diff --git a/services/ml/app/config/config.py b/services/ml/app/config/config.py
index 1441ea2..2bd8192 100644
--- a/services/ml/app/config/config.py
+++ b/services/ml/app/config/config.py
@@ -2,14 +2,14 @@
 
 class Config:
     PORT: int = int(os.environ.get("PORT", 8000))
-    QDRANT_HOST: str = os.environ.get("QDRANT_HOST", "http://localhost:6333")
+    QDRANT_HOST: str = os.environ.get("QDRANT_HOST", "http://84.201.156.82:6333")
     QDRANT_COLLECTION_NAME: str = os.environ.get("QDRANT_COLLECTION_NAME", "docs_768")
-    QDRANT_API_KEY: str = os.environ.get("QDRANT_API_KEY")
+    QDRANT_API_KEY: str = os.environ.get("QDRANT_API_KEY", "somwerhjsadimqwe")
     LLM_SOURCE: str = os.environ.get("LLM_SOURCE", "openai")
 
     def __init__(self):
         if self.LLM_SOURCE == "openai":
-            self.MODEL = os.environ.get("MODEL", "meta-llama/llama-3.1-8b-instruct:free")
+            self.MODEL = os.environ.get("MODEL", "meta-llama/llama-3.1-8b-instruct")
             self.OPENAI_BASE_URL = os.environ.get("OPENAI_BASE_URL", "https://openrouter.ai/api/v1")
             self.OPENAI_API_KEY = os.environ.get("")
             self.EMBEDDING_MODEL = os.environ.get("EMBEDDING_MODEL", "sentence-transformers/all-mpnet-base-v2")
diff --git a/services/ml/app/server.py b/services/ml/app/server.py
index 70403c0..5b6af90 100644
--- a/services/ml/app/server.py
+++ b/services/ml/app/server.py
@@ -9,25 +9,42 @@
 from packages.retriever import Retriever
 from langchain_core.output_parsers import StrOutputParser
 from langchain_core.runnables import RunnableLambda
+from sentence_transformers import CrossEncoder
+from langchain.prompts import ChatPromptTemplate
+from operator import itemgetter
 
 from .config import config
 
 llm_instance = LLMInstance(config)
 retriever_instance = Retriever(llm_instance.get_embeddings(), host=config.QDRANT_HOST, collection_name=config.QDRANT_COLLECTION_NAME, api_key=config.QDRANT_API_KEY)
 
+cross_encoder = CrossEncoder('cross-encoder/ms-marco-MiniLM-L-12-v2')
+
+retriever = retriever_instance.get_retriever()
+retriever.search_kwargs['k'] = 20
+
+def rerank_documents_with_crossencoder(query_and_docs):
+    docs = query_and_docs["docs"]
+    query = query_and_docs["question"]
+    scores = cross_encoder.predict([(query, doc.page_content) for doc in docs])
+    ranked_docs = [doc for _, doc in sorted(zip(scores, docs), key=lambda x: x[0], reverse=True)]
+    return {"docs": ranked_docs[:3], "question": query}
+
+def join_docs(docs):
+    return " ".join([doc.page_content for doc in docs])
 
-# Modify the qa_chain definition
 qa_chain = (
     RunnableParallel(
-        {"context": retriever_instance.get_retriever(), "question": RunnablePassthrough()}
+        {"docs": retriever, "question": itemgetter("question")}
     )
+    | RunnableLambda(rerank_documents_with_crossencoder)
+    | RunnableParallel({"context": itemgetter("docs") | RunnableLambda(join_docs), "question": itemgetter("question")})
     | prompt
     | llm_instance.get_llm()
     | StrOutputParser()
     | (lambda x: {"result": x})
 ).with_types(input_type=InputChat, output_type=OutputChat)
 
-
 app = FastAPI(
     title="LangChain Server",
     version="1.0",
@@ -57,6 +74,17 @@
 
 add_routes(app, path="/search", runnable=retriever_chain)
 
+vqa_chain = (
+    RunnableParallel(
+        {"context": retriever_instance.get_retriever(), "question": RunnablePassthrough()}
+    )
+    | RunnableLambda(lambda x: x.replace("s3://afana-propdoc-production", "https://"))
+    | prompt
+    | llm_instance.get_llm()
+).with_types(input_type=InputChat, output_type=OutputChat)
+
+add_routes(app, path="/vqa", runnable=vqa_chain)
+
 if __name__ == "__main__":
     import uvicorn
 
diff --git a/services/ml/packages/prompts/prompt_qa.py b/services/ml/packages/prompts/prompt_qa.py
index 188aecc..972f14e 100644
--- a/services/ml/packages/prompts/prompt_qa.py
+++ b/services/ml/packages/prompts/prompt_qa.py
@@ -2,6 +2,8 @@
 
 prompt = ChatPromptTemplate.from_template("""Вы являетесь помощником в выполнении поиска ответов на вопросы по нормативной документации по строительству объектов. 
                                           Используйте приведенные ниже фрагменты извлеченного контекста, чтобы ответить на вопрос.
+                                          При ответе ссылайтесь на источник, откуда вы взяли информацию (например, информация может лежать в метаданных в headers или pdf_file). 
+                                          Старайтесь изложить информацию четко и подробно.
                                           Если вы не знаете ответа, просто скажите, что вы не знаете.
         Question: {question} 
         Context: {context}