Merge branch 'main' into update-exa-phi-2406

cookbook/agent_concepts/knowledge/embedders/azure_embedder.py

@@ -2,7 +2,7 @@
 from agno.embedder.azure_openai import AzureOpenAIEmbedder
 from agno.vectordb.pgvector import PgVector
 
-embeddings = AzureOpenAIEmbedder().get_embedding(
+embeddings = AzureOpenAIEmbedder(id="text-embedding-3-small").get_embedding(
     "The quick brown fox jumps over the lazy dog."
 )
 

cookbook/examples/agents/shopping_partner.py

@@ -1,7 +1,6 @@
 from agno.agent import Agent
 from agno.models.openai import OpenAIChat
 from agno.tools.exa import ExaTools
-from agno.tools.firecrawl import FirecrawlTools
 
 agent = Agent(
     name="shopping partner",

cookbook/examples/apps/agentic_rag/app.py

@@ -18,6 +18,7 @@
     about_widget,
     add_message,
     display_tool_calls,
+
     export_chat_history,
     rename_session_widget,
     session_selector_widget,

cookbook/examples/apps/agentic_rag/utils.py

@@ -138,7 +138,6 @@ def session_selector_widget(agent: Agent, model_id: str) -> None:
             )
             st.rerun()
 
-
 def about_widget() -> None:
     """Display an about section in the sidebar"""
     st.sidebar.markdown("---")

cookbook/examples/apps/game_generator/README.md

@@ -15,7 +15,7 @@ source ~/.venvs/aienv/bin/activate
 pip install -r cookbook/examples/apps/game_generator/requirements.txt
 ```
 
-### 3. Export `OPENAI_API_KEY` 
+### 3. Export `OPENAI_API_KEY`
 
 ```shell
 export OPENAI_API_KEY=sk-***

cookbook/models/anthropic/README.md

@@ -68,7 +68,13 @@ export OPENAI_API_KEY=***
 python cookbook/models/anthropic/knowledge.py
 ```
 
-### 9. Run Agent that analyzes an image
+### 9. Run Agent that uses memory   
+
+```shell
+python cookbook/models/anthropic/memory.py
+```
+
+### 10. Run Agent that analyzes an image
 
 ```shell
 python cookbook/models/anthropic/image_agent.py

cookbook/models/anthropic/memory.py

@@ -0,0 +1,44 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
+2. Run: `pip install anthropic sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/models/anthropic/memory.py` to run the agent
+"""
+
+from agno.agent import Agent, AgentMemory
+from agno.memory.db.postgres import PgMemoryDb
+from agno.models.anthropic import Claude
+from agno.storage.agent.postgres import PostgresAgentStorage
+from rich.pretty import pprint
+
+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=PostgresAgentStorage(
+        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)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+
+# Ask about the conversation
+agent.print_response(
+    "What have we been talking about, do you know my name?", stream=True
+)

cookbook/models/cohere/README.md

@@ -60,3 +60,9 @@ python cookbook/models/cohere/storage.py
 ```shell
 python cookbook/models/cohere/knowledge.py
 ```
+
+### 9. Run Agent that uses memory
+
+```shell
+python cookbook/models/cohere/memory.py
+```

cookbook/models/cohere/memory.py

@@ -0,0 +1,43 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
+2. Run: `pip install cohere sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/models/cohere/memory.py` to run the agent
+"""
+
+from agno.agent import Agent, AgentMemory
+from agno.memory.db.postgres import PgMemoryDb
+from agno.models.cohere import Cohere
+from agno.storage.agent.postgres import PostgresAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+agent = Agent(
+    model=Cohere(id="command-r-08-2024"),
+    # 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=PostgresAgentStorage(
+        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)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+
+# Ask about the conversation
+agent.print_response(
+    "What have we been talking about, do you know my name?", stream=True
+)

cookbook/models/mistral/README.md

@@ -49,3 +49,9 @@ python cookbook/models/mistral/tool_use.py
 ```shell
 python cookbook/models/mistral/structured_output.py
 ```
+
+### 7. Run Agent that uses memory
+
+```shell
+python cookbook/models/mistral/memory.py
+```

cookbook/models/mistral/basic.py

@@ -1,15 +1,9 @@
-import os
-
 from agno.agent import Agent, RunResponse  # noqa
 from agno.models.mistral import MistralChat
 
-mistral_api_key = os.getenv("MISTRAL_API_KEY")
 
 agent = Agent(
-    model=MistralChat(
-        id="mistral-large-latest",
-        api_key=mistral_api_key,
-    ),
+    model=MistralChat(id="mistral-large-latest"),
     markdown=True,
 )
 

cookbook/models/mistral/memory.py

@@ -0,0 +1,43 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
+2. Run: `pip install mistralai sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/models/mistral/memory.py` to run the agent
+"""
+
+from agno.agent import Agent, AgentMemory
+from agno.memory.db.postgres import PgMemoryDb
+from agno.models.mistral.mistral import MistralChat
+from agno.storage.agent.postgres import PostgresAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+agent = Agent(
+    model=MistralChat(id="mistral-large-latest"),
+    # 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=PostgresAgentStorage(
+        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)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+
+# Ask about the conversation
+agent.print_response(
+    "What have we been talking about, do you know my name?", stream=True
+)

cookbook/models/mistral/mistral_small.py

@@ -10,4 +10,4 @@
     show_tool_calls=True,
     markdown=True,
 )
-agent.print_response("Whats happening in France?", stream=True)
+agent.print_response("Tell me about mistrall small, any news", stream=True)

cookbook/models/ollama/README.md

@@ -63,7 +63,13 @@ python cookbook/models/ollama/storage.py
 python cookbook/models/ollama/knowledge.py
 ```
 
-### 9. Run Agent that interprets an image
+### 9. Run Agent that uses memory   
+
+```shell
+python cookbook/models/ollama/memory.py
+```
+
+### 10. Run Agent that interprets an image
 
 Pull the llama3.2 vision model
 
@@ -75,14 +81,13 @@ ollama pull llama3.2-vision
 python cookbook/models/ollama/image_agent.py
 ```
 
-### 10. Run Agent that manually sets the Ollama client
+### 11. Run Agent that manually sets the Ollama client
 
 ```shell
 python cookbook/models/ollama/set_client.py
 ```
 
-
-### 11. See demos of some well-known Ollama models
+### 12. See demos of some well-known Ollama models
 
 ```shell
 python cookbook/models/ollama/demo_deepseek_r1.py

cookbook/models/ollama/memory.py

@@ -0,0 +1,43 @@
+"""
+This recipe shows how to use personalized memories and summaries in an agent.
+Steps:
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
+2. Run: `pip install ollama sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
+3. Run: `python cookbook/models/ollama/memory.py` to run the agent
+"""
+
+from agno.agent import Agent, AgentMemory
+from agno.memory.db.postgres import PgMemoryDb
+from agno.models.ollama.chat import Ollama
+from agno.storage.agent.postgres import PostgresAgentStorage
+
+db_url = "postgresql+psycopg://ai:ai@localhost:5532/ai"
+agent = Agent(
+    model=Ollama(id="qwen2.5:latest"),
+    # 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=PostgresAgentStorage(
+        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)
+
+# -*- Share personal information
+agent.print_response("I live in nyc?", stream=True)
+
+# -*- Share personal information
+agent.print_response("I'm going to a concert tomorrow?", stream=True)
+
+# Ask about the conversation
+agent.print_response(
+    "What have we been talking about, do you know my name?", stream=True
+)

cookbook/models/openai/memory.py

@@ -1,7 +1,7 @@
 """
 This recipe shows how to use personalized memories and summaries in an agent.
 Steps:
-1. Run: `./cookbook/run_pgvector.sh` to start a postgres container with pgvector
+1. Run: `./cookbook/scripts/run_pgvector.sh` to start a postgres container with pgvector
 2. Run: `pip install openai sqlalchemy 'psycopg[binary]' pgvector` to install the dependencies
 3. Run: `python cookbook/agents/personalized_memories_and_summaries.py` to run the agent
 """