Update src/chatbot.py

src/chatbot.py

@@ -1,296 +1,298 @@
-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
+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/Ministral-8B-Instruct-2410"
+    #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