b2 (#204)

aws/react-bedrock1.py

@@ -0,0 +1,128 @@
+# This code is Apache 2 licensed:
+# https://www.apache.org/licenses/LICENSE-2.0
+
+from dotenv import load_dotenv
+load_dotenv()
+
+import boto3
+import json
+
+import re
+import httpx
+
+class ChatBot:
+    def __init__(self, system=""):
+        self.client = boto3.client(service_name="bedrock-runtime", region_name="us-west-2")
+        self.system = system
+        self.messages = ""
+        if self.system:
+            # self.messages.append({"role": "system", "content": system})
+            self.messages = f"system: {system}\n"
+
+    def __call__(self, message):
+        self.messages = f"{self.messages}user: {message}\n"
+        print("before call >>>>>>", self.messages)
+        result = self.execute()
+        self.messages = f"{self.messages}assistant: {result}\n"
+        print("after call >>>>>>", self.messages)
+
+        return result
+
+    def execute(self):
+        body = json.dumps({
+            "inputText": self.messages, 
+            "textGenerationConfig":{  
+                "maxTokenCount":70,
+                "stopSequences":[], #define phrases that signal the model to conclude text generation.
+                "temperature":0, #Temperature controls randomness; higher values increase diversity, lower values boost predictability.
+                "topP":0.9 # Top P is a text generation technique, sampling from the most probable tokens in a distribution.
+            }
+        })
+
+        response = self.client.invoke_model(
+            body=body,
+            modelId="amazon.titan-text-express-v1",
+            accept="application/json", 
+            contentType="application/json"
+        )
+
+        response_body = json.loads(response.get('body').read())
+        outputText = response_body.get('results')[0].get('outputText')
+        return outputText
+
+prompt = """
+You run in a loop of Thought, Action, PAUSE, Observation.
+At the end of the loop you output an Answer
+Use Thought to describe your thoughts about the question you have been asked.
+Use Action to run one of the actions available to you - then return PAUSE.
+Observation will be the result of running those actions.
+
+Your available actions are:
+
+calculate:
+e.g. calculate: 4 * 7 / 3
+Runs a calculation and returns the number - uses Python so be sure to use floating point syntax if necessary
+
+wikipedia:
+e.g. wikipedia: LLM
+Returns a summary from searching Wikipedia
+
+Example session:
+
+Question: What is the capital of Hebei?
+Thought: I should look up Hebei on Wikipedia
+Action: wikipedia: Hebei
+PAUSE
+
+You will be called again with this:
+
+Observation: Hebei is a province in China. The capital is Shijiazhuang.
+
+You then output:
+
+Answer: The capital of Hebei is Shijiazhuang
+""".strip()
+
+
+action_re = re.compile('^Action: (\w+): (.*)$')
+
+def query(question, max_turns=3):
+    i = 0
+    bot = ChatBot(prompt)
+    next_prompt = question
+    while i < max_turns:
+        i += 1
+        result = bot(next_prompt)
+        # print(result)
+        actions = [action_re.match(a) for a in result.split('\n') if action_re.match(a)]
+        if actions:
+            # There is an action to run
+            action, action_input = actions[0].groups()
+            if action not in known_actions:
+                raise Exception("Unknown action: {}: {}".format(action, action_input))
+            print(" -- running {} {}".format(action, action_input))
+            observation = known_actions[action](action_input)
+            print("Observation:", observation)
+            next_prompt = "Observation: {}".format(observation)
+        else:
+            return
+
+
+def wikipedia(q):
+    return httpx.get("https://en.wikipedia.org/w/api.php", params={
+        "action": "query",
+        "list": "search",
+        "srsearch": q,
+        "format": "json"
+    }).json()["query"]["search"][0]["snippet"]
+
+
+def calculate(what):
+    return eval(what)
+
+known_actions = {
+    "wikipedia": wikipedia,
+    "calculate": calculate,
+}
+
+query("What is the captical of Hebei")

openai/react-openai.py

@@ -0,0 +1,15 @@
+from dotenv import load_dotenv
+load_dotenv()
+
+from openai import OpenAI
+client = OpenAI()
+
+completion = client.chat.completions.create(
+  model="o1-preview-2024-09-12",
+  messages=[
+    {"role": "system", "content": "You are a poetic assistant, skilled in explaining complex programming concepts with creative flair."},
+    {"role": "user", "content": "Compose a poem that explains the concept of recursion in programming."}
+  ]
+)
+
+print(completion)

react/react1.py

@@ -20,15 +20,14 @@ def __init__(self, system=""):
 
     def __call__(self, message):
         self.messages.append({"role": "user", "content": message})
+        print(self.messages)
         result = self.execute()
         self.messages.append({"role": "assistant", "content": result})
+        print("after call >>>>>> ", self.messages)
         return result
 
     def execute(self):
         completion = self.client.chat.completions.create(model="gpt-3.5-turbo", messages=self.messages)
-        # Uncomment this to print out token usage each time, e.g.
-        # {"completion_tokens": 86, "prompt_tokens": 26, "total_tokens": 112}
-        # print(completion.usage)
         return completion.choices[0].message.content
 
 prompt = """
@@ -74,7 +73,7 @@ def query(question, max_turns=5):
     while i < max_turns:
         i += 1
         result = bot(next_prompt)
-        print(result)
+        # print(result)
         actions = [action_re.match(a) for a in result.split('\n') if action_re.match(a)]
         if actions:
             # There is an action to run