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import os
import getpass
import sentence_transformers
import streamlit as st
VECTOR_DB ="c8af7dfa-bcad-46e5-b69d-cd85ce9315d1"
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 = VECTOR_DB
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")
# 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)
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