Create app.py

app.py

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+import os
+import getpass
+
+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()
+
+    # 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")
+
+# User input in Streamlit
+question = st.text_input("Enter your question:")
+
+if question:
+    # Retrieve relevant grounding context
+    grounding = 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.
+
+{grounding}
+
+Question: {question}<|end_of_text|>
+<|start_of_role|>assistant<|end_of_role|>"""
+
+    # Placeholder for a prompt input (Optional)
+    prompt_input = ""  # Set this dynamically if needed
+    prompt = f"""{prompt_input}{formatted_question}"""
+
+    # Simulated AI response (Replace with actual model call)
+    generated_response = f"AI Response based on: {prompt}"
+
+    # Display results
+    st.subheader("📌 Retrieved Context")
+    st.write(grounding)
+
+    st.subheader("🤖 AI Response")
+    st.write(generated_response)