agent framework for bedrock

aws/bedrock2.py

@@ -44,11 +44,14 @@
     "max_tokens": 512,
     "temperature": 0.5,
     "messages": [
-
         {
             "role": "user",
             "content": [{"type": "text", "text": "Tell me about AWS Bedrock"}],
-        }
+        },
+        {
+            "role": "assistant",
+            "content": [{"type": "text", "text": "Bedrock is an AI Platform"}],
+        },
 
     ],
 }

aws/react-bedrock2.py

@@ -0,0 +1,135 @@
+# 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
+
+from botocore.exceptions import ClientError
+
+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 = []
+    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):
+        native_request = {
+            "system": self.system,
+            "anthropic_version": "bedrock-2023-05-31",
+            "max_tokens": 512,
+            "temperature": 0.5,
+            "messages": self.messages,
+        }
+
+
+        # Convert the native request to JSON.
+        request = json.dumps(native_request)
+
+        model_id = "anthropic.claude-v2"
+
+
+        try:
+            # Invoke the model with the request.
+            response = self.client.invoke_model(modelId=model_id, body=request)
+
+        except (ClientError, Exception) as e:
+            print(f"ERROR: Can't invoke '{model_id}'. Reason: {e}")
+            exit(1)
+
+        # Decode the response body.
+        model_response = json.loads(response["body"].read())
+
+        # Extract and print the response text.
+        response_text = model_response["content"][0]["text"]
+        return response_text
+
+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")