diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..dd36fbf61691635b2b4b324e22282de64a91febc --- /dev/null +++ b/.gitignore @@ -0,0 +1,25 @@ +# See https://help.github.com/articles/ignoring-files/ for more about ignoring files. + +# dependencies +/node_modules +/.pnp +.pnp.js + +# testing +/coverage + +# production +.venv/ +__pycache__/ +input/ +.env +# misc +.DS_Store +.env.local +.env.development.local +.env.test.local +.env.production.local + +npm-debug.log* +yarn-debug.log* +yarn-error.log* diff --git a/README.md b/README.md new file mode 100644 index 0000000000000000000000000000000000000000..54830e23307eeed0e9baeb398e6e6e981b3ec670 --- /dev/null +++ b/README.md @@ -0,0 +1,13 @@ +--- +title: LabAid V2 +emoji: 🧪 +colorFrom: indigo +colorTo: red +sdk: docker +app_file: Dockerfile +pinned: false +--- + +# LabAid V2 - HPLC Instrument Assistant + +This Space combines a FastAPI RAG backend (powered by LangChain + Together AI) and a React frontend. It provides HPLC troubleshooting assistance by retrieving knowledge from a Chroma vector database. diff --git a/api_service.py b/api_service.py new file mode 100644 index 0000000000000000000000000000000000000000..d28cbe8f65d2edf46810f8f975354812273dcf88 --- /dev/null +++ b/api_service.py @@ -0,0 +1,145 @@ +import os +import sqlite3 # May be used elsewhere +from dotenv import load_dotenv +from fastapi import FastAPI, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +from typing import List +from contextlib import asynccontextmanager + +from langchain.schema import Document +from langchain_together import ChatTogether, TogetherEmbeddings +from langchain_core.prompts import ChatPromptTemplate +from langchain_core.output_parsers import StrOutputParser +from langchain_core.runnables import RunnablePassthrough +from langchain_core.documents import Document + +import chromadb +from langchain_community.vectorstores import Chroma # ✅ Updated import + +# --- 1. Environment & Constants --- +load_dotenv() + +TOGETHER_API_KEY = os.getenv("TOGETHER_API_KEY") +if not TOGETHER_API_KEY: + raise ValueError("TOGETHER_API_KEY environment variable not set. Please check your .env file.") + +VECTOR_DB_DIR = "vector_db_chroma" +COLLECTION_NAME = "my_instrument_manual_chunks" + +LLM_MODEL_NAME = "meta-llama/Meta-Llama-3.1-8B-Instruct-Turbo" +EMBEDDINGS_MODEL_NAME = "togethercomputer/m2-bert-80M-32k-retrieval" + +# --- 2. Lifespan Event Handler --- +@asynccontextmanager +async def lifespan(app: FastAPI): + global rag_chain, retriever, prompt, llm + + print("--- Initializing RAG components ---") + try: + llm = ChatTogether( + model=LLM_MODEL_NAME, + temperature=0.3, + api_key=TOGETHER_API_KEY + ) + print(f"LLM {LLM_MODEL_NAME} initialized.") + + embeddings = TogetherEmbeddings( + model=EMBEDDINGS_MODEL_NAME, + api_key=TOGETHER_API_KEY + ) + client = chromadb.PersistentClient(path=VECTOR_DB_DIR) + vectorstore = Chroma( + client=client, + collection_name=COLLECTION_NAME, + embedding_function=embeddings + ) + retriever = vectorstore.as_retriever(search_kwargs={"k": 5}) + print("Retriever initialized.") + + answer_prompt = """ You are a professional HPLC instrument troubleshooting expert who specializes in helping junior researchers and students. + Your task is to answer the user's troubleshooting questions in detail and clearly based on the HPLC instrument knowledge provided below. + If there is no direct answer in the knowledge, please provide the most reasonable speculative suggestions based on your expert judgment, or ask further clarifying questions. + Please ensure that your answers are logically clear, easy to understand, and directly address the user's questions.""" + prompt = ChatPromptTemplate.from_messages([ + ("system", answer_prompt), + ("user", "context: {context}\n\nquestion: {question}"), + ]) + + def format_docs(docs: List[Document]) -> str: + return "\n\n".join(doc.page_content for doc in docs) + + rag_chain = ( + {"context": retriever | format_docs, "question": RunnablePassthrough()} + | prompt + | llm + | StrOutputParser() + ) + print("RAG chain ready.") + + except Exception as e: + raise RuntimeError(f"Failed to initialize RAG chain: {e}") + + yield # Keep app running + +# --- 3. Initialize FastAPI App --- +app = FastAPI( + title="LabAid AI", + description="API service for a Retrieval-Augmented Generation (RAG) AI assistant.", + version="1.0.0", + lifespan=lifespan # ✅ Updated lifespan hook +) + +# --- 4. CORS Middleware --- +origins = [ + "http://localhost", + "http://localhost:3000", + "http://127.0.0.1:8000", +] + +app.add_middleware( + CORSMiddleware, + allow_origins=origins, + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +# --- 5. Request/Response Models --- +class QueryRequest(BaseModel): + query: str + +class QueryResponse(BaseModel): + answer: str + source_documents: List[str] + +# --- 6. RAG Query Endpoint --- +@app.post("/ask", response_model=QueryResponse) +async def ask_rag(request: QueryRequest): + if rag_chain is None or retriever is None: + raise HTTPException(status_code=500, detail="RAG chain not initialized.") + + try: + user_query = request.query + print(f"Received query: {user_query}") + + retrieved_docs = retriever.invoke(user_query) + formatted_context = "\n\n".join(doc.page_content for doc in retrieved_docs) + + answer = (prompt | llm | StrOutputParser()).invoke({ + "context": formatted_context, + "question": user_query + }) + + sources = [doc.page_content for doc in retrieved_docs] + return QueryResponse(answer=answer, source_documents=sources) + + except Exception as e: + print(f"Error: {e}") + raise HTTPException(status_code=500, detail=f"Failed to process query: {e}") + +# --- 7. 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r,l=t.pendingProps,a=!1,i=0!==(128&t.flags);if((r=i)||(r=(null===e||null!==e.memoizedState)&&0!==(2&ci.current)),r&&(a=!0,t.flags&=-129),r=0!==(32&t.flags),t.flags&=-33,null===e){if(al){if(a?oi(t):ui(),al){var u,s=ll;if(u=s){e:{for(u=s,s=il;8!==u.nodeType;){if(!s){s=null;break e}if(null===(u=yf(u.nextSibling))){s=null;break e}}s=u}null!==s?(t.memoizedState={dehydrated:s,treeContext:null!==Yr?{id:Xr,overflow:Jr}:null,retryLane:536870912,hydrationErrors:null},(u=Dr(18,null,null,0)).stateNode=s,u.return=t,t.child=u,rl=t,ll=null,u=!0):u=!1}u||sl(t)}if(null!==(s=t.memoizedState)&&null!==(s=s.dehydrated))return vf(s)?t.lanes=32:t.lanes=536870912,null;si(t)}return 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Ni(e,t,t.pendingProps,n),t.child;case 8:case 12:return Ni(e,t,t.pendingProps.children,n),t.child;case 10:return r=t.pendingProps,bl(0,t.type,r.value),Ni(e,t,r.children,n),t.child;case 9:return l=t.type._context,r=t.pendingProps.children,Cl(t),r=r(l=Pl(l)),t.flags|=1,Ni(e,t,r,n),t.child;case 14:return Ti(e,t,t.type,t.pendingProps,n);case 15:return Li(e,t,t.type,t.pendingProps,n);case 19:return Ki(e,t,n);case 31:return r=t.pendingProps,n=t.mode,r={mode:r.mode,children:r.children},null===e?((n=Vi(r,n)).ref=t.ref,t.child=n,n.return=t,t=n):((n=Mr(e.child,r)).ref=t.ref,t.child=n,n.return=t,t=n),t;case 22:return Oi(e,t,n);case 24:return 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n=null;null!==t;)null!==t.alternate&&(n=t),t=t.sibling;null===n?e.tail=null:n.sibling=null;break;case"collapsed":n=e.tail;for(var r=null;null!==n;)null!==n.alternate&&(r=n),n=n.sibling;null===r?t||null===e.tail?e.tail=null:e.tail.sibling=null:r.sibling=null}}function nu(e){var t=null!==e.alternate&&e.alternate.child===e.child,n=0,r=0;if(t)for(var l=e.child;null!==l;)n|=l.lanes|l.childLanes,r|=65011712&l.subtreeFlags,r|=65011712&l.flags,l.return=e,l=l.sibling;else for(l=e.child;null!==l;)n|=l.lanes|l.childLanes,r|=l.subtreeFlags,r|=l.flags,l.return=e,l=l.sibling;return e.subtreeFlags|=r,e.childLanes=n,t}function ru(e,t,n){var r=t.pendingProps;switch(nl(t),t.tag){case 31:case 16:case 15:case 0:case 11:case 7:case 8:case 12:case 9:case 14:case 1:return nu(t),null;case 3:return 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