Update chatbot.py

chatbot.py

@@ -1,68 +1,68 @@
-from langchain.text_splitter import CharacterTextSplitter
-from langchain_community.document_loaders import TextLoader
-from langchain.schema.runnable import RunnablePassthrough
-from langchain.schema.output_parser import StrOutputParser
-from langchain_pinecone import PineconeVectorStore
-from langchain.prompts import PromptTemplate
-from langchain_google_genai import GoogleGenerativeAI, GoogleGenerativeAIEmbeddings
-from dotenv import load_dotenv, find_dotenv
-import os
-from pinecone import Pinecone, PodSpec
-
-load_dotenv(find_dotenv())
-
-class Chatbot():
-
-    loader = TextLoader('dataset.txt', autodetect_encoding=True)
-    documents = loader.load()
-    text_splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=4)
-    docs = text_splitter.split_documents(documents)
-
-    embeddings = GoogleGenerativeAIEmbeddings(
-    model="models/embedding-001", task_type="retrieval_query", google_api_key=os.getenv("GEMINI_API_KEY")
-)
-
-    pinecone = Pinecone(
-        api_key=os.environ.get("PINECONE_API_KEY")
-        # host='gcp-starter'
-    )
-
-    index_name = "thehexatechchatbot"
-
-    if index_name not in pinecone.list_indexes().names():
-        pinecone.create_index(name=index_name, metric="cosine", dimension=768, spec=PodSpec(environment="gcp-starter"))
-        docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)
-    else:
-         docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings)
-
-
-    llm = GoogleGenerativeAI(model="Gemini 2.5 Flash-Lite Preview 06-17", google_api_key=os.getenv("GEMINI_API_KEY"))
-
-    template = """
-    INSTRUCTION: Act as Customer Support chatbot of The Hexatech, an IT Startup that provides Daas \
-    to a user who wants to get his query solved about The Hexatech. Use the CONTEXT to answer in a helpful manner to the QUESTION. \
-    If you don't know any ANSWER, say you don't know \
-    Always follow general guardrails before generating any response. \
-    Always try to keep the conversation in context to The Hexatech. Keep your replies short \
-    compassionate and informative.\
-    Give the answer from the CONTEXT\
-    You should help user to get his query solved and also try to increase engagement and promoting The Hexatech & its services.\
-        
-    CONTEXT: {context}
-    QUESTION: {question}
-    ANSWER:
-    """
-
-    prompt = PromptTemplate(
-    template=template, 
-    input_variables=["context", "question"]
-    )
-
-
-    rag_chain = (
-    {"context": docsearch.as_retriever(),  "question": RunnablePassthrough()} 
-    | prompt 
-    | llm
-    | StrOutputParser() 
-    )
-
+from langchain.text_splitter import CharacterTextSplitter
+from langchain_community.document_loaders import TextLoader
+from langchain.schema.runnable import RunnablePassthrough
+from langchain.schema.output_parser import StrOutputParser
+from langchain_pinecone import PineconeVectorStore
+from langchain.prompts import PromptTemplate
+from langchain_google_genai import GoogleGenerativeAI, GoogleGenerativeAIEmbeddings
+from dotenv import load_dotenv, find_dotenv
+import os
+from pinecone import Pinecone, PodSpec
+
+load_dotenv(find_dotenv())
+
+class Chatbot():
+
+    loader = TextLoader('dataset.txt', autodetect_encoding=True)
+    documents = loader.load()
+    text_splitter = CharacterTextSplitter(chunk_size=512, chunk_overlap=4)
+    docs = text_splitter.split_documents(documents)
+
+    embeddings = GoogleGenerativeAIEmbeddings(
+    model="models/embedding-001", task_type="retrieval_query", google_api_key=os.getenv("GEMINI_API_KEY")
+)
+
+    pinecone = Pinecone(
+        api_key=os.environ.get("PINECONE_API_KEY")
+        # host='gcp-starter'
+    )
+
+    index_name = "thehexatechchatbot"
+
+    if index_name not in pinecone.list_indexes().names():
+        pinecone.create_index(name=index_name, metric="cosine", dimension=768, spec=PodSpec(environment="gcp-starter"))
+        docsearch = PineconeVectorStore.from_documents(docs, embeddings, index_name=index_name)
+    else:
+         docsearch = PineconeVectorStore.from_existing_index(index_name, embeddings)
+
+
+    llm = GoogleGenerativeAI(model="gemini-2.5-flash-lite-preview-06-17", google_api_key=os.getenv("GEMINI_API_KEY"))
+
+    template = """
+    INSTRUCTION: Act as Customer Support chatbot of The Hexatech, an IT Startup that provides Daas \
+    to a user who wants to get his query solved about The Hexatech. Use the CONTEXT to answer in a helpful manner to the QUESTION. \
+    If you don't know any ANSWER, say you don't know \
+    Always follow general guardrails before generating any response. \
+    Always try to keep the conversation in context to The Hexatech. Keep your replies short \
+    compassionate and informative.\
+    Give the answer from the CONTEXT\
+    You should help user to get his query solved and also try to increase engagement and promoting The Hexatech & its services.\
+        
+    CONTEXT: {context}
+    QUESTION: {question}
+    ANSWER:
+    """
+
+    prompt = PromptTemplate(
+    template=template, 
+    input_variables=["context", "question"]
+    )
+
+
+    rag_chain = (
+    {"context": docsearch.as_retriever(),  "question": RunnablePassthrough()} 
+    | prompt 
+    | llm
+    | StrOutputParser() 
+    )
+