Spaces:
Running
on
T4
Running
on
T4
colpali fix
Browse files
pages/Multimodal_Conversational_Search.py
CHANGED
@@ -208,11 +208,6 @@ def handle_input(state,dummy):
|
|
208 |
if key.startswith('input_'):
|
209 |
inputs[key.removeprefix('input_')] = st.session_state[key]
|
210 |
st.session_state.inputs_ = inputs
|
211 |
-
|
212 |
-
#######
|
213 |
-
|
214 |
-
|
215 |
-
#st.write(inputs)
|
216 |
question_with_id = {
|
217 |
'question': inputs["query"],
|
218 |
'id': len(st.session_state.questions_)
|
@@ -230,32 +225,7 @@ def handle_input(state,dummy):
|
|
230 |
'image': out_['image'],
|
231 |
'table':out_['table']
|
232 |
})
|
233 |
-
|
234 |
-
|
235 |
-
|
236 |
-
|
237 |
-
# search_type = st.selectbox('Select the Search type',
|
238 |
-
# ('Conversational Search (RAG)',
|
239 |
-
# 'OpenSearch vector search',
|
240 |
-
# 'LLM Text Generation'
|
241 |
-
# ),
|
242 |
-
|
243 |
-
# key = 'input_searchType',
|
244 |
-
# help = "Select the type of retriever\n1. Conversational Search (Recommended) - This will include both the OpenSearch and LLM in the retrieval pipeline \n (note: This will put opensearch response as context to LLM to answer) \n2. OpenSearch vector search - This will put only OpenSearch's vector search in the pipeline, \n(Warning: this will lead to unformatted results )\n3. LLM Text Generation - This will include only LLM in the pipeline, \n(Warning: This will give hallucinated and out of context answers_)"
|
245 |
-
# )
|
246 |
-
|
247 |
-
# col1, col2, col3, col4 = st.columns(4)
|
248 |
-
|
249 |
-
# with col1:
|
250 |
-
# st.text_input('Temperature', value = "0.001", placeholder='LLM Temperature', key = 'input_temperature',help = "Set the temperature of the Large Language model. \n Note: 1. Set this to values lower to 1 in the order of 0.001, 0.0001, such low values reduces hallucination and creativity in the LLM response; 2. This applies only when LLM is a part of the retriever pipeline")
|
251 |
-
# with col2:
|
252 |
-
# st.number_input('Top K', value = 200, placeholder='Top K', key = 'input_topK', step = 50, help = "This limits the LLM's predictions to the top k most probable tokens at each step of generation, this applies only when LLM is a prt of the retriever pipeline")
|
253 |
-
# with col3:
|
254 |
-
# st.number_input('Top P', value = 0.95, placeholder='Top P', key = 'input_topP', step = 0.05, help = "This sets a threshold probability and selects the top tokens whose cumulative probability exceeds the threshold while the tokens are generated by the LLM")
|
255 |
-
# with col4:
|
256 |
-
# st.number_input('Max Output Tokens', value = 500, placeholder='Max Output Tokens', key = 'input_maxTokens', step = 100, help = "This decides the total number of tokens generated as the final response. Note: Values greater than 1000 takes longer response time")
|
257 |
-
|
258 |
-
# st.markdown('---')
|
259 |
|
260 |
|
261 |
def write_user_message(md):
|
|
|
208 |
if key.startswith('input_'):
|
209 |
inputs[key.removeprefix('input_')] = st.session_state[key]
|
210 |
st.session_state.inputs_ = inputs
|
|
|
|
|
|
|
|
|
|
|
211 |
question_with_id = {
|
212 |
'question': inputs["query"],
|
213 |
'id': len(st.session_state.questions_)
|
|
|
225 |
'image': out_['image'],
|
226 |
'table':out_['table']
|
227 |
})
|
228 |
+
st.session_state.input_query=""
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
229 |
|
230 |
|
231 |
def write_user_message(md):
|