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