Merge remote-tracking branch 'origin/dev' into pr/21

app.py

@@ -12,9 +12,11 @@ from climateqa.engine.reranker import get_reranker
 from climateqa.engine.graph import make_graph_agent,make_graph_agent_poc
 from climateqa.engine.chains.retrieve_papers import find_papers
 from climateqa.chat import start_chat, chat_stream, finish_chat
+from climateqa.engine.talk_to_data.main import ask_vanna
 
 from front.tabs import (create_config_modal, create_examples_tab, create_papers_tab, create_figures_tab, create_chat_interface, create_about_tab)
 from front.utils import process_figures
+from gradio_modal import Modal
 
 
 from utils import create_user_id
@@ -67,9 +69,9 @@ vectorstore_graphs = get_pinecone_vectorstore(embeddings_function, index_name=os
 vectorstore_region = get_pinecone_vectorstore(embeddings_function, index_name=os.getenv("PINECONE_API_INDEX_LOCAL_V2"))
 
 llm = get_llm(provider="openai",max_tokens = 1024,temperature = 0.0)
-if os.getenv("ENV")=="GRADIO_ENV":
+if os.environ["GRADIO_ENV"] == "local":
     reranker = get_reranker("nano")
-else:
+else :
     reranker = get_reranker("large")
 
 agent = make_graph_agent(llm=llm, vectorstore_ipcc=vectorstore, vectorstore_graphs=vectorstore_graphs, vectorstore_region = vectorstore_region, reranker=reranker, threshold_docs=0.2)
@@ -94,7 +96,7 @@ async def chat_poc(query, history, audience, sources, reports, relevant_content_
 # Function to update modal visibility
 def update_config_modal_visibility(config_open):
     new_config_visibility_status = not config_open
-    return gr.update(visible=new_config_visibility_status), new_config_visibility_status
+    return Modal(visible=new_config_visibility_status), new_config_visibility_status
     
 
 def update_sources_number_display(sources_textbox, figures_cards, current_graphs, papers_html):
@@ -142,7 +144,7 @@ def cqa_tab(tab_name):
 
                             # Papers subtab
                             with gr.Tab("Papers", elem_id="tab-citations", id=4) as tab_papers:
-                                papers_summary, papers_html, citations_network, papers_modal = create_papers_tab()
+                                papers_direct_search, papers_summary, papers_html, citations_network, papers_modal = create_papers_tab()
 
                             # Graphs subtab
                             with gr.Tab("Graphs", elem_id="tab-graphs", id=5) as tab_graphs:
@@ -150,6 +152,20 @@ def cqa_tab(tab_name):
                                     "<h2>There are no graphs to be displayed at the moment. Try asking another question.</h2>",
                                     elem_id="graphs-container"
                                 )
+                            with gr.Tab("DRIAS", elem_id="tab-vanna", id=6) as tab_vanna:
+                                vanna_direct_question = gr.Textbox(label="Direct Question", placeholder="You can write direct question here",elem_id="direct-question", interactive=True)
+                                with gr.Accordion("Details",elem_id = 'vanna-details', open=False) as vanna_details :
+                                    vanna_sql_query = gr.Textbox(label="SQL Query Used", elem_id="sql-query", interactive=False)
+                                    show_vanna_table = gr.Button("Show Table", elem_id="show-table")
+                                    with Modal(visible=False) as vanna_table_modal:
+                                        vanna_table = gr.DataFrame([], elem_id="vanna-table")
+                                        close_vanna_modal = gr.Button("Close", elem_id="close-vanna-modal")
+                                        close_vanna_modal.click(lambda: Modal(visible=False),None, [vanna_table_modal])
+                                    show_vanna_table.click(lambda: Modal(visible=True),None ,[vanna_table_modal])
+
+                                vanna_display = gr.Plot()
+                                vanna_direct_question.submit(ask_vanna, [vanna_direct_question], [vanna_sql_query ,vanna_table, vanna_display])
+                                
     return {
         "chatbot": chatbot,
         "textbox": textbox,
@@ -162,6 +178,7 @@ def cqa_tab(tab_name):
         "figures_cards": figures_cards,
         "gallery_component": gallery_component,
         "config_button": config_button,
+        "papers_direct_search" : papers_direct_search,
         "papers_html": papers_html,
         "citations_network": citations_network,
         "papers_summary": papers_summary,
@@ -170,10 +187,23 @@ def cqa_tab(tab_name):
         "tab_figures": tab_figures,
         "tab_graphs": tab_graphs,
         "tab_papers": tab_papers,
-        "graph_container": graphs_container
+        "graph_container": graphs_container,
+        "vanna_sql_query": vanna_sql_query,
+        "vanna_table" : vanna_table,
+        "vanna_display": vanna_display
     }
                                 
- 
+def config_event_handling(main_tabs_components : list[dict], config_componenets : dict):
+    config_open = config_componenets["config_open"]
+    config_modal = config_componenets["config_modal"]
+    close_config_modal = config_componenets["close_config_modal_button"]
+    
+    for button in [close_config_modal] + [main_tab_component["config_button"] for main_tab_component in main_tabs_components]:
+        button.click(
+            fn=update_config_modal_visibility,
+            inputs=[config_open],
+            outputs=[config_modal, config_open]
+        ) 
     
 def event_handling(
     main_tab_components,
@@ -190,7 +220,8 @@ def event_handling(
     sources_textbox = main_tab_components["sources_textbox"]
     figures_cards = main_tab_components["figures_cards"]
     gallery_component = main_tab_components["gallery_component"]
-    config_button = main_tab_components["config_button"]
+    # config_button = main_tab_components["config_button"]
+    papers_direct_search = main_tab_components["papers_direct_search"]
     papers_html = main_tab_components["papers_html"]
     citations_network = main_tab_components["citations_network"]
     papers_summary = main_tab_components["papers_summary"]
@@ -200,9 +231,13 @@ def event_handling(
     tab_graphs = main_tab_components["tab_graphs"]
     tab_papers = main_tab_components["tab_papers"]
     graphs_container = main_tab_components["graph_container"]
+    vanna_sql_query = main_tab_components["vanna_sql_query"]
+    vanna_table = main_tab_components["vanna_table"]
+    vanna_display = main_tab_components["vanna_display"]
     
-    config_open = config_components["config_open"]
-    config_modal = config_components["config_modal"]
+    
+    # config_open = config_components["config_open"]
+    # config_modal = config_components["config_modal"]
     dropdown_sources = config_components["dropdown_sources"]
     dropdown_reports = config_components["dropdown_reports"]
     dropdown_external_sources = config_components["dropdown_external_sources"]
@@ -211,18 +246,18 @@ def event_handling(
     after = config_components["after"]
     output_query = config_components["output_query"]
     output_language = config_components["output_language"]
-    close_config_modal = config_components["close_config_modal_button"]
+    # close_config_modal = config_components["close_config_modal_button"]
     
     new_sources_hmtl = gr.State([])
-    
-    print("textbox id : ", textbox.elem_id)
+    ttd_data = gr.State([])
+
      
-    for button in [config_button, close_config_modal]:
-        button.click(
-            fn=update_config_modal_visibility,
-            inputs=[config_open],
-            outputs=[config_modal, config_open]
-        )
+    # for button in [config_button, close_config_modal]:
+    #     button.click(
+    #         fn=update_config_modal_visibility,
+    #         inputs=[config_open],
+    #         outputs=[config_modal, config_open]
+    #     )
     
     if tab_name == "ClimateQ&A":
         print("chat cqa - message sent")
@@ -265,10 +300,14 @@ def event_handling(
         component.change(update_sources_number_display, [sources_textbox, figures_cards, current_graphs, papers_html], [tab_recommended_content, tab_sources, tab_figures, tab_graphs, tab_papers])
     
     # Search for papers
-    for component in [textbox, examples_hidden]:
+    for component in [textbox, examples_hidden, papers_direct_search]:
         component.submit(find_papers, [component, after, dropdown_external_sources], [papers_html, citations_network, papers_summary])
+    
         
 
+    # if tab_name == "Beta - POC Adapt'Action": # Not untill results are good enough
+    #     # Drias search
+    #     textbox.submit(ask_vanna, [textbox], [vanna_sql_query ,vanna_table, vanna_display])
 
 def main_ui():
     # config_open = gr.State(True)
@@ -282,7 +321,9 @@ def main_ui():
             create_about_tab()
         
         event_handling(cqa_components, config_components, tab_name = 'ClimateQ&A')
-        event_handling(local_cqa_components, config_components, tab_name = 'Beta - POC Adapt\'Action')
+        event_handling(local_cqa_components, config_components, tab_name = "Beta - POC Adapt'Action")
+        
+        config_event_handling([cqa_components,local_cqa_components] ,config_components)
         
         demo.queue()
         

climateqa/chat.py

@@ -53,6 +53,13 @@ def log_interaction_to_azure(history, output_query, sources, docs, share_client,
         print(f"Error logging on Azure Blob Storage: {e}")
         error_msg = f"ClimateQ&A Error: {str(e)[:100]} - The error has been noted, try another question and if the error remains, you can contact us :)"
         raise gr.Error(error_msg)
+
+def handle_numerical_data(event):
+    if event["name"] == "retrieve_drias_data" and event["event"] == "on_chain_end":
+        numerical_data = event["data"]["output"]["drias_data"]
+        sql_query = event["data"]["output"]["drias_sql_query"]
+        return numerical_data, sql_query
+    return None, None
     
 # Main chat function
 async def chat_stream(
@@ -121,6 +128,7 @@ async def chat_stream(
     used_documents = []
     retrieved_contents = []
     answer_message_content = ""
+    vanna_data = {}
 
     # Define processing steps
     steps_display = {
@@ -142,6 +150,14 @@ async def chat_stream(
                     history, used_documents, retrieved_contents = handle_retrieved_documents(
                         event, history, used_documents, retrieved_contents
                     )
+                # Handle Vanna retrieval
+                # if event["event"] == "on_chain_end" and event["name"] in ["retrieve_documents","retrieve_local_data"] and event["data"]["output"] != None:
+                #     df_output_vanna, sql_query = handle_numerical_data(
+                #         event
+                #     )
+                #     vanna_data = {"df_output": df_output_vanna, "sql_query": sql_query}
+                 
+                    
                 if event["event"] == "on_chain_end" and event["name"] == "answer_search" :
                     docs = event["data"]["input"]["documents"]
                     docs_html = convert_to_docs_to_html(docs)                    
@@ -184,7 +200,7 @@ async def chat_stream(
                         sub_questions = [q["question"] + "-> relevant sources : " + str(q["sources"]) for q in event["data"]["output"]["questions_list"]]
                         history[-1].content += "Decompose question into sub-questions:\n\n - " + "\n - ".join(sub_questions)
 
-            yield history, docs_html, output_query, output_language, related_contents, graphs_html 
+            yield history, docs_html, output_query, output_language, related_contents, graphs_html#, vanna_data
 
     except Exception as e:
         print(f"Event {event} has failed")
@@ -195,4 +211,4 @@ async def chat_stream(
     # Call the function to log interaction
     log_interaction_to_azure(history, output_query, sources, docs, share_client, user_id)
 
-    yield history, docs_html, output_query, output_language, related_contents, graphs_html 
+    yield history, docs_html, output_query, output_language, related_contents, graphs_html#, vanna_data

climateqa/engine/chains/drias_retriever.py

@@ -0,0 +1,16 @@
+import sys
+import os
+from climateqa.engine.talk_to_data.main import ask_vanna
+
+
+def make_drias_retriever_node(llm):
+    
+    def retrieve_drias_data(state):
+        print("---- Retrieving data from DRIAS ----")
+        query = state["query"]
+        sql_query, df, fig = ask_vanna(query)
+        state["drias_data"] = df
+        state["drias_sql_query"] = sql_query
+        return state
+    
+    return retrieve_drias_data

climateqa/engine/chains/intent_categorization.py

@@ -29,7 +29,7 @@ class IntentCategorizer(BaseModel):
             Examples:
             - ai_impact = Environmental impacts of AI: "What are the environmental impacts of AI", "How does AI affect the environment"
             - search = Searching for any quesiton about climate change, energy, biodiversity, nature, and everything we can find the IPCC or IPBES reports or scientific papers,
-            - chitchat = Any general question that is not related to the environment or climate change or just conversational, or if you don't think searching the IPCC or IPBES reports would be relevant
+            - chitchat = Any general question that is not related to the environment or climate change or just conversational, or if you don't think searching the IPCC or IPBES reports would be relevant. If it can be interprated as a climate related question, please use the search intent.
         """,
             # - geo_info = Geolocated info about climate change: Any question where the user wants to know localized impacts of climate change, eg: "What will be the temperature in Marseille in 2050"
             # - esg = Any question about the ESG regulation, frameworks and standards like the CSRD, TCFD, SASB, GRI, CDP, etc.
@@ -57,6 +57,7 @@ def make_intent_categorization_node(llm):
     categorization_chain = make_intent_categorization_chain(llm)
 
     def categorize_message(state):
+        print("Input Message : ", state["user_input"])
         print("---- Categorize_message ----")
 
         output = categorization_chain.invoke({"input": state["user_input"]})

climateqa/engine/graph.py

@@ -23,6 +23,7 @@ from .chains.retrieve_documents import make_IPx_retriever_node, make_POC_retriev
 from .chains.answer_rag import make_rag_node
 from .chains.graph_retriever import make_graph_retriever_node
 from .chains.chitchat_categorization import make_chitchat_intent_categorization_node
+from .chains.drias_retriever import make_drias_retriever_node
 # from .chains.set_defaults import set_defaults
 
 class GraphState(TypedDict):
@@ -49,6 +50,8 @@ class GraphState(TypedDict):
     recommended_content : List[Document] # OWID Graphs  # TODO merge with related_contents
     search_only : bool = False
     reports : List[str] = []
+    drias_data: pd.DataFrame
+    drias_sql_query : str
 
 def dummy(state):
     return 
@@ -240,6 +243,7 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     answer_rag = make_rag_node(llm, with_docs=True)
     answer_rag_no_docs = make_rag_node(llm, with_docs=False)
     chitchat_categorize_intent = make_chitchat_intent_categorization_node(llm)
+    # retrieve_drias_data = make_drias_retriever_node(llm) # WIP
 
     # Define the nodes
     # workflow.add_node("set_defaults", set_defaults)
@@ -258,6 +262,7 @@ def make_graph_agent_poc(llm, vectorstore_ipcc, vectorstore_graphs, vectorstore_
     workflow.add_node("retrieve_documents", retrieve_documents)
     workflow.add_node("answer_rag", answer_rag)
     workflow.add_node("answer_rag_no_docs", answer_rag_no_docs)
+    # workflow.add_node("retrieve_drias_data", retrieve_drias_data)# WIP
 
     # Entry point
     workflow.set_entry_point("categorize_intent")

climateqa/engine/talk_to_data/main.py

@@ -19,7 +19,7 @@ VANNA_MODEL = os.getenv('VANNA_MODEL')
 
 #Vanna object
 vn = MyVanna(config = {"temperature": 0, "api_key": OPENAI_API_KEY, 'model': VANNA_MODEL, 'pc_api_key': PC_API_KEY, 'index_name': INDEX_NAME, "top_k" : 4})
-db_vanna_path = os.path.join(os.path.dirname(__file__), "database/drias.db")
+db_vanna_path = os.path.join(os.path.dirname(os.path.dirname(os.path.dirname(os.path.dirname(__file__)))), "data/drias/drias.db")
 vn.connect_to_sqlite(db_vanna_path)
 
 llm = get_llm(provider="openai")

climateqa/engine/talk_to_data/myVanna.py

@@ -0,0 +1,13 @@
+from dotenv import load_dotenv
+from climateqa.engine.talk_to_data.vanna_class import MyCustomVectorDB
+from vanna.openai import OpenAI_Chat
+import os
+
+load_dotenv()
+
+OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+
+class MyVanna(MyCustomVectorDB, OpenAI_Chat):
+    def __init__(self, config=None):
+        MyCustomVectorDB.__init__(self, config=config)
+        OpenAI_Chat.__init__(self, config=config)

climateqa/engine/talk_to_data/utils.py

@@ -0,0 +1,98 @@
+import re
+import openai
+import pandas as pd
+from geopy.geocoders import Nominatim
+import sqlite3
+import ast
+
+
+def detect_location_with_openai(api_key, sentence):
+    """
+    Detects locations in a sentence using OpenAI's API.
+    """
+    openai.api_key = api_key
+
+    prompt = f"""
+    Extract all locations (cities, countries, states, or geographical areas) mentioned in the following sentence.
+    Return the result as a Python list. If no locations are mentioned, return an empty list.
+    
+    Sentence: "{sentence}"
+    """
+
+    response = openai.chat.completions.create(
+        model="gpt-4o-mini",  
+        messages=[
+            {"role": "system", "content": "You are a helpful assistant skilled in identifying locations in text."},
+            {"role": "user", "content": prompt}
+        ],
+        max_tokens=100,
+        temperature=0
+    )
+
+    return response.choices[0].message.content.split("\n")[1][2:-2]
+
+
+def detectTable(sql_query):
+    pattern = r'(?i)\bFROM\s+((?:`[^`]+`|"[^"]+"|\'[^\']+\'|\w+)(?:\.(?:`[^`]+`|"[^"]+"|\'[^\']+\'|\w+))*)'
+    matches = re.findall(pattern, sql_query)
+    return matches
+
+
+
+def loc2coords(location : str):
+    geolocator = Nominatim(user_agent="city_to_latlong")
+    location = geolocator.geocode(location)
+    return (location.latitude, location.longitude)
+
+
+def coords2loc(coords : tuple):
+    geolocator = Nominatim(user_agent="coords_to_city")
+    try:
+        location = geolocator.reverse(coords)
+        return location.address
+    except Exception as e:
+        print(f"Error: {e}")
+        return "Unknown Location"  
+
+
+def nearestNeighbourSQL(db: str, location: tuple, table : str):
+    conn = sqlite3.connect(db)
+    long = round(location[1], 3)
+    lat = round(location[0], 3)
+    cursor  = conn.cursor()
+    cursor.execute(f"SELECT lat, lon FROM {table} WHERE lat BETWEEN {lat - 0.3} AND {lat + 0.3} AND lon BETWEEN {long - 0.3} AND {long + 0.3}")
+    results = cursor.fetchall()
+    return results[0]
+
+def detect_relevant_tables(user_question, llm):
+    table_names_list = [
+        "Frequency_of_rainy_days_index",
+        "Winter_precipitation_total",
+        "Summer_precipitation_total",
+        "Annual_precipitation_total",
+        # "Remarkable_daily_precipitation_total_(Q99)",
+        "Frequency_of_remarkable_daily_precipitation",
+        "Extreme_precipitation_intensity",
+        "Mean_winter_temperature",
+        "Mean_summer_temperature",
+        "Number_of_tropical_nights",
+        "Maximum_summer_temperature",
+        "Number_of_days_with_Tx_above_30C",
+        "Number_of_days_with_Tx_above_35C",
+        "Drought_index"
+    ]
+    prompt = (
+        f"You are helping to build a sql query to retrieve relevant data for a user question."
+        f"The different tables are {table_names_list}."
+        f"The user question is {user_question}. Write the relevant tables to use. Answer only a python list of table name."
+    )
+    table_names = ast.literal_eval(llm.invoke(prompt).content.strip("```python\n").strip())
+    return table_names
+
+def replace_coordonates(coords, query, coords_tables):
+    n = query.count(str(coords[0]))
+
+    for i in range(n):
+        query = query.replace(str(coords[0]), str(coords_tables[i][0]),1)
+        query = query.replace(str(coords[1]), str(coords_tables[i][1]),1)
+    return query

climateqa/engine/talk_to_data/vanna_class.py

@@ -0,0 +1,325 @@
+from vanna.base import VannaBase
+from pinecone import Pinecone
+from climateqa.engine.embeddings import get_embeddings_function
+import pandas as pd
+import hashlib
+
+class MyCustomVectorDB(VannaBase):
+    
+    """
+    VectorDB class for storing and retrieving vectors from Pinecone.
+    
+    args : 
+        config (dict) : Configuration dictionary containing the Pinecone API key and the index name :
+            - pc_api_key (str) : Pinecone API key
+            - index_name (str) : Pinecone index name
+            - top_k (int) : Number of top results to return (default = 2)
+            
+    """
+    
+    def __init__(self,config):
+        super().__init__(config = config)
+        try : 
+            self.api_key = config.get('pc_api_key')
+            self.index_name = config.get('index_name')
+        except : 
+            raise Exception("Please provide the Pinecone API key and the index name")
+        
+        self.pc = Pinecone(api_key = self.api_key)
+        self.index = self.pc.Index(self.index_name)
+        self.top_k = config.get('top_k', 2)
+        self.embeddings = get_embeddings_function()
+        
+        
+    def check_embedding(self, id, namespace):
+        fetched = self.index.fetch(ids = [id], namespace = namespace)
+        if fetched['vectors'] == {}: 
+            return False
+        return True
+    
+    def generate_hash_id(self, data: str) -> str:
+        """
+        Generate a unique hash ID for the given data.
+        
+        Args:
+            data (str): The input data to hash (e.g., a concatenated string of user attributes).
+        
+        Returns:
+            str: A unique hash ID as a hexadecimal string.
+        """
+        
+        data_bytes = data.encode('utf-8')
+        hash_object = hashlib.sha256(data_bytes)
+        hash_id = hash_object.hexdigest()
+        
+        return hash_id
+    
+    def add_ddl(self, ddl: str, **kwargs) -> str:
+        id = self.generate_hash_id(ddl) + '_ddl'
+        
+        if self.check_embedding(id, 'ddl'):
+            print(f"DDL having id {id} already exists")
+            return id
+        
+        self.index.upsert(
+            vectors = [(id, self.embeddings.embed_query(ddl), {'ddl': ddl})],
+            namespace = 'ddl'
+        )
+        
+        return id
+
+    def add_documentation(self, doc: str, **kwargs) -> str:
+        id = self.generate_hash_id(doc) + '_doc'
+        
+        if self.check_embedding(id, 'documentation'):
+            print(f"Documentation having id {id} already exists")
+            return id
+        
+        self.index.upsert(
+            vectors = [(id, self.embeddings.embed_query(doc), {'doc': doc})],
+            namespace = 'documentation'
+        )
+        
+        return id
+
+    def add_question_sql(self, question: str, sql: str, **kwargs) -> str:
+        id = self.generate_hash_id(question) + '_sql'
+        
+        if self.check_embedding(id, 'question_sql'):
+            print(f"Question-SQL pair having id {id} already exists")
+            return id
+        
+        self.index.upsert(
+            vectors = [(id, self.embeddings.embed_query(question + sql), {'question': question, 'sql': sql})],
+            namespace = 'question_sql'
+        )
+        
+        return id
+
+    def get_related_ddl(self, question: str, **kwargs) -> list:
+        res = self.index.query(
+            vector=self.embeddings.embed_query(question),
+            top_k=self.top_k,
+            namespace='ddl',
+            include_metadata=True
+        )
+        
+        return [match['metadata']['ddl'] for match in res['matches']]
+
+    def get_related_documentation(self, question: str, **kwargs) -> list:
+        res = self.index.query(
+            vector=self.embeddings.embed_query(question),
+            top_k=self.top_k,
+            namespace='documentation',
+            include_metadata=True
+        )
+        
+        return [match['metadata']['doc'] for match in res['matches']]
+
+    def get_similar_question_sql(self, question: str, **kwargs) -> list:
+        res = self.index.query(
+            vector=self.embeddings.embed_query(question),
+            top_k=self.top_k,
+            namespace='question_sql',
+            include_metadata=True
+        )
+        
+        return [(match['metadata']['question'], match['metadata']['sql']) for match in res['matches']]
+
+    def get_training_data(self, **kwargs) -> pd.DataFrame:
+        
+        list_of_data = []
+        
+        namespaces = ['ddl', 'documentation', 'question_sql']
+        
+        for namespace in namespaces:
+            
+            data = self.index.query(
+            top_k=10000,
+            namespace=namespace,
+            include_metadata=True,
+            include_values=False
+            )
+            
+            for match in data['matches']:
+                list_of_data.append(match['metadata'])
+                
+        return pd.DataFrame(list_of_data)
+            
+
+
+    def remove_training_data(self, id: str, **kwargs) -> bool:
+        if id.endswith("_ddl"):
+            self.Index.delete(ids=[id], namespace="_ddl")
+            return True
+        if id.endswith("_sql"):
+            self.index.delete(ids=[id], namespace="_sql")
+            return True
+        
+        if id.endswith("_doc"):
+            self.Index.delete(ids=[id], namespace="_doc")
+            return True
+        
+        return False
+    
+    def generate_embedding(self, text, **kwargs):
+        # Implement the method here
+        pass
+
+
+    def get_sql_prompt(
+            self,
+            initial_prompt : str,
+            question: str,
+            question_sql_list: list,
+            ddl_list: list,
+            doc_list: list,
+            **kwargs,
+        ):
+            """
+            Example:
+            ```python
+            vn.get_sql_prompt(
+                question="What are the top 10 customers by sales?",
+                question_sql_list=[{"question": "What are the top 10 customers by sales?", "sql": "SELECT * FROM customers ORDER BY sales DESC LIMIT 10"}],
+                ddl_list=["CREATE TABLE customers (id INT, name TEXT, sales DECIMAL)"],
+                doc_list=["The customers table contains information about customers and their sales."],
+            )
+
+            ```
+
+            This method is used to generate a prompt for the LLM to generate SQL.
+
+            Args:
+                question (str): The question to generate SQL for.
+                question_sql_list (list): A list of questions and their corresponding SQL statements.
+                ddl_list (list): A list of DDL statements.
+                doc_list (list): A list of documentation.
+
+            Returns:
+                any: The prompt for the LLM to generate SQL.
+            """
+
+            if initial_prompt is None:
+                initial_prompt = f"You are a {self.dialect} expert. " + \
+                "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. "
+
+            initial_prompt = self.add_ddl_to_prompt(
+                initial_prompt, ddl_list, max_tokens=self.max_tokens
+            )
+
+            if self.static_documentation != "":
+                doc_list.append(self.static_documentation)
+
+            initial_prompt = self.add_documentation_to_prompt(
+                initial_prompt, doc_list, max_tokens=self.max_tokens
+            )
+
+            # initial_prompt = self.add_sql_to_prompt(
+            #     initial_prompt, question_sql_list, max_tokens=self.max_tokens
+            # )
+
+
+            initial_prompt += (
+                "===Response Guidelines \n"
+                "1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \n"
+                "2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \n"
+                "3. If the provided context is insufficient, please give a sql query based on your knowledge and the context provided. \n"
+                "4. Please use the most relevant table(s). \n"
+                "5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \n"
+                f"6. Ensure that the output SQL is {self.dialect}-compliant and executable, and free of syntax errors. \n"
+                f"7. Add a description of the table in the result of the sql query, if relevant. \n"
+                "8 Make sure to include the relevant KPI in the SQL query. The query should return impactfull data \n"
+                # f"8. If a set of latitude,longitude is provided, make a intermediate query to find the nearest value in the table and replace the coordinates in the sql query. \n"
+                # "7. Add a description of the table in the result of the sql query."
+                # "7. If the question is about a specific latitude, longitude, query an interval of 0.3 and keep only the first set of coordinate. \n"
+                # "7. Table names should be included in the result of the sql query. Use for example Mean_winter_temperature AS table_name in the query \n"
+            )
+
+
+            message_log = [self.system_message(initial_prompt)]
+
+            for example in question_sql_list:
+                if example is None:
+                    print("example is None")
+                else:
+                    if example is not None and "question" in example and "sql" in example:
+                        message_log.append(self.user_message(example["question"]))
+                        message_log.append(self.assistant_message(example["sql"]))
+
+            message_log.append(self.user_message(question))
+
+            return message_log
+        
+
+# def get_sql_prompt(
+#         self,
+#         initial_prompt : str,
+#         question: str,
+#         question_sql_list: list,
+#         ddl_list: list,
+#         doc_list: list,
+#         **kwargs,
+#     ):
+#         """
+#         Example:
+#         ```python
+#         vn.get_sql_prompt(
+#             question="What are the top 10 customers by sales?",
+#             question_sql_list=[{"question": "What are the top 10 customers by sales?", "sql": "SELECT * FROM customers ORDER BY sales DESC LIMIT 10"}],
+#             ddl_list=["CREATE TABLE customers (id INT, name TEXT, sales DECIMAL)"],
+#             doc_list=["The customers table contains information about customers and their sales."],
+#         )
+
+#         ```
+
+#         This method is used to generate a prompt for the LLM to generate SQL.
+
+#         Args:
+#             question (str): The question to generate SQL for.
+#             question_sql_list (list): A list of questions and their corresponding SQL statements.
+#             ddl_list (list): A list of DDL statements.
+#             doc_list (list): A list of documentation.
+
+#         Returns:
+#             any: The prompt for the LLM to generate SQL.
+#         """
+
+#         if initial_prompt is None:
+#             initial_prompt = f"You are a {self.dialect} expert. " + \
+#             "Please help to generate a SQL query to answer the question. Your response should ONLY be based on the given context and follow the response guidelines and format instructions. "
+
+#         initial_prompt = self.add_ddl_to_prompt(
+#             initial_prompt, ddl_list, max_tokens=self.max_tokens
+#         )
+
+#         if self.static_documentation != "":
+#             doc_list.append(self.static_documentation)
+
+#         initial_prompt = self.add_documentation_to_prompt(
+#             initial_prompt, doc_list, max_tokens=self.max_tokens
+#         )
+
+#         initial_prompt += (
+#             "===Response Guidelines \n"
+#             "1. If the provided context is sufficient, please generate a valid SQL query without any explanations for the question. \n"
+#             "2. If the provided context is almost sufficient but requires knowledge of a specific string in a particular column, please generate an intermediate SQL query to find the distinct strings in that column. Prepend the query with a comment saying intermediate_sql \n"
+#             "3. If the provided context is insufficient, please explain why it can't be generated. \n"
+#             "4. Please use the most relevant table(s). \n"
+#             "5. If the question has been asked and answered before, please repeat the answer exactly as it was given before. \n"
+#             f"6. Ensure that the output SQL is {self.dialect}-compliant and executable, and free of syntax errors. \n"
+#         )
+
+#         message_log = [self.system_message(initial_prompt)]
+
+#         for example in question_sql_list:
+#             if example is None:
+#                 print("example is None")
+#             else:
+#                 if example is not None and "question" in example and "sql" in example:
+#                     message_log.append(self.user_message(example["question"]))
+#                     message_log.append(self.assistant_message(example["sql"]))
+
+#         message_log.append(self.user_message(question))
+
+#         return message_log

front/tabs/tab_papers.py

@@ -3,6 +3,8 @@ from gradio_modal import Modal
 
 
 def create_papers_tab():
+    direct_search_textbox = gr.Textbox(label="Direct search for papers", placeholder= "What is climate change ?", elem_id="papers-search")
+
     with gr.Accordion(
         visible=True,
         elem_id="papers-summary-popup",
@@ -32,5 +34,5 @@ def create_papers_tab():
         papers_modal
     )
     
-    return papers_summary, papers_html, citations_network, papers_modal
+    return direct_search_textbox, papers_summary, papers_html, citations_network, papers_modal
 

requirements.txt

@@ -19,3 +19,5 @@ langchain-community==0.2
 msal==1.31
 matplotlib==3.9.2
 gradio-modal==0.0.4
+vanna==0.7.5
+geopy==2.4.1

sandbox/talk_to_data/20250306 - CQA - Drias.ipynb

@@ -0,0 +1,94 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Import the function in main.py"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sys\n",
+    "import os\n",
+    "sys.path.append(os.path.dirname(os.path.dirname(os.getcwd())))\n",
+    "\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2\n",
+    "\n",
+    "from climateqa.engine.talk_to_data.main import ask_vanna\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Create a human query"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# query = \"Compare the winter and summer precipitation in 2050 in Marseille\"\n",
+    "# query = \"What is the impact of climate  in Bordeaux?\"\n",
+    "# query = \"what is the number of days where the temperature above 35 in 2050 in Marseille\"\n",
+    "# query = \"Quelle sera la température à Marseille sur les prochaines années ?\"\n",
+    "# query = \"Comment vont évoluer les températures à Marseille ?\"\n",
+    "query = \"Comment vont évoluer les températures à marseille ?\""
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Call the function ask vanna, it gives an output of a the sql query and the dataframe of the result (tuple)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "sql_query, df, fig = ask_vanna(query)\n",
+    "print(df.head())\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "climateqa",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}

style.css

@@ -481,14 +481,13 @@ a {
         max-height: calc(100vh - 190px) !important;
         overflow: hidden;
     }
-
     div#tab-examples,
     div#sources-textbox,
     div#tab-config {
         height: calc(100vh - 190px) !important;
         overflow-y: scroll !important;
     }
-
+    div#tab-vanna,
     div#sources-figures,
     div#graphs-container,
     div#tab-citations {
@@ -606,3 +605,16 @@ a {
 #checkbox-config:checked {
     display: block;
 }
+
+#vanna-display {
+    max-height: 300px;
+    /* overflow-y: scroll; */
+}
+#sql-query{
+    max-height: 100px;
+    overflow-y:scroll;
+}
+#vanna-details{
+    max-height: 500px;
+    overflow-y:scroll;
+}