update-claude-phi-1742

cookbook/knowledge/custom_references.py

@@ -11,13 +11,13 @@
 from phi.agent import Agent
 from phi.document import Document
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
-from phi.vectordb.pgvector import PgVector2
+from phi.vectordb.pgvector import PgVector
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
     urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    vector_db=PgVector2(collection="recipes", db_url=db_url),
+    vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 # Comment out after first run
 # knowledge_base.load(recreate=False)

cookbook/knowledge/json_kb.py

@@ -2,15 +2,15 @@
 
 from phi.agent import Agent
 from phi.knowledge.json import JSONKnowledgeBase
-from phi.vectordb.pgvector import PgVector2
+from phi.vectordb.pgvector import PgVector
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Initialize the JSONKnowledgeBase
 knowledge_base = JSONKnowledgeBase(
     path=Path("data/docs"),  # Table name: ai.json_documents
-    vector_db=PgVector2(
-        collection="json_documents",
+    vector_db=PgVector(
+        table_name="json_documents",
         db_url=db_url,
     ),
     num_documents=5,  # Number of documents to return on search

cookbook/knowledge/pdf.py

@@ -1,14 +1,14 @@
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFKnowledgeBase, PDFReader
-from phi.vectordb.pgvector import PgVector2
+from phi.vectordb.pgvector import PgVector
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 # Create a knowledge base with the PDFs from the data/pdfs directory
 knowledge_base = PDFKnowledgeBase(
     path="data/pdfs",
-    vector_db=PgVector2(
-        collection="pdf_documents",
+    vector_db=PgVector(
+        table_name="pdf_documents",
         # Can inspect database via psql e.g. "psql -h localhost -p 5432 -U ai -d ai"
         db_url=db_url,
     ),

cookbook/knowledge/pdf_url.py

@@ -1,12 +1,12 @@
 from phi.agent import Agent
 from phi.knowledge.pdf import PDFUrlKnowledgeBase
-from phi.vectordb.pgvector import PgVector2
+from phi.vectordb.pgvector import PgVector
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 knowledge_base = PDFUrlKnowledgeBase(
     urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
-    vector_db=PgVector2(collection="recipes", db_url=db_url),
+    vector_db=PgVector(table_name="recipes", db_url=db_url),
 )
 knowledge_base.load(recreate=False)  # Comment out after first run
 

cookbook/knowledge/text.py

@@ -2,16 +2,16 @@
 
 from phi.agent import Agent
 from phi.knowledge.text import TextKnowledgeBase
-from phi.vectordb.pgvector import PgVector2
+from phi.vectordb.pgvector import PgVector
 
 db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
 
 
 # Initialize the TextKnowledgeBase
 knowledge_base = TextKnowledgeBase(
     path=Path("data/docs"),  # Table name: ai.text_documents
-    vector_db=PgVector2(
-        collection="text_documents",
+    vector_db=PgVector(
+        table_name="text_documents",
         db_url=db_url,
     ),
     num_documents=5,  # Number of documents to return on search

cookbook/providers/claude/README.md

@@ -74,3 +74,21 @@ python cookbook/providers/claude/finance.py
 ```shell
 python cookbook/providers/claude/structured_output.py
 ```
+
+### 7. Run Agent that uses memory
+
+```shell
+python cookbook/providers/claude/memory.py
+```
+
+### 8. Run Agent that uses storage
+
+```shell
+python cookbook/providers/claude/storage.py
+```
+
+### 9. Run Agent that uses knowledge
+
+```shell
+python cookbook/providers/claude/knowledge.py
+```

cookbook/providers/claude/agent.py

@@ -5,7 +5,7 @@
 from phi.tools.yfinance import YFinanceTools
 
 agent = Agent(
-    model=Claude(id="claude-3-5-sonnet-20240620"),
+    model=Claude(id="claude-3-5-sonnet-20241022"),
     tools=[YFinanceTools(stock_price=True)],
     show_tool_calls=True,
     markdown=True,

cookbook/providers/claude/agent_stream.py

@@ -6,7 +6,7 @@
 from phi.tools.yfinance import YFinanceTools
 
 agent = Agent(
-    model=Claude(id="claude-3-5-sonnet-20240620"),
+    model=Claude(id="claude-3-5-sonnet-20241022"),
     tools=[YFinanceTools(stock_price=True)],
     show_tool_calls=True,
     markdown=True,

cookbook/providers/claude/basic.py

@@ -1,7 +1,7 @@
 from phi.agent import Agent, RunResponse  # noqa
 from phi.model.anthropic import Claude
 
-agent = Agent(model=Claude(id="claude-3-5-sonnet-20240620"), markdown=True)
+agent = Agent(model=Claude(id="claude-3-5-sonnet-20241022"), markdown=True)
 
 # Get the response in a variable
 # run: RunResponse = agent.run("Share a 2 sentence horror story")

cookbook/providers/claude/basic_stream.py

@@ -2,7 +2,7 @@
 from phi.agent import Agent, RunResponse  # noqa
 from phi.model.anthropic import Claude
 
-agent = Agent(model=Claude(id="claude-3-5-sonnet-20240620"), markdown=True)
+agent = Agent(model=Claude(id="claude-3-5-sonnet-20241022"), markdown=True)
 
 # Get the response in a variable
 # run_response: Iterator[RunResponse] = agent.run("Share a 2 sentence horror story", stream=True)

cookbook/providers/claude/data_analyst.py

@@ -11,7 +11,7 @@
 )
 
 agent = Agent(
-    model=Claude(id="claude-3-5-sonnet-20240620"),
+    model=Claude(id="claude-3-5-sonnet-20241022"),
     tools=[duckdb_tools],
     markdown=True,
     show_tool_calls=True,

cookbook/providers/claude/finance_agent.py

@@ -5,7 +5,7 @@
 from phi.tools.yfinance import YFinanceTools
 
 agent = Agent(
-    model=Claude(id="claude-3-5-sonnet-20240620"),
+    model=Claude(id="claude-3-5-sonnet-20241022"),
     tools=[YFinanceTools(stock_price=True, analyst_recommendations=True, stock_fundamentals=True)],
     show_tool_calls=True,
     description="You are an investment analyst that researches stocks and helps users make informed decisions.",

cookbook/providers/claude/knowledge.py

@@ -0,0 +1,23 @@
+"""Run `pip install duckduckgo-search sqlalchemy pgvector pypdf anthropic openai` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.anthropic import Claude
+from phi.knowledge.pdf import PDFUrlKnowledgeBase
+from phi.vectordb.pgvector import PgVector
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+knowledge_base = PDFUrlKnowledgeBase(
+    urls=["https://phi-public.s3.amazonaws.com/recipes/ThaiRecipes.pdf"],
+    vector_db=PgVector(table_name="recipes", db_url=db_url),
+)
+knowledge_base.load(recreate=True)  # Comment out after first run
+
+agent = Agent(
+    model=Claude(id="claude-3-5-sonnet-20241022"),
+    knowledge_base=knowledge_base,
+    use_tools=True,
+    show_tool_calls=True,
+    debug_mode=True,
+)
+agent.print_response("How to make Thai curry?", markdown=True)

cookbook/providers/claude/memory.py

@@ -0,0 +1,51 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/run_pgvector.sh` to start a postgres and pgvector instance
+2. Run: `pip install anthropic sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/agents/personalized_memories_and_summaries.py` to run the agent
+"""
+
+from rich.pretty import pprint
+
+from phi.agent import Agent, AgentMemory
+from phi.model.anthropic import Claude
+from phi.memory.db.postgres import PgMemoryDb
+from phi.storage.agent.postgres import PgAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+agent = Agent(
+    model=Claude(id="claude-3-5-sonnet-20241022"),
+    # Store the memories and summary in a database
+    memory=AgentMemory(
+        db=PgMemoryDb(table_name="agent_memory", db_url=db_url), create_user_memories=True, create_session_summary=True
+    ),
+    # Store agent sessions in a database
+    storage=PgAgentStorage(table_name="personalized_agent_sessions", db_url=db_url),
+    # Show debug logs so you can see the memory being created
+    # debug_mode=True,
+)
+
+# -*- Share personal information
+agent.print_response("My name is john billings?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+# -*- Print memories
+pprint(agent.memory.memories)
+# -*- Print summary
+pprint(agent.memory.summary)
+
+# Ask about the conversation
+agent.print_response("What have we been talking about, do you know my name?", stream=True)

cookbook/providers/claude/storage.py

@@ -0,0 +1,17 @@
+"""Run `pip install duckduckgo-search sqlalchemy anthropic` to install dependencies."""
+
+from phi.agent import Agent
+from phi.model.anthropic import Claude
+from phi.tools.duckduckgo import DuckDuckGo
+from phi.storage.agent.postgres import PgAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+
+agent = Agent(
+    model=Claude(),
+    storage=PgAgentStorage(table_name="agent_sessions", db_url=db_url),
+    tools=[DuckDuckGo()],
+    add_history_to_messages=True,
+)
+agent.print_response("How many people live in Canada?")
+agent.print_response("What is their national anthem called?")

phi/model/anthropic/claude.py

@@ -72,7 +72,7 @@ class Claude(Model):
         client (Optional[AnthropicClient]): A pre-configured instance of the Anthropic client.
     """
 
-    id: str = "claude-3-5-sonnet-20240620"
+    id: str = "claude-3-5-sonnet-20241022"
     name: str = "Claude"
     provider: str = "Anthropic"