src/chatbot.py

from langchain_core.prompts import ChatPromptTemplate
from langchain_community.llms.huggingface_hub import HuggingFaceHub
from langchain_community.embeddings import HuggingFaceEmbeddings

from src.vectordatabase import RAG, get_vectorstore
import pandas as pd

# Load environmental variables from .env-file
# from dotenv import load_dotenv, find_dotenv
# load_dotenv(find_dotenv())

# Define important variables 
embeddings = HuggingFaceEmbeddings(model_name="paraphrase-multilingual-MiniLM-L12-v2") # Remove embedding input parameter from functions?
llm = HuggingFaceHub(
    # ToDo: Try different models here
    repo_id="mistralai/Mixtral-8x7B-Instruct-v0.1",
    # repo_id="CohereForAI/c4ai-command-r-v01", # too large 69gb
    # repo_id="CohereForAI/c4ai-command-r-v01-4bit", # too large 22gb
    # repo_id="meta-llama/Meta-Llama-3-8B", # too large 16 gb
    task="text-generation",
    model_kwargs={
        "max_new_tokens": 512,
        "top_k": 30,
        "temperature": 0.1,
        "repetition_penalty": 1.03,
        }
)
# ToDo: Experiment with different templates
prompt_test = ChatPromptTemplate.from_template("""<s>[INST] 

                    Instruction: Beantworte die folgende Frage auf deutsch und nur auf der Grundlage des angegebenen Kontexts:



                    Context: {context}



                    Question: {input}  

                    [/INST]"""
                    
) 
prompt_de = ChatPromptTemplate.from_template("""Beantworte die folgende Frage auf deutsch und nur auf der Grundlage des angegebenen Kontexts:



        <context>

        {context}

        </context>



        Frage: {input}

        """
        # Returns the answer in German
)
prompt_en = ChatPromptTemplate.from_template("""Answer the following question in English and solely based on the provided context:



        <context>

        {context}

        </context>



        Question: {input}

        """
        # Returns the answer in English
)



def chatbot(message, history, db_inputs, prompt_language, llm=llm):
    """

    Generate a response from the chatbot based on the provided message, history, database inputs, prompt language, and LLM model.



    Parameters:

    -----------

    message : str

        The message or question to be answered by the chatbot.

        

    history : list

        The history of previous interactions or messages.

        

    db_inputs : list

        A list of strings specifying which vector stores to combine. Each string represents a specific index or a special keyword "All".

        

    prompt_language : str

        The language of the prompt to be used for generating the response. Should be either "DE" for German or "EN" for English.

        

    llm : LLM, optional

        An instance of the Language Model to be used for generating the response. Defaults to the global variable `llm`.



    Returns:

    --------

    str

        The response generated by the chatbot.

    """
    
    db = get_vectorstore(inputs = db_inputs, embeddings=embeddings)
    
    # Select prompt based on user input
    if prompt_language == "DE":
        prompt = prompt_de
        raw_response = RAG(llm=llm, prompt=prompt, db=db, question=message)
        # Only necessary because mistral does include it´s json structure in the output including its input content
        try:
            response = raw_response['answer'].split("Antwort: ")[1]
        except:  
            response = raw_response['answer']
        return response
    else:
        prompt = prompt_en
        raw_response = RAG(llm=llm, prompt=prompt, db=db, question=message)
        # Only necessary because mistral does include it´s json structure in the output including its input content
        try:
            response = raw_response['answer'].split("Answer: ")[1]
        except:  
            response = raw_response['answer']
    
    return response  


def keyword_search(query, n=10, embeddings=embeddings, method='ss', party_filter='All'):
    """

    Retrieve speech contents based on keywords using a specified method.



    Parameters:

    ----------

    db : FAISS

        The FAISS vector store containing speech embeddings.



    query : str

        The keyword(s) to search for in the speech contents.



    n : int, optional

        The number of speech contents to retrieve (default is 10).



    embeddings : Embeddings, optional

        An instance of embeddings used for embedding queries (default is embeddings).



    method : str, optional

        The method used for retrieving speech contents. Options are 'ss' (semantic search) and 'mmr' 

        (maximal marginal relevance) (default is 'ss').



    party_filter : str, optional

        A filter for retrieving speech contents by party affiliation. Specify 'All' to retrieve 

        speeches from all parties (default is 'All').



    Returns:

    -------

    pandas.DataFrame

        A DataFrame containing the speech contents, dates, and party affiliations.

    

    Notes:

    -----

    - The `db` parameter should be a FAISS vector store containing speech embeddings.

    - The `query` parameter specifies the keyword(s) to search for in the speech contents.

    - The `n` parameter determines the number of speech contents to retrieve (default is 10).

    - The `embeddings` parameter is an instance of embeddings used for embedding queries (default is embeddings).

    - The `method` parameter specifies the method used for retrieving speech contents. Options are 'ss' (semantic search) 

      and 'mmr' (maximal marginal relevance) (default is 'ss').

    - The `party_filter` parameter is a filter for retrieving speech contents by party affiliation. Specify 'All' to retrieve 

      speeches from all parties (default is 'All').

    """
    
    db = get_vectorstore(inputs=["All"], embeddings=embeddings)
    query_embedding = embeddings.embed_query(query)

    # Maximal Marginal Relevance
    if method == 'mmr':
        df_res = pd.DataFrame(columns=['Speech Content', 'Date', 'Party', 'Relevance'])
        results = db.max_marginal_relevance_search_with_score_by_vector(query_embedding, k=n)
        for doc in results:
            party = doc[0].metadata["party"]
            if party != party_filter and party_filter != 'All':
                continue
            speech_content = doc[0].page_content
            speech_date = doc[0].metadata["date"]
            score = round(doc[1], ndigits=2)
            df_res = pd.concat([df_res, pd.DataFrame({'Speech Content': [speech_content],
                                                      'Date': [speech_date],
                                                      'Party': [party],
                                                      'Relevance': [score]})], ignore_index=True)
        df_res.sort_values('Relevance', inplace=True, ascending=True)

    # Similarity Search
    else:
        df_res = pd.DataFrame(columns=['Speech Content', 'Date', 'Party'])
        results = db.similarity_search_by_vector(query_embedding, k=n)
        for doc in results:
            party = doc.metadata["party"]
            if party != party_filter and party_filter != 'All':
                continue
            speech_content = doc.page_content
            speech_date = doc.metadata["date"]
            df_res = pd.concat([df_res, pd.DataFrame({'Speech Content': [speech_content],
                                                      'Date': [speech_date],
                                                      'Party': [party]})], ignore_index=True)
    return df_res