LLAMA3.3 support

README.md

@@ -140,7 +140,7 @@ def count_letters(character: str, word: str) -> str:
 # Initialize agent with tools and LLM options
 agent = ChainOfThoughtAgent(
     tools=[count_letters],
-    llm_options=llm_options.LLAMA3_2_VISION
+    llm_options=llm_options.LLAMA3_3
 )
 
 # Optional: Add callback to see all messages

core/just_agents/llm_options.py

@@ -29,6 +29,20 @@ class LLMOptionsBase(ModelOptions, extra="allow"):
     "tools": []
 }
 
+LLAMA3_3: dict[str, Any] = {
+    "model": "groq/llama-3.3-70b-versatile",
+    "api_base": "https://api.groq.com/openai/v1",
+    "temperature": 0.0,
+    "tools": []
+}
+
+LLAMA3_3_specdec: dict[str, Any] = {
+    "model": "groq/llama-3.3-70b-specdec",
+    "api_base": "https://api.groq.com/openai/v1",
+    "temperature": 0.0,
+    "tools": []
+}
+
 
 MISTRAL_8x22B = {
     "model": "mistral/open-mixtral-8x22b",

core/just_agents/patterns/chain_of_throught.py

@@ -32,9 +32,11 @@ class ChainOfThoughtAgent(BaseAgent, IThinkingAgent[SupportedMessages, Supported
 You are an expert AI assistant that explains your reasoning step by step. 
   For each step, provide a title that describes what you're doing in that step, along with the content. 
   Decide if you need another step or if you're ready to give the final answer. 
-  Respond in JSON format with 'title', 'content', and 'next_action' (either 'continue' or 'final_answer') keys. 
-  Make sure you send only one JSON step object. You response should be a valid JSON object. In the JSON use Use Triple Quotes for Multi-line Strings.
-  USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 3. 
+  Respond in JSON format with "title", "content", and "next_action" (either "continue" or "final_answer") keys. 
+  Make sure you send only one JSON step object AND NOTHING ELSE. 
+  You response should be a valid JSON object. 
+  In the JSON use Use Triple Quotes for Multi-line Strings.
+  USE AS MANY REASONING STEPS AS POSSIBLE. AT LEAST 2. 
   BE AWARE OF YOUR LIMITATIONS AS AN LLM AND WHAT YOU CAN AND CANNOT DO. 
   IN YOUR REASONING, INCLUDE EXPLORATION OF ALTERNATIVE ANSWERS. 
   CONSIDER YOU MAY BE WRONG, AND IF YOU ARE WRONG IN YOUR REASONING, WHERE IT WOULD BE. 
@@ -43,12 +45,11 @@ class ChainOfThoughtAgent(BaseAgent, IThinkingAgent[SupportedMessages, Supported
   DO NOT JUST SAY YOU ARE RE-EXAMINING. USE AT LEAST 3 METHODS TO DERIVE THE ANSWER. USE BEST PRACTICES.
   
               Example of a valid JSON response:
-              ```json
               {
                   "title": "Identifying Key Information",
                   "content": "To begin solving this problem, we need to carefully examine the given information and identify the crucial elements that will guide our solution process. This involves...",
                   "next_action": "continue"
-              }```
+              }
 """
 
 

core/just_agents/patterns/interfaces/IThinkingAgent.py

@@ -31,6 +31,6 @@ def think(self,
         thought = self.thought_query(query, **kwargs) #queries itself with thought as expected output
         new_chain = [*current_chain, thought] #updates chain with the new thought
         if thought.is_final() or max_iter <= 0:
-            return (self.thought_query(query, **kwargs), new_chain) #returns the final thought and the chain that preceded it
+            return (thought, new_chain) #returns the final thought and the chain that preceded it
         else:
             return self.think(query, max_iter - 1, new_chain, **kwargs) #continues the thought process

examples/just_agents/examples/basic/agent_profiles.yaml

@@ -3,7 +3,7 @@ agent_profiles:
     class_qualname: just_agents.patterns.chain_of_throught.ChainOfThoughtAgent
     llm_options:
       api_base: https://api.groq.com/openai/v1
-      model: groq/llama-3.2-90b-vision-preview
+      model: groq/llama-3.3-70b-versatile
       temperature: 0.0
       tool_choice: auto
       tools: []
@@ -35,7 +35,7 @@ agent_profiles:
     class_qualname: just_agents.base_agent.BaseAgent
     llm_options:
       api_base: https://api.groq.com/openai/v1
-      model: groq/llama-3.2-90b-vision-preview
+      model: groq/llama-3.3-70b-versatile
       temperature: 0.0
       tool_choice: auto
       tools: []

examples/just_agents/examples/basic/agent_serialization.py

@@ -20,7 +20,7 @@
     config_path = basic_examples_dir / "agent_profiles.yaml"
 
     created_agent = BaseAgent(
-        llm_options=llm_options.LLAMA3_2_VISION,
+        llm_options=llm_options.LLAMA3_3,
         config_path=config_path,
         tools=[mock_get_current_weather]
     )

examples/just_agents/examples/basic/chain_of_thought_example.py

@@ -25,7 +25,7 @@
     # - LLAMA 3.2 Vision as the language model
     agent: ChainOfThoughtAgent = ChainOfThoughtAgent(  # type: ignore
         tools=tools,
-        llm_options=llm_options.LLAMA3_2_VISION
+        llm_options=llm_options.LLAMA3_3
     )
 
     # Add a callback to print all messages that the agent processes

examples/just_agents/examples/basic/function_calling_example.py

@@ -34,7 +34,7 @@ def get_current_weather(location: str):
     # 1. LLAMA3_2_VISION as the language model
     # 2. get_current_weather function as an available tool
     agent = BaseAgent(  # type: ignore
-        llm_options=llm_options.LLAMA3_2_VISION,
+        llm_options=llm_options.LLAMA3_3,
         tools=[get_current_weather]
     )
 

examples/notebooks/01_just_agents_colab.ipynb

@@ -322,7 +322,7 @@
       "source": [
         "from just_agents import llm_options\n",
         "\n",
-        "llm_options: dict = llm_options.LLAMA3_2_VISION\n",
+        "llm_options: dict = llm_options.LLAMA3_3\n",
         "\n",
         "\"\"\"\n",
         "llm_options here are just dictionary of the following form:\n",
@@ -546,7 +546,7 @@
         "  return \"Weather is snowy, there were numerous frozen lakes\"\n",
         "\n",
         "session: BaseAgent = BaseAgent(\n",
-        "    llm_options=llm_options.LLAMA3_2_VISION,\n",
+        "    llm_options=llm_options.LLAMA3_3,\n",
         "    tools=[get_weather_by_city]\n",
         "    #if you rename the function, then do not forget to change here\n",
         ")\n",
@@ -732,7 +732,7 @@
         "from just_agents import llm_options\n",
         "\n",
         "session: BaseAgent = BaseAgent(\n",
-        "    llm_options=llm_options.LLAMA3_2_VISION,\n",
+        "    llm_options=llm_options.LLAMA3_3,\n",
         "    tools=[get_weather_by_city]\n",
         "    #if you rename the function, then do not forget to change here\n",
         ")\n",
@@ -952,7 +952,7 @@
       "source": [
         "from just_agents import llm_options\n",
         "\n",
-        "kamala_options = llm_options.LLAMA3_2_VISION\n",
+        "kamala_options = llm_options.LLAMA3_3\n",
         "trump_options: dict = {\n",
         "    \"model\": \"groq/mixtral-8x7b-32768\",\n",
         "    \"api_base\": \"https://api.groq.com/openai/v1\",\n",

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "just-agents"
-version = "0.4.2"
+version = "0.4.3"
 description = "Just Agents - A lightweight, straightforward library for LLM agents that focuses on simplicity over unnecessary abstractions."
 authors = [
     "Alex Karmazin <karmazinalex@gmail.com>",
@@ -27,12 +27,12 @@ just-agents-router = { path = "router", develop = true }
 just-agents-examples = { path = "examples", develop = true }
 
 [tool.poetry.group.publish.dependencies]
-just-agents-core = "0.4.2"
-just-agents-tools = "0.4.2"
-just-agents-coding = "0.4.2"
-just-agents-web = "0.4.2"
-just-agents-router = "0.4.2"
-just-agents-examples = "0.4.2"
+just-agents-core = "0.4.3"
+just-agents-tools = "0.4.3"
+just-agents-coding = "0.4.3"
+just-agents-web = "0.4.3"
+just-agents-router = "0.4.3"
+just-agents-examples = "0.4.3"
 
 [tool.poetry.group.dev.dependencies]
 pytest = "^7.4.4"

tests/test_chain_of_thought.py

@@ -15,7 +15,7 @@ def count_letters(character:str, word:str) -> str:
 def test_function_query():
     load_dotenv(override = True)
 
-    llm_options = just_agents.llm_options.OPENAI_GPT4oMINI.copy()
+    llm_options = just_agents.llm_options.LLAMA3_3
     agent: ChainOfThoughtAgent = ChainOfThoughtAgent(llm_options=llm_options, tools=[count_letters])
     result, thoughts = agent.think("Count the number of occurrences of the letter 'L' in the word - 'LOLLAPALOOZA'.")
     print("Thoughts: ", thoughts)

tests/test_session.py

@@ -25,7 +25,7 @@ def get_current_weather(location: str):
 def test_sync_function_calling():
     load_dotenv(override=True)
     session: LLMSession = LLMSession(
-        llm_options=llm_options.LLAMA3_2_VISION,
+        llm_options=llm_options.LLAMA3_3,
         tools=[get_current_weather]
     )
     result = session.query("What's the weather like in San Francisco, Tokyo, and Paris?")
@@ -40,7 +40,7 @@ async def process_stream(async_generator):
 def test_stream_function_calling():
     load_dotenv(override=True)
     session: LLMSession = LLMSession(
-        llm_options=llm_options.LLAMA3_2_VISION,
+        llm_options=llm_options.LLAMA3_3,
         tools=[get_current_weather]
     )
     stream = session.stream("What's the weather like in San Francisco, Tokyo, and Paris?")
@@ -55,7 +55,7 @@ def test_stream_function_calling():
 def test_stream_genetics_function_calling():
     load_dotenv(override=True)
     session: LLMSession = LLMSession(
-        llm_options=llm_options.LLAMA3_2_VISION,
+        llm_options=llm_options.LLAMA3_3,
         tools=[hybrid_search, rsid_lookup, gene_lookup, pathway_lookup, disease_lookup, sequencing_info, clinical_trails_full_trial]
     )
     stream = session.stream("What is the influence of different alleles in rs10937739?")