changed embeddings to snowflake, web search tool used by llama3.2 now

rag_tutorials/deepseek_local_rag_agent/README.md

@@ -1,17 +1,17 @@
 # Deepseek Local RAG Reasoning Agent 🤔
 
-A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via Ollama), Gemini for embeddings, Qdrant for vector storage, and Agno for agent orchestration, this application offers both simple local chat and advanced RAG-enhanced interactions with comprehensive document processing and web search capabilities.
+A powerful reasoning agent that combines local Deepseek models with RAG capabilities. Built using Deepseek (via Ollama), Snowflake for embeddings, Qdrant for vector storage, and Agno for agent orchestration, this application offers both simple local chat and advanced RAG-enhanced interactions with comprehensive document processing and web search capabilities.
 
 ## Features
 
 - **Dual Operation Modes**
   - Local Chat Mode: Direct interaction with Deepseek locally
-  - RAG Mode: Enhanced reasoning with document context and web search integration
+  - RAG Mode: Enhanced reasoning with document context and web search integration - llama3.1:8b
 
 - **Document Processing** (RAG Mode)
   - PDF document upload and processing
   - Web page content extraction
-  - Automatic text chunking
+  - Automatic text chunking and embedding
   - Vector storage in Qdrant cloud
 
 - **Intelligent Querying** (RAG Mode)
@@ -32,7 +32,7 @@ A powerful reasoning agent that combines local Deepseek models with RAG capabili
   - Flexible model selection:
     - Deepseek r1 1.5b (lighter, suitable for most laptops)
     - Deepseek r1 7b (more capable, requires better hardware)
-  - Gemini Embedding model for vector embeddings
+  - Snowflake Arctic Embedding model (SOTA) for vector embeddings
   - Agno Agent framework for orchestration
   - Streamlit-based interactive interface
 
@@ -47,6 +47,9 @@ ollama pull deepseek-r1:1.5b
 
 # For the more capable model (if your hardware supports it)
 ollama pull deepseek-r1:7b
+
+ollama pull snowflake-arctic-embed
+ollama pull llama3.1:8b
 ```
 
 ### 2. Google API Key (for RAG Mode)

rag_tutorials/deepseek_local_rag_agent/deepseek_rag_agent.py

@@ -15,27 +15,22 @@ from qdrant_client import QdrantClient
 from qdrant_client.models import Distance, VectorParams
 from langchain_core.embeddings import Embeddings
 from agno.tools.exa import ExaTools
+from agno.embedder.ollama import OllamaEmbedder
 
 
-class GeminiEmbedder(Embeddings):
-    def __init__(self, model_name="models/text-embedding-004"):
-        genai.configure(api_key=st.session_state.google_api_key)
-        self.model = model_name
+class OllamaEmbedderr(Embeddings):
+    def __init__(self, model_name="snowflake-arctic-embed"):
+        self.embedder = OllamaEmbedder(id=model_name, dimensions=1024)
 
     def embed_documents(self, texts: List[str]) -> List[List[float]]:
         return [self.embed_query(text) for text in texts]
 
     def embed_query(self, text: str) -> List[float]:
-        response = genai.embed_content(
-            model=self.model,
-            content=text,
-            task_type="retrieval_document"
-        )
-        return response['embedding']
+        return self.embedder.get_embedding(text)
 
 
 # Constants
-COLLECTION_NAME = "deepseek-r1-agno"
+COLLECTION_NAME = "test-deepseek-r1"
 
 
 # Streamlit App Initialization
@@ -98,14 +93,12 @@ if st.sidebar.button("🗑️ Clear Chat History"):
 # Show API Configuration only if RAG is enabled
 if st.session_state.rag_enabled:
     st.sidebar.header("🔑 API Configuration")
-    google_api_key = st.sidebar.text_input("Google API Key", type="password", value=st.session_state.google_api_key)
     qdrant_api_key = st.sidebar.text_input("Qdrant API Key", type="password", value=st.session_state.qdrant_api_key)
     qdrant_url = st.sidebar.text_input("Qdrant URL", 
                                      placeholder="https://your-cluster.cloud.qdrant.io:6333",
                                      value=st.session_state.qdrant_url)
 
     # Update session state
-    st.session_state.google_api_key = google_api_key
     st.session_state.qdrant_api_key = qdrant_api_key
     st.session_state.qdrant_url = qdrant_url
     
@@ -228,7 +221,7 @@ def create_vector_store(client, texts):
             client.create_collection(
                 collection_name=COLLECTION_NAME,
                 vectors_config=VectorParams(
-                    size=768,  # Gemini embedding-004 dimension
+                    size=1024,  
                     distance=Distance.COSINE
                 )
             )
@@ -241,7 +234,7 @@ def create_vector_store(client, texts):
         vector_store = QdrantVectorStore(
             client=client,
             collection_name=COLLECTION_NAME,
-            embedding=GeminiEmbedder()
+            embedding=OllamaEmbedderr()
         )
         
         # Add documents
@@ -254,37 +247,11 @@ def create_vector_store(client, texts):
         st.error(f"🔴 Vector store error: {str(e)}")
         return None
 
-
-# Add this after the GeminiEmbedder class
-def get_query_rewriter_agent() -> Agent:
-    """Initialize a query rewriting agent."""
-    return Agent(
-        name="Query Rewriter",
-        model=Gemini(id="gemini-exp-1206"),
-        instructions="""You are an expert at reformulating questions to be more precise and detailed. 
-        1. Analyze the user's question
-        2. Rewrite it to be more specific and search-friendly
-        3. Expand any acronyms or technical terms
-        4. Return ONLY the rewritten query without any additional text or explanations
-        
-        Example 1:
-        User: "What does it say about ML?"
-        Output: "What are the key concepts, techniques, and applications of Machine Learning (ML) discussed in the context?"
-        
-        Example 2:
-        User: "Tell me about transformers"
-        Output: "Explain the architecture, mechanisms, and applications of Transformer neural networks in natural language processing and deep learning"
-        """,
-        show_tool_calls=False,
-        markdown=True,
-    )
-
-
 def get_web_search_agent() -> Agent:
     """Initialize a web search agent."""
     return Agent(
         name="Web Search Agent",
-        model=Gemini(id="gemini-exp-1206", api_key=st.session_state.google_api_key),
+        model=Ollama(id="llama3.2"),
         tools=[ExaTools(
             api_key=st.session_state.exa_api_key,
             include_domains=search_domains,
@@ -340,10 +307,6 @@ def check_document_relevance(query: str, vector_store, threshold: float = 0.7) -
     return bool(docs), docs
 
 
-# Main Application Flow
-
-# Chat Interface
-# Create two columns for chat input and search toggle
 chat_col, toggle_col = st.columns([0.9, 0.1])
 
 with chat_col:
@@ -352,16 +315,8 @@ with chat_col:
 with toggle_col:
     st.session_state.force_web_search = st.toggle('🌐', help="Force web search")
 
-# Check if RAG is enabled but no API key
-if st.session_state.rag_enabled and not st.session_state.google_api_key:
-    st.error("Please enter your Google API Key to continue with RAG mode")
-    st.stop()
-
-# Initialize Qdrant and configure APIs if RAG is enabled
-if st.session_state.rag_enabled and st.session_state.google_api_key:
-    os.environ["GOOGLE_API_KEY"] = st.session_state.google_api_key
-    genai.configure(api_key=st.session_state.google_api_key)
-    
+# Check if RAG is enabled 
+if st.session_state.rag_enabled:
     qdrant_client = init_qdrant()
     
     # File/URL Upload Section
@@ -411,15 +366,14 @@ if prompt:
         st.write(prompt)
 
     if st.session_state.rag_enabled:
+
             # Existing RAG flow remains unchanged
-            with st.spinner("🤔 Reformulating query..."):
+            with st.spinner("🤔Evaluating the Query..."):
                 try:
-                    query_rewriter = get_query_rewriter_agent()
-                    rewritten_query = query_rewriter.run(prompt).content
+                    rewritten_query = prompt
                     
-                    with st.expander("🔄 See rewritten query"):
-                        st.write(f"Original: {prompt}")
-                        st.write(f"Rewritten: {rewritten_query}")
+                    with st.expander("Evaluating the query"):
+                        st.write(f"User's Prompt: {prompt}")
                 except Exception as e:
                     st.error(f"❌ Error rewriting query: {str(e)}")
                     rewritten_query = prompt
@@ -469,11 +423,9 @@ if prompt:
                         full_prompt = f"""Context: {context}
 
 Original Question: {prompt}
-Rewritten Question: {rewritten_query}
-
 Please provide a comprehensive answer based on the available information."""
                     else:
-                        full_prompt = f"Original Question: {prompt}\nRewritten Question: {rewritten_query}"
+                        full_prompt = f"Original Question: {prompt}\n"
                         st.info("ℹ️ No relevant information found in documents or web search.")
 
                     response = rag_agent.run(full_prompt)