FinFeed_version4.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from dotenv import load_dotenv\n",
    "from langchain_openai.chat_models import ChatOpenAI\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "from langchain.prompts import ChatPromptTemplate\n",
    "from langchain_community.document_loaders import TextLoader\n",
    "from langchain.text_splitter import RecursiveCharacterTextSplitter\n",
    "from langchain_openai.embeddings import OpenAIEmbeddings\n",
    "from pinecone import Pinecone,  PodSpec\n",
    "from langchain_pinecone import PineconeVectorStore\n",
    "from langchain_core.runnables import RunnablePassthrough,RunnableLambda\n",
    "from langchain_core.pydantic_v1 import BaseModel, Field\n",
    "from transformers import AutoTokenizer, AutoModelForSequenceClassification\n",
    "\n",
    "import re\n",
    "from collections import Counter\n",
    "import nltk\n",
    "from nltk.corpus import stopwords\n",
    "from nltk.tokenize import word_tokenize\n",
    "from transformers import pipeline\n",
    "import pandas as pd\n",
    "from openai import OpenAI\n",
    "import matplotlib\n",
    "matplotlib.use('agg')\n",
    "import matplotlib.pyplot as plt\n",
    "import ast\n",
    "# load_dotenv(\"API_KEYS\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load environment variables from .env file\n",
    "load_dotenv(\"API_KEYS.env\")\n",
    "\n",
    "# get the environment variable\n",
    "client = OpenAI(api_key=os.environ.get(\"OPENAI_API_KEY\"))\n",
    "openai_api_key = os.getenv('OPENAI_API_KEY')\n",
    "pinecone_api_key = os.getenv('PINECONE_API_KEY')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Classification(BaseModel):\n",
    "    answer: str = Field(description=\"\"\"Do Answer the question based on the context below but pretend like you are a news reporter who just received the context\n",
    "                         as the latest news. \n",
    "                        If you can't answer the question, reply 'I do not have enough information to answer this question' \"\"\")\n",
    "    sentiment: str = Field(description=\"The sentiment of the comments. Rate each comment.\")\n",
    "    aggressiveness: str = Field(description=\"How aggressive the comments is on a scale from 1 to 10. 1 being polite and 10 means aggressive.\"\n",
    "    )\n",
    "    language: str = Field(description=\"The language the text is written in\")\n",
    "    political_tendency : str = Field(description= \"\"\"The political leaning of the comment. Emphasize political leaning on both economic andsocial policies. \n",
    "                                     Justify your answer.\"\"\")\n",
    "\n",
    "\n",
    "class FinFeedRAG:\n",
    "    def __init__(self, pinecone_index,pine_cone_api_key= pinecone_api_key, openai_api_key= openai_api_key,\n",
    "                  embeddings_model= OpenAIEmbeddings(),model='gpt-3.5-turbo', path_to_df = 'combined_df.pkl'):\n",
    "        self.openai_api_key=openai_api_key\n",
    "        self.api_key_pinecone = pine_cone_api_key\n",
    "        self.pinecone_index = pinecone_index\n",
    "        # Initialize Pinecone connection\n",
    "        self.vector_db = None\n",
    "        self.embeddings=embeddings_model\n",
    "        self.model=model\n",
    "        #read latest dataframe\n",
    "        self.df_youtube_data= pd.read_pickle(path_to_df)\n",
    "        self.template_classification = \"\"\"\n",
    "                            Answer the question based on the context below but pretend like you are a news reporter who just received the context as the latest news. \n",
    "                            If you can't answer the question, reply \"I do not have enough information to answer this question\".\n",
    "                \n",
    "                            Extract the properties mentioned in the 'Classification' function and also justify your analysis.\n",
    "\n",
    "                            Context: {context}\n",
    "\n",
    "                            Public Comments: {comments}\n",
    "                            \n",
    "                            Question: {question}\n",
    "                            \"\"\"\n",
    "        self.template = \"\"\"\n",
    "                        Answer the question based on the context provided below, which is structured in a dictionary format. Assume the role of a news reporter.\n",
    "                        Each time you use information from the context, you must cite it explicitly. Cite the source accompanying each context entry by including \n",
    "                        it directly in your response. Additionally, for each context, comments from public is provided. At the end of the answer, please provide some \n",
    "                        examples from people's opinion.\n",
    "                        \n",
    "                        Use as many contexts as possible to provide a comprehensive answer. If you lack sufficient information to formulate a response, please state:\n",
    "                        \"I do not have enough information to answer this question.\"\n",
    "                        \n",
    "                        Contexts:\n",
    "                        {context}\n",
    "                        \n",
    "                        Question:\n",
    "                        {question}\n",
    "                        \n",
    "                        Citing the context:\n",
    "                        When referencing a specific context in your answer, use the format:\n",
    "                        'According to [source], ...'. For example, if drawing from the first context, you would write:\n",
    "                        'According to Yahoo Finance, ...'.\n",
    "                        \n",
    "                        Providing people's opinion:\n",
    "                        At the end of your answer, include public opinion using the format:\n",
    "                        \"Public Opinon:\\n\n",
    "                        Here are some examples of people's reactions to related news: [public opinion quotes].\"\n",
    "                        \n",
    "                        \"\"\"\n",
    "        \n",
    "        self.template_prompt_engineer = \"\"\"\n",
    "Transform the following user query into a concise and optimized prompt suitable for retrieving relevant chunks from vector data base which consists of news on \n",
    "finance, economics, and politics. Ensure the rephrased prompt clearly reflects key terms and concepts from these fields to improve accuracy in data querying.\n",
    "Original Query: '{question}'\n",
    "\"\"\"\n",
    "\n",
    "\n",
    "\n",
    "#####################################################\n",
    "    def extract_comments(self, query):\n",
    "        retriever = PineconeVectorStore.from_existing_index(\n",
    "            index_name=self.pinecone_index,\n",
    "            embedding=self.embeddings\n",
    "        ).as_retriever(\n",
    "            search_type='similarity',\n",
    "            search_kwargs={'k': 10}\n",
    "        )\n",
    "\n",
    "        documents = retriever.invoke(query)\n",
    "        comments = []\n",
    "        for doc in documents:\n",
    "            comments.extend(doc.metadata.get('youtube_comments', []))\n",
    "        comments_str = \"\\n\".join(comments)\n",
    "        return comments_str\n",
    "    \n",
    "    def prompt_tagging(self,template=None):\n",
    "        if template is None:\n",
    "            template = self.template_classification\n",
    "        return ChatPromptTemplate.from_template(template)\n",
    "\n",
    "    def tagging_chain(self, query):\n",
    "        retriever = PineconeVectorStore.from_existing_index(\n",
    "            index_name=self.pinecone_index,\n",
    "            embedding=self.embeddings\n",
    "        ).as_retriever(\n",
    "            search_type='similarity',\n",
    "            search_kwargs={'k': 10}\n",
    "        )\n",
    "\n",
    "        comments = self.extract_comments(query)\n",
    "        comments_runnable = RunnableLambda(lambda _: comments)\n",
    "\n",
    "        chaining = (\n",
    "            {\n",
    "                \"context\": retriever,\n",
    "                \"comments\": comments_runnable,\n",
    "                \"question\": RunnablePassthrough()\n",
    "            }\n",
    "            | self.prompt_tagging() #uses self.template_classification\n",
    "            | self.llm_tagging() #for structured output\n",
    "        )\n",
    "\n",
    "        return chaining.invoke(query).dict()\n",
    "\n",
    "    \n",
    "####################################Sentiment\n",
    "    def get_all_vector_ids(self,  input_vector= [0.0] * 1536, top_k = 10000, index= None):   # Replace 768 with your vector dimension\n",
    "        if index is None:\n",
    "            index = self.pinecone_index\n",
    "            \n",
    "        results = index.query(vector=input_vector, top_k= top_k, include_values=False)\n",
    "        # ids = set()\n",
    "        ids=[]\n",
    "        for result in results['matches']:\n",
    "            # ids.add(result['id'])\n",
    "            ids.append(result['id'])\n",
    "        return ids\n",
    "\n",
    "\n",
    "    def insert_youtube_metadata(self, top_k=10000, index= None):\n",
    "        \"\"\"Set top_k >= total_vector_count of vector database\n",
    "        \"\"\"\n",
    "        if index is None:\n",
    "            index= self.pinecone_index\n",
    "        assert top_k > self.vector_db.describe_index_stats().total_vector_count, \"top_k is less that total_vector_count of vector database\"\n",
    "\n",
    "        #my_index = Pinecone(self.api_key_pinecone).Index(index)\n",
    "        all_ids = self.get_all_vector_ids([0.0] * 1536, top_k= top_k , index=index)  \n",
    "\n",
    "        for id in all_ids:\n",
    "        \n",
    "            old_metadata = index.fetch([id])['vectors'][id]['metadata']\n",
    "            index.update(\n",
    "            id= id, \n",
    "            set_metadata={'youtube_response_metadata': str(self.df_youtube_data[self.df_youtube_data['txt_file_path'].apply(lambda x : x.replace('\\\\','/')) == \n",
    "                                                                old_metadata['source']]['response_metadata'].values[0]),\n",
    "                        'youtube_comments': self.df_youtube_data[self.df_youtube_data['txt_file_path'].apply(lambda x : x.replace('\\\\','/')) \n",
    "                                                                 == old_metadata['source']]['list_of_comments'].values[0],\n",
    "                        'youtube_video_url': str(self.df_youtube_data[self.df_youtube_data['txt_file_path'].apply(lambda x : x.replace('\\\\','/')) \n",
    "                                                                 == old_metadata['source']]['video_urls'].values[0])  \n",
    "        }, \n",
    "            namespace=\"\"\n",
    "        )\n",
    "  \n",
    "\n",
    "    def llm_tagging(self, model = 'gpt-3.5-turbo'):\n",
    "       \n",
    "        llm = ChatOpenAI(openai_api_key=self.openai_api_key, model=model).with_structured_output(Classification)\n",
    "        return llm\n",
    "\n",
    "    \n",
    "    def initialize_pinecone(self):\n",
    "        if self.vector_db is None:  # Check if it's already initialized\n",
    "            pc = Pinecone(api_key=self.api_key_pinecone)\n",
    "            self.vector_db = pc.Index(self.pinecone_index)  # Connect to the index and store the connection\n",
    "        return self.vector_db\n",
    "        \n",
    "    \n",
    "    def preprocess_youtube_text(self, text_file, chunksize,chunkoverlap, preprocess_yt=True):\n",
    "        if preprocess_yt:\n",
    "            self.preprocess_input(text_file,save_back_to_file=True)\n",
    "        else:\n",
    "            None\n",
    "        loader = TextLoader(text_file) #text instance of langchain\n",
    "        text_documents = loader.load() \n",
    "        # Assuming RecursiveCharacterTextSplitter is a class you have access to or have created\n",
    "        splitter = RecursiveCharacterTextSplitter(chunk_size=chunksize, chunk_overlap=chunkoverlap)\n",
    "        processed_text = splitter.split_documents(text_documents)\n",
    "        # Further processing can be done here if necessary\n",
    "        return processed_text\n",
    "\n",
    "    def upload_to_vb(self,text,embeddings,chunksize, chunkoverlap,index=None,preprocess_yt=True):\n",
    "        if index is None:\n",
    "            index = self.pinecone_index\n",
    "        return PineconeVectorStore.from_documents(self.preprocess_youtube_text(text,chunksize,chunkoverlap,preprocess_yt), self.embeddings, index_name=index)\n",
    "\n",
    "    def add_to_vb(self,text,chunksize, chunkoverlap,index=None,preprocess_yt=True):\n",
    "        if index is None:\n",
    "            index = self.pinecone_index\n",
    "        embed= self.embeddings.embed_documents(self.preprocess_youtube_text(text,chunksize,chunkoverlap,preprocess_yt))\n",
    "        return PineconeVectorStore.add_documents(embed, index_name=index)   \n",
    "\n",
    "    def preprocess_input(self, text_file,save_back_to_file=True):\n",
    "        # Simple text preprocessing: lowercasing, removing punctuation need to add more preprocessing steps do research on it\n",
    "        # Read and process the content and rewrite it\n",
    "        if save_back_to_file==True:\n",
    "            with open(text_file, 'r') as file:\n",
    "                # Read the contents of the file\n",
    "                text = file.read()\n",
    "            processed_text = text.lower()\n",
    "            processed_text = re.sub(r'[^\\w\\s]', '', processed_text)\n",
    "            tokens = word_tokenize(processed_text)\n",
    "            filtered_words = [word for word in tokens if word.lower() not in stopwords.words('english')]\n",
    "            # Join words back into a single string\n",
    "            final_text = ' '.join(filtered_words)\n",
    "            # Write the processed content back, replacing the original\n",
    "            with open(text_file, 'w') as file:\n",
    "                file.write(final_text)\n",
    "        else:\n",
    "            with open(text_file, 'r') as file:\n",
    "                # Read the contents of the file\n",
    "                text = file.read()\n",
    "            processed_text = text.lower()\n",
    "            processed_text = re.sub(r'[^\\w\\s]', '', processed_text)\n",
    "            tokens = word_tokenize(processed_text)\n",
    "            filtered_words = [word for word in tokens if word.lower() not in stopwords.words('english')]\n",
    "            # Join words back into a single string\n",
    "            final_text = ' '.join(filtered_words)\n",
    "            return final_text\n",
    "        \n",
    "    def most_common(self, input_text_file,most_common=10):\n",
    "        # Preprocess the text\n",
    "        processed_text = self.preprocess_input(input_text_file,save_back_to_file=False)    \n",
    "        # Extract keywords based on frequency, assuming more frequent terms are more relevant\n",
    "        words = processed_text.split()\n",
    "        word_freq = Counter(words)\n",
    "        common_words = word_freq.most_common(most_common)  # Get the top 5 words       \n",
    "        # Form a query by joining the most common words\n",
    "        query = ' '.join(word for word, _ in common_words)\n",
    "        return query\n",
    "\n",
    "    def retrieve_embeddings(self, query, most_similar=10):\n",
    "        assert self.vector_db is not None, \"Initialize Pinecone first\"\n",
    "        query_result = self.vector_db.query(vector=self.embeddings.embed_query(query), top_k=most_similar)\n",
    "        ids = [item['id'] for item in query_result['matches']]\n",
    "        return [self.vector_db.fetch(ids)['vectors'][id]['values'] for id in ids]\n",
    "\n",
    "    def provide_context(self, query,index=None,most_similar=10):\n",
    "        if index is None:\n",
    "            index = self.pinecone_index\n",
    "        # Provide context to LLM\n",
    "        return PineconeVectorStore.from_existing_index(index_name=index,embedding=self.embeddings).as_retriever(search_type='similarity',\n",
    "                search_kwargs={\n",
    "                'k':most_similar}).invoke(query)\n",
    "        \n",
    "    def prompt(self,template=None):\n",
    "        if template is None:\n",
    "            template = self.template\n",
    "        return ChatPromptTemplate.from_template(template)\n",
    "\n",
    "    def prompt_eng(self,template=None):\n",
    "        if template is None:\n",
    "            template = self.template_prompt_engineer\n",
    "        return ChatPromptTemplate.from_template(template)\n",
    "        \n",
    "    def llm(self,model=None):\n",
    "        if model is None:\n",
    "            model = self.model\n",
    "        return ChatOpenAI(openai_api_key=self.openai_api_key, model=model)\n",
    "        \n",
    "    def parser(self):\n",
    "        return StrOutputParser()\n",
    "    def chain_prompt_eng(self,query):\n",
    "        chaining_eng =  (\n",
    "        {\n",
    "         \"question\": RunnablePassthrough()}\n",
    "        | self.prompt_eng()\n",
    "        | self.llm()\n",
    "        | self.parser())\n",
    "        return chaining_eng.invoke(query)\n",
    "\n",
    "    def chain(self,query):\n",
    "        #complete_query = self.prompt().format(context=self.provide_context(query),question=query)\n",
    "        #response = self.llm().invoke(complete_query)\n",
    "        #return self.parser().invoke(response)\n",
    "        chaining = (\n",
    "        {\"context\": PineconeVectorStore.from_existing_index(index_name=self.pinecone_index,embedding=self.embeddings).as_retriever(search_type='similarity',\n",
    "                search_kwargs={\n",
    "                'k': 10}), \n",
    "         \"question\": RunnablePassthrough()}\n",
    "        | self.prompt()\n",
    "        | self.llm()\n",
    "        | self.parser())\n",
    "        #query=str(self.prompt_eng(query))\n",
    "        return chaining.invoke(query)\n",
    "    \n",
    "\n",
    "   \n",
    "    def chain1(self, query):\n",
    "        # Initialize the retriever using an existing Pinecone index with specified embeddings\n",
    "     \n",
    "        retriever = PineconeVectorStore.from_existing_index(\n",
    "        index_name=self.pinecone_index,\n",
    "        embedding=self.embeddings\n",
    "    ).as_retriever(\n",
    "        search_type='similarity',\n",
    "        search_kwargs={'k': 10}  # Retrieve top 10 similar results\n",
    "    )\n",
    "\n",
    "    # Invoke the retriever with the query and process metadata\n",
    "        retrieved_items = retriever.invoke(query)\n",
    "        metadata = []\n",
    "        for item in retrieved_items:                # Extract and evaluate the nested metadata string if it exists\n",
    "            meta_string = item.metadata['youtube_reponse_metadata']\n",
    "            metadata.append(meta_string) \n",
    "        \n",
    "        # Combine retrieved metadata into a dictionary\n",
    "        dic = {i: meta for i, meta in enumerate(metadata)}\n",
    "    \n",
    "        # Create a chaining operation where metadata is included as context\n",
    "       \n",
    "        chaining = (\n",
    "            {\"context\": retriever, \n",
    "             \"metadata\": RunnablePassthrough(dic),\n",
    "             \"question\": RunnablePassthrough()}\n",
    "            | self.prompt()\n",
    "            | self.llm()\n",
    "            | self.parser()\n",
    "        )\n",
    "    \n",
    "        # Invoke the complete chain with the initial query\n",
    "        return chaining.invoke(query)\n",
    "\n",
    "    \n",
    "\n",
    "    def chain2(self, query):\n",
    "        # Initialize the retriever using an existing Pinecone index with specified embeddings\n",
    "        retriever = PineconeVectorStore.from_existing_index(\n",
    "            index_name=self.pinecone_index,\n",
    "            embedding=self.embeddings\n",
    "        ).as_retriever(\n",
    "            search_type='similarity',\n",
    "            search_kwargs={'k': 10}  # Retrieve top 10 similar results\n",
    "        )\n",
    "      \n",
    "        # Invoke the retriever with the query and process metadata\n",
    "        retrieved_items = retriever.invoke(query)\n",
    "        metadata = []\n",
    "        for item in retrieved_items:\n",
    "            content=item.page_content\n",
    "            meta = ast.literal_eval(item.metadata['youtube_response_metadata'])['snippet']['channelTitle']\n",
    "            context_entry = {\n",
    "                \"text\": content,\n",
    "                \"source\": meta\n",
    "            }\n",
    "            metadata.append(context_entry)\n",
    "    \n",
    "        # Combine retrieved metadata into a dictionary\n",
    "        dic = {f'Context {i}': context for i, context in enumerate(metadata)}\n",
    "\n",
    "\n",
    "    \n",
    "        # Use a lambda function for passing context and question to prompt\n",
    "        context_and_question = RunnablePassthrough(lambda: {'context': dic ,'question': query})\n",
    "    \n",
    "        # Create a sequence of operations\n",
    "        # Assuming your self.prompt(), self.llm(), and self.parser() are methods that handle their respective parts\n",
    "        result = {'context': RunnablePassthrough(lambda x : dic) ,'question': RunnablePassthrough()} | self.prompt() | self.llm() | self.parser()\n",
    "    \n",
    "        # Invoke the complete chain with the initial query\n",
    "        return result.invoke(query)\n",
    "\n",
    "\n",
    "    def chain3(self, query):\n",
    "        # Initialize the retriever using an existing Pinecone index with specified embeddings\n",
    "        retriever = PineconeVectorStore.from_existing_index(\n",
    "            index_name=self.pinecone_index,\n",
    "            embedding=self.embeddings\n",
    "        ).as_retriever(\n",
    "            search_type='similarity',\n",
    "            search_kwargs={'k': 5}  # Retrieve top 10 similar results\n",
    "        )\n",
    "      \n",
    "        # Invoke the retriever with the query and process metadata\n",
    "        retrieved_items = retriever.invoke(query)\n",
    "        metadata = []\n",
    "        comments = []\n",
    "        youtube_urls =[]\n",
    "        contexts=[]\n",
    "       \n",
    "        \n",
    "        for item in retrieved_items:\n",
    "            yt_url = item.metadata['youtube_video_url']\n",
    "            content=item.page_content\n",
    "            comments=item.metadata[\"youtube_comments\"]\n",
    "            meta = ast.literal_eval(item.metadata['youtube_response_metadata'])['snippet']['channelTitle']\n",
    "            context_entry = {\n",
    "                \"text\": content,\n",
    "                \"source\": meta,\n",
    "                \"public_opinion\":comments\n",
    "            }\n",
    "            metadata.append(context_entry)\n",
    "            comments.extend(item.metadata.get('youtube_comments', []))\n",
    "            youtube_urls.append(yt_url)\n",
    "            contexts.append(content)\n",
    "\n",
    "        comments_str = \"\\n\".join(comments)\n",
    "        dic = {f'Context {i}': context for i, context in enumerate(metadata)}\n",
    "        # Use a lambda function for passing context and question to prompt\n",
    "        context_and_question = RunnablePassthrough(lambda: {'context': dic ,'question': query})\n",
    "        \n",
    "        # Create a sequence of operations\n",
    "        # Assuming your self.prompt(), self.llm(), and self.parser() are methods that handle their respective parts\n",
    "        result = {'context': RunnablePassthrough(lambda x : dic) ,'question': RunnablePassthrough()} | self.prompt() | self.llm() | self.parser()\n",
    "        \n",
    "        comments_runnable = RunnableLambda(lambda _: comments)\n",
    "        sentiment_chain = (\n",
    "            {\n",
    "                \"context\": retriever,\n",
    "                \"comments\": comments_runnable,\n",
    "                \"question\": RunnablePassthrough()\n",
    "            }\n",
    "            | self.prompt_tagging() #uses self.template_classification\n",
    "            | self.llm_tagging() #for structured output\n",
    "        )\n",
    "\n",
    "        dic_sentiment = sentiment_chain.invoke(query).dict()\n",
    "\n",
    "        sentiment = dic_sentiment['sentiment']\n",
    "        aggresiveness = dic_sentiment['aggressiveness']\n",
    "        political_tendency=dic_sentiment['political_tendency']\n",
    "        # Invoke the complete chain with the initial query\n",
    "\n",
    "        fig,ax=self.analyze_and_plot_sentiment(contexts)\n",
    "        return result.invoke(query),sentiment,aggresiveness,political_tendency, youtube_urls,ax,fig\n",
    "\n",
    "\n",
    "    def analyze_and_plot_sentiment(self,contexts):\n",
    "        # Initialize tokenizer and model\n",
    "        tokenizer = AutoTokenizer.from_pretrained(\"ProsusAI/finbert\")\n",
    "        model = AutoModelForSequenceClassification.from_pretrained(\"ProsusAI/finbert\")\n",
    "        sentiment_analyzer = pipeline(\"sentiment-analysis\", model=model, tokenizer=tokenizer)\n",
    "        \n",
    "        list_sent = []\n",
    "        data_sent = pd.DataFrame({'labels': []})\n",
    "        \n",
    "        # Analyze sentiment for each context\n",
    "        for item in contexts:\n",
    "            list_sent.append(sentiment_analyzer(item)[0][\"label\"])\n",
    "        \n",
    "        data_sent['labels'] = list_sent\n",
    "        \n",
    "        plt.style.use('seaborn-v0_8-dark')\n",
    "        \n",
    "        # Count the sentiment labels\n",
    "        sentiment_counts = data_sent['labels'].value_counts()\n",
    "        \n",
    "        # Create a figure and a set of subplots\n",
    "        fig, ax = plt.subplots(figsize=(8, 6))\n",
    "        \n",
    "        # Define colors consistent with the seaborn-dark theme\n",
    "        colors = ['#4c72b0' if label == 'positive' else '#c44e52' if label == 'negative' else '#dd8452' for label in sentiment_counts.index]\n",
    "        \n",
    "        # Plot the sentiment counts as a bar plot\n",
    "        ax.bar(sentiment_counts.index, sentiment_counts.values, color=colors)\n",
    "        \n",
    "        # Customize the plot\n",
    "        ax.set_xlabel('Sentiment')\n",
    "        ax.set_ylabel('Number of Chunks')\n",
    "        #ax.set_title('Sentiment Analysis of Context Chunks')\n",
    "        ax.set_xticks(range(len(sentiment_counts)))\n",
    "        ax.set_xticklabels(sentiment_counts.index, rotation=0)\n",
    "        ax.grid(True)\n",
    "        \n",
    "        # Set the face color of the figure and axes to white\n",
    "        fig.patch.set_facecolor('white')\n",
    "        ax.set_facecolor('white')\n",
    "        \n",
    "        # Adjust layout\n",
    "        plt.tight_layout()\n",
    "        \n",
    "        # Close the figure to prevent it from displaying automatically\n",
    "        plt.close(fig)\n",
    "        \n",
    "        return fig, ax\n",
    "            \n",
    "\n",
    "    \n",
    "     "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "bot = FinFeedRAG(pinecone_index='news-data')\n",
    "# bot1= FinFeedRAG(pinecone_index='latest-news')\n",
    "pine= bot.initialize_pinecone()\n",
    "# pine1= bot1.initialize_pinecone()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "data/text/#uk_liberal_democrats_leader_falls_off_paddle_board.txt\n",
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      "data/text/47%_of_american_workers_feel_financially_well_report.txt\n",
      "data/text/abukarsh_every_central_bank_wants_to_take_right_step.txt\n",
      "data/text/airfares_peaking_as_travelers_in_europe,_asia_seek_savings__reuters.txt\n",
      "data/text/ai_weekly_musk_startup_generates_$6_billion_in_fresh_funding__reuters.txt\n",
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      "data/text/apple_sees_big_iphone_rebound_in_china.txt\n",
      "data/text/apple_turns_it_around,_trump_trial_closing_arguments,_israel_strikes_rafah_#bloombergbrief_#shorts.txt\n",
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      "data/text/as_the_world_condemns_the_rafah_attack,_israels_war_on_gaza_continues_#shorts.txt\n",
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      "data/text/crypto_news_eth_etfs,_pro-crypto_politics,_uni,_doge_&_more!.txt\n",
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      "data/text/cyclone_remal_leaves_millions_without_power_in_bangladesh_and_india__al_jazeera_newsfeed.txt\n",
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      "data/text/end_the_genocide_says_us_libertarian_presidential_candidate_chase_oliver__#ajshorts.txt\n",
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      "data/text/south_korea's_pandemic_innovations_highlight_global_cooperation_challenges.txt\n",
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      "data/text/tallbacken's_purves_says_s&p_500_could_hit_6,000.txt\n",
      "data/text/tesla_ceo_elon_musk_new_china_ev_tariffs_are_not_good_#shorts.txt\n",
      "data/text/tesla_reportedly_cuts_model_y_production_at_giga_shanghai_#shorts.txt\n",
      "data/text/texas_storms_windstorms_knock_out_power_for_645,000_in_dallas_area.txt\n",
      "data/text/the_art_of_resistance_raoul_peck_and_viet_thanh_nguyen__studio_b_unscripted.txt\n",
      "data/text/the_big_gamble_the_world_of_online_sports_betting_in_the_us__fault_lines_documentary.txt\n",
      "data/text/the_closing_arguments_in_trumps_new_york_hush_money_trial.txt\n",
      "data/text/the_investment_trend_that_won't_go_away.txt\n",
      "data/text/thousands_feared_buried_alive_in_papua_landslide.txt\n",
      "data/text/thousands_feared_buried_alive_in_papua_landslide__news.txt\n",
      "data/text/thunderstorms_spawn_tornadoes_in_kentucky.txt\n",
      "data/text/toyota_investors_urged_to_vote_against_founding_family_chair_toyoda.txt\n",
      "data/text/treasuries_hit_as_us_sales_struggle_to_lure_buyers__bloomberg_markets_the_close_5282024.txt\n",
      "data/text/trilateral_summit_japanese,_south_korean_&_chinese_leaders_meet.txt\n",
      "data/text/trumps_legal_troubles__explained__start_here.txt\n",
      "data/text/trump_closing_statements,_israel's_airstrike_in_rafah.txt\n",
      "data/text/trump_jury_hears_closing_arguments_as_deliberations_loom.txt\n",
      "data/text/trump_lawyer_seeks_to_discredit_cohen_as_hush-money_trial_nears_end.txt\n",
      "data/text/tuesday,_may_28,_2024.txt\n",
      "data/text/us_consumer_confidence_unexpectedly_rises_for_first_time_in_four_months.txt\n",
      "data/text/us_lawmaker_visits_taiwan_to_affirm_support_against_chinas_aggression__aj_#shorts.txt\n",
      "data/text/valliere_on_israel's_rafah_actions_escalating_tensions.txt\n",
      "data/text/wall_street_trades_are_going_to_settle_faster_starting_today_#stocks_#shorts.txt\n",
      "data/text/war_on_gaza_at_least_21_killed_in_israeli_attack_on_gazas_al-mawasi_safe_zone.txt\n",
      "data/text/war_on_gaza_israeli_air_raids_rain_down_on_palestinians_returning_home_near_jabalia.txt\n",
      "data/text/we_don't_know_where_to_go_says_family_displaced_for_8th_time_in_gaza__al_jazeera_newsfeed.txt\n",
      "data/text/what's_behind_the_rise_in_political_violence_in_mexico__inside_story.txt\n",
      "data/text/what_to_expect_from_the_#uk_snap_election_#politics.txt\n",
      "data/text/white_house_press_secretary_karine_jean-pierre_holds_briefing.txt\n",
      "data/text/why_access_to_healthy_food_is_difficult_salad_and_go_ceo.txt\n",
      "data/text/why_all_eyes_are_on_rafah__the_take.txt\n",
      "data/text/why_uk_stock_market_is_'attractive'_according_to_lombard_odier.txt\n",
      "data/text/wild_orangutan_spotted_self-medicating_a_wound__aj_#shorts.txt\n",
      "data/text/will_tiktok_be_banned_in_the_us__ft_#shorts.txt\n",
      "data/text/windstorms_knock_out_power_to_645,000_in_dallas_area.txt\n",
      "data/text/zelenskiy_secures_military_aid_from_belgium,_spain__bloomberg's_the_pulse.txt\n"
     ]
    }
   ],
   "source": [
    "directory_path = \"data/text/\"\n",
    "for filename in os.listdir(directory_path):\n",
    "    if filename.endswith(('.txt')):\n",
    "        # Perform your desired actions here\n",
    "        print(directory_path + filename)\n",
    "        bot.upload_to_vb(directory_path + filename,embeddings=OpenAIEmbeddings(openai_api_key=openai_api_key),\n",
    "                         chunksize=1000,chunkoverlap=100,index='news-data', preprocess_yt= True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'dimension': 1536,\n",
      " 'index_fullness': 0.0,\n",
      " 'namespaces': {'': {'vector_count': 2193}},\n",
      " 'total_vector_count': 2193}\n"
     ]
    }
   ],
   "source": [
    "print(pine.describe_index_stats())\n",
    "# print(pine1.describe_index_stats())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       " '47b1ef9a-5c16-4894-b006-657dfa9391ca',\n",
       " '1cd0ddd0-187f-4fb3-a8a1-6f887cd6d2c8',\n",
       " 'fe520030-31cd-4129-a9f3-1a26806f237d',\n",
       " 'ba610c76-4ae4-4d46-bfd1-55a4c78841be',\n",
       " 'e235abb4-4243-4e2f-a37d-739f1d422212',\n",
       " '7b18108c-8feb-432e-a688-12c8cea74c34',\n",
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       " '185be7d9-7f55-435f-a839-879ab8103a1d',\n",
       " '8541d1b7-ba7f-413a-9348-3f9f8743acbb',\n",
       " '06e00b26-6fbb-4d3c-bbbc-033b18b15930',\n",
       " '68eb0036-2e72-4d2d-9b95-9b2276ae7c24',\n",
       " 'd3a9961d-cf47-4285-8eb6-b752c994c306',\n",
       " '54130c24-60c8-4141-a88d-d18d60ab4f81',\n",
       " '6459658a-115d-4018-a5f9-6d3e7132bdd8',\n",
       " '654f8ff0-37c7-491a-965d-ffe1ac5d76ae',\n",
       " 'c719efe6-51ae-4d86-8578-b2b92c7575d0',\n",
       " '2a53cfe8-d9be-4edb-987f-d007b500779f',\n",
       " 'a6c59daa-88a3-4bab-83b4-f591b97235a0',\n",
       " 'a2ccdb12-abee-4995-980b-180c9dc842a1',\n",
       " 'c2a76866-c9b1-4c01-b9c1-7ee7261fc552',\n",
       " '51e9c922-397b-4e3c-83b5-5c2ef17058cb',\n",
       " '5d36f968-5cf8-4196-a83c-4bd0c9ae2293',\n",
       " '87d2f08b-f52f-4cff-bbfc-8b0686cc25f7',\n",
       " 'fab197ba-43b8-4818-87b4-b333d638ddf4',\n",
       " '712febec-f2ce-444a-8a1e-47b6f2219782',\n",
       " '1590be28-57d5-4d70-b8c2-bb0fcdf4cee1',\n",
       " 'b80e1a12-d3d4-4948-a1a2-9fbfa947287e',\n",
       " '28bd7763-af84-4be1-a34b-a27ce2aa2e2c',\n",
       " '315dfe23-a699-43bc-a8e6-0d429082b003',\n",
       " '06c8f33d-64d4-4b9e-b6dc-05afe49b3b9a',\n",
       " '262c3d17-0f4a-45ef-83b1-cc8d355df086',\n",
       " '693a97ef-a2cd-49bd-9508-5ee96c41774d',\n",
       " 'bffb1a48-cb56-4101-9827-31fbfc96d7e4',\n",
       " 'd1d0fcd5-dd87-47a7-92c2-8919cbb4d340',\n",
       " 'f96bdc84-8b3e-42e9-8ec7-7fd42eb61b10',\n",
       " '299c1bb2-4bc4-49ca-a7fb-326ad034924a',\n",
       " 'f78f2d82-9758-4985-9f95-de72b6920d42',\n",
       " 'a2080a87-bb80-496d-a6c6-aaa8dc645571',\n",
       " 'a0275bd9-0498-46cb-ae25-f6d302a863a9',\n",
       " '5bc2bfc8-e465-4d95-9fbd-6aa7ce88b51c',\n",
       " 'd50b4c46-d39f-40ec-9ef4-05aaed3940a1',\n",
       " ...]"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bot.get_all_vector_ids(index=pine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "bot.insert_youtube_metadata(index= pine)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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