src/chatbot.py

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


from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain.chains import create_retrieval_chain

from langchain_community.docstore.in_memory import InMemoryDocstore
from faiss import IndexFlatL2

#import functools
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
)

db_all = FAISS.load_local(folder_path="./src/FAISS", index_name="speeches_1949_09_12",
                                           embeddings=embeddings, allow_dangerous_deserialization=True)

def get_vectorstore(inputs, embeddings):
    """

    Combine multiple FAISS vector stores into a single vector store based on the specified inputs.



    Parameters

    ----------

    inputs : list of str

        A list of strings specifying which vector stores to combine. Each string represents a specific 

        index or a special keyword "All". If "All" is the first entry in the list, 

        it directly return the pre-defined vectorstore for all speeches

        

    embeddings : Embeddings

        An instance of embeddings that will be used to load the vector stores. The specific type and

        structure of `embeddings` depend on the implementation of the `get_vectorstore` function.



    Returns

    -------

    FAISS

        A FAISS vector store that combines the specified indices into a single vector store.

    

    """

    # Default folder path
    folder_path = "./src/FAISS"
 

    if inputs[0] == "All" or inputs[0] is None:
        return db_all

    # Initialize empty db
    embedding_function = embeddings 
    dimensions = len(embedding_function.embed_query("dummy"))

    db = FAISS(
        embedding_function=embedding_function,
        index=IndexFlatL2(dimensions),
        docstore=InMemoryDocstore(),
        index_to_docstore_id={},
        normalize_L2=False
    )

    # Retrieve inputs: 20. Legislaturperiode, 19. Legislaturperiode, ...
    for input in inputs:
        # Ignore if user also selected All among other legislatures
        if input == "All":
            continue
        # Retrieve selected index and merge vector stores
        index = input.split(".")[0]
        index_name = f'{index}_legislature'
        local_db = FAISS.load_local(folder_path=folder_path, index_name=index_name,
                                    embeddings=embeddings, allow_dangerous_deserialization=True)
        db.merge_from(local_db)
        print('Successfully merged inputs')
    return db

def RAG(llm, prompt, db, question):
    """

    Apply Retrieval-Augmented Generation (RAG) by providing the context and the question to the 

    language model using a predefined template.



    Parameters:

    ----------

    llm : LanguageModel

        An instance of the language model to be used for generating responses.

        

    prompt : str

        A predefined template or prompt that structures how the context and question are presented to the language model.

        

    db : VectorStore

        A vector store instance that supports retrieval of relevant documents based on the input question.

        

    question : str

        The question or query to be answered by the language model.



    Returns:

    -------

    str

        The response generated by the language model, based on the retrieved context and provided question.

    """
    # Create a document chain using the provided language model and prompt template
    document_chain = create_stuff_documents_chain(llm=llm, prompt=prompt)
    # Convert the vector store into a retriever
    retriever = db.as_retriever()
    # Create a retrieval chain that integrates the retriever with the document chain
    retrieval_chain = create_retrieval_chain(retriever, document_chain)
    # Invoke the retrieval chain with the input question to get the final response
    response = retrieval_chain.invoke({"input": question})
    
    return response


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
    elif method == "ss":
        kws_data = [] 
        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"]
            speech_date = speech_date.strftime("%Y-%m-%d")
            print(speech_date)
            # Error here?
            kws_entry = {'Speech Content': speech_content,
                        'Date': speech_date,
                        'Party': party}
            
            kws_data.append(kws_entry)
    
    df_res = pd.DataFrame(kws_data)

    return df_res