final version of the upgraded graphrag accelerator

backend/graphrag_app/api/index.py

@@ -13,6 +13,7 @@ from fastapi import (
     HTTPException,
     UploadFile,
 )
+from graphrag.config.enums import IndexingMethod
 from kubernetes import (
     client as kubernetes_client,
 )
@@ -56,8 +57,12 @@ async def schedule_index_job(
     index_container_name: str,
     entity_extraction_prompt: UploadFile | None = None,
     entity_summarization_prompt: UploadFile | None = None,
-    community_summarization_prompt: UploadFile | None = None,
+    community_summarization_graph_prompt: UploadFile | None = None,
+    community_summarization_text_prompt: UploadFile | None = None,
+    indexing_method: IndexingMethod = IndexingMethod.Standard.value,
 ):
+    indexing_method = IndexingMethod(indexing_method).value
+
     azure_client_manager = AzureClientManager()
     blob_service_client = azure_client_manager.get_blob_service_client()
     pipelinejob = PipelineJob()
@@ -86,9 +91,14 @@ async def schedule_index_job(
         if entity_summarization_prompt
         else None
     )
-    community_summarization_prompt_content = (
+    community_summarization_graph_content = (
-        community_summarization_prompt.file.read().decode("utf-8")
+        community_summarization_graph_prompt.file.read().decode("utf-8")
-        if community_summarization_prompt
+        if community_summarization_graph_prompt
+        else None
+    )
+    community_summarization_text_content = (
+        community_summarization_text_prompt.file.read().decode("utf-8")
+        if community_summarization_text_prompt
         else None
     )
 
@@ -119,9 +129,14 @@ async def schedule_index_job(
         ) = []
         existing_job._entity_extraction_prompt = entity_extraction_prompt_content
         existing_job._entity_summarization_prompt = entity_summarization_prompt_content
-        existing_job._community_summarization_prompt = (
+        existing_job.community_summarization_graph_prompt = (
-            community_summarization_prompt_content
+            community_summarization_graph_content
         )
+        existing_job.community_summarization_text_prompt = (
+            community_summarization_text_content
+        )
+        existing_job._indexing_method = indexing_method
+
         existing_job._epoch_request_time = int(time())
         existing_job.update_db()
     else:
@@ -131,7 +146,9 @@ async def schedule_index_job(
             human_readable_storage_name=storage_container_name,
             entity_extraction_prompt=entity_extraction_prompt_content,
             entity_summarization_prompt=entity_summarization_prompt_content,
-            community_summarization_prompt=community_summarization_prompt_content,
+            community_summarization_graph_prompt=community_summarization_graph_content,
+            community_summarization_text_prompt=community_summarization_text_content,
+            indexing_method=indexing_method,
             status=PipelineJobState.SCHEDULED,
         )
 

backend/graphrag_app/api/prompt_tuning.py

@@ -12,8 +12,8 @@ from fastapi import (
     Depends,
     HTTPException,
 )
-from graphrag.config.create_graphrag_config import create_graphrag_config
+from graphrag.config.load_config import load_config
-
+from graphrag.config.models.graph_rag_config import GraphRagConfig
 from graphrag_app.logger.load_logger import load_pipeline_logger
 from graphrag_app.utils.azure_clients import AzureClientManager
 from graphrag_app.utils.common import sanitize_name, subscription_key_check
@@ -46,12 +46,15 @@ async def generate_prompts(
             detail=f"Storage container '{container_name}' does not exist.",
         )
 
-    # load pipeline configuration file (settings.yaml) for input data and other settings
+    # load custom pipeline settings
-    ROOT_DIR = Path(__file__).resolve().parent.parent.parent
+    ROOT_DIR = Path(__file__).resolve().parent.parent.parent / "scripts/settings.yaml"
-    with (ROOT_DIR / "scripts/settings.yaml").open("r") as f:
+    
-        data = yaml.safe_load(f)
+    # layer the custom settings on top of the default configuration settings of graphrag
-    data["input"]["container_name"] = sanitized_container_name
+    graphrag_config: GraphRagConfig = load_config(
-    graphrag_config = create_graphrag_config(values=data, root_dir=".")
+        root_dir=ROOT_DIR.parent, 
+        config_filepath=ROOT_DIR
+    )
+    graphrag_config.input.container_name = sanitized_container_name
 
     # generate prompts
     try:

backend/graphrag_app/utils/common.py

@@ -5,9 +5,8 @@ import hashlib
 import os
 import sys
 import traceback
-from typing import Annotated
 from pathlib import Path
-from typing import Dict, List
+from typing import Annotated, Dict, List
 
 import pandas as pd
 from azure.core.exceptions import ResourceNotFoundError
@@ -17,7 +16,6 @@ from azure.storage.blob.aio import ContainerClient
 from fastapi import Header, HTTPException
 from graphrag.config.load_config import load_config
 from graphrag.config.models.graph_rag_config import GraphRagConfig
-from graphrag.config.models.vector_store_config import VectorStoreConfig
 
 from graphrag_app.logger.load_logger import load_pipeline_logger
 from graphrag_app.typing.models import QueryData
@@ -287,6 +285,10 @@ def get_data_tables(
         root_dir=ROOT_DIR.parent, 
         config_filepath=ROOT_DIR
     )
+    # update the config to use the correct blob storage containers
+    config.cache.container_name = index_names["sanitized_name"]
+    config.reporting.container_name = index_names["sanitized_name"]
+    config.output.container_name = index_names["sanitized_name"]
     # dynamically assign the sanitized index name 
     config.vector_store["default_vector_store"].container_name = sanitized_name
     

backend/graphrag_app/utils/pipeline.py

@@ -7,6 +7,7 @@ from typing import (
 )
 
 from azure.cosmos.exceptions import CosmosHttpResponseError
+from graphrag.config.enums import IndexingMethod
 
 from graphrag_app.typing.pipeline import PipelineJobState
 from graphrag_app.utils.azure_clients import AzureClientManager
@@ -39,7 +40,9 @@ class PipelineJob:
 
     _entity_extraction_prompt: str = field(default=None, init=False)
     _entity_summarization_prompt: str = field(default=None, init=False)
-    _community_summarization_prompt: str = field(default=None, init=False)
+    _community_summarization_graph_prompt: str = field(default=None, init=False)
+    _community_summarization_text_prompt: str = field(default=None, init=False)
+    _indexing_method: str = field(default=IndexingMethod.Standard.value, init=False)
 
     @staticmethod
     def _jobs_container():
@@ -56,7 +59,9 @@ class PipelineJob:
         human_readable_storage_name: str,
         entity_extraction_prompt: str | None = None,
         entity_summarization_prompt: str | None = None,
-        community_summarization_prompt: str | None = None,
+        community_summarization_graph_prompt: str | None = None,
+        community_summarization_text_prompt: str | None = None,
+        indexing_method: str = IndexingMethod.Standard.value,
         **kwargs,
     ) -> "PipelineJob":
         """
@@ -112,7 +117,10 @@ class PipelineJob:
 
         instance._entity_extraction_prompt = entity_extraction_prompt
         instance._entity_summarization_prompt = entity_summarization_prompt
-        instance._community_summarization_prompt = community_summarization_prompt
+        instance._community_summarization_graph_prompt = community_summarization_graph_prompt
+        instance._community_summarization_text_prompt = community_summarization_text_prompt
+
+        instance._indexing_method = IndexingMethod(indexing_method).value
 
         # Create the item in the database
         instance.update_db()
@@ -160,9 +168,15 @@ class PipelineJob:
         instance._entity_summarization_prompt = db_item.get(
             "entity_summarization_prompt"
         )
-        instance._community_summarization_prompt = db_item.get(
+        instance._community_summarization_graph_prompt = db_item.get(
-            "community_summarization_prompt"
+            "community_summarization_graph_prompt"
         )
+        instance._community_summarization_text_prompt = db_item.get(
+            "community_summarization_text_prompt"
+        )
+
+        instance._indexing_method = db_item.get("indexing_method")
+
         return instance
 
     @staticmethod
@@ -200,14 +214,19 @@ class PipelineJob:
             "status": self._status.value,
             "percent_complete": self._percent_complete,
             "progress": self._progress,
+            "indexing_method": self._indexing_method,
         }
         if self._entity_extraction_prompt:
             model["entity_extraction_prompt"] = self._entity_extraction_prompt
         if self._entity_summarization_prompt:
             model["entity_summarization_prompt"] = self._entity_summarization_prompt
-        if self._community_summarization_prompt:
+        if self._community_summarization_graph_prompt:
-            model["community_summarization_prompt"] = (
+            model["community_summarization_graph_prompt"] = (
-                self._community_summarization_prompt
+                self._community_summarization_graph_prompt
+            )
+        if self._community_summarization_text_prompt:
+            model["community_summarization_text_prompt"] = (
+                self._community_summarization_text_prompt
             )
         return model
 
@@ -291,14 +310,34 @@ class PipelineJob:
         self.update_db()
 
     @property
-    def community_summarization_prompt(self) -> str:
+    def community_summarization_graph_prompt(self) -> str:
-        return self._community_summarization_prompt
+        return self._community_summarization_graph_prompt
 
-    @community_summarization_prompt.setter
+    @community_summarization_graph_prompt.setter
-    def community_summarization_prompt(
+    def community_summarization_graph_prompt(
-        self, community_summarization_prompt: str
+        self, community_summarization_graph_prompt: str
     ) -> None:
-        self._community_summarization_prompt = community_summarization_prompt
+        self._community_summarization_graph_prompt = community_summarization_graph_prompt
+        self.update_db()
+    
+    @property
+    def community_summarization_text_prompt(self) -> str:
+        return self._community_summarization_text_prompt
+
+    @community_summarization_text_prompt.setter
+    def community_summarization_text_prompt(
+        self, community_summarization_text_prompt: str
+    ) -> None:
+        self._community_summarization_text_prompt = community_summarization_text_prompt
+        self.update_db()
+
+    @property
+    def indexing_method(self) -> str:
+        return self._indexing_method
+    
+    @indexing_method.setter
+    def indexing_method(self, indexing_method: str) -> None:
+        self._indexing_method = IndexingMethod(indexing_method).value
         self.update_db()
 
     @property

backend/scripts/indexer.py

@@ -7,11 +7,15 @@ import traceback
 from pathlib import Path
 
 import graphrag.api as api
-import yaml
 from graphrag.callbacks.workflow_callbacks import WorkflowCallbacks
-from graphrag.config.create_graphrag_config import create_graphrag_config
+
-from graphrag.index.create_pipeline_config import create_pipeline_config
+# from graphrag.index.create_pipeline_config import create_pipeline_config
-from graphrag.index.typing import PipelineRunResult
+from graphrag.config.enums import IndexingMethod
+from graphrag.config.load_config import load_config
+from graphrag.config.models.community_reports_config import CommunityReportsConfig
+from graphrag.config.models.graph_rag_config import GraphRagConfig
+from graphrag.index.typing.pipeline_run_result import PipelineRunResult
+from graphrag.index.workflows.factory import PipelineFactory
 
 from graphrag_app.logger import (
     PipelineJobUpdater,
@@ -48,54 +52,76 @@ def start_indexing_job(index_name: str):
     storage_name = pipeline_job.human_readable_index_name
 
     # load custom pipeline settings
-    SCRIPT_DIR = Path(__file__).resolve().parent
+    ROOT_DIR = Path(__file__).resolve().parent / "settings.yaml"
-    with (SCRIPT_DIR / "settings.yaml").open("r") as f:
+    config: GraphRagConfig = load_config(
-        data = yaml.safe_load(f)
+        root_dir=ROOT_DIR.parent, 
-    # dynamically set some values
+        config_filepath=ROOT_DIR
-    data["input"]["container_name"] = sanitized_storage_name
-    data["storage"]["container_name"] = sanitized_index_name
-    data["reporting"]["container_name"] = sanitized_index_name
-    data["cache"]["container_name"] = sanitized_index_name
-    if "vector_store" in data["embeddings"]:
-        data["embeddings"]["vector_store"]["collection_name"] = (
-            f"{sanitized_index_name}_description_embedding"
     )
+    # dynamically assign the sanitized index name 
+    config.vector_store["default_vector_store"].container_name = sanitized_index_name
 
-    # set prompt for entity extraction
+    # dynamically set indexing storage values
+    config.input.container_name = sanitized_storage_name
+    config.output.container_name = sanitized_index_name
+    config.reporting.container_name = sanitized_index_name
+    config.cache.container_name = sanitized_index_name
+    
+    # update extraction prompts
+    PROMPT_DIR = Path(__file__).resolve().parent
+
+    # set prompt for entity extraction / graph construction
     if pipeline_job.entity_extraction_prompt:
-        fname = "entity-extraction-prompt.txt"
+        # use the default prompt
-        with open(fname, "w") as outfile:
+        config.extract_graph.prompt = None
-            outfile.write(pipeline_job.entity_extraction_prompt)
-        data["entity_extraction"]["prompt"] = fname
     else:
-        data.pop("entity_extraction")
+        # try to load the custom prompt
+        fname = "extract_graph.txt"
+        with open(PROMPT_DIR / fname, "w") as file:
+            file.write(pipeline_job.entity_extraction_prompt)
+        config.extract_graph.prompt = fname
 
     # set prompt for entity summarization
-    if pipeline_job.entity_summarization_prompt:
+    if pipeline_job.entity_summarization_prompt is None:
-        fname = "entity-summarization-prompt.txt"
+        # use the default prompt
-        with open(fname, "w") as outfile:
+        config.summarize_descriptions.prompt = None
-            outfile.write(pipeline_job.entity_summarization_prompt)
-        data["summarize_descriptions"]["prompt"] = fname
     else:
-        data.pop("summarize_descriptions")
+        # try to load the custom prompt
+        fname = "summarize_descriptions.txt"
+        with open(PROMPT_DIR / fname, "w") as file:
+            file.write(pipeline_job.entity_summarization_prompt)
+        config.summarize_descriptions.prompt = fname
 
-    # set prompt for community summarization
+    # set prompt for community graph summarization
-    if pipeline_job.community_summarization_prompt:
+    if pipeline_job.community_summarization_graph_prompt is None:
-        fname = "community-summarization-prompt.txt"
+        # use the default prompt
-        with open(fname, "w") as outfile:
+        config.community_reports.graph_prompt = None
-            outfile.write(pipeline_job.community_summarization_prompt)
-        data["community_reports"]["prompt"] = fname
     else:
-        data.pop("community_reports")
+        # try to load the custom prompt
+        fname = "community_report_graph.txt"
+        with open(PROMPT_DIR / fname, "w") as file:
+            file.write(pipeline_job.community_summarization_graph_prompt)
+        pipeline_job.community_summarization_graph_prompt = fname
 
-    # generate default graphrag config parameters and override with custom settings
+    # set prompt for community text summarization
-    parameters = create_graphrag_config(data, ".")
+    if pipeline_job.community_summarization_text_prompt is None:
+        # use the default prompt
+        config.community_reports.text_prompt = None
+    else:
+        fname = "community_report_text.txt"
+        # try to load the custom prompt
+        with open(PROMPT_DIR / fname, "w") as file:
+            file.write(pipeline_job.community_summarization_text_prompt)
+        config.community_reports.text_prompt = fname
+
+    # set the extraction strategy
+    indexing_method = IndexingMethod(pipeline_job.indexing_method)
+    pipeline_workflows = PipelineFactory.create_pipeline(config, indexing_method)
 
     # reset pipeline job details
     pipeline_job.status = PipelineJobState.RUNNING
-    pipeline_config = create_pipeline_config(parameters)
+
     pipeline_job.all_workflows = [
-        workflow.name for workflow in pipeline_config.workflows
+        workflow for workflow in pipeline_workflows.names()
     ]
     pipeline_job.completed_workflows = []
     pipeline_job.failed_workflows = []
@@ -117,7 +143,7 @@ def start_indexing_job(index_name: str):
         print("Building index...")
         pipeline_results: list[PipelineRunResult] = asyncio.run(
             api.build_index(
-                config=parameters,
+                config=config,
                 callbacks=[logger, pipeline_job_updater],
             )
         )

backend/scripts/settings.yaml

@@ -68,7 +68,7 @@ models:
 vector_store:
   default_vector_store:
     type: cosmosdb # or [lancedb, azure_ai_search, cosmosdb]
-    url: ${COSMOS_URL}
+    url: ${COSMOS_URI_ENDPOINT}
     container_name: PLACEHOLDER
     overwrite: True
     database_name: vectordb
@@ -85,7 +85,7 @@ extract_graph:
 
 extract_graph_nlp:
   text_analyzer:
-    extractor_type: syntactic_parser # [regex_english, syntactic_parser, cfg]
+    extractor_type: regex_english # [regex_english, syntactic_parser, cfg]
 
 summarize_descriptions:
   model_id: default_chat_model

infra/deploy.sh

@@ -29,6 +29,8 @@ GRAPHRAG_EMBEDDING_MODEL="text-embedding-ada-002"
 GRAPHRAG_EMBEDDING_MODEL_VERSION="2"
 GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME="text-embedding-ada-002"
 GRAPHRAG_EMBEDDING_MODEL_QUOTA="300"
+GRAPHRAG_LLM_MODEL_CONCURRENT_REQUEST="15"
+GRAPHRAG_EMBEDDING_MODEL_CONCURRENT_REQUEST="15"
 
 requiredParams=(
     LOCATION
@@ -56,6 +58,8 @@ optionalParams=(
     GRAPHRAG_EMBEDDING_MODEL_QUOTA
     GRAPHRAG_EMBEDDING_MODEL_VERSION
     GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME
+    GRAPHRAG_LLM_MODEL_CONCURRENT_REQUEST
+    GRAPHRAG_EMBEDDING_MODEL_CONCURRENT_REQUEST
 )
 
 errorBanner () {
@@ -527,6 +531,9 @@ installGraphRAGHelmChart () {
         exitIfValueEmpty "$graphragEmbeddingModelDeployment" "Unable to parse embedding model deployment name from deployment outputs, exiting..."
     fi
 
+    graphragLlmModelConcurrentRequest="$GRAPHRAG_LLM_MODEL_CONCURRENT_REQUEST"
+    graphragEmbeddingModelConcurrentRequest="$GRAPHRAG_EMBEDDING_MODEL_CONCURRENT_REQUEST"
+
     reset_x=true
     if ! [ -o xtrace ]; then
         set -x
@@ -552,7 +559,10 @@ installGraphRAGHelmChart () {
         --set "graphragConfig.GRAPHRAG_LLM_DEPLOYMENT_NAME=$graphragLlmModelDeployment" \
         --set "graphragConfig.GRAPHRAG_EMBEDDING_MODEL=$graphragEmbeddingModel" \
         --set "graphragConfig.GRAPHRAG_EMBEDDING_DEPLOYMENT_NAME=$graphragEmbeddingModelDeployment" \
-        --set "graphragConfig.STORAGE_ACCOUNT_BLOB_URL=$storageAccountBlobUrl"
+        --set "graphragConfig.STORAGE_ACCOUNT_BLOB_URL=$storageAccountBlobUrl" \
+        --set "graphragConfig.GRAPHRAG_LLM_MODEL_CONCURRENT_REQUEST=$GRAPHRAG_LLM_MODEL_CONCURRENT_REQUEST" \
+        --set "graphragConfig.GRAPHRAG_EMBEDDING_MODEL_CONCURRENT_REQUEST=$GRAPHRAG_EMBEDDING_MODEL_CONCURRENT_REQUEST"
+      
 
     local helmResult
     helmResult=$?