Update app.py

app.py

@@ -5,144 +5,6 @@ import sentence_transformers
 
 import streamlit as st
 
-def get_credentials():
-	return {
-		"url" : "https://us-south.ml.cloud.ibm.com",
-		"apikey" : os.getenv("IBM_API_KEY")
-	}
-
-model_id = "ibm/granite-3-8b-instruct"
-
-parameters = {
-    "decoding_method": "greedy",
-    "max_new_tokens": 900,
-    "min_new_tokens": 0,
-    "repetition_penalty": 1
-}
-
-project_id = os.getenv("IBM_PROJECT_ID")
-space_id = os.getenv("IBM_SPACE_ID")
-
-from ibm_watsonx_ai.foundation_models import ModelInference
-
-model = ModelInference(
-	model_id = model_id,
-	params = parameters,
-	credentials = get_credentials(),
-	project_id = project_id,
-	space_id = space_id
-	)
-
-from ibm_watsonx_ai.client import APIClient
-
-wml_credentials = get_credentials()
-client = APIClient(credentials=wml_credentials, project_id=project_id)  #, space_id=space_id)
-
-vector_index_id = "14c14504-5f45-4e6c-8f0f-25f2378a1d99"
-vector_index_details = client.data_assets.get_details(vector_index_id)
-vector_index_properties = vector_index_details["entity"]["vector_index"]
-
-top_n = 20 if vector_index_properties["settings"].get("rerank") else int(vector_index_properties["settings"]["top_k"])
-
-def rerank( client, documents, query, top_n ):
-    from ibm_watsonx_ai.foundation_models import Rerank
-
-    reranker = Rerank(
-        model_id="cross-encoder/ms-marco-minilm-l-12-v2",
-        api_client=client,
-        params={
-            "return_options": {
-                "top_n": top_n
-            },
-            "truncate_input_tokens": 512
-        }
-    )
-
-    reranked_results = reranker.generate(query=query, inputs=documents)["results"]
-
-    new_documents = []
-    
-    for result in reranked_results:
-        result_index = result["index"]
-        new_documents.append(documents[result_index])
-        
-    return new_documents
-
-from ibm_watsonx_ai.foundation_models.embeddings.sentence_transformer_embeddings import SentenceTransformerEmbeddings
-
-emb = SentenceTransformerEmbeddings('sentence-transformers/all-MiniLM-L6-v2')
-
-import subprocess
-import gzip
-import json
-import chromadb
-import random
-import string
-
-def hydrate_chromadb():
-    data = client.data_assets.get_content(vector_index_id)
-    content = gzip.decompress(data)
-    stringified_vectors = str(content, "utf-8")
-    vectors = json.loads(stringified_vectors)
-    
-    #chroma_client = chromadb.Client()
-    #chroma_client = chromadb.InMemoryClient()
-    chroma_client = chromadb.PersistentClient(path="./chroma_db")
-
-    # make sure collection is empty if it already existed
-    collection_name = "my_collection"
-    try:
-        collection = chroma_client.delete_collection(name=collection_name)
-    except:
-        print("Collection didn't exist - nothing to do.")
-    collection = chroma_client.create_collection(name=collection_name)
-
-    vector_embeddings = []
-    vector_documents = []
-    vector_metadatas = []
-    vector_ids = []
-
-    for vector in vectors:
-        vector_embeddings.append(vector["embedding"])
-        vector_documents.append(vector["content"])
-        metadata = vector["metadata"]
-        lines = metadata["loc"]["lines"]
-        clean_metadata = {}
-        clean_metadata["asset_id"] = metadata["asset_id"]
-        clean_metadata["asset_name"] = metadata["asset_name"]
-        clean_metadata["url"] = metadata["url"]
-        clean_metadata["from"] = lines["from"]
-        clean_metadata["to"] = lines["to"]
-        vector_metadatas.append(clean_metadata)
-        asset_id = vector["metadata"]["asset_id"]
-        random_string = ''.join(random.choices(string.ascii_uppercase + string.digits, k=10))
-        id = "{}:{}-{}-{}".format(asset_id, lines["from"], lines["to"], random_string)
-        vector_ids.append(id)
-
-    collection.add(
-        embeddings=vector_embeddings,
-        documents=vector_documents,
-        metadatas=vector_metadatas,
-        ids=vector_ids
-    )
-    return collection
-
-chroma_collection = hydrate_chromadb()
-
-def proximity_search( question ):
-    query_vectors = emb.embed_query(question)
-    query_result = chroma_collection.query(
-        query_embeddings=query_vectors,
-        n_results=top_n,
-        include=["documents", "metadatas", "distances"]
-    )
-
-    documents = list(reversed(query_result["documents"][0]))
-
-    if vector_index_properties["settings"].get("rerank"):
-        documents = rerank(client, documents, question, vector_index_properties["settings"]["top_k"])
-
-    return "\n".join(documents)
 
 # Streamlit UI
 st.title("🔍 IBM Watson RAG Chatbot")
@@ -152,7 +14,7 @@ question = st.text_input("Enter your question:")
 
 if question:
     # Retrieve relevant grounding context
-    grounding = proximity_search(question)
+    grounding = RAG_proximity_search(question)
 
     # Format the question with retrieved context
     formatted_question = f"""<|start_of_role|>user<|end_of_role|>Use the following pieces of context to answer the question.