Bedrock, Cohere, and Mixed Support (#6)

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

trustgraph/graph_rag.py

@@ -228,16 +228,13 @@ class GraphRag:
 
         kg =  self.get_cypher(query)
 
-        prompt=f"""<instructions>Study the knowledge graph provided, and use
-the information to answer the question.  The question should be answered
-in plain English only.
-</instructions>
-<knowledge-graph>
+        prompt=f"""Study the following set of knowledge statements. The statements are written in Cypher format that has been extracted from a knowledge graph. Use only the provided set of knowledge statements in your response. Do not speculate if the answer is not found in the provided set of knowledge statements.
+
+Here's the knowledge statements:
 {kg}
-</knowledge-graph>
-<question>
+
+Use only the provided knowledge statements to respond to the following:
 {query}
-</question>
 """
 
         return prompt

trustgraph/model/text_completion/bedrock/__init__.py

@@ -0,0 +1,3 @@
+
+from . llm import *
+

trustgraph/model/text_completion/bedrock/__main__.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+
+from . llm import run
+
+if __name__ == '__main__':
+    run()
+

trustgraph/model/text_completion/bedrock/llm.py

@@ -0,0 +1,159 @@
+
+"""
+Simple LLM service, performs text prompt completion using AWS Bedrock.
+Input is prompt, output is response. Mistral is default.
+"""
+
+import boto3
+import json
+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 = 'mistral.mistral-large-2407-v1:0'
+default_region = 'us-west-2'
+
+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)
+        aws_id = params.get("aws_id_key")
+        aws_secret = params.get("aws_secret")
+        aws_region = params.get("aws_region", default_region)
+
+        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.session = boto3.Session(
+            aws_access_key_id=aws_id,
+            aws_secret_access_key=aws_secret,
+            region_name=aws_region
+        )
+
+        self.bedrock = self.session.client(service_name='bedrock-runtime')
+
+        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
+
+        promptbody = json.dumps({
+            "prompt": prompt,
+            "max_tokens": 8192,
+            "temperature": 0.0,
+            "top_p": 0.99,
+            "top_k": 40
+        })
+
+        accept = 'application/json'
+        contentType = 'application/json'
+
+        response = self.bedrock.invoke_model(body=promptbody, modelId=self.model, accept=accept, contentType=contentType)
+        
+        # Mistral Response Structure
+        if self.model.startswith("mistral"):
+            response_body = json.loads(response.get("body").read())
+            outputtext = response_body['outputs'][0]['text']
+
+        # Claude Response Structure
+        elif self.model.startswith("anthropic"):
+            model_response = json.loads(response["body"].read())
+            outputtext = model_response['content'][0]['text']
+
+        # Llama 3.1 Response Structure
+        elif self.model.startswith("meta"):
+            model_response = json.loads(response["body"].read())
+            outputtext = model_response["generation"]
+
+        # Use Mistral as default
+        else:
+            response_body = json.loads(response.get("body").read())
+            outputtext = response_body['outputs'][0]['text']            
+ 
+        print(outputtext, flush=True)
+       
+        # Parse output for ```json``` delimiters
+        pattern = r'```json\s*([\s\S]*?)\s*```'
+        match = re.search(pattern, outputtext)
+
+        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 = outputtext.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="mistral.mistral-large-2407-v1:0",
+            help=f'Bedrock model (default: Mistral-Large-2407)'
+        )
+
+        parser.add_argument(
+            '-z', '--aws-id-key',
+            help=f'AWS ID Key'
+        )
+
+        parser.add_argument(
+            '-k', '--aws-secret',
+            help=f'AWS Secret Key'
+        )
+
+        parser.add_argument(
+            '-r', '--aws-region',
+            help=f'AWS Region (default: us-west-2)'
+        )
+
+def run():
+
+    Processor.start(module, __doc__)
+
+    

trustgraph/model/text_completion/cohere/__init__.py

@@ -0,0 +1,3 @@
+
+from . llm import *
+

trustgraph/model/text_completion/cohere/__main__.py

@@ -0,0 +1,7 @@
+#!/usr/bin/env python3
+
+from . llm import run
+
+if __name__ == '__main__':
+    run()
+

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__)
+
+