GML-2015 Add UI page for configurations, Role base access

common/embeddings/embedding_services.py

@@ -184,9 +184,9 @@ class VertexAI_PaLM_Embedding(EmbeddingModel):
 
     def __init__(self, config):
         super().__init__(config, model_name=config.get("model_name", "VertexAI PaLM"))
-        from langchain.embeddings import VertexAIEmbeddings
+        from langchain_google_vertexai import VertexAIEmbeddings
 
-        self.embeddings = VertexAIEmbeddings(model_name=self.model_name)
+        self.embeddings = VertexAIEmbeddings(model=self.model_name)
 
 
 class GenAI_Embedding(EmbeddingModel):
@@ -243,3 +243,4 @@ def __init__(self, config):
             model=self.model_name,
             base_url=base_url
         )
+

common/llm_services/google_vertexai_service.py

@@ -9,11 +9,11 @@
 class GoogleVertexAI(LLM_Model):
     def __init__(self, config):
         super().__init__(config)
-        from langchain_community.llms import VertexAI
+        from langchain_google_vertexai import VertexAI
 
         model_name = config["llm_model"]
         self.llm = VertexAI(
-            model_name=model_name, max_output_tokens=1000, **config["model_kwargs"]
+            model=model_name, max_output_tokens=1000, **config["model_kwargs"]
         )
 
         self.prompt_path = config["prompt_path"]
@@ -38,3 +38,4 @@ def entity_relationship_extraction_prompt(self):
     @property
     def model(self):
         return self.llm
+

common/prompts/aws_bedrock_claude3haiku/community_summarization.txt

@@ -0,0 +1,11 @@
+You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
+Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
+Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
+If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
+Make sure it is written in third person, and include the entity names so we the have full context.
+
+#######
+-Data-
+Commuinty Title: {entity_name}
+Description List: {description_list}
+

common/prompts/custom/aml/community_summarization.txt

@@ -0,0 +1,11 @@
+You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
+Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
+Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
+If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
+Make sure it is written in third person, and include the entity names so we the have full context.
+
+#######
+-Data-
+Commuinty Title: {entity_name}
+Description List: {description_list}
+

common/prompts/gcp_vertexai_palm/community_summarization.txt

@@ -0,0 +1,11 @@
+You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
+Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
+Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
+If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
+Make sure it is written in third person, and include the entity names so we the have full context.
+
+#######
+-Data-
+Commuinty Title: {entity_name}
+Description List: {description_list}
+

common/prompts/google_gemini/community_summarization.txt

@@ -0,0 +1,11 @@
+You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
+Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
+Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
+If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
+Make sure it is written in third person, and include the entity names so we the have full context.
+
+#######
+-Data-
+Commuinty Title: {entity_name}
+Description List: {description_list}
+

common/prompts/openai_gpt4/community_summarization.txt

@@ -0,0 +1,11 @@
+You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
+Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
+Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
+If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
+Make sure it is written in third person, and include the entity names so we the have full context.
+
+#######
+-Data-
+Commuinty Title: {entity_name}
+Description List: {description_list}
+

common/requirements.txt

@@ -80,6 +80,7 @@ kiwisolver==1.4.8
 langchain>=0.3.26
 langchain-core>=0.3.26
 langchain_google_genai==2.1.8
+langchain-google-vertexai==2.1.2
 langchain-community==0.3.26
 langchain-experimental==0.3.5rc1
 langchain-groq==0.3.4

docs/tutorials/configs/nginx.conf

@@ -14,6 +14,16 @@ server {
     proxy_pass http://graphrag-ui:3000/;
   }
 
+  location /setup {
+    rewrite ^/setup$ / break;
+    proxy_pass http://graphrag-ui:3000;
+  }
+
+  location /setup/ {
+    rewrite ^/setup/.*$ / break;
+    proxy_pass http://graphrag-ui:3000;
+  }
+
 
   location /chat-dialog {
     proxy_pass http://graphrag-ui:3000/;

ecc/app/ecc_util.py

@@ -1,5 +1,5 @@
 from common.chunkers import character_chunker, regex_chunker, semantic_chunker, markdown_chunker, recursive_chunker, html_chunker, single_chunker
-from common.config import graphrag_config, embedding_service, llm_config
+from common.config import graphrag_config, embedding_service, llm_config, get_completion_config
 from common.llm_services import (
     AWS_SageMaker_Endpoint,
     AWSBedrock,
@@ -55,24 +55,27 @@ def get_chunker(chunker_type: str = ""):
     return chunker
 
 
-def get_llm_service():
-    if llm_config["completion_service"]["llm_service"].lower() == "openai":
-        llm_provider = OpenAI(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "azure":
-        llm_provider = AzureOpenAI(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "sagemaker":
-        llm_provider = AWS_SageMaker_Endpoint(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "vertexai":
-        llm_provider = GoogleVertexAI(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "genai":
-        llm_provider = GoogleGenAI(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "bedrock":
-        llm_provider = AWSBedrock(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "groq":
-        llm_provider = Groq(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "ollama":
-        llm_provider = Ollama(llm_config["completion_service"])
-    elif llm_config["completion_service"]["llm_service"].lower() == "huggingface":
-        llm_provider = HuggingFaceEndpoint(llm_config["completion_service"])
+def get_llm_service(graphname=None):
+    config = get_completion_config(graphname)
+    if config["llm_service"].lower() == "openai":
+        llm_provider = OpenAI(config)
+    elif config["llm_service"].lower() == "azure":
+        llm_provider = AzureOpenAI(config)
+    elif config["llm_service"].lower() == "sagemaker":
+        llm_provider = AWS_SageMaker_Endpoint(config)
+    elif config["llm_service"].lower() == "vertexai":
+        llm_provider = GoogleVertexAI(config)
+    elif config["llm_service"].lower() == "genai":
+        llm_provider = GoogleGenAI(config)
+    elif config["llm_service"].lower() == "bedrock":
+        llm_provider = AWSBedrock(config)
+    elif config["llm_service"].lower() == "groq":
+        llm_provider = Groq(config)
+    elif config["llm_service"].lower() == "ollama":
+        llm_provider = Ollama(config)
+    elif config["llm_service"].lower() == "huggingface":
+        llm_provider = HuggingFaceEndpoint(config)
+    else:
+        raise Exception("LLM Completion Service Not Supported")
 
     return llm_provider

ecc/app/graphrag/community_summarizer.py

@@ -12,26 +12,45 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 
+import os
 import re
+import logging
 
 from langchain_core.prompts import PromptTemplate
 
 from common.llm_services import LLM_Model
 from common.py_schemas import CommunitySummary
+from common.config import completion_config
+
+logger = logging.getLogger(__name__)
+
+
+# Load prompt from file
+def load_community_prompt():
+    prompt_path = completion_config.get("prompt_path", "./common/prompts/openai_gpt4/")
+    if prompt_path.startswith("./"):
+        prompt_path = prompt_path[2:]
+    prompt_path = prompt_path.rstrip("/")
+
+    prompt_file = os.path.join(prompt_path, "community_summarization.txt")
+    if not os.path.exists(prompt_file):
+        error_msg = f"Community summarization prompt file not found: {prompt_file}. Please ensure the file exists in the configured prompt path."
+        logger.error(error_msg)
+        raise FileNotFoundError(error_msg)
+
+    try:
+        with open(prompt_file, "r", encoding="utf-8") as f:
+            content = f.read()
+            logger.info(f"Successfully loaded community summarization prompt from: {prompt_file}")
+            return content
+    except Exception as e:
+        error_msg = f"Failed to read community summarization prompt from {prompt_file}: {str(e)}"
+        logger.error(error_msg)
+        raise Exception(error_msg)
+
 
 # src: https://github.com/microsoft/graphrag/blob/main/graphrag/index/graph/extractors/summarize/prompts.py
-SUMMARIZE_PROMPT = PromptTemplate.from_template("""
-You are a helpful assistant responsible for generating a comprehensive summary of the data provided below.
-Given one or two entities, and a list of descriptions, all related to the same entity or group of entities.
-Please concatenate all of these into a single, comprehensive description. Make sure to include information collected from all the descriptions.
-If the provided descriptions are contradictory, please resolve the contradictions and provide a single, coherent summary, but do not add any information that is not in the description.
-Make sure it is written in third person, and include the entity names so we the have full context.
-
-#######
--Data-
-Commuinty Title: {entity_name}
-Description List: {description_list}
-""")
+SUMMARIZE_PROMPT = PromptTemplate.from_template(load_community_prompt())
 
 id_pat = re.compile(r"[_\d]*")
 
@@ -58,4 +77,4 @@ async def summarize(self, name: str, text: list[str]) -> CommunitySummary:
             )
         except Exception as e:
             return {"error": True, "summary": "", "message": str(e)}
-        return {"error": False, "summary": summary.summary}
+        return {"error": False, "summary": summary.summary}

ecc/app/graphrag/workers.py

@@ -440,7 +440,7 @@ async def process_community(
         if len(children) == 1:
             summary = children[0]
         else:
-            llm = ecc_util.get_llm_service()
+            llm = ecc_util.get_llm_service(conn.graphname)
             summarizer = community_summarizer.CommunitySummarizer(llm)
             summary = await summarizer.summarize(comm_id, children)
             if summary["error"]:

ecc/app/main.py

@@ -36,6 +36,7 @@
     embedding_service,
     get_llm_service,
     llm_config,
+    reload_db_config,
 )
 from common.db.connections import elevate_db_connection_to_token, get_db_connection_id_token
 from common.embeddings.base_embedding_store import EmbeddingStore
@@ -213,6 +214,41 @@ async def run_with_tracking(task_key: str, run_func, graphname: str, conn):
     try:
         running_tasks[task_key] = {"status": "running", "started_at": time.time()}
         LogWriter.info(f"Starting ECC task: {task_key}")
+
+        # Reload config at the start of each job to ensure latest settings are used
+        LogWriter.info("📥 Reloading configuration for new job...")
+        from common.config import reload_llm_config, reload_graphrag_config, reload_db_config
+
+        llm_result = reload_llm_config()
+        if llm_result["status"] == "success":
+            LogWriter.info(f"✅ LLM config reloaded: {llm_result['message']}")
+            completion_service = llm_config.get("completion_service", {})
+            ecc_model = completion_service.get("llm_model", "unknown")
+            ecc_provider = completion_service.get("llm_service", "unknown")
+            LogWriter.info(
+                f"[ECC] Using completion model={ecc_model} (provider={ecc_provider})"
+            )
+        else:
+            LogWriter.warning(f"⚠️ LLM config reload had issues: {llm_result['message']}")
+
+        db_result = reload_db_config()
+        if db_result["status"] == "success":
+            LogWriter.info(
+                f"✅ DB config reloaded: {db_result['message']} "
+                f"(host={db_config.get('hostname')}, "
+                f"restppPort={db_config.get('restppPort')}, "
+                f"gsPort={db_config.get('gsPort')})"
+            )
+        else:
+            LogWriter.warning(f"⚠️ DB config reload had issues: {db_result['message']}")
+
+        graphrag_result = reload_graphrag_config()
+        if graphrag_result["status"] == "success":
+            LogWriter.info(f"✅ GraphRAG config reloaded: {graphrag_result['message']}")
+        else:
+            LogWriter.warning(f"⚠️ GraphRAG config reload had issues: {graphrag_result['message']}")
+
+        # Now run the actual job with fresh config
         await run_func(graphname, conn)
         running_tasks[task_key] = {"status": "completed", "completed_at": time.time()}
         LogWriter.info(f"Completed ECC task: {task_key}")
@@ -242,6 +278,17 @@ def consistency_update(
     response: Response,
     credentials = Depends(auth_credentials),
 ):
+    db_result = reload_db_config()
+    if db_result["status"] == "success":
+        LogWriter.info(
+            f"✅ DB config reloaded: {db_result['message']} "
+            f"(host={db_config.get('hostname')}, "
+            f"restppPort={db_config.get('restppPort')}, "
+            f"gsPort={db_config.get('gsPort')})"
+        )
+    else:
+        LogWriter.warning(f"⚠️ DB config reload had issues: {db_result['message']}")
+
     if isinstance(credentials, HTTPBasicCredentials):
         conn = elevate_db_connection_to_token(
             db_config.get("hostname"),

graphrag-ui/src/components/Bot.tsx

@@ -85,6 +85,7 @@ const Bot = ({ layout, getConversationId }: { layout?: string | undefined, getCo
   const handleSelect = (value) => {
     setSelectedGraph(value);
     localStorage.setItem("selectedGraph", value);
+    window.dispatchEvent(new Event("graphrag:selectedGraph"));
     navigate("/chat");
     //window.location.reload();
   };