Delete ai_generate

ai_generate

@@ -1,221 +0,0 @@
-import os
-from langchain_community.document_loaders import PyMuPDFLoader
-from langchain_core.documents import Document
-from langchain_community.embeddings.sentence_transformer import (
-    SentenceTransformerEmbeddings,
-)
-from langchain.schema import StrOutputParser
-from langchain_community.vectorstores import Chroma
-from langchain_text_splitters import RecursiveCharacterTextSplitter
-from langchain import hub
-from langchain_core.output_parsers import StrOutputParser
-from langchain_core.runnables import RunnablePassthrough
-from langchain_groq import ChatGroq
-from langchain_openai import ChatOpenAI
-from langchain_google_genai import ChatGoogleGenerativeAI
-from langchain_anthropic import ChatAnthropic
-from dotenv import load_dotenv
-from langchain_core.output_parsers import XMLOutputParser
-from langchain.prompts import ChatPromptTemplate
-
-load_dotenv()
-
-# suppress grpc and glog logs for gemini
-os.environ["GRPC_VERBOSITY"] = "ERROR"
-os.environ["GLOG_minloglevel"] = "2"
-
-# RAG parameters
-CHUNK_SIZE = 1024
-CHUNK_OVERLAP = CHUNK_SIZE // 8
-K = 10
-FETCH_K = 20
-
-llm_model_translation = {
-    "LLaMA 3": "llama3-70b-8192",
-    "OpenAI GPT 4o Mini": "gpt-4o-mini",
-    "OpenAI GPT 4o": "gpt-4o",
-    "OpenAI GPT 4": "gpt-4-turbo",
-    "Gemini 1.5 Pro": "gemini-1.5-pro",
-    "Claude Sonnet 3.5": "claude-3-5-sonnet-20240620",
-}
-
-llm_classes = {
-    "llama3-70b-8192": ChatGroq,
-    "gpt-4o-mini": ChatOpenAI,
-    "gpt-4o": ChatOpenAI,
-    "gpt-4-turbo": ChatOpenAI,
-    "gemini-1.5-pro": ChatGoogleGenerativeAI,
-    "claude-3-5-sonnet-20240620": ChatAnthropic,
-}
-
-xml_system = """You're a helpful AI assistant. Given a user prompt and some related sources, \
-fulfill all the requirements of the prompt and provide citations. If a part of the generated text does \
-not use any of the sources, don't put a citation for that part. Otherwise, list all sources used for that part of the answer.
-At the end of each relevant part, add a citation in square brackets, numbered sequentially starting from [0], regardless of the source's original ID.
-
-
-Remember, you must return both the requested text and citations. A citation consists of a VERBATIM quote that \
-justifies the answer and a sequential number (starting from 0) for the quote's article. Return a citation for every quote across all articles \
-that justify the answer. Use the following format for your final output:
-
-<cited_answer>
-    <answer></answer>
-    <citations>
-        <citation><source_id></source_id><source></source><quote></quote></citation>
-        <citation><source_id></source_id><source></source><quote></quote></citation>
-        ...
-    </citations>
-</cited_answer>
-
-Here are the sources:{context}"""
-xml_prompt = ChatPromptTemplate.from_messages(
-    [("system", xml_system), ("human", "{input}")]
-)
-
-def format_docs_xml(docs: list[Document]) -> str:
-    formatted = []
-    for i, doc in enumerate(docs):
-        doc_str = f"""\
-    <source>
-        <source>{doc.metadata['source']}</source>
-        <title>{doc.metadata['title']}</title>
-        <article_snippet>{doc.page_content}</article_snippet>
-    </source>"""
-        formatted.append(doc_str)
-    return "\n\n<sources>" + "\n".join(formatted) + "</sources>"
-
-
-def citations_to_html(citations_data):
-    if citations_data:
-        html_output = "<ul>"
-        
-        for index, citation in enumerate(citations_data):
-            source_id = citation['citation'][0]['source_id']
-            source = citation['citation'][1]['source']
-            quote = citation['citation'][2]['quote']
-            
-            html_output += f"""
-            <li>
-                [{index}] - "{source}" <br>
-                "{quote}"
-            </li>
-            """
-        
-        html_output += "</ul>"
-        return html_output
-    return ""
-
-
-def load_llm(model: str, api_key: str, temperature: float = 1.0, max_length: int = 2048):
-    model_name = llm_model_translation.get(model)
-    llm_class = llm_classes.get(model_name)
-    if not llm_class:
-        raise ValueError(f"Model {model} not supported.")
-    try:
-        llm = llm_class(model_name=model_name, temperature=temperature, max_tokens=max_length)
-    except Exception as e:
-        print(f"An error occurred: {e}")
-        llm = None
-    return llm
-
-
-def create_db_with_langchain(path: list[str], url_content: dict):
-    all_docs = []
-    text_splitter = RecursiveCharacterTextSplitter(chunk_size=CHUNK_SIZE, chunk_overlap=CHUNK_OVERLAP)
-    embedding_function = SentenceTransformerEmbeddings(model_name="all-mpnet-base-v2")
-    if path:
-        for file in path:
-            loader = PyMuPDFLoader(file)
-            data = loader.load()
-            # split it into chunks
-            docs = text_splitter.split_documents(data)
-            all_docs.extend(docs)
-
-    if url_content:
-        for url, content in url_content.items():
-            doc = Document(page_content=content, metadata={"source": url})
-            # split it into chunks
-            docs = text_splitter.split_documents([doc])
-            all_docs.extend(docs)
-
-    # print docs
-    for idx, doc in enumerate(all_docs):
-        print(f"Doc: {idx} | Length = {len(doc.page_content)}")
-
-    assert len(all_docs) > 0, "No PDFs or scrapped data provided"
-    db = Chroma.from_documents(all_docs, embedding_function)
-    return db
-
-
-def generate_rag(
-    prompt: str,
-    topic: str,
-    model: str,
-    url_content: dict,
-    path: list[str],
-    temperature: float = 1.0,
-    max_length: int = 2048,
-    api_key: str = "",
-    sys_message="",
-):
-    llm = load_llm(model, api_key, temperature, max_length)
-    if llm is None:
-        print("Failed to load LLM. Aborting operation.")
-        return None
-    db = create_db_with_langchain(path, url_content)
-    retriever = db.as_retriever(search_type="mmr", search_kwargs={"k": K, "fetch_k": FETCH_K})
-    rag_prompt = hub.pull("rlm/rag-prompt")
-
-    def format_docs(docs):
-        if all(isinstance(doc, Document) for doc in docs):
-            return "\n\n".join(doc.page_content for doc in docs)
-        else:
-            raise TypeError("All items in docs must be instances of Document.")
-
-    docs = retriever.get_relevant_documents(topic)
-    # formatted_docs = format_docs(docs)
-    # rag_chain = (
-    #     {"context": lambda _: formatted_docs, "question": RunnablePassthrough()} | rag_prompt | llm | StrOutputParser()
-    # )
-    # return rag_chain.invoke(prompt)
-
-    formatted_docs = format_docs_xml(docs)
-    rag_chain = (
-        RunnablePassthrough.assign(context=lambda _: formatted_docs)
-        | xml_prompt
-        | llm
-        | XMLOutputParser()
-    )
-    result = rag_chain.invoke({"input": prompt})
-    print(result)
-    return result['cited_answer'][0]['answer'], result['cited_answer'][1]['citations']
-
-
-def generate_base(
-    prompt: str, topic: str, model: str, temperature: float, max_length: int, api_key: str, sys_message=""
-):
-    llm = load_llm(model, api_key, temperature, max_length)
-    if llm is None:
-        print("Failed to load LLM. Aborting operation.")
-        return None, None
-    try:
-        output = llm.invoke(prompt).content
-        return output, None
-    except Exception as e:
-        print(f"An error occurred while running the model: {e}")
-        return None, None
-
-
-def generate(
-    prompt: str,
-    topic: str,
-    model: str,
-    url_content: dict,
-    path: list[str],
-    temperature: float = 1.0,
-    max_length: int = 2048,
-    api_key: str = "",
-    sys_message="",
-):
-    return generate_rag(prompt, topic, model, url_content, path, temperature, max_length, api_key, sys_message)
-