Refactor out Retrieval/Generator LangGraph

generic_rag/app.py

@@ -6,14 +6,13 @@ from pathlib import Path
 
 import chainlit as cl
 from backend.models import BackendType, get_chat_model, get_embedding_model
+from graphs.ret_gen import RetGenLangGraph
 from chainlit.cli import run_chainlit
-from langchain import hub
+
 from langchain_chroma import Chroma
-from langchain_core.documents import Document
+
-from langgraph.graph import START, StateGraph
-from langgraph.pregel.io import AddableValuesDict
 from parsers.parser import add_pdf_files, add_urls
-from typing_extensions import List, TypedDict
+
 
 logging.basicConfig(level=logging.INFO)
 logger = logging.getLogger(__name__)
@@ -65,62 +64,26 @@ parser.add_argument(
 parser.add_argument("-r", "--reset-chrome-db", action="store_true", help="Reset the Chroma DB.")
 args = parser.parse_args()
 
-
-class State(TypedDict):
-    question: str
-    context: List[Document]
-    answer: str
-
-
-def retrieve(state: State):
-    vector_store = cl.user_session.get("vector_store")
-
-    retrieved_docs = vector_store.similarity_search(state["question"])
-
-    return {"context": retrieved_docs}
-
-
-def generate(state: State):
-    prompt = cl.user_session.get("prompt")
-    llm = cl.user_session.get("chat_model")
-
-    docs_content = "\n\n".join(doc.page_content for doc in state["context"])
-    messages = prompt.invoke({"question": state["question"], "context": docs_content})
-    response = llm.invoke(messages)
-
-    return {"answer": response.content}
-
-
-@cl.on_chat_start
-async def on_chat_start():
 vector_store = Chroma(
     collection_name="generic_rag",
-        embedding_function=get_embedding_model(args.back_end),
+    embedding_function=get_embedding_model(args.backend),
     persist_directory=str(args.chroma_db_location),
 )
 
-    cl.user_session.set("vector_store", vector_store)
+ret_gen_graph = RetGenLangGraph(
-    cl.user_session.set("emb_model", get_embedding_model(args.back_end))
+    vector_store, chat_model=get_chat_model(args.backend), embedding_model=get_embedding_model(args.backend)
-    cl.user_session.set("chat_model", get_chat_model(args.back_end))
+)
-    cl.user_session.set("prompt", hub.pull("rlm/rag-prompt"))
-
-    graph_builder = StateGraph(State).add_sequence([retrieve, generate])
-    graph_builder.add_edge(START, "retrieve")
-    graph = graph_builder.compile()
-
-    cl.user_session.set("graph", graph)
 
 
 @cl.on_message
 async def on_message(message: cl.Message):
-    graph = cl.user_session.get("graph")
+    response = ret_gen_graph.invoke(message.content)
-    response = graph.invoke({"question": message.content})
 
     answer = response["answer"]
     answer += "\n\n"
 
-    pdf_sources = get_pdf_sources(response)
+    pdf_sources = ret_gen_graph.get_last_pdf_sources()
-    web_sources = get_web_sources(response)
+    web_sources = ret_gen_graph.get_last_web_sources()
 
     elements = []
     if len(pdf_sources) > 0:
@@ -128,7 +91,7 @@ async def on_message(message: cl.Message):
         for source, page_numbers in pdf_sources.items():
             page_numbers = list(page_numbers)
             page_numbers.sort()
-            # display="side" seems to be not supported by chainlit for PDF's, so we use "inline" instead
+            # display="side" seems to be not supported by chainlit for PDF's, so we use "inline" instead.
             elements.append(cl.Pdf(name="pdf", display="inline", path=source, page=page_numbers[0]))
             answer += f"'{source}' on page(s): {page_numbers}\n"
 
@@ -138,39 +101,6 @@ async def on_message(message: cl.Message):
     await cl.Message(content=answer, elements=elements).send()
 
 
-def get_pdf_sources(response: AddableValuesDict) -> dict[str, list[int]]:
-    """
-    Function that retrieves the PDF sources with page numbers from a response.
-    """
-    pdf_sources = {}
-    for context in response["context"]:
-        try:
-            if context.metadata["filetype"] == "application/pdf":
-                source = context.metadata["source"]
-                page_number = context.metadata["page_number"]
-                if source in pdf_sources:
-                    pdf_sources[source].add(page_number)
-                else:
-                    pdf_sources[source] = {page_number}
-        except KeyError:
-            pass
-    return pdf_sources
-
-
-def get_web_sources(response: AddableValuesDict) -> set:
-    """
-    Function that retrieves the web sources from a response.
-    """
-    web_sources = set()
-    for context in response["context"]:
-        try:
-            if context.metadata["filetype"] == "web":
-                web_sources.add(context.metadata["source"])
-        except KeyError:
-            pass
-    return web_sources
-
-
 @cl.set_starters
 async def set_starters():
     chainlit_starters = os.environ["CHAINLIT_STARTERS"]
@@ -193,12 +123,6 @@ async def set_starters():
 
 
 if __name__ == "__main__":
-    vector_store = Chroma(
-        collection_name="generic_rag",
-        embedding_function=get_embedding_model(args.back_end),
-        persist_directory=str(args.chroma_db_location),
-    )
-
     if args.reset_chrome_db:
         vector_store.reset_collection()
 

generic_rag/graphs/ret_gen.py

@@ -0,0 +1,80 @@
+from langgraph.graph import START, END, StateGraph
+from typing_extensions import List, TypedDict
+from langchain_core.documents import Document
+from langchain import hub
+from typing import Any, Union
+
+
+class State(TypedDict):
+    question: str
+    context: List[Document]
+    answer: str
+
+
+class RetGenLangGraph:
+    def __init__(self, vector_store, chat_model, embedding_model):
+        self.vector_store = vector_store
+        self.chat_model = chat_model
+        self.embedding_model = embedding_model
+        self.prompt = hub.pull("rlm/rag-prompt")
+
+        graph_builder = StateGraph(State).add_sequence([self._retrieve, self._generate])
+        graph_builder.add_edge(START, "_retrieve")
+        graph_builder.add_edge("_retrieve", "_generate")
+        graph_builder.add_edge("_generate", END)
+
+        self.graph = graph_builder.compile()
+        self.last_invoke = None
+
+    def invoke(self, message: str) -> Union[dict[str, Any], Any]:
+        self.last_invoke = self.graph.invoke(message)
+        return self.last_invoke
+    
+    def _retrieve(self, state: State) -> dict:
+        retrieved_docs = self.vector_store.similarity_search(state["question"])
+        return {"context": retrieved_docs}
+
+    def _generate(self, state: State) -> dict:
+        docs_content = "\n\n".join(doc.page_content for doc in state["context"])
+        messages = self.prompt.invoke({"question": state["question"], "context": docs_content})
+        response = self.chat_model.invoke(messages)
+        return {"answer": response.content}
+
+    def get_last_pdf_sources(self) -> dict[str, list[int]]:
+        """
+        Method that retrieves the PDF sources used during the last invoke.
+        """
+        if self.last_invoke is None:
+            return []
+        
+        pdf_sources = {}
+        for context in self.last_invoke["context"]:
+            try:
+                if context.metadata["filetype"] == "application/pdf":
+                    source = context.metadata["source"]
+                    page_number = context.metadata["page_number"]
+                    if source in pdf_sources:
+                        pdf_sources[source].add(page_number)
+                    else:
+                        pdf_sources[source] = {page_number}
+            except KeyError:
+                pass
+
+        return pdf_sources
+
+    def get_last_web_sources(self) -> set:
+        """
+        Method that retrieves the web sources used during the last invoke.
+        """
+        if self.last_invoke is None:
+            return set()
+        
+        web_sources = set()
+        for context in self.last_invoke["context"]:
+            try:
+                if context.metadata["filetype"] == "web":
+                    web_sources.add(context.metadata["source"])
+            except KeyError:
+                pass
+
+        return web_sources