Delete embeddings.py

embeddings.py

@@ -1,50 +0,0 @@
-##return embedding vector for a given text
-##uses senetence based emebdings
-from langchain_text_splitters import CharacterTextSplitter
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_core.documents import Document
-from langchain_chroma import Chroma
-
-model_name = "sentence-transformers/all-MiniLM-L6-v2"
-model_kwargs = {"device": "cpu"}
-encode_kwargs = {"normalize_embeddings": True}
-hf = HuggingFaceEmbeddings(
-model_name=model_name, model_kwargs=model_kwargs, encode_kwargs=encode_kwargs
-)   
-
-
-vector_store = Chroma(
-collection_name="collection",
-embedding_function=hf,
-persist_directory="chroma_langchain_db",
-)
-
-
-def set_embedding(text:str,doc_id:str,user_id:str):
-    text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
-    encoding_name="cl100k_base", chunk_size=300, chunk_overlap=40
-)
-    texts = text_splitter.split_text(text)
-    print(type(texts[0]))## IT IS LIST OF STRINGS 
-    
-    for i in range(len(texts)):
-        ##vector=hf.embed_query(texts[i])
-        vector_id=(user_id+doc_id+str(i))
-        globals()[f"document_{i}"]=Document(
-            page_content= texts[i],
-            metadata={"doc_id": doc_id, "user_id": user_id},
-            id= vector_id,
-        )
-        vector_store.add_documents([globals()[f"document_{i}"]])
-        print(f"Added document {i} with id {vector_id}")
-
-def get_chunks(query:str,user_id:str,doc_id:str):
-    results = vector_store.similarity_search(
-    query,
-    k=5,
-    filter={"user_id": user_id}
-)
-    list_of_chunks=[]
-    for res in results:
-        list_of_chunks.append(res.page_content)
-    return list_of_chunks