Update to use fine tuned embeddings model

app.py

@@ -92,7 +92,7 @@ graph = compiled_graph  # This is what we'll import in the script
 
 @cl.on_chat_start
 async def on_chat_start():
-    await cl.Message("Hello! I'm your AI assistant. How can I help you today?").send()
+    await cl.Message("Hello! I'm your chief joy officer, here to help you with finding fun things to do in London! You can ask me anything but I'm particularly good at finding events and activities. What can I help you with today?").send()
 
 @cl.on_message
 async def on_message(message: cl.Message):

pyproject.toml

@@ -19,5 +19,6 @@ dependencies = [
     "langchain-qdrant>=0.2.0",
     "tavily-python>=0.3.1",
     "typing-extensions>=4.9.0",
-    "beautifulsoup4==4.13.3"
+    "beautifulsoup4==4.13.3",
+    "sentence-transformers==3.4.1"
 ]

rag.py

@@ -6,6 +6,8 @@ from langchain_qdrant import QdrantVectorStore
 from qdrant_client import QdrantClient
 from qdrant_client.http.models import Distance, VectorParams
 from langchain_community.document_loaders import WebBaseLoader
+from langchain_community.embeddings import HuggingFaceEmbeddings
+
 import tiktoken
 
 def load_web_documents(urls: List[str]) -> List[Document]:
@@ -22,9 +24,16 @@ def load_web_documents(urls: List[str]) -> List[Document]:
     return loader.load()
 
 def create_rag_pipeline(collection_name: str = "rag_collection"):
-    # Initialize embedding model
-    embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
-    embedding_dim = 1536  # Dimension for text-embedding-3-small
+    # OpenAI embedding model
+    # embedding_model = OpenAIEmbeddings(model="text-embedding-3-small")
+    # Fine tuned embedding model
+    embedding_model = HuggingFaceEmbeddings(
+        model_name="ric9176/cjo-ft-v0", 
+        model_kwargs={'device': 'cpu'},  # Or 'cuda' if using GPU
+        encode_kwargs={'normalize_embeddings': True}  # Optional: normalize the embeddings
+    )
+    # embedding_dim = 1536  # Dimension for text-embedding-3-small
+    embedding_dim = 1024  # Dimension for Snowflake/snowflake-arctic-embed-l
 
     # Initialize Qdrant client (in-memory for development)
     client = QdrantClient(":memory:")

uv.lock

@@ -18,6 +18,7 @@ dependencies = [
     { name = "openai" },
     { name = "pydantic" },
     { name = "pypdf2" },
+    { name = "sentence-transformers" },
     { name = "tavily-python" },
     { name = "typing-extensions" },
     { name = "websockets" },
@@ -37,6 +38,7 @@ requires-dist = [
     { name = "openai", specifier = ">=1.59.9" },
     { name = "pydantic", specifier = "==2.10.1" },
     { name = "pypdf2", specifier = ">=3.0.1" },
+    { name = "sentence-transformers", specifier = "==3.4.1" },
     { name = "tavily-python", specifier = ">=0.3.1" },
     { name = "typing-extensions", specifier = ">=4.9.0" },
     { name = "websockets", specifier = ">=14.2" },
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