Spaces:
Sleeping
Sleeping
File size: 5,216 Bytes
66b3608 0a1026c 66b3608 3bc66ce 66b3608 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 |
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)
|