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thomfoolery commited on 29 days ago

Commit

95b0fa1

1 Parent(s): 465a7e3

updates

Browse files

Files changed (4) hide show

app.py +7 -3
pyproject.toml +1 -0
rag_graph.py +24 -13
uv.lock +252 -0

app.py CHANGED Viewed

@@ -4,21 +4,25 @@ from rag_graph import RagGraph
 @cl.cache
 def get_qdrant_client():
   from qdrant_client import QdrantClient
   return QdrantClient(path='data/vectors')
 @cl.on_chat_start
 async def on_chat_start():
   qdrant_client = get_qdrant_client()
   rag_graph = RagGraph(qdrant_client)
-  rag_graph.create_rag_graph()
   cl.user_session.set("rag_graph", rag_graph)
 @cl.on_message
 async def on_message(question: cl.Message):
   msg = cl.Message(content="")
-  await msg.send()  # Initialize the message first
   rag_graph = cl.user_session.get("rag_graph")
-  await rag_graph.stream(question.content, msg)  # Update the message when streaming is complete

 @cl.cache
 def get_qdrant_client():
+  """Create a QdrantClient instance and cache it for restarts during development."""
   from qdrant_client import QdrantClient
   return QdrantClient(path='data/vectors')
 @cl.on_chat_start
 async def on_chat_start():
+  """Create the RAG graph and store it in the user session."""
   qdrant_client = get_qdrant_client()
   rag_graph = RagGraph(qdrant_client)
   cl.user_session.set("rag_graph", rag_graph)
 @cl.on_message
 async def on_message(question: cl.Message):
+  """Stream the response to the user."""
   msg = cl.Message(content="")
+  # Send a message to the user to indicate that the response is being generated
+  await msg.send()
   rag_graph = cl.user_session.get("rag_graph")
+   # Update the message when streaming is complete
+  await rag_graph.stream(question.content, msg)

pyproject.toml CHANGED Viewed

@@ -22,4 +22,5 @@ dependencies = [
     "dotenv>=0.9.9",
     "unstructured>=0.14.8",
     "chainlit>=2.2.1",
 ]

     "dotenv>=0.9.9",
     "unstructured>=0.14.8",
     "chainlit>=2.2.1",
+    "ragas>=0.2.13",
 ]

rag_graph.py CHANGED Viewed

@@ -16,15 +16,20 @@ from langchain.text_splitter import RecursiveCharacterTextSplitter
 from qdrant_client.http.models import Distance, VectorParams
 import nltk
 nltk.download('punkt_tab')
 nltk.download('averaged_perceptron_tagger_eng')
 CHUNK_SIZE = 1000
 CHUNK_OVERLAP = CHUNK_SIZE // 2
 RAG_PROMPT = """\
-You are a helpful assistant who answers questions based on provided context.
 You must only use the provided context, and cannot use your own knowledge.
 ### Question
@@ -64,8 +69,10 @@ class RagGraph:
     self.vector_db_retriever = self.vector_store.as_retriever(search_kwargs={"k": 5})
     self.graph = None
-  def create_rag_graph(self):
     """Create the RAG graph."""
     class State(TypedDict):
       """State for the conversation."""
@@ -74,8 +81,8 @@ class RagGraph:
     def retrieve(state):
       question = state["question"]
-      retrieved_docs = self.vector_db_retriever.invoke(question)
-      return {"question": state["question"], "context": retrieved_docs}
     async def stream(state):
       """LangGraph node that streams responses"""
@@ -90,23 +97,27 @@ class RagGraph:
     self.graph = graph_builder.compile()
   def run(self, question):
-    """Run the graph."""
-    retrieved_docs = self.vector_db_retriever.invoke(question)
-    docs_content = "\n\n".join(doc.page_content for doc in retrieved_docs)
-    messages = self.rag_prompt.format_messages(question=question, context=docs_content)
     response = self.llm.invoke(messages)
-    return {"response": response.content}
   async def stream(self, question, msg):
-    """Stream the graph."""
     async for event in self.graph.astream({"question": question, "context": []}, stream_mode=["messages"]):
-      _, (message_chunk, metadata) = event
       if message_chunk.content:
         await msg.stream_token(message_chunk.content)
     await msg.send()
 def main():
   """Test the RAG graph."""
   load_dotenv()

 from qdrant_client.http.models import Distance, VectorParams
+# Necessary for dependencies for DirectoryLoader
 import nltk
 nltk.download('punkt_tab')
 nltk.download('averaged_perceptron_tagger_eng')
+# Chunk configuration
 CHUNK_SIZE = 1000
 CHUNK_OVERLAP = CHUNK_SIZE // 2
+# RAG prompt template
 RAG_PROMPT = """\
+You are a helpful assistant who helps Shopify merchants automate their businesses.
+Your goal is to provide a helpful response to the merchant's question in straight forward, non technical language.
+Try to be brief and to the point, but explain technical jargon.
 You must only use the provided context, and cannot use your own knowledge.
 ### Question
     self.vector_db_retriever = self.vector_store.as_retriever(search_kwargs={"k": 5})
     self.graph = None
+    self.create()
+  def create(self):
     """Create the RAG graph."""
     class State(TypedDict):
       """State for the conversation."""
     def retrieve(state):
       question = state["question"]
+      context = self.vector_db_retriever.invoke(question)
+      return {"question": state["question"], "context": context}
     async def stream(state):
       """LangGraph node that streams responses"""
     self.graph = graph_builder.compile()
   def run(self, question):
+    """Invoke RAG response without streaming."""
+    chunks = self.vector_db_retriever.invoke(question)
+    context = "\n\n".join(doc.page_content for doc in chunks)
+    messages = self.rag_prompt.format_messages(question=question, context=context)
     response = self.llm.invoke(messages)
+    return {
+      "response": response.content,
+      "context": chunks
+    }
   async def stream(self, question, msg):
+    """Stream RAG response."""
     async for event in self.graph.astream({"question": question, "context": []}, stream_mode=["messages"]):
+      _event_name, (message_chunk, _metadata) = event
       if message_chunk.content:
         await msg.stream_token(message_chunk.content)
     await msg.send()
+# Run RAG with CLI (no streaming)
 def main():
   """Test the RAG graph."""
   load_dotenv()

uv.lock CHANGED Viewed

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