remove llm call

02-household-queries/optimize_encoder.py

@@ -13,22 +13,13 @@
 
 # from langchain_google_genai import ChatGoogleGenerativeAI, GoogleGenerativeAIEmbeddings
 
-from llm import ollama_client
 from retrieval import create_retriever
 from langchain_community.vectorstores import Chroma
 # import nltk
 # import spacy
 
 dotenv.load_dotenv()
 
-_llm_model_name = os.environ.get("LLM_MODEL_NAME", "mistral")
-
-
-llm = {
- "gpt4all_path": "./mistral-7b-instruct-v0.1.Q4_0.gguf",
- "ollama": ollama_client(_llm_model_name, settings={"temperature":0.1})
-}
-
 EMBEDDINGS = {
  "st_all-MiniLM-L6-v2" : {"func": SentenceTransformerEmbeddings(model_name="all-MiniLM-L6-v2")},
  "hf_all-MiniLM-L6-v2" : {"func": HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")},
@@ -61,6 +52,7 @@ def get_text_chunks_langchain(text, source, chunk_size, chunk_overlap):
  # text_splitter= NLTKTextSplitter()
  # text_splitter= SpacyTextSplitter()
  texts = text_splitter.split_text(source + "\n\n" + text)
+ # print(" Split into", len(texts))
 
  docs = [
  Document(page_content=t, metadata={"source": source.strip()}) for t in texts
@@ -129,7 +121,6 @@ def evaluate_retrieval(vectordb, recall_results):
  recall_results["result"] = results
  recall_results["recall_percentage"] = recall/15
  recall_results["extra_card_count"] = extra_card_count
-
  return recall_results
 
 def run_embedding_func_and_eval_retrieval(embeddings, chunk_size, chunk_overlap):