Release v0.9.0

ai_adapter.py

@@ -1,15 +1,18 @@
+import json
 from azure.ai.inference.models import SystemMessage, UserMessage
 from alkemio_virtual_contributor_engine.events.response import Response
 from alkemio_virtual_contributor_engine.events.input import Input
+from create_context import create_context
 from logger import setup_logger
-from logger import setup_logger
-from models import get_retriever, invoke_model
+from models import invoke_model
 from alkemio_virtual_contributor_engine.utils import (
-    combine_documents,
-    get_language_by_code,
     history_as_text,
 )
-from prompts import chat_system_prompt, condense_prompt
+from prompts import (
+    condense_prompt,
+    bok_system_prompt,
+    response_system_prompt,
+)
 from config import env
 
 logger = setup_logger(__name__)
@@ -60,32 +63,36 @@ async def query_chain(input: Input) -> Response:
     else:
         logger.info("No history to handle, initial interaction")
 
-    documents = get_retriever().invoke(message)
-    logger.info("Context retrieved.")
-    logger.debug(f"Context is {documents}")
-    context = combine_documents(documents)
+    documents, context = create_context(message)
 
     messages = [
-        SystemMessage(
-            content=chat_system_prompt.format(
-                context=context, language=get_language_by_code(input.language)
-            )
-        ),
+        SystemMessage(content=bok_system_prompt.format(knowledge=context)),
+        SystemMessage(content=response_system_prompt.format(context=context)),
         UserMessage(content=message),
     ]
 
     logger.info("Invoking LLM.")
-    result = invoke_model(messages)
+    response = json.loads(invoke_model(messages))
     logger.info("LLM invocation completed.")
-    logger.info(f"LLM message is: {result}")
+    logger.info(f"LLM message is: {response}")
+
+    sources = []
+    for index, metadata in enumerate(documents["metadatas"][0]):
+        index = str(index)
+        if (
+            {"uri": metadata["source"]} not in sources
+            and index in response["source_scores"]
+            and response["source_scores"][index] > 0
+        ):
+            sources.append({"uri": metadata["source"]})
 
     return Response(
         {
-            "result": result,
-            "original_result": result,
+            "result": response["result"],
+            "original_result": response["result"],
             "human_language": input.language,
             "result_language": input.language,
             "knowledge_language": "en",
-            "sources": [{"uri": document.metadata["source"]} for document in documents],
+            "sources": sources,
         }
     )

config.py

@@ -11,6 +11,8 @@
 class Env:
     model_name: str
     embeddings_model_name: str
+    openai_api_key: str
+    openai_endpoint: str
     openai_api_version: str
     site_url: str
     welcome_site_url: str
@@ -41,6 +43,8 @@ def __init__(self):
 
         self.model_name = os.getenv("LLM_DEPLOYMENT_NAME", "")
         self.embeddings_model_name = os.getenv("EMBEDDINGS_DEPLOYMENT_NAME", "")
+        self.openai_api_key = os.getenv("AZURE_OPENAI_API_KEY", "")
+        self.openai_endpoint = os.getenv("AZURE_OPENAI_ENDPOINT", "")
         self.openai_api_version = os.getenv("OPENAI_API_VERSION", "")
 
         self.site_url = os.getenv("AI_SOURCE_WEBSITE", "")

create_context.py

@@ -0,0 +1,57 @@
+from chromadb.api.types import IncludeEnum
+from models import embed_func
+from logger import setup_logger
+
+from alkemio_virtual_contributor_engine.chromadb_client import chromadb_client
+
+
+logger = setup_logger(__name__)
+
+
+def combine_documents(docs, document_separator="\n\n"):
+    chunks_array = []
+    for index, document in enumerate(docs["documents"][0]):
+        chunks_array.append(f"[source:{index}] {document}")
+
+    return document_separator.join(chunks_array)
+
+
+def get_documents(message: str):
+
+    collections = [
+        "alkem.io-knowledge",
+        "welcome.alkem.io-knowledge",
+        "www.alkemio.org-knowledge",
+    ]
+    result = {"documents": [[]], "metadatas": [[]], "distances": [[]]}
+
+    for collection in collections:
+        collection = chromadb_client.get_collection(
+            collection, embedding_function=embed_func
+        )
+        tmp_result = collection.query(
+            query_texts=[message],
+            include=[
+                IncludeEnum.documents,
+                IncludeEnum.metadatas,
+                IncludeEnum.distances,
+            ],
+            n_results=3,
+        )
+        if (
+            tmp_result
+            and tmp_result["documents"]
+            and tmp_result["distances"]
+            and tmp_result["metadatas"]
+        ):
+            result["distances"][0] += tmp_result["distances"][0]
+            result["documents"][0] += tmp_result["documents"][0]
+            result["metadatas"][0] += tmp_result["metadatas"][0]
+    return result
+
+
+def create_context(message):
+    documents = get_documents(message)
+    logger.info("Context retrieved.")
+    logger.debug(f"Context is {documents}")
+    return documents, combine_documents(documents)

main.py

@@ -1,35 +1,25 @@
-import ai_adapter
 import asyncio
+
+import ai_adapter
 from alkemio_virtual_contributor_engine.alkemio_vc_engine import (
     AlkemioVirtualContributorEngine,
 )
-from alkemio_virtual_contributor_engine.events.input import Input, InvocationOperation
+from alkemio_virtual_contributor_engine.events import Input
 from alkemio_virtual_contributor_engine.events.response import Response
 
-from ingest import ensure_ingested
 from logger import setup_logger
 
 logger = setup_logger(__name__)
 
-# Lock to prevent multiple ingestions from happening at the same time
-ingestion_lock = asyncio.Lock()
+engine = AlkemioVirtualContributorEngine()
 
 
 async def query(input: Input) -> Response:
     logger.info("Query method invoked.")
-    if input.operation is InvocationOperation.INGEST:
-        logger.info("Operation is INGEST.")
-        async with ingestion_lock:
-            await ensure_ingested(True)
-            # return empty response - we can extend this to give valid feedback
-            return Response()
-
-    logger.info("Operation is QUERY.")
     result = await ai_adapter.invoke(input)
     logger.info("Query method completed.")
     return result
 
 
-engine = AlkemioVirtualContributorEngine()
 engine.register_handler(query)
 asyncio.run(engine.start())

manifests/25-virtual-contributor-engine-guidance-deployment-dev.yaml

@@ -40,6 +40,8 @@ spec:
                 secretKeyRef:
                   name: alkemio-rabbitmq-cluster-default-user
                   key: password
+            - name: RABBITMQ_QUEUE
+              value: virtual-contributor-engine-guidance
           envFrom:
             - secretRef:
                 name: alkemio-secrets