update

app/Pipfile

@@ -0,0 +1,11 @@
+[[source]]
+url = "https://pypi.org/simple"
+verify_ssl = true
+name = "pypi"
+
+[packages]
+
+[dev-packages]
+
+[requires]
+python_version = "3.11"

app/Pipfile.lock

@@ -0,0 +1,20 @@
+{
+    "_meta": {
+        "hash": {
+            "sha256": "ed6d5d614626ae28e274e453164affb26694755170ccab3aa5866f093d51d3e4"
+        },
+        "pipfile-spec": 6,
+        "requires": {
+            "python_version": "3.11"
+        },
+        "sources": [
+            {
+                "name": "pypi",
+                "url": "https://pypi.org/simple",
+                "verify_ssl": true
+            }
+        ]
+    },
+    "default": {},
+    "develop": {}
+}

app/backend/Pipfile

@@ -8,6 +8,12 @@ fastapi = "*"
 pydantic = "*"
 uvicorn = "*"
 pydantic-settings = "==2.1.0"
+pyyaml = "==6.0.1"
+pip = "==24.0.0"
+docker = "*"
+chromadb = "*"
+sentence-transformers = "*"
+langchain = "*"
 
 [dev-packages]
 httpx = "==0.26.0"

app/backend/api/query.py

@@ -1,10 +1,465 @@
-from typing import List
+import os
+import logging
+import argparse
 
-from fastapi import APIRouter
+from decouple import config
+from langchain_deepseek import ChatDeepSeek
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores.chroma import Chroma
+from fastapi import FastAPI, APIRouter, HTTPException, Request
+from fastapi.responses import JSONResponse
+from pydantic import BaseModel, Field
+from typing import List, Dict, Any
+from typing_extensions import TypedDict
 
-from models.adaptive_rag.router import RouteQuery
+from langchain_core.prompts import ChatPromptTemplate
-from models.adaptive_rag.grading import GradeAnswer, GradeDocuments, GradeHallucinations
+from langchain_community.tools.tavily_search import TavilySearchResults
+from langchain.prompts import PromptTemplate, HumanMessagePromptTemplate
 
+from langchain.schema import Document
+from pprint import pprint
+from langgraph.graph import END, StateGraph, START
+
+from app.backend.models.adaptive_rag.data_models import (
+    RouteQuery, 
+    GradeDocuments, 
+    GradeHallucinations, 
+    GradeAnswer
+)
+from app.backend.models.adaptive_rag.prompts_library import (
+    system_router, 
+    system_retriever_grader,
+    system_hallucination_grader,
+    system_answer_grader,
+    system_question_rewriter,
+    qa_prompt_template
+)
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)-15s %(message)s")
+logger = logging.getLogger()
+
+os.environ["DEEPSEEK_API_KEY"] = config("DEEPSEEK_API_KEY", cast=str)
+os.environ["TAVILY_API_KEY"] = config("TAVILY_API_KEY", cast=str)
+os.environ["TOKENIZERS_PARALLELISM"] = "false"
+
+app = FastAPI()
 router = APIRouter()
 
-@router.post("/", response_model=SummaryResponseSchema, status_code=201)
+class QueryRequest(BaseModel):
+    query: str = Field(..., description="The question to ask the model")
+    input_chromadb_artifact: str = Field(..., description="Fully-qualified name for the chromadb artifact")
+    embedding_model: str = Field("paraphrase-multilingual-mpnet-base-v2", description="Sentence Transformer model name")
+    chat_model_provider: str = Field("gemini", description="Chat model provider")
+
+
+class QueryResponse(BaseModel):
+    response: str = Field(..., description="The model's response")
+
+
+@router.post("/query", response_model=QueryResponse, response_model_exclude_none=True)
+async def query_endpoint(request: Request, query_request: QueryRequest):
+    try:
+        args = argparse.Namespace(
+            query=query_request.query,
+            input_chromadb_artifact=query_request.input_chromadb_artifact,
+            embedding_model=query_request.embedding_model,
+            chat_model_provider=query_request.chat_model_provider
+        )
+        result = go(args)
+        return {"response": result["response"]}
+    except Exception as e:
+        logger.exception(f"Error processing query: {e}")
+        raise HTTPException(status_code=500, detail=f"Error processing query: {e}")
+
+
+def go(args):
+
+        logger.info("Downloading chromadb artifact")
+        artifact_chromadb_local_path = args.input_chromadb_artifact #modified
+        # shutil.unpack_archive(artifact_chromadb_local_path, "chroma_db") #removed
+
+        # Initialize embedding model (do this ONCE)
+        embedding_model = HuggingFaceEmbeddings(model_name=args.embedding_model) 
+        llm = ChatDeepSeek(
+                model="deepseek-chat", 
+                temperature=0,
+                max_tokens=None,
+                timeout=None,
+                max_retries=2,
+        )
+
+        # Load data from ChromaDB
+        # db_folder = "chroma_db" #removed
+        # db_path = os.path.join(os.getcwd(), db_folder) #removed
+        # collection_name = "rag-chroma" #removed
+        vectorstore = Chroma(persist_directory=artifact_chromadb_local_path, collection_name="rag-chroma", embedding_function=embedding_model) #modified
+        retriever = vectorstore.as_retriever()
+
+        ##########################################
+        # Routing to vectorstore or web search
+        structured_llm_router = llm.with_structured_output(RouteQuery)
+        # Prompt
+        route_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_router),
+                ("human", "{question}"),
+            ]
+        )
+        question_router = route_prompt | structured_llm_router
+
+        ##########################################
+        ### Retrieval Grader
+        structured_llm_grader = llm.with_structured_output(GradeDocuments)
+        # Prompt
+        grade_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_retriever_grader),
+                ("human", "Retrieved document: \n\n {document} \n\n User question: {question}"),
+            ]
+        )
+        retrieval_grader = grade_prompt | structured_llm_grader
+
+        ##########################################
+        ### Generate
+        from langchain_core.output_parsers import StrOutputParser
+
+        # Create a PromptTemplate with the given prompt
+        new_prompt_template = PromptTemplate(
+            input_variables=["context", "question"],
+            template=qa_prompt_template,
+        )
+
+        # Create a new HumanMessagePromptTemplate with the new PromptTemplate
+        new_human_message_prompt_template = HumanMessagePromptTemplate(
+            prompt=new_prompt_template
+        )
+        prompt_qa = ChatPromptTemplate.from_messages([new_human_message_prompt_template])
+
+        # Chain
+        rag_chain = prompt_qa | llm | StrOutputParser()
+
+
+        ##########################################
+        ### Hallucination Grader
+        structured_llm_grader = llm.with_structured_output(GradeHallucinations)
+
+        # Prompt
+        hallucination_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_hallucination_grader),
+                ("human", "Set of facts: \n\n {documents} \n\n LLM generation: {generation}"),
+            ]
+        )
+
+        hallucination_grader = hallucination_prompt | structured_llm_grader
+
+        ##########################################
+        ### Answer Grader
+        structured_llm_grader = llm.with_structured_output(GradeAnswer)
+
+        # Prompt
+        answer_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_answer_grader),
+                ("human", "User question: \n\n {question} \n\n LLM generation: {generation}"),
+            ]
+        )
+        answer_grader = answer_prompt | structured_llm_grader
+
+        ##########################################
+        ### Question Re-writer
+        # Prompt
+        re_write_prompt = ChatPromptTemplate.from_messages(
+            [
+                ("system", system_question_rewriter),
+                (
+                    "human",
+                    "Here is the initial question: \n\n {question} \n Formulate an improved question.",
+                ),
+            ]
+        )   
+        question_rewriter = re_write_prompt | llm | StrOutputParser()
+
+        ### Search
+        web_search_tool = TavilySearchResults(k=3)
+
+        class GraphState(TypedDict):
+            """
+            Represents the state of our graph.
+
+            Attributes:
+                question: question
+                generation: LLM generation
+                documents: list of documents
+            """
+
+            question: str
+            generation: str
+            documents: List[str]
+
+
+
+        def retrieve(state):
+            """
+            Retrieve documents
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                state (dict): New key added to state, documents, that contains retrieved documents
+            """
+            print("---RETRIEVE---")
+            question = state["question"]
+
+            # Retrieval
+            documents = retriever.invoke(question)
+
+            print(documents)
+            return {"documents": documents, "question": question}
+
+
+        def generate(state):
+            """
+            Generate answer
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                state (dict): New key added to state, generation, that contains LLM generation
+            """
+            print("---GENERATE---")
+            question = state["question"]
+            documents = state["documents"]
+
+            # RAG generation
+            generation = rag_chain.invoke({"context": documents, "question": question})
+            return {"documents": documents, "question": question, "generation": generation}
+
+
+        def grade_documents(state):
+            """
+            Determines whether the retrieved documents are relevant to the question.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                state (dict): Updates documents key with only filtered relevant documents
+            """
+
+            print("---CHECK DOCUMENT RELEVANCE TO QUESTION---")
+            question = state["question"]
+            documents = state["documents"]
+
+            # Score each doc
+            filtered_docs = []
+            for d in documents:
+                score = retrieval_grader.invoke(
+                    {"question": question, "document": d.page_content}
+                )
+                grade = score.binary_score
+                if grade == "yes":
+                    print("---GRADE: DOCUMENT RELEVANT---")
+                    filtered_docs.append(d)
+                else:
+                    print("---GRADE: DOCUMENT NOT RELEVANT---")
+                    continue
+            return {"documents": filtered_docs, "question": question}
+
+
+        def transform_query(state):
+            """
+            Transform the query to produce a better question.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                state (dict): Updates question key with a re-phrased question
+            """
+
+            print("---TRANSFORM QUERY---")
+            question = state["question"]
+            documents = state["documents"]
+
+            # Re-write question
+            better_question = question_rewriter.invoke({"question": question})
+            return {"documents": documents, "question": better_question}
+
+
+        def web_search(state):
+            """
+            Web search based on the re-phrased question.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                state (dict): Updates documents key with appended web results
+            """
+
+            print("---WEB SEARCH---")
+            question = state["question"]
+
+            # Web search
+            docs = web_search_tool.invoke({"query": question})
+            web_results = "\n".join([d["content"] for d in docs])
+            web_results = Document(page_content=web_results)
+
+            return {"documents": web_results, "question": question}
+
+
+        ### Edges ###
+        def route_question(state):
+            """
+            Route question to web search or RAG.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                str: Next node to call
+            """
+
+            print("---ROUTE QUESTION---")
+            question = state["question"]
+            source = question_router.invoke({"question": question})
+            if source.datasource == "web_search":
+                print("---ROUTE QUESTION TO WEB SEARCH---")
+                return "web_search"
+            elif source.datasource == "vectorstore":
+                print("---ROUTE QUESTION TO RAG---")
+                return "vectorstore"
+
+
+        def decide_to_generate(state):
+            """
+            Determines whether to generate an answer, or re-generate a question.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                str: Binary decision for next node to call
+            """
+
+            print("---ASSESS GRADED DOCUMENTS---")
+            state["question"]
+            filtered_documents = state["documents"]
+
+            if not filtered_documents:
+                # All documents have been filtered check_relevance
+                # We will re-generate a new query
+                print(
+                    "---DECISION: ALL DOCUMENTS ARE NOT RELEVANT TO QUESTION, TRANSFORM QUERY---"
+                )
+                return "transform_query"
+            else:
+                # We have relevant documents, so generate answer
+                print("---DECISION: GENERATE---")
+                return "generate"
+
+
+        def grade_generation_v_documents_and_question(state):
+            """
+            Determines whether the generation is grounded in the document and answers question.
+
+            Args:
+                state (dict): The current graph state
+
+            Returns:
+                str: Decision for next node to call
+            """
+
+            print("---CHECK HALLUCINATIONS---")
+            question = state["question"]
+            documents = state["documents"]
+            generation = state["generation"]
+
+            score = hallucination_grader.invoke(
+                {"documents": documents, "generation": generation}
+            )
+            grade = score.binary_score
+
+            # Check hallucination
+            if grade == "yes":
+                print("---DECISION: GENERATION IS GROUNDED IN DOCUMENTS---")
+                # Check question-answering
+                print("---GRADE GENERATION vs QUESTION---")
+                score = answer_grader.invoke({"question": question, "generation": generation})
+                grade = score.binary_score
+                if grade == "yes":
+                    print("---DECISION: GENERATION ADDRESSES QUESTION---")
+                    return "useful"
+                else:
+                    print("---DECISION: GENERATION DOES NOT ADDRESS QUESTION---")
+                    return "not useful"
+            else:
+                pprint("---DECISION: GENERATION IS NOT GROUNDED IN DOCUMENTS, RE-TRY---")
+                return "not supported"
+
+        workflow = StateGraph(GraphState)
+
+        # Define the nodes
+        workflow.add_node("web_search", web_search)  # web search
+        workflow.add_node("retrieve", retrieve)  # retrieve
+        workflow.add_node("grade_documents", grade_documents)  # grade documents
+        workflow.add_node("generate", generate)  # generatae
+        workflow.add_node("transform_query", transform_query)  # transform_query
+
+        # Build graph
+        workflow.add_conditional_edges(
+            START,
+            route_question,
+            {
+                "web_search": "web_search",
+                "vectorstore": "retrieve",
+            },
+        )
+        workflow.add_edge("web_search", "generate")
+        workflow.add_edge("retrieve", "grade_documents")
+        workflow.add_conditional_edges(
+            "grade_documents",
+            decide_to_generate,
+            {
+                "transform_query": "transform_query",
+                "generate": "generate",
+            },
+        )
+        workflow.add_edge("transform_query", "retrieve")
+        workflow.add_conditional_edges(
+            "generate",
+            grade_generation_v_documents_and_question,
+            {
+                "not supported": "generate",
+                "useful": END,
+                "not useful": "transform_query",
+            },
+        )
+
+        # Compile
+        app = workflow.compile()
+
+        # Run
+        inputs = {
+            "question": args.query
+        }
+        for output in app.stream(inputs):
+            for key, value in output.items():
+                # Node
+                pprint(f"Node '{key}':")
+                # Optional: print full state at each node
+                # pprint.pprint(value["keys"], indent=2, width=80, depth=None)
+            pprint("\n---\n")
+
+        # Final generation
+        print(value["generation"])
+
+        return {"response": value["generation"]}
+    
+
+
+app.include_router(router, prefix="/query", tags=["query"])

app/backend/models/adaptive_rag/output.py

@@ -1,5 +0,0 @@
-from pydantic import BaseModel
-
-class final_answer(BaseModel):
-    """Final answer to be returned to the user."""
-    answer: str

app/backend/models/adaptive_rag/query.py

@@ -0,0 +1,9 @@
+from pydantic import BaseModel, Field
+
+
+class QueryRequest(BaseModel):
+    query: str = Field(..., description="The question to ask the model")
+
+class QueryResponse(BaseModel):
+    response: str = Field(..., description="The model's response")
+

app/frontend/package.json

@@ -8,9 +8,11 @@
     "build": "tsc -b && vite build",
     "lint": "eslint .",
     "preview": "vite preview",
-    "test": "vitest"
+    "test": "vitest",
+    "test:run": "vitest run"
   },
   "dependencies": {
+    "daisyui": "^5.0.17",
     "react": "^19.0.0",
     "react-dom": "^19.0.0"
   },
@@ -21,15 +23,17 @@
     "@types/react": "^19.0.10",
     "@types/react-dom": "^19.0.4",
     "@vitejs/plugin-react": "^4.3.4",
+    "autoprefixer": "^10.4.21",
     "eslint": "^9.21.0",
-    "eslint-plugin-react": "^7.37.5",
+    "eslint-plugin-react-hooks": "^5.1.0",
-    "eslint-plugin- react-hooks": "^5.1.0",
     "eslint-plugin-react-refresh": "^0.4.19",
     "globals": "^15.15.0",
     "jsdom": "^26.0.0",
+    "postcss": "^8.5.3",
+    "tailwindcss": "^3.4.17",
     "typescript": "~5.7.2",
     "typescript-eslint": "^8.24.1",
     "vite": "^6.2.0",
     "vitest": "^3.1.1"
   }
-}
+}

app/frontend/postcss.config.js

@@ -0,0 +1,6 @@
+export default {
+  plugins: {
+    tailwindcss: {},
+    autoprefixer: {},
+  },
+}

app/frontend/src/App.css

@@ -1,42 +0,0 @@
-#root {
-  max-width: 1280px;
-  margin: 0 auto;
-  padding: 2rem;
-  text-align: center;
-}
-
-.logo {
-  height: 6em;
-  padding: 1.5em;
-  will-change: filter;
-  transition: filter 300ms;
-}
-.logo:hover {
-  filter: drop-shadow(0 0 2em #646cffaa);
-}
-.logo.react:hover {
-  filter: drop-shadow(0 0 2em #61dafbaa);
-}
-
-@keyframes logo-spin {
-  from {
-    transform: rotate(0deg);
-  }
-  to {
-    transform: rotate(360deg);
-  }
-}
-
-@media (prefers-reduced-motion: no-preference) {
-  a:nth-of-type(2) .logo {
-    animation: logo-spin infinite 20s linear;
-  }
-}
-
-.card {
-  padding: 2em;
-}
-
-.read-the-docs {
-  color: #888;
-}

app/frontend/src/App.tsx

@@ -1,7 +1,6 @@
 import { useState } from 'react'
 import reactLogo from './assets/react.svg'
 import viteLogo from '/vite.svg'
-import './App.css'
 
 function App() {
   const [count, setCount] = useState(0)
@@ -10,22 +9,22 @@ function App() {
     <>
       <div>
         <a href="https://vite.dev" target="_blank">
-          <img src={viteLogo} className="logo" alt="Vite logo" />
+          <img src={viteLogo} className="h-6 w-6" alt="Vite logo" />
         </a>
         <a href="https://react.dev" target="_blank">
-          <img src={reactLogo} className="logo react" alt="React logo" />
+          <img src={reactLogo} className="h-6 w-6" alt="React logo" />
         </a>
       </div>
       <h1>Vite + React</h1>
-      <div className="card">
+      <div className="p-4 bg-gray-100 rounded shadow">
         <button onClick={() => setCount((count) => count + 1)}>
           count is {count}
         </button>
-        <p>
+        <p className="text-gray-700">
           Edit <code>src/App.tsx</code> and save to test HMR
         </p>
       </div>
-      <p className="read-the-docs">
+      <p className="text-gray-500 text-sm">
         Click on the Vite and React logos to learn more
       </p>
     </>

app/frontend/src/index.css

@@ -1,68 +1,3 @@
-:root {
+@tailwind base;
-  font-family: system-ui, Avenir, Helvetica, Arial, sans-serif;
+@tailwind components;
-  line-height: 1.5;
+@tailwind utilities;
-  font-weight: 400;
-
-  color-scheme: light dark;
-  color: rgba(255, 255, 255, 0.87);
-  background-color: #242424;
-
-  font-synthesis: none;
-  text-rendering: optimizeLegibility;
-  -webkit-font-smoothing: antialiased;
-  -moz-osx-font-smoothing: grayscale;
-}
-
-a {
-  font-weight: 500;
-  color: #646cff;
-  text-decoration: inherit;
-}
-a:hover {
-  color: #535bf2;
-}
-
-body {
-  margin: 0;
-  display: flex;
-  place-items: center;
-  min-width: 320px;
-  min-height: 100vh;
-}
-
-h1 {
-  font-size: 3.2em;
-  line-height: 1.1;
-}
-
-button {
-  border-radius: 8px;
-  border: 1px solid transparent;
-  padding: 0.6em 1.2em;
-  font-size: 1em;
-  font-weight: 500;
-  font-family: inherit;
-  background-color: #1a1a1a;
-  cursor: pointer;
-  transition: border-color 0.25s;
-}
-button:hover {
-  border-color: #646cff;
-}
-button:focus,
-button:focus-visible {
-  outline: 4px auto -webkit-focus-ring-color;
-}
-
-@media (prefers-color-scheme: light) {
-  :root {
-    color: #213547;
-    background-color: #ffffff;
-  }
-  a:hover {
-    color: #747bff;
-  }
-  button {
-    background-color: #f9f9f9;
-  }
-}

app/frontend/tailwind.config.js

@@ -0,0 +1,11 @@
+/** @type {import('tailwindcss').Config} */
+export default {
+  content: [
+    "./src/**/*.{js,jsx,ts,tsx}",
+  ],
+  theme: {
+    extend: {},
+  },
+  plugins: [require("daisyui")],
+}
+

app/frontend/vite.config.ts

@@ -1,7 +1,7 @@
 import { defineConfig } from 'vite'
 import react from '@vitejs/plugin-react'
 
-// https://vitejs.dev/config/
+// https://vite.dev/config/
 export default defineConfig({
   plugins: [react()],
   test: {
@@ -9,4 +9,4 @@ export default defineConfig({
     environment: "jsdom",
     setupFiles: "./tests/setup.ts",
   },
-});
+});