
+
+config = load_config_and_device("../../../backend/config.json")
+config["persist_dir"] = "../../data/doc_examples/chroma_db/"
+config["data_dir"] = "../../data/doc_examples/"
+config["type_of_data"] = "dataset"
+config["training"] = True
+config["test_subset"] = True  # set this to false while training, this is for demo
+# load the persistent database using ChromaDB
+client = chromadb.PersistentClient(path=config["persist_dir"])
+print(config)
+
+# ## Embedding model
+# - Pick a model from HF
+
+config["embedding_model"] = "BAAI/bge-large-en-v1.5"
+
+# ## LLM model
+
+# - Pick a model from Ollama - https://ollama.com/library?sort=popular
+# - eg : mistral
+#
+
+config["llm_model"] = "mistral"
+
+# +
+qa_dataset_handler = QASetup(
+    config=config,
+    data_type=config["type_of_data"],
+    client=client,
+)
+
+qa_dataset, _ = qa_dataset_handler.setup_vector_db_and_qa()
+# -
+
+# # IMPORTANT
+# - Do NOT forget to change the model to the best model in ollama/get_ollama.sh