updated ragas

app.py

@@ -10,8 +10,11 @@ from langchain_core.messages import HumanMessage
 from langgraph.graph.message import add_messages
 from qdrant_client import QdrantClient
 from langchain_openai import OpenAIEmbeddings
-
 import os
+from ragas import evaluate
+from ragas.metrics import answer_relevancy
+from langchain_core.documents import Document
+
 
 load_dotenv()
 
@@ -20,7 +23,6 @@ llm = ChatOpenAI(model="gpt-4o-mini")
 qd_api_key = os.getenv("QDRANT_CLOUD_API_KEY")
 
 
-
 embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
 
 # ✅ Initialize Qdrant Client
@@ -57,6 +59,47 @@ def search(query_vector, top_k=1) -> list:
 
     return return_hits
 
+def evaluate_retrieved_docs(question: str, retrieved_docs: list):
+    """Evaluate the retrieved documents using RAGAS metrics."""
+    
+    # ✅ Extract document content from metadata
+    ragas_docs = [
+        Document(page_content=hit["metadata"].get("content", ""))
+        for hit in retrieved_docs
+        if "content" in hit["metadata"] and hit["metadata"]["content"]
+    ]
+
+    # 🚨 Debugging Output
+    print("🔍 Debug: RAGAS Docs Format:", ragas_docs)
+
+    if not ragas_docs:
+        print("⚠️ No relevant documents to evaluate.")
+        return 0  # Return low score if no documents found
+
+    # ✅ Construct required input
+    queries = [question]
+    contexts = [[doc.page_content for doc in ragas_docs]]
+
+    print("✅ Debug: Queries ->", queries)
+    print("✅ Debug: Contexts ->", contexts)
+
+    # ✅ Run evaluation
+    scores = evaluate(
+        queries=queries,
+        contexts=contexts,
+        metrics=[answer_relevancy]
+    )
+
+    print("📊 Debug: Raw Scores Output ->", scores)
+
+    relevance_score = scores.iloc[0]["answer_relevancy"]
+    print(f"📊 RAGAS Answer Relevancy Score: {relevance_score}")
+
+    return relevance_score
+
+
+
+
 def get_document_by_name(doc_name: str) -> str:
     """Retrieve the raw HTML content of a document by its name from the `data/` folder."""
     
@@ -138,30 +181,34 @@ format_prompt = ChatPromptTemplate.from_template(ot_formatted_prompt)
 def research_node(state) -> dict:
     question = state["messages"][-1].content
 
-    # ✅ Convert the text question to an embedding using text-embedding-3-small
+    # ✅ Convert the text question to an embedding using OpenAI Embeddings
     query_vector = embedding_model.embed_query(question)
 
     # ✅ Query Qdrant with the vector
     relevant_docs = search(query_vector=query_vector, top_k=1)
 
-    if relevant_docs[0]["score"] > 0:
+    # ✅ Evaluate retrieved documents using RAGAS
+    relevance_score = evaluate_retrieved_docs(question, relevant_docs)
+
+    if relevance_score > 0.5:  # Threshold for good retrieval quality
         # ✅ Found relevant document → Summarize it
-        # we will pull our document data from a html file since HF will not let me upload pdfs
         document_name = relevant_docs[0]["metadata"].get("document_name", "No source available.")
         document_text = get_document_by_name(document_name)
-        #document_text = relevant_docs[0]["metadata"].get("content", "No content available.")
+
         messages = summary_prompt.format_messages(document=document_text)
         response = llm.invoke(messages)
 
         return {**state, "messages": state["messages"] + [HumanMessage(content=response.content)], "_next": "post_processing"}
-
+    
     else:
-        # ✅ No relevant document → Query LLM with full prompt
+        # ✅ No relevant document or low RAGAS score → Query LLM directly
+        print("⚠️ RAGAS score too low, defaulting to LLM.")
         messages = rag_prompt.format_messages(question=question, context="No relevant documents found.")
         response = llm.invoke(messages)
 
         return {**state, "messages": state["messages"] + [HumanMessage(content=response.content)], "_next": "post_processing"}
 
+
 # **Post-Processing Node: Formats response using `ot_formatted_prompt`**
 def post_processing_node(state) -> dict:
     response_text = state["messages"][-1].content

pyproject.toml

@@ -13,6 +13,7 @@ dependencies = [
     "langgraph>=0.2.74",
     "nltk>=3.9.1",
     "qdrant-client>=1.13.2",
+    "ragas>=0.2.13",
     "unstructured>=0.14.8",
     "websockets>=15.0",
 ]

uv.lock

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+    { name = "pandas" },
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@@ -1161,6 +1270,7 @@ dependencies = [
     { name = "langgraph" },
     { name = "nltk" },
     { name = "qdrant-client" },
+    { name = "ragas" },
     { name = "unstructured" },
     { name = "websockets" },
 ]
@@ -1175,6 +1285,7 @@ requires-dist = [
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     { name = "nltk", specifier = ">=3.9.1" },
     { name = "qdrant-client", specifier = ">=1.13.2" },
+    { name = "ragas", specifier = ">=0.2.13" },
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