Merge pull request 'Uses a single prompt for both graph's' (#23) from prompt_fix into main

Reviewed-on: AI_team/generic-RAG-demo#23

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",
@@ -84,27 +91,38 @@ vector_store = Chroma(
 
 if args.use_conditional_graph:
     graph = CondRetGenLangGraph(
-        vector_store,
+        vector_store=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,
+        vector_store=vector_store,
         chat_model=get_chat_model(args.chat_backend),
         embedding_model=get_embedding_model(args.emb_backend),
+        system_prompt=system_prompt,
     )
 
 
 @cl.on_message
 async def on_message(message: cl.Message):
+    config = {"configurable": {"thread_id": cl.user_session.get("id")}}
+
+    chainlit_response = cl.Message(content="")
+
+    async for response in graph.stream(message.content, config=config):
+        await chainlit_response.stream_token(response)
+
+    if isinstance(graph, RetGenLangGraph):
+        await add_sources(chainlit_response, graph.get_last_pdf_sources(), graph.get_last_web_sources())
     if isinstance(graph, CondRetGenLangGraph):
-        await process_cond_response(message)
+        await add_sources(chainlit_response, graph.last_retrieved_docs, graph.last_retrieved_sources)
-    elif isinstance(graph, RetGenLangGraph):
+
-        await process_response(message)
+    await chainlit_response.send()
 
 
-async def add_sources(chainlit_response: cl.Message, pdf_sources: dict, web_sources: set | list):
+async def add_sources(chainlit_response: cl.Message, pdf_sources: dict, web_sources: set | list) -> None:
     if len(pdf_sources) > 0:
         await chainlit_response.stream_token("\n\nThe following PDF source were consulted:\n")
         for source, page_numbers in pdf_sources.items():
@@ -114,40 +132,13 @@ async def add_sources(chainlit_response: cl.Message, pdf_sources: dict, web_sour
             chainlit_response.elements.append(cl.Pdf(name="pdf", display="inline", path=source, page=page_numbers[0]))
             await chainlit_response.update()
             await chainlit_response.stream_token(f"- '{source}' on page(s): {page_numbers}\n")
+
     if len(web_sources) > 0:
         await chainlit_response.stream_token("\n\nThe following web sources were consulted:\n")
         for source in web_sources:
             await chainlit_response.stream_token(f"- {source}\n")
 
 
-async def process_response(message):
-    config = {"configurable": {"thread_id": cl.user_session.get("id")}}
-
-    chainlit_response = cl.Message(content="")
-
-    async for response in graph.stream(message.content, config=config):
-        await chainlit_response.stream_token(response)
-
-    pdf_sources = graph.get_last_pdf_sources()
-    web_sources = graph.get_last_web_sources()
-    await add_sources(chainlit_response, pdf_sources, web_sources)
-
-    await chainlit_response.send()
-
-
-async def process_cond_response(message):
-    config = {"configurable": {"thread_id": cl.user_session.get("id")}}
-
-    chainlit_response = cl.Message(content="")
-
-    for response in graph.stream(message.content, config=config):
-        await chainlit_response.stream_token(response)
-
-    await add_sources(chainlit_response, graph.last_retrieved_docs, graph.last_retrieved_sources)
-
-    await chainlit_response.send()
-
-
 @cl.set_starters
 async def set_starters():
     chainlit_starters = os.environ.get("CHAINLIT_STARTERS", None)

generic_rag/graphs/cond_ret_gen.py

@@ -1,16 +1,16 @@
-import logging
-from typing import Any, Iterator
-import re
 import ast
+import logging
+import re
 from pathlib import Path
+from typing import Any, AsyncGenerator
 
 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 langchain_core.tools import tool
 from langchain_core.runnables.config import RunnableConfig
+from langchain_core.tools import tool
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, MessagesState, StateGraph
 from langgraph.prebuilt import InjectedStore, ToolNode, tools_condition
@@ -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 = (
+        self.system_prompt = 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."
-        )
 
         memory = MemorySaver()
         tools = ToolNode([self._retrieve])
@@ -52,8 +49,8 @@ class CondRetGenLangGraph:
         self.last_retrieved_docs = {}
         self.last_retrieved_sources = set()
 
-    def stream(self, message: str, config: RunnableConfig | None = None) -> Iterator[str]:
+    async def stream(self, message: str, config: RunnableConfig | None = None) -> AsyncGenerator[Any, Any]:
-        for llm_response, metadata in self.graph.stream(
+        async for llm_response, metadata in self.graph.astream(
             {"messages": [{"role": "user", "content": message}]}, stream_mode="messages", config=config
         ):
             if llm_response.content and metadata["langgraph_node"] == "_generate":

generic_rag/graphs/ret_gen.py

@@ -2,11 +2,12 @@ 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 langchain_core.runnables.config import RunnableConfig
 from langgraph.checkpoint.memory import MemorySaver
 from langgraph.graph import END, START, StateGraph
 from typing_extensions import List, TypedDict
@@ -17,15 +18,17 @@ 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])
@@ -36,20 +39,23 @@ class RetGenLangGraph:
         self.graph = graph_builder.compile(memory)
         self.last_retrieved_docs = []
 
-    async def stream(self, message: str, config: dict) -> AsyncGenerator[Any, Any]:
+    async def stream(self, message: str, config: RunnableConfig | None = None) -> AsyncGenerator[Any, Any]:
         async for response, _ in self.graph.astream({"question": message}, stream_mode="messages", config=config):
             yield response.content
 
-    def _retrieve(self, state: State) -> dict:
+    def _retrieve(self, state: State) -> dict[str, list]:
         logger.debug(f"querying VS for: {state["question"]}")
         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})
+        system_message_content = self.system_prompt + f"\n\n{docs_content}"
-        async for response in self.chat_model.astream(messages):
+
-            yield {"answer": response.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]]:
         """