Bedrock, Cohere, and Mixed Support (#6)

* Added Cohere support
* Tweaks to rag prompt
* Bedrock support
* Version to 0.5.7

trustgraph/model/text_completion/cohere/llm.py

@@ -0,0 +1,117 @@
+
+"""
+Simple LLM service, performs text prompt completion using Cohere.
+Input is prompt, output is response.
+"""
+
+import cohere
+import re
+
+from .... schema import TextCompletionRequest, TextCompletionResponse
+from .... schema import text_completion_request_queue
+from .... schema import text_completion_response_queue
+from .... log_level import LogLevel
+from .... base import ConsumerProducer
+
+module = ".".join(__name__.split(".")[1:-1])
+
+default_input_queue = text_completion_request_queue
+default_output_queue = text_completion_response_queue
+default_subscriber = module
+default_model = 'c4ai-aya-23-8b'
+
+class Processor(ConsumerProducer):
+
+    def __init__(self, **params):
+    
+        input_queue = params.get("input_queue", default_input_queue)
+        output_queue = params.get("output_queue", default_output_queue)
+        subscriber = params.get("subscriber", default_subscriber)
+        model = params.get("model", default_model)
+        api_key = params.get("api_key")
+
+        super(Processor, self).__init__(
+            **params | {
+                "input_queue": input_queue,
+                "output_queue": output_queue,
+                "subscriber": subscriber,
+                "input_schema": TextCompletionRequest,
+                "output_schema": TextCompletionResponse,
+                "model": model,
+            }
+        )
+
+        self.model = model
+
+        self.cohere = cohere.Client(api_key=api_key)
+
+        print("Initialised", flush=True)
+
+    def handle(self, msg):
+
+        v = msg.value()
+
+        # Sender-produced ID
+
+        id = msg.properties()["id"]
+
+        print(f"Handling prompt {id}...", flush=True)
+
+        prompt = v.prompt
+
+        output = self.cohere.chat( 
+        model=self.model,
+        message=prompt,
+        preamble = "You are a helpful AI-assistant."
+        temperature=0.0,
+        chat_history=[],
+        prompt_truncation='auto',
+        connectors=[]
+        ) 
+
+        resp = output.text
+        print(resp, flush=True)
+
+        # Parse output for ```json``` delimiters
+        pattern = r'```json\s*([\s\S]*?)\s*```'
+        match = re.search(pattern, resp)
+
+        if match:
+            # If delimiters are found, extract the JSON content
+            json_content = match.group(1)
+            json_resp = json_content.strip()
+        
+        else:
+            # If no delimiters are found, return the original text
+            json_resp = resp.strip()
+        
+        print("Send response...", flush=True)
+        r = TextCompletionResponse(response=json_resp)
+        self.send(r, properties={"id": id})
+
+        print("Done.", flush=True)
+
+    @staticmethod
+    def add_args(parser):
+
+        ConsumerProducer.add_args(
+            parser, default_input_queue, default_subscriber,
+            default_output_queue,
+        )
+
+        parser.add_argument(
+            '-m', '--model',
+            default="c4ai-aya-23-8b",
+            help=f'Cohere model (default: c4ai-aya-23-8b)'
+        )
+
+        parser.add_argument(
+            '-k', '--api-key',
+            help=f'Cohere API key'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+
+