Update api.py

api.py

@@ -1,81 +1,81 @@
-from fastapi import FastAPI
-from pydantic import BaseModel
-from langchain_groq import ChatGroq
-from langchain_huggingface import HuggingFaceEmbeddings
-from langchain_community.vectorstores import Chroma
-from langchain_core.prompts import ChatPromptTemplate
-from dotenv import load_dotenv
-import os
-import re
-
-load_dotenv()
-
-app = FastAPI()
-
-CHROMA_PATH = "./chroma_db_wilson"
-
-PROMPT_TEMPLATE = """
-Answer the question based only on the following context:
-
-{context}
-
----
-
-Answer the question based on the above context: {question}
-"""
-
-llm = ChatGroq(
-    model="qwen/qwen3-32b",
-    api_key=os.getenv("GROQ_API_KEY")
-)
-
-# Load RAG components sekali saat startup
-embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
-db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
-
-class PromptRequest(BaseModel):
-    prompt: str
-
-
-def clean_text(text: str) -> str:
-    # Hapus <think> tags
-    text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
-    # Hapus bold/italic (**text**, *text*, __text__)
-    text = re.sub(r'\*\*(.+?)\*\*', r'\1', text)
-    text = re.sub(r'\*(.+?)\*', r'\1', text)
-    text = re.sub(r'__(.+?)__', r'\1', text)
-    # Hapus bullet points (- item atau * item)
-    text = re.sub(r'^\s*[-*]\s+', '', text, flags=re.MULTILINE)
-    # Hapus heading (## Title)
-    text = re.sub(r'#+\s+', '', text)
-    # Hapus \n (newline characters)
-    text = text.replace('\n', ' ')   # ← tambahkan ini
-    # Hapus spasi berlebihan
-    text = re.sub(r' {2,}', ' ', text)  # ← tambahkan ini
-    return text.strip()
-
-@app.post("/generate")
-def generate(request: PromptRequest):
-    # 1. Cari dokumen relevan dari Chroma
-    results = db.similarity_search_with_relevance_scores(request.prompt, k=3)
-
-    # 2. Cek threshold
-    if len(results) == 0 or results[0][1] < 0.2:
-        return {"response": "I’m not sure yet. For more information, please contact Wilson in the email below."}
-
-    # 3. Gabungkan context
-    context_text = "\n\n---\n\n".join([doc.page_content for doc, _ in results])
-
-    # 4. Buat prompt dengan context
-    prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE).format(
-        context=context_text,
-        question=request.prompt
-    )
-
-    
-
-    # 5. Kirim ke LLM
-    response = llm.invoke(prompt)
-    clean_content = clean_text(response.content)
-
+from fastapi import FastAPI
+from pydantic import BaseModel
+from langchain_groq import ChatGroq
+from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_community.vectorstores import Chroma
+from langchain_core.prompts import ChatPromptTemplate
+from dotenv import load_dotenv
+import os
+import re
+
+load_dotenv()
+
+app = FastAPI()
+
+CHROMA_PATH = "./chroma_db_wilson"
+
+PROMPT_TEMPLATE = """
+Answer the question based only on the following context:
+
+{context}
+
+---
+
+Answer the question based on the above context: {question}
+"""
+
+llm = ChatGroq(
+    model="qwen/qwen3-32b",
+    api_key=os.getenv("GROQ_API_KEY")
+)
+
+# Load RAG components sekali saat startup
+embeddings = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
+db = Chroma(persist_directory=CHROMA_PATH, embedding_function=embeddings)
+
+class PromptRequest(BaseModel):
+    prompt: str
+
+
+def clean_text(text: str) -> str:
+    # Hapus <think> tags
+    text = re.sub(r'<think>.*?</think>', '', text, flags=re.DOTALL)
+    # Hapus bold/italic (**text**, *text*, __text__)
+    text = re.sub(r'\*\*(.+?)\*\*', r'\1', text)
+    text = re.sub(r'\*(.+?)\*', r'\1', text)
+    text = re.sub(r'__(.+?)__', r'\1', text)
+    # Hapus bullet points (- item atau * item)
+    text = re.sub(r'^\s*[-*]\s+', '', text, flags=re.MULTILINE)
+    # Hapus heading (## Title)
+    text = re.sub(r'#+\s+', '', text)
+    # Hapus \n (newline characters)
+    text = text.replace('\n', ' ')   # ← tambahkan ini
+    # Hapus spasi berlebihan
+    text = re.sub(r' {2,}', ' ', text)  # ← tambahkan ini
+    return text.strip()
+
+@app.post("/generate")
+def generate(request: PromptRequest):
+    # 1. Cari dokumen relevan dari Chroma
+    results = db.similarity_search_with_relevance_scores(request.prompt, k=3)
+
+    # 2. Cek threshold
+    if len(results) == 0 or results[0][1] < 0.5:
+        return {"response": "I’m not sure yet. For more information, please contact Wilson in the email below."}
+
+    # 3. Gabungkan context
+    context_text = "\n\n---\n\n".join([doc.page_content for doc, _ in results])
+
+    # 4. Buat prompt dengan context
+    prompt = ChatPromptTemplate.from_template(PROMPT_TEMPLATE).format(
+        context=context_text,
+        question=request.prompt
+    )
+
+    
+
+    # 5. Kirim ke LLM
+    response = llm.invoke(prompt)
+    clean_content = clean_text(response.content)
+
     return {"response": clean_content}