From 848c26ed4139eac3d24fc3bb7a98b2baf5d5e34a Mon Sep 17 00:00:00 2001 From: Prins Kumar Date: Tue, 10 Mar 2026 21:19:07 +0530 Subject: [PATCH 1/3] feat: Configuration pages, role-based access to pages --- common/config.py | 302 +++- common/embeddings/embedding_services.py | 5 +- .../llm_services/google_vertexai_service.py | 5 +- common/requirements.txt | 1 + docs/tutorials/configs/nginx.conf | 10 + ecc/app/common | 1 - ecc/app/configs | 1 - ecc/app/ecc_util.py | 43 +- ecc/app/graphrag/community_summarizer.py | 45 +- ecc/app/graphrag/workers.py | 2 +- ecc/app/main.py | 47 + graphrag-ui/src/components/Bot.tsx | 1 + graphrag-ui/src/components/ModeToggle.tsx | 82 +- graphrag-ui/src/main.tsx | 46 +- .../src/pages/setup/CustomizePrompts.tsx | 298 ++++ graphrag-ui/src/pages/setup/GraphDBConfig.tsx | 437 ++++++ .../src/pages/setup/GraphRAGConfig.tsx | 408 +++++ graphrag-ui/src/pages/setup/IngestGraph.tsx | 1392 +++++++++++++++++ graphrag-ui/src/pages/setup/KGAdmin.tsx | 595 +++++++ graphrag-ui/src/pages/setup/LLMConfig.tsx | 1344 ++++++++++++++++ graphrag-ui/src/pages/setup/SetupLayout.tsx | 260 +++ graphrag/app/agent/agent.py | 45 +- graphrag/app/common | 1 - graphrag/app/configs | 1 - graphrag/app/routers/ui.py | 901 ++++++++++- 25 files changed, 6164 insertions(+), 109 deletions(-) delete mode 120000 ecc/app/common delete mode 120000 ecc/app/configs create mode 100644 graphrag-ui/src/pages/setup/CustomizePrompts.tsx create mode 100644 graphrag-ui/src/pages/setup/GraphDBConfig.tsx create mode 100644 graphrag-ui/src/pages/setup/GraphRAGConfig.tsx create mode 100644 graphrag-ui/src/pages/setup/IngestGraph.tsx create mode 100644 graphrag-ui/src/pages/setup/KGAdmin.tsx create mode 100644 graphrag-ui/src/pages/setup/LLMConfig.tsx create mode 100644 graphrag-ui/src/pages/setup/SetupLayout.tsx delete mode 120000 graphrag/app/common delete mode 120000 graphrag/app/configs diff --git a/common/config.py b/common/config.py index 18a4288..a611594 100644 --- a/common/config.py +++ b/common/config.py @@ -13,9 +13,12 @@ # limitations under the License. import json +import logging import os from fastapi.security import HTTPBasic + +logger = logging.getLogger(__name__) from pyTigerGraph import TigerGraphConnection from common.embeddings.embedding_services import ( @@ -51,6 +54,34 @@ # Configs SERVER_CONFIG = os.getenv("SERVER_CONFIG", "configs/server_config.json") + + +def get_server_config_path(graphname=None): + """Return graph-specific server config path if it exists, else the default.""" + if graphname: + graph_path = f"configs/{graphname}/server_config.json" + if os.path.exists(graph_path): + return graph_path + return SERVER_CONFIG + + +def get_completion_config(graphname=None): + """ + Return completion_service config for the given graph. + Uses configs/{graphname}/server_config.json if it exists, else falls back to default. + Auth credentials always come from the live default config so key rotations propagate. + """ + config_path = get_server_config_path(graphname) + if config_path != SERVER_CONFIG: + logger.info(f"[get_completion_config] graph={graphname} using graph-specific config: {config_path}") + with open(config_path, "r") as f: + graph_config = json.load(f) + graph_completion = graph_config.get("llm_config", {}).get("completion_service", {}).copy() + if "authentication_configuration" in llm_config: + graph_completion["authentication_configuration"] = llm_config["authentication_configuration"] + return graph_completion + logger.info(f"[get_completion_config] graph={graphname} using default config") + return llm_config["completion_service"].copy() PATH_PREFIX = os.getenv("PATH_PREFIX", "") PRODUCTION = os.getenv("PRODUCTION", "false").lower() == "true" @@ -83,13 +114,25 @@ if llm_config is None: raise Exception("llm_config is not found in SERVER_CONFIG") +# Inject authentication_configuration into service configs so they have everything they need +if "authentication_configuration" in llm_config: + if "completion_service" in llm_config: + llm_config["completion_service"]["authentication_configuration"] = llm_config["authentication_configuration"] + if "embedding_service" in llm_config: + llm_config["embedding_service"]["authentication_configuration"] = llm_config["authentication_configuration"] + if "multimodal_service" in llm_config: + llm_config["multimodal_service"]["authentication_configuration"] = llm_config["authentication_configuration"] + completion_config = llm_config.get("completion_service") if completion_config is None: raise Exception("completion_service is not found in llm_config") -if "llm_service" not in completion_config: - raise Exception("llm_service is not found in completion_service") -if "llm_model" not in completion_config: - raise Exception("llm_model is not found in completion_service") + +# Log which model will be used for chatbot and ECC/GraphRAG +if "chatbot_llm" in completion_config: + logger.info(f"[CHATBOT] Using chatbot_llm: {completion_config['chatbot_llm']} (Provider: {completion_config['llm_service']})") + logger.info(f"[ECC/GraphRAG] Using llm_model: {completion_config['llm_model']} (Provider: {completion_config['llm_service']})") +else: + logger.info(f"[CHATBOT & ECC/GraphRAG] Using llm_model: {completion_config['llm_model']} (Provider: {completion_config['llm_service']})") embedding_config = llm_config.get("embedding_service") if embedding_config is None: raise Exception("embedding_service is not found in llm_config") @@ -99,6 +142,9 @@ raise Exception("model_name is not found in embedding_service") embedding_dimension = embedding_config.get("dimensions", 1536) +# Log which embedding model will be used +logger.info(f"[EMBEDDING] Using model: {embedding_config.get('model_name', 'N/A')} (Provider: {embedding_config.get('embedding_model_service', 'N/A')})") + # Get context window size from llm_config # <=0 means unlimited tokens (no truncation), otherwise use the specified limit if "token_limit" in llm_config: @@ -109,6 +155,8 @@ # Get multimodal_service config (optional, for vision/image tasks) multimodal_config = llm_config.get("multimodal_service") +if multimodal_config: + logger.info(f"[MULTIMODAL] Using model: {multimodal_config.get('llm_model', 'N/A')} (Provider: {multimodal_config.get('llm_service', 'N/A')})") # Merge shared authentication configuration from llm_config level into service configs # Services can still override by defining their own authentication_configuration @@ -175,27 +223,44 @@ else: raise Exception("Embedding service not implemented") -def get_llm_service(llm_config) -> LLM_Model: - if llm_config["completion_service"]["llm_service"].lower() == "openai": - return OpenAI(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "azure": - return AzureOpenAI(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "sagemaker": - return AWS_SageMaker_Endpoint(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "vertexai": - return GoogleVertexAI(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "genai": - return GoogleGenAI(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "bedrock": - return AWSBedrock(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "groq": - return Groq(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "ollama": - return Ollama(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "huggingface": - return HuggingFaceEndpoint(llm_config["completion_service"]) - elif llm_config["completion_service"]["llm_service"].lower() == "watsonx": - return IBMWatsonX(llm_config["completion_service"]) +def get_llm_service(llm_config, for_chatbot=False) -> LLM_Model: + """ + Get LLM service for either Chatbot or GraphRAG/ECC tasks. + + Args: + llm_config: The LLM configuration dictionary + for_chatbot: If True, uses chatbot_llm if specified, otherwise uses llm_model. + If False (default), always uses llm_model for ECC/GraphRAG. + """ + # Use completion_service which already has authentication_configuration injected + service_config = llm_config["completion_service"].copy() + + # For chatbot: use chatbot_llm if specified, otherwise use llm_model + # For ECC/GraphRAG: always use llm_model + if for_chatbot and "chatbot_llm" in service_config: + service_config["llm_model"] = service_config["chatbot_llm"] + # If llm_model doesn't exist, it will raise KeyError in the service constructor + + if service_config["llm_service"].lower() == "openai": + return OpenAI(service_config) + elif service_config["llm_service"].lower() == "azure": + return AzureOpenAI(service_config) + elif service_config["llm_service"].lower() == "sagemaker": + return AWS_SageMaker_Endpoint(service_config) + elif service_config["llm_service"].lower() == "vertexai": + return GoogleVertexAI(service_config) + elif service_config["llm_service"].lower() == "genai": + return GoogleGenAI(service_config) + elif service_config["llm_service"].lower() == "bedrock": + return AWSBedrock(service_config) + elif service_config["llm_service"].lower() == "groq": + return Groq(service_config) + elif service_config["llm_service"].lower() == "ollama": + return Ollama(service_config) + elif service_config["llm_service"].lower() == "huggingface": + return HuggingFaceEndpoint(service_config) + elif service_config["llm_service"].lower() == "watsonx": + return IBMWatsonX(service_config) else: raise Exception("LLM Completion Service Not Supported") @@ -269,3 +334,190 @@ def get_multimodal_service() -> LLM_Model: support_ai_instance=True, ) service_status["embedding_store"] = {"status": "ok", "error": None} + + +def reload_llm_config(new_llm_config: dict = None): + """ + Reload LLM configuration and reinitialize services. + + Args: + new_llm_config: If provided, saves this config to file first. + If None, just reloads from existing file. + + Returns: + dict: Status of reload operation + """ + global llm_config, embedding_service, completion_config, embedding_config, multimodal_config + + try: + # If new config provided, save it first + if new_llm_config is not None: + with open(SERVER_CONFIG, "r") as f: + server_config = json.load(f) + + # Preserve existing API keys if not provided in new config + existing_llm_config = server_config.get("llm_config", {}) + + # Directly save the new LLM config without preserving old API keys + server_config["llm_config"] = new_llm_config + + with open(SERVER_CONFIG, "w") as f: + json.dump(server_config, f, indent=2) + + # Read/reload from file + with open(SERVER_CONFIG, "r") as f: + server_config = json.load(f) + + # Validate before updating + new_llm_config = server_config.get("llm_config") + if new_llm_config is None: + raise Exception("llm_config is not found in SERVER_CONFIG") + + # Inject authentication_configuration into service configs BEFORE updating globals + if "authentication_configuration" in new_llm_config: + if "completion_service" in new_llm_config: + new_llm_config["completion_service"]["authentication_configuration"] = new_llm_config["authentication_configuration"] + if "embedding_service" in new_llm_config: + new_llm_config["embedding_service"]["authentication_configuration"] = new_llm_config["authentication_configuration"] + if "multimodal_service" in new_llm_config: + new_llm_config["multimodal_service"]["authentication_configuration"] = new_llm_config["authentication_configuration"] + + new_completion_config = new_llm_config.get("completion_service") + new_embedding_config = new_llm_config.get("embedding_service") + new_multimodal_config = new_llm_config.get("multimodal_service") + + if new_completion_config is None: + raise Exception("completion_service is not found in llm_config") + if new_embedding_config is None: + raise Exception("embedding_service is not found in llm_config") + + # Update llm_config in-place to preserve references in other modules (ui.py imports this) + llm_config.clear() + llm_config.update(new_llm_config) + + # Update service configs in-place to preserve references + completion_config.clear() + completion_config.update(new_completion_config) + + embedding_config.clear() + embedding_config.update(new_embedding_config) + + # multimodal_config can be reassigned (not imported elsewhere) + multimodal_config = new_multimodal_config + + # Re-initialize embedding service + if embedding_config["embedding_model_service"].lower() == "openai": + embedding_service = OpenAI_Embedding(embedding_config) + elif embedding_config["embedding_model_service"].lower() == "azure": + embedding_service = AzureOpenAI_Ada002(embedding_config) + elif embedding_config["embedding_model_service"].lower() == "vertexai": + embedding_service = VertexAI_PaLM_Embedding(embedding_config) + elif embedding_config["embedding_model_service"].lower() == "genai": + embedding_service = GenAI_Embedding(embedding_config) + elif embedding_config["embedding_model_service"].lower() == "bedrock": + embedding_service = AWS_Bedrock_Embedding(embedding_config) + elif embedding_config["embedding_model_service"].lower() == "ollama": + embedding_service = Ollama_Embedding(embedding_config) + else: + raise Exception("Embedding service not implemented") + + return { + "status": "success", + "message": "LLM configuration reloaded successfully" + } + + except Exception as e: + return { + "status": "error", + "message": f"Failed to reload LLM config: {str(e)}" + } + + +def reload_db_config(new_db_config: dict = None): + """ + Reload DB configuration from server_config.json and update in-memory config. + + Args: + new_db_config: If provided, saves this config to file first. + If None, just reloads from existing file. + + Returns: + dict: Status of reload operation + """ + global db_config + + try: + if new_db_config is not None: + with open(SERVER_CONFIG, "r") as f: + server_config = json.load(f) + + server_config["db_config"] = new_db_config + + temp_file = f"{SERVER_CONFIG}.tmp" + with open(temp_file, "w") as f: + json.dump(server_config, f, indent=2) + os.replace(temp_file, SERVER_CONFIG) + + with open(SERVER_CONFIG, "r") as f: + server_config = json.load(f) + + new_db_config = server_config.get("db_config") + if new_db_config is None: + raise Exception("db_config is not found in SERVER_CONFIG") + + db_config.clear() + db_config.update(new_db_config) + + return { + "status": "success", + "message": "DB configuration reloaded successfully" + } + except Exception as e: + return { + "status": "error", + "message": f"Failed to reload DB config: {str(e)}" + } + + +def reload_graphrag_config(): + """ + Reload GraphRAG configuration from server_config.json. + Updates the in-memory graphrag_config dict to reflect changes immediately. + + Returns: + dict: Status of reload operation + """ + global graphrag_config + + try: + # Read from file + with open(SERVER_CONFIG, "r") as f: + server_config = json.load(f) + + # Validate + new_graphrag_config = server_config.get("graphrag_config") + if new_graphrag_config is None: + new_graphrag_config = {"reuse_embedding": True} + + # Set defaults (same as startup logic) + if "chunker" not in new_graphrag_config: + new_graphrag_config["chunker"] = "semantic" + if "extractor" not in new_graphrag_config: + new_graphrag_config["extractor"] = "llm" + + # Update graphrag_config in-place to preserve references in other modules + graphrag_config.clear() + graphrag_config.update(new_graphrag_config) + + logger.info(f"GraphRAG config reloaded: extractor={graphrag_config.get('extractor')}, chunker={graphrag_config.get('chunker')}, reuse_embedding={graphrag_config.get('reuse_embedding')}") + + return { + "status": "success", + "message": "GraphRAG configuration reloaded successfully" + } + + except Exception as e: + return { + "status": "error", + "message": f"Failed to reload GraphRAG config: {str(e)}" + } \ No newline at end of file diff --git a/common/embeddings/embedding_services.py b/common/embeddings/embedding_services.py index 1597cd2..6f170d0 100644 --- a/common/embeddings/embedding_services.py +++ b/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 ) + diff --git a/common/llm_services/google_vertexai_service.py b/common/llm_services/google_vertexai_service.py index 22679f5..2fec35b 100644 --- a/common/llm_services/google_vertexai_service.py +++ b/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 + diff --git a/common/requirements.txt b/common/requirements.txt index d5a2d5b..12c9fcf 100644 --- a/common/requirements.txt +++ b/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 diff --git a/docs/tutorials/configs/nginx.conf b/docs/tutorials/configs/nginx.conf index dc09929..975d8a0 100644 --- a/docs/tutorials/configs/nginx.conf +++ b/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/; diff --git a/ecc/app/common b/ecc/app/common deleted file mode 120000 index dc879ab..0000000 --- a/ecc/app/common +++ /dev/null @@ -1 +0,0 @@ -../../common \ No newline at end of file diff --git a/ecc/app/configs b/ecc/app/configs deleted file mode 120000 index 5992d10..0000000 --- a/ecc/app/configs +++ /dev/null @@ -1 +0,0 @@ -../../configs \ No newline at end of file diff --git a/ecc/app/ecc_util.py b/ecc/app/ecc_util.py index 75a3f87..aa6c5ed 100644 --- a/ecc/app/ecc_util.py +++ b/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 diff --git a/ecc/app/graphrag/community_summarizer.py b/ecc/app/graphrag/community_summarizer.py index 0bab35b..803ad17 100644 --- a/ecc/app/graphrag/community_summarizer.py +++ b/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} \ No newline at end of file diff --git a/ecc/app/graphrag/workers.py b/ecc/app/graphrag/workers.py index 78f38be..4f8543a 100644 --- a/ecc/app/graphrag/workers.py +++ b/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"]: diff --git a/ecc/app/main.py b/ecc/app/main.py index 5468391..b74989a 100644 --- a/ecc/app/main.py +++ b/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"), diff --git a/graphrag-ui/src/components/Bot.tsx b/graphrag-ui/src/components/Bot.tsx index 1f4e4e6..266fe2e 100644 --- a/graphrag-ui/src/components/Bot.tsx +++ b/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(); }; diff --git a/graphrag-ui/src/components/ModeToggle.tsx b/graphrag-ui/src/components/ModeToggle.tsx index a8109bc..8049162 100644 --- a/graphrag-ui/src/components/ModeToggle.tsx +++ b/graphrag-ui/src/components/ModeToggle.tsx @@ -1,4 +1,5 @@ import { Moon, Sun, LogOut, Settings } from "lucide-react"; +import React from "react"; import { useLocation, useNavigate } from "react-router-dom"; import { Button } from "@/components/ui/button"; @@ -17,6 +18,67 @@ export function ModeToggle() { const location = useLocation(); const isLoginRoute = location.pathname === "/"; const [confirm, confirmDialog] = useConfirm(); + const [userRoles, setUserRoles] = React.useState([]); + const [graphRoles, setGraphRoles] = React.useState>({}); + const [rolesLoaded, setRolesLoaded] = React.useState(false); + const [selectedGraph, setSelectedGraph] = React.useState( + localStorage.getItem("selectedGraph") || "" + ); + const isGraphAdmin = (graphRoles[selectedGraph] || []).includes("admin"); + const canAccessSetup = + userRoles.includes("superuser") || + userRoles.includes("globaldesigner") || + isGraphAdmin; + + React.useEffect(() => { + const loadRoles = async () => { + try { + const creds = localStorage.getItem("creds"); + if (!creds) { + setUserRoles([]); + setRolesLoaded(true); + return; + } + const response = await fetch("/ui/roles", { + headers: { Authorization: `Basic ${creds}` }, + }); + if (!response.ok) { + setUserRoles([]); + setRolesLoaded(true); + return; + } + const data = await response.json(); + const roles = Array.isArray(data.roles) ? data.roles : []; + setUserRoles(roles.map((role: string) => role.toLowerCase())); + setGraphRoles( + data.graph_roles && typeof data.graph_roles === "object" + ? Object.fromEntries( + Object.entries(data.graph_roles).map(([graph, roles]) => [ + graph, + Array.isArray(roles) + ? roles.map((role: string) => role.toLowerCase()) + : [], + ]) + ) + : {} + ); + setSelectedGraph(localStorage.getItem("selectedGraph") || ""); + } finally { + setRolesLoaded(true); + } + }; + loadRoles(); + }, [location.pathname]); + + React.useEffect(() => { + const handleGraphChange = () => { + setSelectedGraph(localStorage.getItem("selectedGraph") || ""); + }; + window.addEventListener("graphrag:selectedGraph", handleGraphChange); + return () => { + window.removeEventListener("graphrag:selectedGraph", handleGraphChange); + }; + }, []); const handleLogout = async () => { // Show confirmation dialog @@ -46,7 +108,7 @@ export function ModeToggle() { return (
- {!isLoginRoute && ( + {!isLoginRoute && rolesLoaded && canAccessSetup && ( + {!isLoginRoute && ( + + )} diff --git a/graphrag-ui/src/main.tsx b/graphrag-ui/src/main.tsx index 69cfa82..5e298b5 100755 --- a/graphrag-ui/src/main.tsx +++ b/graphrag-ui/src/main.tsx @@ -1,10 +1,16 @@ import ReactDOM from "react-dom/client"; import App from "./App.tsx"; import "./index.css"; -import { Outlet, RouterProvider, createBrowserRouter } from "react-router-dom"; +import { Outlet, RouterProvider, createBrowserRouter, Navigate } from "react-router-dom"; import Chat from "./pages/Chat"; import ChatDialog from "./pages/ChatDialog.tsx"; -import Setup from "./pages/Setup.tsx"; +import SetupLayout from "./pages/setup/SetupLayout.tsx"; +import KGAdmin from "./pages/setup/KGAdmin.tsx"; +import IngestGraph from "./pages/setup/IngestGraph.tsx"; +import LLMConfig from "./pages/setup/LLMConfig.tsx"; +import GraphDBConfig from "./pages/setup/GraphDBConfig.tsx"; +import GraphRAGConfig from "./pages/setup/GraphRAGConfig.tsx"; +import CustomizePrompts from "./pages/setup/CustomizePrompts.tsx"; import { ThemeProvider } from "./components/ThemeProvider.tsx"; import { ModeToggle } from "@/components/ModeToggle.tsx"; @@ -42,7 +48,41 @@ const router = createBrowserRouter([ }, { path: "/setup", - element: , + element: , + children: [ + { + path: "", + element: , + }, + { + path: "kg-admin", + element: , + }, + { + path: "kg-admin/ingest", + element: , + }, + { + path: "server-config", + element: , + }, + { + path: "server-config/llm", + element: , + }, + { + path: "server-config/graphdb", + element: , + }, + { + path: "server-config/graphrag", + element: , + }, + { + path: "prompts", + element: , + }, + ], }, ], }, diff --git a/graphrag-ui/src/pages/setup/CustomizePrompts.tsx b/graphrag-ui/src/pages/setup/CustomizePrompts.tsx new file mode 100644 index 0000000..ef13dfc --- /dev/null +++ b/graphrag-ui/src/pages/setup/CustomizePrompts.tsx @@ -0,0 +1,298 @@ +import React, { useState, useEffect } from "react"; +import { FileText, Save, Loader2 } from "lucide-react"; +import { Button } from "@/components/ui/button"; +import { Input } from "@/components/ui/input"; + +const ALL_PROMPT_TYPES = [ + { id: "chatbot_response", name: "Chatbot Responses", description: "Customize how the chatbot responds to user questions" }, + { id: "entity_relationship", name: "Entity Relationships", description: "Configure entity and relationship extraction from document chunks" }, + { id: "community_summarization", name: "Community Summarization", description: "Define how community summaries are generated" }, + { id: "query_generation", name: "Schema Instructions", description: "Configure instructions for schema filtering and schema generation" }, +]; + +const CustomizePrompts = () => { + const [configuredProvider, setConfiguredProvider] = useState(""); + const [isLoading, setIsLoading] = useState(true); + const [expandedPrompt, setExpandedPrompt] = useState(null); + // Only the prompt types returned by the backend (filtered by access level) + const [availablePromptIds, setAvailablePromptIds] = useState([]); + + // Prompts loaded from backend (editable content only) + const [prompts, setPrompts] = useState({ + chatbot_response: "", + entity_relationship: "", + community_summarization: "", + query_generation: "", + }); + + // Template variables that should not be edited (stored separately) + const [promptTemplates, setPromptTemplates] = useState({ + chatbot_response: "", + entity_relationship: "", + community_summarization: "", + query_generation: "", + }); + + // Only render prompt types the backend returned for this user + const promptTypes = ALL_PROMPT_TYPES.filter(p => availablePromptIds.includes(p.id)); + + const [isSaving, setIsSaving] = useState(false); + const [saveMessage, setSaveMessage] = useState(""); + const [saveMessageType, setSaveMessageType] = useState<"success" | "error" | "">(""); + const selectedGraph = localStorage.getItem("selectedGraph") || ""; + + const handleSavePrompt = async (promptId: string) => { + setIsSaving(true); + setSaveMessage(""); + setSaveMessageType(""); + + try { + const creds = localStorage.getItem("creds"); + const query = selectedGraph ? `?graphname=${encodeURIComponent(selectedGraph)}` : ""; + const response = await fetch(`/ui/prompts${query}`, { + method: "POST", + headers: { + "Content-Type": "application/json", + Authorization: `Basic ${creds}`, + }, + body: JSON.stringify({ + prompt_type: promptId, + editable_content: prompts[promptId as keyof typeof prompts], + template_variables: promptTemplates[promptId as keyof typeof promptTemplates], + graphname: selectedGraph || undefined, + }), + }); + + if (!response.ok) { + const errorData = await response.json(); + throw new Error(errorData.detail || "Failed to save prompt"); + } + + const result = await response.json(); + setSaveMessage(`✅ ${result.message}`); + setSaveMessageType("success"); + setExpandedPrompt(null); // Collapse after successful save + } catch (error: any) { + console.error("Error saving prompt:", error); + setSaveMessage(`❌ Error: ${error.message}`); + setSaveMessageType("error"); + } finally { + setIsSaving(false); + } + }; + + const handlePromptChange = (promptId: string, value: string) => { + setPrompts(prev => ({ ...prev, [promptId]: value })); + }; + + // Fetch prompts and configured LLM provider from server + useEffect(() => { + const fetchPrompts = async () => { + setIsLoading(true); + try { + const creds = localStorage.getItem("creds"); + const query = selectedGraph ? `?graphname=${encodeURIComponent(selectedGraph)}` : ""; + const response = await fetch(`/ui/prompts${query}`, { + headers: { Authorization: `Basic ${creds}` }, + }); + + if (!response.ok) { + throw new Error("Failed to fetch prompts"); + } + + const data = await response.json(); + + // Track which prompts this user is allowed to see (backend filters by role) + setAvailablePromptIds(Object.keys(data.prompts)); + + // Update prompts with fetched data (editable content only) + setPrompts({ + chatbot_response: data.prompts.chatbot_response?.editable_content !== undefined + ? data.prompts.chatbot_response.editable_content + : (typeof data.prompts.chatbot_response === 'string' ? data.prompts.chatbot_response : ""), + entity_relationship: data.prompts.entity_relationship?.editable_content !== undefined + ? data.prompts.entity_relationship.editable_content + : (typeof data.prompts.entity_relationship === 'string' ? data.prompts.entity_relationship : ""), + community_summarization: data.prompts.community_summarization?.editable_content !== undefined + ? data.prompts.community_summarization.editable_content + : (typeof data.prompts.community_summarization === 'string' ? data.prompts.community_summarization : ""), + query_generation: data.prompts.query_generation?.editable_content !== undefined + ? data.prompts.query_generation.editable_content + : (typeof data.prompts.query_generation === 'string' ? data.prompts.query_generation : ""), + }); + + // Store template variables separately + setPromptTemplates({ + chatbot_response: data.prompts.chatbot_response?.template_variables || "", + entity_relationship: data.prompts.entity_relationship?.template_variables || "", + community_summarization: data.prompts.community_summarization?.template_variables || "", + query_generation: data.prompts.query_generation?.template_variables || "", + }); + + // Set configured provider + const providerMap: Record = { + openai: "OpenAI", + azure: "Azure OpenAI", + genai: "Google GenAI (Gemini)", + vertexai: "Google Vertex AI", + bedrock: "AWS Bedrock", + ollama: "Ollama", + }; + const provider = data.configured_provider?.toLowerCase() || "openai"; + setConfiguredProvider(providerMap[provider] || data.configured_provider || "OpenAI"); + } catch (error) { + console.error("Error loading prompts:", error); + setConfiguredProvider("OpenAI"); + } finally { + setIsLoading(false); + } + }; + + fetchPrompts(); + }, []); + + return ( +
+
+
+
+
+ +
+
+

+ Customize Prompts +

+

+ Customize the three core prompts used by GraphRAG +

+

+ {selectedGraph + ? `Editing prompt overrides for graph: ${selectedGraph}` + : "Editing default prompts for all graphs"} +

+
+
+
+ +
+
+ {/* Configured Provider - Read Only */} +
+ +
+ + {isLoading && ( +
+ +
+ )} +
+

+ Prompts are configured for your currently active LLM provider. Change provider in Server Configuration. +

+
+ + {/* Save Message */} + {saveMessage && ( +
+ {saveMessage} +
+ )} + + {/* Prompt Templates */} +
+

+ Prompt Templates +

+ +
+ {promptTypes.map((prompt) => ( +
+
+
+

+ {prompt.name} +

+

+ {prompt.description} +

+
+ +
+ + {expandedPrompt === prompt.id && ( +
+