Personalization

cookbook/agents/requirements.txt

@@ -136,7 +136,7 @@ peewee==3.17.5
     # via yfinance
 pgvector==0.2.5
     # via -r cookbook/llm_os/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llm_os/requirements.in
 pillow==10.3.0
     # via

cookbook/examples/auto_rag/requirements.txt

@@ -107,7 +107,7 @@ pandas==2.2.2
     #   streamlit
 pgvector==0.2.5
     # via -r cookbook/llms/openai/auto_rag/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/openai/auto_rag/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/examples/data_eng/requirements.txt

@@ -86,7 +86,7 @@ pandas==2.2.2
     #   -r cookbook/examples/data_eng/requirements.in
     #   altair
     #   streamlit
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/examples/data_eng/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/examples/memory/README.md → cookbook/examples/personalization/README.md

@@ -1,9 +1,8 @@
-# Autonomous RAG with Personalized Memory
+# Personalized Memory & Autonomous RAG
 
-This cookbook shows how to do Autonomous retrieval-augmented generation with GPT4.
+This cookbook implements Personalized Memory & Autonomous retrieval-augmented generation.
 
-Auto-RAG is just a fancy name for giving the LLM tools like "search_knowledge_base", "read_chat_history", "search_the_web"
-and letting it decide how to retrieve the information it needs to answer the question.
+i.e. the Assistant will remember details about the user across runs. Similar to how [ChatGPT implements Memory](https://openai.com/index/memory-and-new-controls-for-chatgpt/).
 
 > Note: Fork and clone this repository if needed
 
@@ -23,7 +22,7 @@ export OPENAI_API_KEY=***
 ### 3. Install libraries
 
 ```shell
-pip install -r cookbook/examples/auto_rag/requirements.txt
+pip install -r cookbook/examples/personalization/requirements.txt
 ```
 
 ### 4. Run PgVector
@@ -50,16 +49,17 @@ docker run -d \
   phidata/pgvector:16
 ```
 
-### 5. Run Autonomous RAG App
+### 5. Run personalized Autonomous RAG App
 
 ```shell
-streamlit run cookbook/examples/auto_rag/app.py
+streamlit run cookbook/examples/personalization/app.py
 ```
 
-- Open [localhost:8501](http://localhost:8501) to view your RAG app.
-- Add websites or PDFs and ask question.
-
-- Example Website: https://techcrunch.com/2024/04/18/meta-releases-llama-3-claims-its-among-the-best-open-models-available/
+- Open [localhost:8501](http://localhost:8501) to view the streamlit app.
+- Add to memory: "call me 'your highness'"
+- Add to memory: "always respond with a nice greeting and salutation"
+- Add to memory: "i like cats so add a cat pun in the response"
+- Add a website to the knowledge base: https://techcrunch.com/2024/04/18/meta-releases-llama-3-claims-its-among-the-best-open-models-available/
 - Ask questions like:
   - What did Meta release?
   - Tell me more about the Llama 3 models?

cookbook/examples/memory/app.py → cookbook/examples/personalization/app.py

@@ -6,23 +6,24 @@
 from phi.document import Document
 from phi.document.reader.pdf import PDFReader
 from phi.document.reader.website import WebsiteReader
+from phi.tools.streamlit.components import get_username_sidebar
 from phi.utils.log import logger
 
-from assistant import get_auto_rag_assistant  # type: ignore
+from assistant import get_personalized_auto_rag_assistant  # type: ignore
 
 nest_asyncio.apply()
 st.set_page_config(
-    page_title="Autonomous RAG",
+    page_title="Personalized Memory & Auto RAG",
     page_icon=":orange_heart:",
 )
-st.title("Autonomous RAG")
+st.title("Personalized Memory & Auto RAG")
 st.markdown("##### :orange_heart: built using [phidata](https://github.com/phidatahq/phidata)")
 
 
 def restart_assistant():
     logger.debug("---*--- Restarting Assistant ---*---")
-    st.session_state["auto_rag_assistant"] = None
-    st.session_state["auto_rag_assistant_run_id"] = None
+    st.session_state["personalized_auto_rag_assistant"] = None
+    st.session_state["personalized_auto_rag_assistant_run_id"] = None
     if "url_scrape_key" in st.session_state:
         st.session_state["url_scrape_key"] += 1
     if "file_uploader_key" in st.session_state:
@@ -31,8 +32,16 @@ def restart_assistant():
 
 
 def main() -> None:
+    # Get username
+    user_id = get_username_sidebar()
+    if user_id:
+        st.sidebar.info(f":technologist: User: {user_id}")
+    else:
+        st.write(":technologist: Please enter a username")
+        return
+
     # Get LLM model
-    llm_model = st.sidebar.selectbox("Select LLM", options=["gpt-4-turbo", "gpt-3.5-turbo"])
+    llm_model = st.sidebar.selectbox("Select LLM", options=["gpt-4o", "gpt-4-turbo"])
     # Set assistant_type in session state
     if "llm_model" not in st.session_state:
         st.session_state["llm_model"] = llm_model
@@ -42,23 +51,26 @@ def main() -> None:
         restart_assistant()
 
     # Get the assistant
-    auto_rag_assistant: Assistant
-    if "auto_rag_assistant" not in st.session_state or st.session_state["auto_rag_assistant"] is None:
+    personalized_auto_rag_assistant: Assistant
+    if (
+        "personalized_auto_rag_assistant" not in st.session_state
+        or st.session_state["personalized_auto_rag_assistant"] is None
+    ):
         logger.info(f"---*--- Creating {llm_model} Assistant ---*---")
-        auto_rag_assistant = get_auto_rag_assistant(llm_model=llm_model)
-        st.session_state["auto_rag_assistant"] = auto_rag_assistant
+        personalized_auto_rag_assistant = get_personalized_auto_rag_assistant(llm_model=llm_model, user_id=user_id)
+        st.session_state["personalized_auto_rag_assistant"] = personalized_auto_rag_assistant
     else:
-        auto_rag_assistant = st.session_state["auto_rag_assistant"]
+        personalized_auto_rag_assistant = st.session_state["personalized_auto_rag_assistant"]
 
     # Create assistant run (i.e. log to database) and save run_id in session state
     try:
-        st.session_state["auto_rag_assistant_run_id"] = auto_rag_assistant.create_run()
+        st.session_state["personalized_auto_rag_assistant_run_id"] = personalized_auto_rag_assistant.create_run()
     except Exception:
         st.warning("Could not create assistant, is the database running?")
         return
 
     # Load existing messages
-    assistant_chat_history = auto_rag_assistant.memory.get_chat_history()
+    assistant_chat_history = personalized_auto_rag_assistant.memory.get_chat_history()
     if len(assistant_chat_history) > 0:
         logger.debug("Loading chat history")
         st.session_state["messages"] = assistant_chat_history
@@ -84,13 +96,13 @@ def main() -> None:
         with st.chat_message("assistant"):
             resp_container = st.empty()
             response = ""
-            for delta in auto_rag_assistant.run(question):
+            for delta in personalized_auto_rag_assistant.run(question):
                 response += delta  # type: ignore
                 resp_container.markdown(response)
             st.session_state["messages"].append({"role": "assistant", "content": response})
 
     # Load knowledge base
-    if auto_rag_assistant.knowledge_base:
+    if personalized_auto_rag_assistant.knowledge_base:
         # -*- Add websites to knowledge base
         if "url_scrape_key" not in st.session_state:
             st.session_state["url_scrape_key"] = 0
@@ -106,7 +118,7 @@ def main() -> None:
                     scraper = WebsiteReader(max_links=2, max_depth=1)
                     web_documents: List[Document] = scraper.read(input_url)
                     if web_documents:
-                        auto_rag_assistant.knowledge_base.load_documents(web_documents, upsert=True)
+                        personalized_auto_rag_assistant.knowledge_base.load_documents(web_documents, upsert=True)
                     else:
                         st.sidebar.error("Could not read website")
                     st.session_state[f"{input_url}_uploaded"] = True
@@ -126,33 +138,43 @@ def main() -> None:
                 reader = PDFReader()
                 auto_rag_documents: List[Document] = reader.read(uploaded_file)
                 if auto_rag_documents:
-                    auto_rag_assistant.knowledge_base.load_documents(auto_rag_documents, upsert=True)
+                    personalized_auto_rag_assistant.knowledge_base.load_documents(auto_rag_documents, upsert=True)
                 else:
                     st.sidebar.error("Could not read PDF")
                 st.session_state[f"{auto_rag_name}_uploaded"] = True
             alert.empty()
 
-    if auto_rag_assistant.knowledge_base and auto_rag_assistant.knowledge_base.vector_db:
+    if personalized_auto_rag_assistant.knowledge_base and personalized_auto_rag_assistant.knowledge_base.vector_db:
         if st.sidebar.button("Clear Knowledge Base"):
-            auto_rag_assistant.knowledge_base.vector_db.clear()
+            personalized_auto_rag_assistant.knowledge_base.vector_db.clear()
             st.sidebar.success("Knowledge base cleared")
 
-    if auto_rag_assistant.storage:
-        auto_rag_assistant_run_ids: List[str] = auto_rag_assistant.storage.get_all_run_ids()
-        new_auto_rag_assistant_run_id = st.sidebar.selectbox("Run ID", options=auto_rag_assistant_run_ids)
-        if st.session_state["auto_rag_assistant_run_id"] != new_auto_rag_assistant_run_id:
-            logger.info(f"---*--- Loading {llm_model} run: {new_auto_rag_assistant_run_id} ---*---")
-            st.session_state["auto_rag_assistant"] = get_auto_rag_assistant(
-                llm_model=llm_model, run_id=new_auto_rag_assistant_run_id
+    if personalized_auto_rag_assistant.storage:
+        personalized_auto_rag_assistant_run_ids: List[str] = personalized_auto_rag_assistant.storage.get_all_run_ids(
+            user_id=user_id
+        )
+        new_personalized_auto_rag_assistant_run_id = st.sidebar.selectbox(
+            "Run ID", options=personalized_auto_rag_assistant_run_ids
+        )
+        if st.session_state["personalized_auto_rag_assistant_run_id"] != new_personalized_auto_rag_assistant_run_id:
+            logger.info(f"---*--- Loading {llm_model} run: {new_personalized_auto_rag_assistant_run_id} ---*---")
+            st.session_state["personalized_auto_rag_assistant"] = get_personalized_auto_rag_assistant(
+                llm_model=llm_model, user_id=user_id, run_id=new_personalized_auto_rag_assistant_run_id
             )
             st.rerun()
 
+    # Show Assistant memory
+    if personalized_auto_rag_assistant.memory.memories and len(personalized_auto_rag_assistant.memory.memories) > 0:
+        logger.info("Loading assistant memory")
+        with st.status("Assistant Memory", expanded=False, state="complete"):
+            with st.container():
+                memory_container = st.empty()
+                memory_container.markdown(
+                    "\n".join([f"- {m.memory}" for m in personalized_auto_rag_assistant.memory.memories])
+                )
+
     if st.sidebar.button("New Run"):
         restart_assistant()
 
-    if "embeddings_model_updated" in st.session_state:
-        st.sidebar.info("Please add documents again as the embeddings model has changed.")
-        st.session_state["embeddings_model_updated"] = False
-
 
 main()

cookbook/examples/memory/assistant.py → cookbook/examples/personalization/assistant.py

@@ -1,34 +1,43 @@
 from typing import Optional
 
-from phi.assistant import Assistant
-from phi.knowledge import AssistantKnowledge
+from phi.assistant import Assistant, AssistantMemory, AssistantKnowledge
 from phi.llm.openai import OpenAIChat
 from phi.tools.duckduckgo import DuckDuckGo
 from phi.embedder.openai import OpenAIEmbedder
 from phi.vectordb.pgvector import PgVector2
+from phi.memory.db.postgres import PgMemoryDb
 from phi.storage.assistant.postgres import PgAssistantStorage
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 
-def get_auto_rag_assistant(
-    llm_model: str = "gpt-4-turbo",
+def get_personalized_auto_rag_assistant(
+    llm_model: str = "gpt-4o",
     user_id: Optional[str] = None,
     run_id: Optional[str] = None,
     debug_mode: bool = True,
 ) -> Assistant:
-    """Get an Auto RAG Assistant."""
+    """Get a Personalized Auto RAG Assistant."""
 
     return Assistant(
-        name="auto_rag_assistant",
+        name="personalized_auto_rag_assistant",
         run_id=run_id,
         user_id=user_id,
         llm=OpenAIChat(model=llm_model),
-        storage=PgAssistantStorage(table_name="auto_rag_assistant_openai", db_url=db_url),
+        # Add personalization to the assistant
+        # by storing memories in a database and adding them to the system prompt
+        memory=AssistantMemory(
+            db=PgMemoryDb(
+                db_url=db_url,
+                table_name="personalized_auto_rag_assistant_memory",
+            ),
+            add_memories=True,
+        ),
+        storage=PgAssistantStorage(table_name="personalized_auto_rag_assistant_openai", db_url=db_url),
         knowledge_base=AssistantKnowledge(
             vector_db=PgVector2(
                 db_url=db_url,
-                collection="auto_rag_documents_openai",
+                collection="personalized_auto_rag_documents_openai",
                 embedder=OpenAIEmbedder(model="text-embedding-3-small", dimensions=1536),
             ),
             # 3 references are added to the prompt

cookbook/examples/memory/requirements.txt → cookbook/examples/personalization/requirements.txt

@@ -107,7 +107,7 @@ pandas==2.2.2
     #   streamlit
 pgvector==0.2.5
     # via -r cookbook/llms/openai/auto_rag/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/openai/auto_rag/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/examples/research/requirements.txt

@@ -99,7 +99,7 @@ pandas==2.2.2
     # via
     #   altair
     #   streamlit
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/examples/research/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/examples/sql/requirements.txt

@@ -87,7 +87,7 @@ pandas==2.2.2
     #   streamlit
 pgvector==0.2.5
     # via -r cookbook/examples/sql/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/examples/sql/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/examples/worldbuilding/requirements.txt

@@ -95,7 +95,7 @@ pandas==2.2.0
     # via
     #   altair
     #   streamlit
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/examples/worldbuilding/requirements.in
 pillow==10.2.0
     # via streamlit

cookbook/integrations/singlestore/ai_apps/requirements.txt

@@ -126,7 +126,7 @@ pandas==2.2.2
     #   yfinance
 peewee==3.17.3
     # via yfinance
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/integrations/singlestore/auto_rag/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/llm_os/requirements.txt

@@ -136,7 +136,7 @@ peewee==3.17.5
     # via yfinance
 pgvector==0.2.5
     # via -r cookbook/llm_os/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llm_os/requirements.in
 pillow==10.3.0
     # via

cookbook/llms/groq/ai_apps/requirements.txt

@@ -127,7 +127,7 @@ peewee==3.17.3
     # via yfinance
 pgvector==0.2.5
     # via -r cookbook/llms/groq/ai_apps/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/ai_apps/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/llms/groq/auto_rag/requirements.txt

@@ -113,7 +113,7 @@ pandas==2.2.2
     #   streamlit
 pgvector==0.2.5
     # via -r cookbook/llms/groq/auto_rag/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/auto_rag/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/llms/groq/investment_researcher/requirements.txt

@@ -124,7 +124,7 @@ pandas==2.2.2
     #   yfinance
 peewee==3.17.3
     # via yfinance
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/investment_researcher/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/llms/groq/news_articles/requirements.txt

@@ -119,7 +119,7 @@ pandas==2.2.2
     #   altair
     #   newspaper4k
     #   streamlit
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/news_articles/requirements.in
 pillow==10.3.0
     # via

cookbook/llms/groq/rag/requirements.txt

@@ -111,7 +111,7 @@ pandas==2.2.2
     #   streamlit
 pgvector==0.2.5
     # via -r cookbook/llms/groq/rag/requirements.in
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/rag/requirements.in
 pillow==10.3.0
     # via streamlit

cookbook/llms/groq/research/requirements.txt

@@ -85,7 +85,7 @@ pandas==2.2.2
     # via
     #   altair
     #   streamlit
-phidata==2.4.8
+phidata==2.4.10
     # via -r cookbook/llms/groq/research/requirements.in
 pillow==10.3.0
     # via streamlit