Update langchain_helper.py

langchain_helper.py

@@ -0,0 +1,60 @@
+from langchain.document_loaders import YoutubeLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
+from langchain.chains import LLMChain
+from langchain.vectorstores import FAISS  # for similarity search
+from langchain.embeddings.openai import OpenAIEmbeddings
+
+
+from dotenv import load_dotenv
+
+load_dotenv()
+
+embeddings = OpenAIEmbeddings()
+
+# video_url = "https://www.youtube.com/watch?v=OyFJWRnt_AY"
+
+
+def create_vector_db_from_ytUrl(video_url: str) -> FAISS:
+    '''First load the youtube video. calling loader.load() will automatically transcribe the youtube video. Next we split the text with RecursiveCharacterTextSplitter. Create and return a vector store object '''
+
+    loader = YoutubeLoader.from_youtube_url(video_url)
+    video_transcript = loader.load()
+
+    text_splitter = RecursiveCharacterTextSplitter(
+        chunk_size=1000, chunk_overlap=100)
+
+    docs = text_splitter.split_documents(video_transcript)
+
+    db = FAISS.from_documents(docs, embeddings)
+    return db
+
+
+def get_response_from_query(db, query, k=4):
+    ''' text_davinci max tokens = 4097 
+    Semantic search '''
+
+    docs = db.similarity_search(query, k=k)
+
+    docs_page_content = " ".join([d.page_content for d in docs])
+
+    llm = OpenAI(model="text-davinci-003")
+
+    prompt = PromptTemplate(
+        input_variables=["question", "docs"],
+        template="""You are a helpful YouTube assistant that can answer questions about videos from video transcripts.
+        Answer the following question: {question}
+        By searching the following video transcript: {docs}
+
+        If you feel like you do not have enough information to give the answer, simply say "I have not much information to answer the question!"
+
+        Your answers should be detailed.
+        """
+    )
+
+    chain = LLMChain(llm=llm, prompt=prompt)
+
+    response = chain.run(question=query, docs=docs_page_content)
+    response = response.replace("\n", "")
+    return response, docs