added genai chatbot

chatbot/LLM_Model/test.py

@@ -0,0 +1,88 @@
+from langchain_community.document_loaders import PyPDFDirectoryLoader
+from langchain.text_splitter import RecursiveCharacterTextSplitter
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from langchain_google_genai import ChatGoogleGenerativeAI
+
+from langchain.vectorstores import Chroma
+
+
+import os
+from dotenv import load_dotenv
+
+load_dotenv()
+google_gemini_api=os.getenv("GOOGLE_API_KEY")
+
+
+
+pdf_loader=PyPDFDirectoryLoader("pdfs")
+data=pdf_loader.load()
+text_splitter = RecursiveCharacterTextSplitter(
+    chunk_size=400,
+    chunk_overlap=100
+)
+text_chunks=text_splitter.split_documents(data)
+
+
+persist_directory="db"
+embedding = GoogleGenerativeAIEmbeddings(model="models/embedding-001")
+vectordb=Chroma.from_documents(
+    documents=text_chunks,
+    embedding=embedding,
+    persist_directory=persist_directory
+)
+vectordb.persist()
+vectordb=None
+vectordb=Chroma(persist_directory=persist_directory,embedding_function=embedding)
+
+
+retriver=vectordb.as_retriever()
+
+llm_model=ChatGoogleGenerativeAI(model="gemini-1.5-pro",google_api_key=google_gemini_api)
+
+system_prompt = (
+    "You have an expertise on Muncipal corporation and you are well aware about the Muncipal Corporation Indore and You have all the information regarding the Indore"
+    "You also have some additional data from the dataset of the IMC indore"
+    "Provide the answer consisely"
+    "Provide the answer Under 150 words"
+    "Context: {context}"
+)
+
+from langchain.chains import create_retrieval_chain
+from langchain.chains.combine_documents import create_stuff_documents_chain
+from langchain_core.prompts import ChatPromptTemplate
+prompt = ChatPromptTemplate.from_messages(
+    [
+        ("system", system_prompt),
+        ("human", "{input}"),
+    ]
+)
+question_answer_chain = create_stuff_documents_chain(llm_model, prompt)
+chain = create_retrieval_chain(retriver, question_answer_chain)
+
+# Basic memory class
+class Memory:
+    def __init__(self):
+        self.history = []
+
+    def add(self, entry):
+        self.history.append(entry)
+        if len(self.history) > 10:  # Limit the history size
+            self.history.pop(0)
+
+    def get_context(self):
+        return " ".join(self.history)
+
+memory = Memory()
+
+def get_response(query):
+    try:
+        context = memory.get_context()
+        response = chain.invoke({"input": query, "context": context})
+        answer = response.get('answer', '')
+        memory.add(f"User: {query}\nAI: {answer}")
+        return answer
+    except Exception as e:
+        return "Sorry, I couldn't process your request."
+
+response=get_response("i found pithole near my home indore")
+print(response)