integrated langchain openai model

main.py

@@ -13,14 +13,16 @@ from fastapi.security import APIKeyHeader
 from fastapi.staticfiles import StaticFiles
 from fastapi.templating import Jinja2Templates
 
-from models.bloom import initialize_index
+from models import bloom, langOpen
 
 load_dotenv()
 app = FastAPI()
 app.secret_key = os.environ.get('SECRET_KEY', 'my-secret-key')
 # openai.api_key = os.environ.get("OPENAI_API_KEY")
 openai_api_key_header = APIKeyHeader(name="token")
-index = None
+
+bloomLlama = None
+langChainAI = None
 # index = initialize_index("index.json")
 OPENAI_API_KEY = ""
 
@@ -117,8 +119,15 @@ async def login(token: str, response: Response):
 @app.post("/initLlamaIndex")
 async def initLlamaIndex(token: str):
     openai.api_key = token
-    global index
-    index = initialize_index("index.json")
+    global bloomLlama
+    bloomLlama = bloom.initialize_index("bloomLlama.json")
+    return {"success": True}
+
+@app.post("/initLangOpen")
+async def initLangOpen(token: str):
+    openai.api_key = token
+    global langChainAI
+    langChainAI = langOpen.initialize_index("langOpen")
     return {"success": True}
 
 # Chatbot endpoint
@@ -128,20 +137,15 @@ async def chatbot(request: Request):
     context = {"request": request, "startTime": startTime}
     return templates.TemplateResponse("chatbot.html", context=context)
 
-
-# @app.get("/chatbot", response_class=HTMLResponse)
-# async def chatbot(request: Request, username: str = Cookie(None)):
-#     if not username:
-#         # Redirect back to the login page if the user is not authenticated
-#         return RedirectResponse(url="/login")
-#     else:
-#         # Render the chatbot page with the username passed to the template
-#         return templates.TemplateResponse("chatbot.html", {"request": request, "username": username})
-
 @app.get("/reply")
-def reply(input: str):
-    bot_reply = index.query(input)
-    return {"bot_reply": bot_reply}
-
+def reply(input: str, modelName: str):
+    if (modelName == "bloom"):
+        bot_reply = bloomLlama.query(input)
+        return {"bot_reply": bot_reply}
+    else:
+        bot_reply = langOpen.answer_question(langChainAI, input)
+        print("bot reply: ", bot_reply)
+        return {"bot_reply": bot_reply}
+    
 if __name__ == "__main__":
     uvicorn.run("main:app", reload=True)

models/bloom.py

@@ -15,10 +15,11 @@ llm_predictor = LLMPredictor(llm=CustomLLM())
 service_context = ServiceContext.from_defaults(llm_predictor=llm_predictor, prompt_helper=prompt_helper)
 
 def initialize_index(index_name):
-    if os.path.exists(index_name):
-        return GPTSimpleVectorIndex.load_from_disk(index_name)
+    path = f"./vectorStores/{index_name}"
+    if os.path.exists(path):
+        return GPTSimpleVectorIndex.load_from_disk(path)
     else:
         documents = [Document(d) for d in data]
         index = GPTSimpleVectorIndex.from_documents(documents, service_context=service_context)
-        index.save_to_disk(index_name)
+        index.save_to_disk(path)
         return index

models/langOpen.py

@@ -1,14 +1,44 @@
 import os
 
+import openai
+from dotenv import load_dotenv
+from langchain.chains import LLMChain
 from langchain.embeddings.openai import OpenAIEmbeddings
+from langchain.llms import OpenAI
+from langchain.prompts import PromptTemplate
 from langchain.vectorstores import FAISS
 
-embedding_function = OpenAIEmbeddings()
+load_dotenv()
+
+embeddings = OpenAIEmbeddings()
+
+prompt_template = """Answer the question using the given context to the best of your ability. 
+If you don't know, answer I don't know.
+Context: {context}
+Topic: {topic}"""
+
+PROMPT = PromptTemplate(
+    template=prompt_template, input_variables=["context", "topic"]
+)
+
+llm = OpenAI(temperature=0)
+
+chain = LLMChain(llm=llm, prompt=PROMPT)
 
 def initialize_index(index_name):  
-    if os.path.exists(index_name):
-        return FAISS.load_local("./", embedding_function, index_name=index_name)
+    path = f"./vectorStores/{index_name}"
+    if os.path.exists(path=path):
+        return FAISS.load_local(folder_path=path, embeddings=embeddings)
     else:
-        faiss = FAISS.from_texts("./data/calregs.txt")
-        faiss.save_local("./")
+        faiss = FAISS.from_texts("./data/updated_calregs.txt", embedding=embeddings)
+        faiss.save_local(path)
         return faiss
+
+
+# faiss = initialize_index("langOpen")
+
+def answer_question(index, input):
+    docs = index.similarity_search(input, k=4)
+    inputs = [{"context": doc.page_content, "topic": input} for doc in docs]
+    result = chain.apply(inputs)[0]['text']
+    return result

models/vectorStores/langOpen/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:6fc144adcdf1a75f209600074611237befd5bcab89913f0ed301f67765547e02
+size 2330

requirements.txt

@@ -16,4 +16,5 @@ langchain
 python-dotenv
 python-multipart
 httpx
-openai
+openai
+faiss-cpu

static/scripts/chatbot.js

@@ -1,13 +1,15 @@
 const msgerForm = get(".msger-inputarea");
 const msgerInput = get(".msger-input");
 const msgerChat = get(".msger-chat");
+const modelSelect = get(".msger-select");
 const isUserVerified = false;
 
 // Icons made by Freepik from www.flaticon.com
 const BOT_IMG = "../static/icons/dialogflow-insights-svgrepo-com.svg";
 const PERSON_IMG = "";
-const BOT_NAME = "    ChatBot";
-const PERSON_NAME = "You";
+
+let BOT_NAME = "    ChatBot";
+let PERSON_NAME = "You";
 
 msgerForm.addEventListener("submit", async (event) => {
     event.preventDefault();
@@ -15,11 +17,12 @@ msgerForm.addEventListener("submit", async (event) => {
     const msgText = msgerInput.value;
     if (!msgText) return;
 
+    const selectedModel = modelSelect.value;
+
     appendMessage(PERSON_NAME, PERSON_IMG, "right", msgText);
     msgerInput.value = "";
 
-    botResponse(msgText);
-    // authenticate(msgText);
+    botResponse(msgText, selectedModel);
 });
 
 function appendMessage(name, img, side, text) {
@@ -44,17 +47,22 @@ function appendMessage(name, img, side, text) {
     msgerChat.scrollTop += 500;
 }
 
-async function botResponse(text) {
+async function botResponse(text, selectedModel) {
     // Bot Response
     try {
-        const response = await fetch(`reply?input=${text}`);
+        const response = await fetch(
+            `reply?input=${text}&modelName=${selectedModel}`
+        );
         const data = await response.json();
-        const reply = data.bot_reply;
-        appendMessage(BOT_NAME, BOT_IMG, "left", reply.response);
+        let reply = data.bot_reply;
+        if (selectedModel == "bloom") {
+            reply = reply.response;
+        }
+        appendMessage(BOT_NAME + " - " + selectedModel, BOT_IMG, "left", reply);
     } catch (error) {
         console.error(error);
         appendMessage(
-            BOT_NAME,
+            BOT_NAME + "- " + selectedModel,
             BOT_IMG,
             "left",
             "Sorry, I'm not able to respond at the moment."

static/scripts/login.js

@@ -23,13 +23,18 @@ loginForm.addEventListener("submit", async (event) => {
 
     if ("redirect" in loginData) {
         // if the login response contains a "redirect" property, it means the login was successful
-        const initResp = await fetch(`initLlamaIndex?token=${token}`, {
+        const initLlamaRep = await fetch(`initLlamaIndex?token=${token}`, {
             method: "POST",
         });
 
-        const initData = await initResp.json();
+        const initLangOpenRep = await fetch(`initLangOpen?token=${token}`, {
+            method: "POST",
+        });
+
+        const initLlamaData = await initLlamaRep.json();
+        const initLangOpenData = await initLangOpenRep.json();
 
-        if ("success" in initData) {
+        if ("success" in initLlamaData || "success" in initLangOpenData) {
             window.location.href = loginData.redirect;
         }
     } else if ("error" in loginData) {

static/styles/chatbot.css

@@ -147,6 +147,13 @@ body {
     background: #ddd;
 }
 
+.msger-select {
+    margin-right: 10px;
+    background: #ddd;
+    font-weight: bold;
+    cursor: pointer;
+}
+
 .msger-send-btn {
     margin-left: 10px;
     background: rgb(0, 196, 65);

templates/chatbot.html

@@ -31,6 +31,10 @@
         </main>
 
         <form class="msger-inputarea">
+            <select id="modelSelect" class="msger-select">
+                <option value="bloom" selected>Llama Bloom</option>
+                <option value="langChain">LangChain OpenAI</option>
+            </select>
             <input type="text" class="msger-input" id="textInput" placeholder="Enter your message...">
             <button type="submit" class="msger-send-btn">Send</button>
         </form>

vectorStores/langOpen/index.pkl

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a46ff99c3fefe49dcfe253ffc702d6358be36780af555e716f8b1f86cb86deb8
+size 2330