ADDED: ChatBot using groq

README.md

@@ -63,6 +63,13 @@ Generative AI, powered by advanced machine learning models, enables the creation
 - **Route:** `/questions_generator`
 - **Description:** The endpoint uses the pdf and generate the questions.It will be helpful for the students or teachers preparing for their exams or test.
 
+### 10. ChatBot Using Groq
+
+- **Route:** `/chat_groq`
+- **Description:** This route utilizes Groq for enhanced language processing speed, with a default model input of mixtral-8x7b-32768, but offering alternatives like llama2-70b-4096 and gemma-7b-it, and a conversational memory length option of 1 to 10, which maintains a list of recent interactions in the conversation, considering only the latest K interactions.
+
+
+
 ## Usage
 
 Each endpoint accepts specific parameters as described in the respective endpoint documentation. Users can make POST requests to these endpoints with the required parameters to perform the desired tasks.

api.py

@@ -1,14 +1,18 @@
+import os
 from fastapi import FastAPI,Form,File,UploadFile
 from fastapi.encoders import jsonable_encoder
 from fastapi.responses import JSONResponse,RedirectResponse
-from typing import List
+from typing import List,Optional
 from pydantic import BaseModel
 import google.generativeai as genai
 from fastapi.middleware.cors import CORSMiddleware
 from settings import invoice_prompt,youtube_transcribe_prompt,text2sql_prompt,EMPLOYEE_DB
 from mongo import MongoDB
 from helper_functions import get_qa_chain,get_gemini_response,get_url_doc_qa,extract_transcript_details,\
     get_gemini_response_health,get_gemini_pdf,read_sql_query,remove_substrings,questions_generator
+from langchain_groq import ChatGroq
+from langchain.chains import ConversationChain
+from langchain.chains.conversation.memory import ConversationBufferWindowMemory
 
 app = FastAPI(title="Genify By Mohd Aquib",
               summary="This API contains routes of different Gen AI usecases")
@@ -227,4 +231,27 @@ async def pdf_questions_generator(pdf: UploadFile = File(...)):
         print(result)
         return ResponseText(response=remove_substrings(out))
     except Exception as e:
-        return ResponseText(response=f"Error: {str(e)}")
+        return ResponseText(response=f"Error: {str(e)}")
+
+@app.post("/chat_groq", description= """This route uses groq for faster response using Language Processing Unit(LPU).
+          \n In model input default is mixtral-8x7b-32768 but you can choose llama2-70b-4096 and gemma-7b-it.
+          \n conversational_memory_length ranges from 1 to 10. It keeps a list of the interactions of the conversation over time.
+          It only uses the last K interactions """)
+async def groq_chatbot(question: str = Form(...), model: Optional[str] = Form('mixtral-8x7b-32768'), 
+    conversational_memory_length: Optional[int] = Form(5)):
+
+    memory=ConversationBufferWindowMemory(k=conversational_memory_length)
+    groq_chat = ChatGroq(groq_api_key= os.environ['GROQ_API_KEY'], model_name=model)
+    conversation = ConversationChain(llm=groq_chat,memory=memory)
+
+    response = conversation.invoke(question)
+    db = MongoDB()
+    payload = {
+        "endpoint" : "/chat_groq",
+        "model" : model,
+        "conversational_memory_length": conversational_memory_length,
+        "output" : response['response']
+        }
+    mongo_data = {"Document": payload}
+    result = db.insert_data(mongo_data)
+    return {"Chatbot": response['response']}

requirements.txt

@@ -13,4 +13,6 @@ youtube_transcript_api
 llama-index
 pypdf
 pymongo
-pytest
+pytest
+groq
+langchain-groq