Update app.py
Browse files
app.py
CHANGED
@@ -141,17 +141,15 @@ def create_vector_db(final_items):
|
|
141 |
from chromadb.utils import embedding_functions
|
142 |
embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
|
143 |
|
|
|
144 |
|
145 |
-
db = client.get_or_create_collection(name=collection_name, embedding_function = embedding_function)
|
146 |
-
|
147 |
-
|
148 |
documents = []
|
149 |
import torch
|
150 |
from llama_cpp import Llama
|
151 |
|
152 |
llm = Llama.from_pretrained(
|
153 |
repo_id="xzlinuxmodels/ollama3.1",
|
154 |
-
|
155 |
)
|
156 |
|
157 |
for item in final_items:
|
@@ -167,13 +165,13 @@ def create_vector_db(final_items):
|
|
167 |
Once the summarizing is done, write 'END'.
|
168 |
"""
|
169 |
|
170 |
-
response2 = llm.generate(
|
171 |
-
prompt
|
172 |
-
)
|
173 |
|
174 |
-
|
175 |
-
|
176 |
-
|
|
|
|
|
177 |
|
178 |
if final_items:
|
179 |
db.add(
|
@@ -183,6 +181,7 @@ def create_vector_db(final_items):
|
|
183 |
|
184 |
return db
|
185 |
|
|
|
186 |
def generate_response(db, query_text, previous_context):
|
187 |
query_results = db.query(
|
188 |
query_texts=query_text,
|
@@ -227,7 +226,7 @@ def generate_response(db, query_text, previous_context):
|
|
227 |
|
228 |
|
229 |
def streamlit_app():
|
230 |
-
st.title("
|
231 |
|
232 |
search_str = st.text_input("Enter search query:")
|
233 |
|
|
|
141 |
from chromadb.utils import embedding_functions
|
142 |
embedding_function = embedding_functions.SentenceTransformerEmbeddingFunction(model_name="all-MiniLM-L6-v2")
|
143 |
|
144 |
+
db = client.get_or_create_collection(name=collection_name, embedding_function=embedding_function)
|
145 |
|
|
|
|
|
|
|
146 |
documents = []
|
147 |
import torch
|
148 |
from llama_cpp import Llama
|
149 |
|
150 |
llm = Llama.from_pretrained(
|
151 |
repo_id="xzlinuxmodels/ollama3.1",
|
152 |
+
filename="unsloth.BF16.gguf",
|
153 |
)
|
154 |
|
155 |
for item in final_items:
|
|
|
165 |
Once the summarizing is done, write 'END'.
|
166 |
"""
|
167 |
|
168 |
+
response2 = list(llm.generate(prompt)) # Convert generator to list
|
|
|
|
|
169 |
|
170 |
+
if response2:
|
171 |
+
response = response2[0]["text"].strip()
|
172 |
+
documents.append(response)
|
173 |
+
else:
|
174 |
+
print("No response received from Llama model.")
|
175 |
|
176 |
if final_items:
|
177 |
db.add(
|
|
|
181 |
|
182 |
return db
|
183 |
|
184 |
+
|
185 |
def generate_response(db, query_text, previous_context):
|
186 |
query_results = db.query(
|
187 |
query_texts=query_text,
|
|
|
226 |
|
227 |
|
228 |
def streamlit_app():
|
229 |
+
st.title("BioModelsRAG")
|
230 |
|
231 |
search_str = st.text_input("Enter search query:")
|
232 |
|