Use a single system prompt everywhere

generic_rag/app.py

@@ -15,6 +15,13 @@ from parsers.parser import add_pdf_files, add_urls
 logging.basicConfig(level=logging.DEBUG)
 logger = logging.getLogger(__name__)
 
+system_prompt = (
+    "You are an assistant for question-answering tasks. "
+    "If the question is in Dutch, answer in Dutch. If the question is in English, answer in English."
+    "Use the following pieces of retrieved context to answer the question. "
+    "If you don't know the answer, say that you don't know."
+)
+
 parser = argparse.ArgumentParser(description="A Sogeti Nederland Generic RAG demo.")
 parser.add_argument(
     "-c",
@@ -87,12 +94,14 @@ if args.use_conditional_graph:
         vector_store,
         chat_model=get_chat_model(args.chat_backend),
         embedding_model=get_embedding_model(args.emb_backend),
+        system_prompt=system_prompt,
     )
 else:
     graph = RetGenLangGraph(
         vector_store,
         chat_model=get_chat_model(args.chat_backend),
         embedding_model=get_embedding_model(args.emb_backend),
+        system_prompt=system_prompt,
     )
 
 

generic_rag/graphs/cond_ret_gen.py

@@ -19,15 +19,12 @@ from typing_extensions import Annotated
 logger = logging.getLogger(__name__)
 
 class CondRetGenLangGraph:
-    def __init__(self, vector_store: Chroma, chat_model: BaseChatModel, embedding_model: Embeddings):
+    def __init__(
+        self, vector_store: Chroma, chat_model: BaseChatModel, embedding_model: Embeddings, system_prompt: str
+    ):
         self.chat_model = chat_model
         self.embedding_model = embedding_model
-        self.system_prompt = (
-            "You are an assistant for question-answering tasks. "
-            "If the question is in Dutch, answer in Dutch. If the question is in English, answer in English."
-            "Use the following pieces of retrieved context to answer the question. "
-            "If you don't know the answer, say that you don't know."
-        )
+        self.system_prompt = system_prompt
 
         memory = MemorySaver()
         tools = ToolNode([self._retrieve])

generic_rag/graphs/ret_gen.py

@@ -2,11 +2,11 @@ import logging
 from pathlib import Path
 from typing import Any, AsyncGenerator
 
-from langchain import hub
 from langchain_chroma import Chroma
 from langchain_core.documents import Document
 from langchain_core.embeddings import Embeddings
 from langchain_core.language_models.chat_models import BaseChatModel
+from langchain_core.messages import BaseMessage, SystemMessage
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, START, StateGraph
 from typing_extensions import List, TypedDict
@@ -17,15 +17,17 @@ logger = logging.getLogger(__name__)
 class State(TypedDict):
     question: str
     context: List[Document]
-    answer: str
+    answer: BaseMessage
 
 
 class RetGenLangGraph:
-    def __init__(self, vector_store: Chroma, chat_model: BaseChatModel, embedding_model: Embeddings):
+    def __init__(
+        self, vector_store: Chroma, chat_model: BaseChatModel, embedding_model: Embeddings, system_prompt: str
+    ):
         self.vector_store = vector_store
         self.chat_model = chat_model
         self.embedding_model = embedding_model
-        self.prompt = hub.pull("rlm/rag-prompt")
+        self.system_prompt = system_prompt
 
         memory = MemorySaver()
         graph_builder = StateGraph(State).add_sequence([self._retrieve, self._generate])
@@ -45,11 +47,14 @@ class RetGenLangGraph:
         self.last_retrieved_docs = self.vector_store.similarity_search(state["question"])
         return {"context": self.last_retrieved_docs}
 
-    async def _generate(self, state: State) -> AsyncGenerator[Any, Any]:
+    def _generate(self, state: State) -> dict[str, list]:
         docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-        messages = await self.prompt.ainvoke({"question": state["question"], "context": docs_content})
-        async for response in self.chat_model.astream(messages):
-            yield {"answer": response.content}
+        system_message_content = self.system_prompt + f"\n\n{docs_content}"
+
+        prompt = [SystemMessage(system_message_content)] + [state["question"]]
+
+        response = self.chat_model.invoke(prompt)
+        return {"answer": [response]}
 
     def get_last_pdf_sources(self) -> dict[str, list[int]]:
         """