fdsf

App.py

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+import streamlit as st
+from streamlit.logger import get_logger
+import datasets
+import pandas as pd
+from langchain_huggingface.embeddings import HuggingFaceEmbeddings
+from langchain_openai import ChatOpenAI
+from langchain_core.prompts import PromptTemplate
+from langchain_core.messages import HumanMessage, SystemMessage
+from sentence_transformers import util
+
+
+
+LOGGER = get_logger(__name__)
+
+
+@st.cache_data
+def get_df() ->object:
+    ds = datasets.load_dataset('sivan22/yalkut-yosef-embeddings')
+    df = pd.DataFrame.from_dict(ds['train'])
+    return df
+
+@st.cache_resource
+def get_model()->object:
+    model_name = "intfloat/multilingual-e5-large"
+    model_kwargs = {'device': 'cpu'} #'cpu' or 'cuda'
+    encode_kwargs = {'normalize_embeddings': False}
+    embeddings_model = HuggingFaceEmbeddings(
+        model_name=model_name,
+        model_kwargs=model_kwargs,
+        encode_kwargs=encode_kwargs
+    )
+    return embeddings_model
+
+@st.cache_resource
+def get_chat_api(api_key:str):
+    chat = ChatOpenAI(model="gpt-3.5-turbo-16k", api_key=api_key)
+    return chat
+
+
+def get_results(embeddings_model,input,df,num_of_results) -> pd.DataFrame:
+    embeddings = embeddings_model.embed_query('query: '+ input)
+    df['similarity'] = df['embeddings'].apply(lambda x: util.dot_score(x,embeddings))
+    results = df.sort_values(by='similarity', ascending=False)
+    return results.head(num_of_results)
+
+def get_llm_results(query,chat,results):   
+
+    prompt_template = PromptTemplate.from_template(
+        """
+    the question is: {query}
+    the possible answers are:
+    {answers}
+
+    """   )
+
+    messages = [
+        SystemMessage(content="You're a helpful assistant. given a question, filter and sort the possible answers to the given question by relevancy, drop the irrelevant answers and return the results in the following JSON format (scores are between 0 and 1): {\"answer\": \"score\", \"answer\": \"score\"}. "),
+        HumanMessage(content=prompt_template.format(query=query, answers=str.join('\n', results['text'].head(10).tolist()))),
+    ]
+
+    response =  chat.invoke(messages)
+    llm_results_df = pd.read_json(response.content, orient='index')
+    return llm_results_df
+
+
+
+def run():
+    
+    st.set_page_config(
+        page_title=" חיפוש סמנטי בילקוט יוסף",
+        page_icon="📚",
+        layout="wide",
+        initial_sidebar_state="expanded"    
+    )
+    
+    st.write("חיפוש חכם בספר ילקוט יוסף קיצור שולחן ערוך")
+    
+    embeddings_model = get_model()    
+    df = get_df()
+    
+    user_input = st.text_input('כתוב כאן את שאלתך', placeholder='כמה נרות מדליקים בכל לילה מלילות החנוכה')    
+    num_of_results = st.sidebar.slider('מספר התוצאות שברצונך להציג:',1,25,5)
+    use_llm = st.sidebar.checkbox("השתמש במודל שפה כדי לשפר תוצאות", False)
+    openAikey = st.sidebar.text_input("OpenAI API key", type="password")
+    
+    if (st.button('חפש') or user_input) and user_input!="":
+        
+        results = get_results(embeddings_model,user_input,df,num_of_results)  
+
+        if use_llm:
+            chat = get_chat_api(openAikey)
+            llm_results = get_llm_results(user_input,chat,results)
+            st.write(llm_results) 
+
+        else:
+            st.write(results[['siman','sek','text']].head(10))
+            
+       
+if __name__ == "__main__":
+    run()

__init__.py

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+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.

requirements.txt

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+pandas
+streamlit
+torch
+transformers
+datasets
+langchain_huggingface
+langchain_openai
+langchain
+sentence_transformers

run.bat

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+pip install -r requirements.txt
+streamlit run app.py

utils.py

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+# Copyright (c) Streamlit Inc. (2018-2022) Snowflake Inc. (2022)
+#
+# Licensed under the Apache License, Version 2.0 (the "License");
+# you may not use this file except in compliance with the License.
+# You may obtain a copy of the License at
+#
+#     http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing, software
+# distributed under the License is distributed on an "AS IS" BASIS,
+# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+# See the License for the specific language governing permissions and
+# limitations under the License.
+
+import inspect
+import textwrap
+
+import streamlit as st
+
+
+def show_code(demo):
+    """Showing the code of the demo."""
+    show_code = st.sidebar.checkbox("Show code", True)
+    if show_code:
+        # Showing the code of the demo.
+        st.markdown("## Code")
+        sourcelines, _ = inspect.getsourcelines(demo)
+        st.code(textwrap.dedent("".join(sourcelines[1:])))