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GraphRAG's primary function is to enhance Large Language Model (LLM) outputs by extracting structured data from unstructured text using knowledge graphs. It directly supports major LLM providers such as OpenAI and Google Gemini, indicating a significant role in advancing LLM capabilities. The core technology relies on knowledge graphs for its data extraction and enhancement processes.\",\n \"findings\": [\n {\n \"summary\": \"GraphRAG as a Central System\",\n \"explanation\": \"GraphRAG is the central entity in this community, defined as a graph-based retrieval-augmented generation system. Its purpose is to improve LLM outputs by leveraging knowledge graphs to extract structured data from unstructured text [Data: Entities (0)]. This system's architecture and functionality are key to understanding the interactions within this community. The system's high degree (5) suggests it is a significant node with many connections and potential influence within its domain [Data: Entities (0)].\"\n },\n {\n \"summary\": \"Microsoft Research's Development Role\",\n \"explanation\": \"Microsoft Research is identified as the developer of the GraphRAG system. This relationship highlights Microsoft's involvement in cutting-edge AI research, specifically in the area of enhancing LLM performance through graph-based methods [Data: Entities (1), Relationships (0)]. The combined degree of 6 for this relationship indicates a strong and significant connection between Microsoft Research and GraphRAG, suggesting substantial investment or strategic importance [Data: Relationships (0)].\"\n },\n {\n \"summary\": \"Support for Major LLM Providers\",\n \"explanation\": \"GraphRAG directly supports prominent LLM providers, including OpenAI and Google Gemini. This indicates that GraphRAG is a foundational technology enabling or improving the capabilities of these major AI players [Data: Entities (2), Entities (3), Relationships (1), Relationships (2)]. The shared combined degree of 6 for these relationships underscores the critical nature of GraphRAG's support for these LLM providers, suggesting a deep integration or dependency [Data: Relationships (1), Relationships (2)].\"\n },\n {\n \"summary\": \"Enhancement of Large Language Models (LLMs)\",\n \"explanation\": \"A primary function of GraphRAG is to enhance LLM outputs. This is achieved by integrating knowledge graphs, which allow for the extraction of structured data from unstructured text, thereby providing LLMs with richer context and more accurate information [Data: Entities (4), Entities (5), Relationships (3), Relationships (4)]. The relationship between GraphRAG and LLMs, with a combined degree of 6, signifies a core operational link and highlights GraphRAG's role in advancing the general capabilities of LLMs [Data: Relationships (3)].\"\n },\n {\n \"summary\": \"Reliance on Knowledge Graphs\",\n \"explanation\": \"Knowledge Graphs are a fundamental component of the GraphRAG system. They are utilized to extract structured data from unstructured text, which is then used to enhance LLM outputs [Data: Entities (4), Relationships (4)]. The description of Knowledge Graphs emphasizes their role in structuring information, which is crucial for GraphRAG's retrieval-augmented generation process. This dependency indicates that the effectiveness and capabilities of GraphRAG are directly tied to the quality and structure of the knowledge graphs it employs [Data: Entities (4), Relationships (4)].\"\n }\n ],\n \"rating\": 7.5,\n \"rating_explanation\": \"The impact severity rating is high due to GraphRAG's foundational role in enhancing major LLM providers and its reliance on knowledge graphs, indicating significant potential influence and dependency in the AI landscape.\"\n}", "role": "assistant", "tool_calls": null, "function_call": null, "images": [], "thinking_blocks": [], "provider_specific_fields": null}}], "usage": {"completion_tokens": 815, "prompt_tokens": 2337, "total_tokens": 3152, "completion_tokens_details": null, "prompt_tokens_details": {"audio_tokens": null, "cached_tokens": null, "text_tokens": 2337, "image_tokens": null}}, "vertex_ai_grounding_metadata": [], "vertex_ai_url_context_metadata": [], "vertex_ai_safety_results": [], "vertex_ai_citation_metadata": []}}} 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TYPE_CHECKING, Any @@ -18,7 +21,72 @@ if TYPE_CHECKING: import pandas as pd -# ruff: noqa: T201 +# Suppress harmless asyncio cleanup warnings on Windows +warnings.filterwarnings("ignore", message=".*coroutine.*was never awaited") +warnings.filterwarnings("ignore", message=".*Fatal error on SSL transport.*") +warnings.filterwarnings("ignore", message=".*Event loop is closed.*") +warnings.filterwarnings("ignore", category=RuntimeWarning, message=".*coroutine.*") + +# Install filtered stderr to suppress SSL cleanup errors +_original_stderr = sys.stderr + + +class FilteredStderr: + """Filter stderr to suppress harmless SSL cleanup errors on Windows.""" + + def __init__(self, original_stderr): + self.original_stderr = original_stderr + self.suppress_file_patterns = [ + "sslproto.py", + "proactor_events.py", + ] + self.in_ssl_traceback = False + + def write(self, message): + msg = message + msg_lower = message.lower() + + # Detect start of SSL cleanup exception - check for "Exception ignored" or "Traceback" + if ("exception ignored" in msg_lower or msg_lower.strip().startswith("traceback")) and \ + ("ssl" in msg_lower or "_sslprotocol" in msg_lower or any(p in msg for p in self.suppress_file_patterns)): + self.in_ssl_traceback = True + return + + # If we're in an SSL traceback, suppress everything until blank line + if self.in_ssl_traceback: + # Stop suppressing on blank line (end of traceback) or if we see a non-SSL error + if message.strip() == "": + self.in_ssl_traceback = False + # Continue suppressing traceback lines + return + + # Also suppress individual SSL/asyncio cleanup error lines that aren't in a traceback + if any(p in msg for p in self.suppress_file_patterns) and \ + ("runtimeerror" in msg_lower or "attributeerror" in msg_lower or "fatal error" in msg_lower or "event loop is closed" in msg_lower): + return + + self.original_stderr.write(message) + + def flush(self): + self.original_stderr.flush() + + def __getattr__(self, name): + return getattr(self.original_stderr, name) + + +# Install filtered stderr to suppress SSL cleanup errors during program execution +sys.stderr = FilteredStderr(_original_stderr) + + +def _run_async_with_cleanup(coro): + """Run an async coroutine and suppress harmless SSL cleanup errors on Windows.""" + result = asyncio.run(coro) + # Small delay to allow async cleanup + try: + asyncio.run(asyncio.sleep(0.1)) + except (RuntimeError, AttributeError): + pass + return result def run_global_search( @@ -59,21 +127,26 @@ def run_global_search( final_community_reports_list = dataframe_dict["community_reports"] index_names = dataframe_dict["index_names"] - response, context_data = asyncio.run( - api.multi_index_global_search( - config=config, - entities_list=final_entities_list, - communities_list=final_communities_list, - community_reports_list=final_community_reports_list, - index_names=index_names, - community_level=community_level, - dynamic_community_selection=dynamic_community_selection, - response_type=response_type, - streaming=streaming, - query=query, - verbose=verbose, - ) - ) + async def run_with_cleanup(): + try: + return await api.multi_index_global_search( + config=config, + entities_list=final_entities_list, + communities_list=final_communities_list, + community_reports_list=final_community_reports_list, + index_names=index_names, + community_level=community_level, + dynamic_community_selection=dynamic_community_selection, + response_type=response_type, + streaming=streaming, + query=query, + verbose=verbose, + ) + finally: + # Give time for async cleanup + await asyncio.sleep(0.1) + + response, context_data = asyncio.run(run_with_cleanup()) print(response) return response, context_data @@ -113,21 +186,31 @@ def on_context(context: Any) -> None: print() return full_response, context_data - return asyncio.run(run_streaming_search()) + async def run_streaming_with_cleanup(): + try: + return await run_streaming_search() + finally: + await asyncio.sleep(0.1) + + return asyncio.run(run_streaming_with_cleanup()) # not streaming - response, context_data = asyncio.run( - api.global_search( - config=config, - entities=final_entities, - communities=final_communities, - community_reports=final_community_reports, - community_level=community_level, - dynamic_community_selection=dynamic_community_selection, - response_type=response_type, - query=query, - verbose=verbose, - ) - ) + async def run_search_with_cleanup(): + try: + return await api.global_search( + config=config, + entities=final_entities, + communities=final_communities, + community_reports=final_community_reports, + community_level=community_level, + dynamic_community_selection=dynamic_community_selection, + response_type=response_type, + query=query, + verbose=verbose, + ) + finally: + await asyncio.sleep(0.1) + + response, context_data = asyncio.run(run_search_with_cleanup()) print(response) return response, context_data @@ -416,7 +499,7 @@ def run_basic_search( final_text_units_list = dataframe_dict["text_units"] index_names = dataframe_dict["index_names"] - response, context_data = asyncio.run( + response, context_data = _run_async_with_cleanup( api.multi_index_basic_search( config=config, text_units_list=final_text_units_list, @@ -461,7 +544,7 @@ def on_context(context: Any) -> None: return asyncio.run(run_streaming_search()) # not streaming - response, context_data = asyncio.run( + response, context_data = _run_async_with_cleanup( api.basic_search( config=config, text_units=final_text_units, diff --git a/graphrag/config/defaults.py b/graphrag/config/defaults.py index 6ec2e2d2bb..54b94d882b 100644 --- a/graphrag/config/defaults.py +++ b/graphrag/config/defaults.py @@ -47,13 +47,13 @@ DEFAULT_OUTPUT_BASE_DIR = "output" DEFAULT_CHAT_MODEL_ID = "default_chat_model" DEFAULT_CHAT_MODEL_TYPE = ModelType.Chat -DEFAULT_CHAT_MODEL = "gpt-4-turbo-preview" +DEFAULT_CHAT_MODEL = "gemini-2.5-flash-lite" DEFAULT_CHAT_MODEL_AUTH_TYPE = AuthType.APIKey DEFAULT_EMBEDDING_MODEL_ID = "default_embedding_model" DEFAULT_EMBEDDING_MODEL_TYPE = ModelType.Embedding -DEFAULT_EMBEDDING_MODEL = "text-embedding-3-small" +DEFAULT_EMBEDDING_MODEL = "gemini-embedding-001" DEFAULT_EMBEDDING_MODEL_AUTH_TYPE = AuthType.APIKey -DEFAULT_MODEL_PROVIDER = "openai" +DEFAULT_MODEL_PROVIDER = "gemini" DEFAULT_VECTOR_STORE_ID = "default_vector_store" ENCODING_MODEL = "cl100k_base" @@ -176,7 +176,7 @@ class EmbedGraphDefaults: class EmbedTextDefaults: """Default values for embedding text.""" - model: str = "text-embedding-3-small" + model: str = "gemini-embedding-001" batch_size: int = 16 batch_max_tokens: int = 8191 model_id: str = DEFAULT_EMBEDDING_MODEL_ID diff --git a/graphrag/config/init_content.py b/graphrag/config/init_content.py index fd16f20d35..45f62f28d8 100644 --- a/graphrag/config/init_content.py +++ b/graphrag/config/init_content.py @@ -22,7 +22,7 @@ type: {defs.DEFAULT_CHAT_MODEL_TYPE.value} model_provider: {defs.DEFAULT_MODEL_PROVIDER} auth_type: {defs.DEFAULT_CHAT_MODEL_AUTH_TYPE.value} # or azure_managed_identity - api_key: ${{GRAPHRAG_API_KEY}} # set this in the generated .env file, or remove if managed identity + api_key: ${{GEMINI_API_KEY}} # set this in the generated .env file, or remove if managed identity model: {defs.DEFAULT_CHAT_MODEL} # api_base: https://.openai.azure.com # api_version: 2024-05-01-preview @@ -37,7 +37,7 @@ type: {defs.DEFAULT_EMBEDDING_MODEL_TYPE.value} model_provider: {defs.DEFAULT_MODEL_PROVIDER} auth_type: {defs.DEFAULT_EMBEDDING_MODEL_AUTH_TYPE.value} - api_key: ${{GRAPHRAG_API_KEY}} + api_key: ${{GEMINI_API_KEY}} model: {defs.DEFAULT_EMBEDDING_MODEL} # api_base: https://.openai.azure.com # api_version: 2024-05-01-preview @@ -160,5 +160,5 @@ """ INIT_DOTENV = """\ -GRAPHRAG_API_KEY= +GEMINI_API_KEY= """ diff --git a/graphrag/config/load_config.py b/graphrag/config/load_config.py index de9026037d..12f44d7a27 100644 --- a/graphrag/config/load_config.py +++ b/graphrag/config/load_config.py @@ -75,10 +75,19 @@ def _load_dotenv(config_path: Path | str) -> None: config_path : Path | str The path to the config file. """ + import os + from dotenv import dotenv_values + config_path = Path(config_path) dotenv_path = config_path.parent / ".env" if dotenv_path.exists(): - load_dotenv(dotenv_path) + # Use dotenv_values to ensure variables are loaded into os.environ + env_config = dotenv_values(str(dotenv_path)) + for key, value in env_config.items(): + if value is not None: + # Strip BOM and whitespace from key names + clean_key = key.strip().lstrip('\ufeff') + os.environ[clean_key] = str(value) def _get_config_path(root_dir: Path, config_filepath: Path | None) -> Path: diff --git a/graphrag/language_model/providers/litellm/chat_model.py b/graphrag/language_model/providers/litellm/chat_model.py index 43cb7ece98..362a44636e 100644 --- a/graphrag/language_model/providers/litellm/chat_model.py +++ b/graphrag/language_model/providers/litellm/chat_model.py @@ -5,9 +5,12 @@ import inspect import json +import logging from collections.abc import AsyncGenerator, Generator from typing import TYPE_CHECKING, Any, cast +logger = logging.getLogger(__name__) + import litellm from azure.identity import DefaultAzureCredential, get_bearer_token_provider from litellm import ( @@ -221,6 +224,9 @@ def __init__( self.name = name self.config = config self.cache = cache.child(self.name) if cache else None + model_provider = config.model_provider + model = config.deployment_name or config.model + logger.info(f"Using LiteLLM provider with model: {model_provider}/{model}") self.completion, self.acompletion = _create_completions( config, self.cache, "chat" ) diff --git a/graphrag/language_model/providers/litellm/embedding_model.py b/graphrag/language_model/providers/litellm/embedding_model.py index 779222c37e..d43cf0de0d 100644 --- a/graphrag/language_model/providers/litellm/embedding_model.py +++ b/graphrag/language_model/providers/litellm/embedding_model.py @@ -3,8 +3,11 @@ """Embedding model implementation using Litellm.""" +import logging from typing import TYPE_CHECKING, Any +logger = logging.getLogger(__name__) + import litellm from azure.identity import DefaultAzureCredential, get_bearer_token_provider from litellm import ( @@ -185,6 +188,9 @@ def __init__( self.name = name self.config = config self.cache = cache.child(self.name) if cache else None + model_provider = config.model_provider + model = config.deployment_name or config.model + logger.info(f"Using LiteLLM provider with model: {model_provider}/{model}") self.embedding, self.aembedding = _create_embeddings( config, self.cache, "embeddings" ) diff --git a/input/sample.txt b/input/sample.txt new file mode 100644 index 0000000000..045edb5738 --- /dev/null +++ b/input/sample.txt @@ -0,0 +1,9 @@ +GraphRAG is a graph-based retrieval-augmented generation system that uses knowledge graphs to enhance LLM outputs. It extracts structured data from unstructured text using the power of large language models. + +The system works by: +1. Processing input documents and extracting entities and relationships +2. Building a knowledge graph from the extracted information +3. Creating community reports and embeddings +4. Enabling semantic search across the knowledge graph + +GraphRAG was developed by Microsoft Research and supports various LLM providers including OpenAI and Google Gemini. diff --git a/output/communities.parquet b/output/communities.parquet new file mode 100644 index 0000000000..3335f31ae1 Binary files /dev/null and b/output/communities.parquet differ diff --git a/output/community_reports.parquet b/output/community_reports.parquet new file mode 100644 index 0000000000..8f97b7d588 Binary files /dev/null and b/output/community_reports.parquet differ diff --git a/output/context.json b/output/context.json new file mode 100644 index 0000000000..9e26dfeeb6 --- /dev/null +++ b/output/context.json @@ -0,0 +1 @@ +{} \ No newline at end of file diff --git a/output/documents.parquet b/output/documents.parquet new file mode 100644 index 0000000000..e298e23053 Binary files /dev/null and b/output/documents.parquet differ diff --git a/output/entities.parquet b/output/entities.parquet new file mode 100644 index 0000000000..53560b5687 Binary files /dev/null and b/output/entities.parquet differ diff --git a/output/relationships.parquet b/output/relationships.parquet new file mode 100644 index 0000000000..eace8d4efc Binary files /dev/null and b/output/relationships.parquet differ diff --git a/output/stats.json b/output/stats.json new file mode 100644 index 0000000000..a710c53f45 --- /dev/null +++ b/output/stats.json @@ -0,0 +1,38 @@ +{ + "total_runtime": 13.642119646072388, + "num_documents": 1, + "update_documents": 0, + "input_load_time": 0, + "workflows": { + "load_input_documents": { + "overall": 0.02956700325012207 + }, + "create_base_text_units": { + "overall": 0.20486879348754883 + }, + "create_final_documents": { + "overall": 0.06813263893127441 + }, + "extract_graph": { + "overall": 5.020992279052734 + }, + "finalize_graph": { + "overall": 0.11890435218811035 + }, + "extract_covariates": { + "overall": 0.0010013580322265625 + }, + "create_communities": { + "overall": 0.1343822479248047 + }, + "create_final_text_units": { + "overall": 0.0707404613494873 + }, + "create_community_reports": { + "overall": 4.517311334609985 + }, + "generate_text_embeddings": { + "overall": 3.4712648391723633 + } + } +} \ No newline at end of file diff --git a/output/text_units.parquet b/output/text_units.parquet new file mode 100644 index 0000000000..1af0889139 Binary files /dev/null and b/output/text_units.parquet differ diff --git a/prompts/basic_search_system_prompt.txt b/prompts/basic_search_system_prompt.txt new file mode 100644 index 0000000000..eb2afc42b8 --- /dev/null +++ b/prompts/basic_search_system_prompt.txt @@ -0,0 +1,67 @@ + +---Role--- + +You are a helpful assistant responding to questions about data in the tables provided. + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all relevant information in the input data tables appropriate for the response length and format. + +You should use the data provided in the data tables below as the primary context for generating the response. + +If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: Sources (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Sources (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "source_id" column in the provided tables. + +Do not include information where the supporting evidence for it is not provided. + + +---Target response length and format--- + +{response_type} + + +---Data tables--- + +{context_data} + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all relevant information in the input data appropriate for the response length and format. + +You should use the data provided in the data tables below as the primary context for generating the response. + +If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: Sources (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Sources (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the source id taken from the "source_id" column in the provided tables. + +Do not include information where the supporting evidence for it is not provided. + + +---Target response length and format--- + +{response_type} + +Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown. diff --git a/prompts/community_report_graph.txt b/prompts/community_report_graph.txt new file mode 100644 index 0000000000..595e7a3e86 --- /dev/null +++ b/prompts/community_report_graph.txt @@ -0,0 +1,149 @@ + +You are an AI assistant that helps a human analyst to perform general information discovery. Information discovery is the process of identifying and assessing relevant information associated with certain entities (e.g., organizations and individuals) within a network. + +# Goal +Write a comprehensive report of a community, given a list of entities that belong to the community as well as their relationships and optional associated claims. The report will be used to inform decision-makers about information associated with the community and their potential impact. The content of this report includes an overview of the community's key entities, their legal compliance, technical capabilities, reputation, and noteworthy claims. + +# Report Structure + +The report should include the following sections: + +- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title. +- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant information associated with its entities. +- IMPACT SEVERITY RATING: a float score between 0-10 that represents the severity of IMPACT posed by entities within the community. IMPACT is the scored importance of a community. +- RATING EXPLANATION: Give a single sentence explanation of the IMPACT severity rating. +- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive. + +Return output as a well-formed JSON-formatted string with the following format: + {{ + "title": , + "summary": , + "rating": , + "rating_explanation": , + "findings": [ + {{ + "summary":, + "explanation": + }}, + {{ + "summary":, + "explanation": + }} + ] + }} + +# Grounding Rules + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (1), Entities (5, 7); Relationships (23); Claims (7, 2, 34, 64, 46, +more)]." + +where 1, 5, 7, 23, 2, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + +Limit the total report length to {max_report_length} words. + +# Example Input +----------- +Text: + +Entities + +id,entity,description +5,VERDANT OASIS PLAZA,Verdant Oasis Plaza is the location of the Unity March +6,HARMONY ASSEMBLY,Harmony Assembly is an organization that is holding a march at Verdant Oasis Plaza + +Relationships + +id,source,target,description +37,VERDANT OASIS PLAZA,UNITY MARCH,Verdant Oasis Plaza is the location of the Unity March +38,VERDANT OASIS PLAZA,HARMONY ASSEMBLY,Harmony Assembly is holding a march at Verdant Oasis Plaza +39,VERDANT OASIS PLAZA,UNITY MARCH,The Unity March is taking place at Verdant Oasis Plaza +40,VERDANT OASIS PLAZA,TRIBUNE SPOTLIGHT,Tribune Spotlight is reporting on the Unity march taking place at Verdant Oasis Plaza +41,VERDANT OASIS PLAZA,BAILEY ASADI,Bailey Asadi is speaking at Verdant Oasis Plaza about the march +43,HARMONY ASSEMBLY,UNITY MARCH,Harmony Assembly is organizing the Unity March + +Output: +{{ + "title": "Verdant Oasis Plaza and Unity March", + "summary": "The community revolves around the Verdant Oasis Plaza, which is the location of the Unity March. The plaza has relationships with the Harmony Assembly, Unity March, and Tribune Spotlight, all of which are associated with the march event.", + "rating": 5.0, + "rating_explanation": "The impact severity rating is moderate due to the potential for unrest or conflict during the Unity March.", + "findings": [ + {{ + "summary": "Verdant Oasis Plaza as the central location", + "explanation": "Verdant Oasis Plaza is the central entity in this community, serving as the location for the Unity March. This plaza is the common link between all other entities, suggesting its significance in the community. The plaza's association with the march could potentially lead to issues such as public disorder or conflict, depending on the nature of the march and the reactions it provokes. [Data: Entities (5), Relationships (37, 38, 39, 40, 41,+more)]" + }}, + {{ + "summary": "Harmony Assembly's role in the community", + "explanation": "Harmony Assembly is another key entity in this community, being the organizer of the march at Verdant Oasis Plaza. The nature of Harmony Assembly and its march could be a potential source of threat, depending on their objectives and the reactions they provoke. The relationship between Harmony Assembly and the plaza is crucial in understanding the dynamics of this community. [Data: Entities(6), Relationships (38, 43)]" + }}, + {{ + "summary": "Unity March as a significant event", + "explanation": "The Unity March is a significant event taking place at Verdant Oasis Plaza. This event is a key factor in the community's dynamics and could be a potential source of threat, depending on the nature of the march and the reactions it provokes. The relationship between the march and the plaza is crucial in understanding the dynamics of this community. [Data: Relationships (39)]" + }}, + {{ + "summary": "Role of Tribune Spotlight", + "explanation": "Tribune Spotlight is reporting on the Unity March taking place in Verdant Oasis Plaza. This suggests that the event has attracted media attention, which could amplify its impact on the community. The role of Tribune Spotlight could be significant in shaping public perception of the event and the entities involved. [Data: Relationships (40)]" + }} + ] +}} + + +# Real Data + +Use the following text for your answer. Do not make anything up in your answer. + +Text: +{input_text} + +The report should include the following sections: + +- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title. +- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant information associated with its entities. +- IMPACT SEVERITY RATING: a float score between 0-10 that represents the severity of IMPACT posed by entities within the community. IMPACT is the scored importance of a community. +- RATING EXPLANATION: Give a single sentence explanation of the IMPACT severity rating. +- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive. + +Return output as a well-formed JSON-formatted string with the following format: + {{ + "title": , + "summary": , + "rating": , + "rating_explanation": , + "findings": [ + {{ + "summary":, + "explanation": + }}, + {{ + "summary":, + "explanation": + }} + ] + }} + +# Grounding Rules + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (1), Entities (5, 7); Relationships (23); Claims (7, 2, 34, 64, 46, +more)]." + +where 1, 5, 7, 23, 2, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + +Limit the total report length to {max_report_length} words. + +Output: \ No newline at end of file diff --git a/prompts/community_report_text.txt b/prompts/community_report_text.txt new file mode 100644 index 0000000000..cfe41eff5f --- /dev/null +++ b/prompts/community_report_text.txt @@ -0,0 +1,95 @@ + +You are an AI assistant that helps a human analyst to perform general information discovery. +Information discovery is the process of identifying and assessing relevant information associated with certain entities (e.g., organizations and individuals) within a network. + +# Goal +Write a comprehensive report of a community, given a list of entities that belong to the community as well as their relationships and optional associated claims. +The report will be used to inform decision-makers about information associated with the community and their potential impact. +The content of this report includes an overview of the community's key entities, their core attributes or capabilities, their connections, and noteworthy claims. +Retain as much time specific information as possible so your end user can build a timeline of events. + +# Report Structure +The report should include the following sections: +- TITLE: community's name that represents its key entities - title should be short but specific. When possible, include representative named entities in the title. Avoid including phrases like 'eligibility assessment' or 'eligibility assessment report' in the title. +- SUMMARY: An executive summary of the community's overall structure, how its entities are related to each other, and significant program-specific or eligibility-related insights. +- IMPORTANCE RATING: A float score between 0-10 that represents the importance of entities within the community.. +- RATING EXPLANATION: Give a single sentence explanation of the importance rating. +- DETAILED FINDINGS: A list of 5-10 key insights about the community. Each insight should have a short summary followed by multiple paragraphs of explanatory text grounded according to the grounding rules below. Be comprehensive. +- DATE RANGE: A range of dates (YYYY-MM-DD) with the format [START, END] which corresponds to the date range of text units and intermediate reports used to build the report. + +Return output as a well-formed JSON-formatted string with the following format. Don't use any unnecessary escape sequences. The output should be a single JSON object that can be parsed by json.loads. + {{ + "title": "", + "summary": "", + "rating": , + "rating_explanation": "", + "findings": [{{"summary":"", "explanation": "", "explanation": "", ""], + + }} + +# Grounding Rules +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids), (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X resolved a major issue with project Y [Data: Sources (1, 5), Date_Range ((2001, 05, 12), (2001, 05, 14))]. He also made major updates to the database of app Y [Data: Reports (2, 4), Sources (7, 23, 2, 34, 46, +more), Date_Range ((2001, 05, 15), (2001, 05, 18))"" + +where 1, 2, 4, 5, 7, 23, 2, 34, and 46 represent the id (not the index) of the relevant data record. + +Limit the total report length to {max_report_length} words. + +# Example Input +----------- +SOURCES +id, text +1, Text: From: compliance.office@enron.com To: management.team@enron.com Cc: legal.team@enron.com, risk@enron.com Date: Wed, 12 Jul 2000 08:30:00 -0600 (CST) Subject: Quick Update on Compliance & Risk Efforts +2, Quick update on what's been cooking in Compliance and Risk Management. Risk Management is stepping up — They've been tightening up on our financial risk assessments and mitigation strategies since early this year. +3, Their efforts are key to keeping us on solid ground financially and in compliance with the latest market regulations as of mid-2000. It's crucial for our strategic planning and helps us stay ahead. +5, Legal's keeping us in check — The Legal Compliance team is on top of ensuring all our operations are up to scratch with legal standards. They're especially focused on improving our corporate governance and contract management as of the second quarter of 2000. This is critical for keeping our operations smooth and legally sound. +9, Working together — Risk Management and Legal Compliance have been syncing up better than ever since the start of Q2 2000. They're making sure our strategies are not just effective but also fully compliant. This coordination is essential for our integrated governance approach. +10, Your thoughts? — How do these updates impact your area? Got ideas on how we can do better? Give your department heads a shout. +11, Thanks for staying engaged. Let's keep pushing for better and smarter ways to work. Cheers, Jane Doe + +Output: + +{{ + "title": "Enron Compliance and Risk Management Overview as of July 2000", + "summary": "This report delves into Enron's key departments focusing on compliance and risk management, illustrating how these entities interact within the organizational framework to uphold regulatory standards and manage financial risks effectively. The information is relevant to the company's operations around mid-2000.", + "rating": 9.2, + "rating_explanation": "The high importance rating reflects the critical roles that the Risk Management and Legal Compliance Departments play in ensuring Enron's adherence to financial and legal regulations, crucial for maintaining the company's integrity and operational stability.", + "findings": [ + {{ + "summary": "Risk Management Operational Scope", + "explanation": "The Risk Management Department at Enron plays a pivotal role in identifying, assessing, and mitigating financial risks. Their proactive approach, highlighted from the beginning of 2000, helps safeguard Enron against potential financial pitfalls and ensures continuous compliance with evolving market regulations. Effective risk management not only prevents financial anomalies but also supports the company's strategic decision-making processes. + +[Data: Sources (2, 3), Date_Range ((2000, 01, 01), (2000, 07, 12))]" + }}, + {{ + "summary": "Legal Compliance and Governance", + "explanation": "The Legal Compliance Department ensures that all Enron's operations adhere to the legal standards set by regulatory bodies. Their focus on corporate governance and contract management, noted starting Q2 2000, is crucial in maintaining Enron's reputation and operational legality, especially in managing complex contracts and corporate agreements. Their efforts underscore the commitment to upholding high legal standards and ethical practices. + +[Data: Source (5), Date_Range ((2000, 04, 01), (2000, 07, 12))]" + }}, + {{ + "summary": "Interdepartmental Collaboration for Compliance", + "explanation": "Collaboration between the Risk Management and Legal Compliance Departments, established in Q2 2000, ensures that risk mitigation strategies are legally sound and that compliance measures consider financial risks. This synergy is vital for holistic governance and has been instrumental in integrating risk management with legal compliance strategies at Enron. Enhanced interdepartmental cooperation during this period plays a crucial role in aligning the company's strategies with regulatory requirements. + +[Data: Sources (9), Date_Range ((2000, 04, 01), (2000, 07, 12))]" + }} + ], + "date_range": ["2000-01-01", "2000-07-12"] +}} + + +# Real Data + +Use the following text for your answer. Do not make anything up in your answer. + +Text: +{input_text} + +Output: diff --git a/prompts/drift_reduce_prompt.txt b/prompts/drift_reduce_prompt.txt new file mode 100644 index 0000000000..88c9cfa17b --- /dev/null +++ b/prompts/drift_reduce_prompt.txt @@ -0,0 +1,60 @@ + +---Role--- + +You are a helpful assistant responding to questions about data in the reports provided. + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input reports appropriate for the response length and format, and incorporating any relevant general knowledge while being as specific, accurate and concise as possible. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (1, 5, 15)]." + +Do not include information where the supporting evidence for it is not provided. + +If you decide to use general knowledge, you should add a delimiter stating that the information is not supported by the data tables. For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing. [Data: General Knowledge (href)]" + +---Data Reports--- + +{context_data} + +---Target response length and format--- + +{response_type} + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input reports appropriate for the response length and format, and incorporating any relevant general knowledge while being as specific, accurate and concise as possible. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (1, 5, 15)]." + +Do not include information where the supporting evidence for it is not provided. + +If you decide to use general knowledge, you should add a delimiter stating that the information is not supported by the data tables. For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing. [Data: General Knowledge (href)]". + +Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown. Now answer the following query using the data above: + diff --git a/prompts/drift_search_system_prompt.txt b/prompts/drift_search_system_prompt.txt new file mode 100644 index 0000000000..eb3d6544e7 --- /dev/null +++ b/prompts/drift_search_system_prompt.txt @@ -0,0 +1,67 @@ + +---Role--- + +You are a helpful assistant responding to questions about data in the tables provided. + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input data tables appropriate for the response length and format, and incorporating any relevant general knowledge. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (15, 16)]." + +where 15, 16, 1, 5, 7, 23, 2, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Pay close attention specifically to the Sources tables as it contains the most relevant information for the user query. You will be rewarded for preserving the context of the sources in your response. + +---Target response length and format--- + +{response_type} + + +---Data tables--- + +{context_data} + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input data tables appropriate for the response length and format, and incorporating any relevant general knowledge. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (15, 16)]." + +where 15, 16, 1, 5, 7, 23, 2, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Pay close attention specifically to the Sources tables as it contains the most relevant information for the user query. You will be rewarded for preserving the context of the sources in your response. + +---Target response length and format--- + +{response_type} + +Add sections and commentary to the response as appropriate for the length and format. + +Additionally provide a score between 0 and 100 representing how well the response addresses the overall research question: {global_query}. Based on your response, suggest up to five follow-up questions that could be asked to further explore the topic as it relates to the overall research question. Do not include scores or follow up questions in the 'response' field of the JSON, add them to the respective 'score' and 'follow_up_queries' keys of the JSON output. Format your response in JSON with the following keys and values: + +{{'response': str, Put your answer, formatted in markdown, here. Do not answer the global query in this section. +'score': int, +'follow_up_queries': List[str]}} diff --git a/prompts/extract_claims.txt b/prompts/extract_claims.txt new file mode 100644 index 0000000000..0b795c3465 --- /dev/null +++ b/prompts/extract_claims.txt @@ -0,0 +1,52 @@ + +-Target activity- +You are an intelligent assistant that helps a human analyst to analyze claims against certain entities presented in a text document. + +-Goal- +Given a text document that is potentially relevant to this activity, an entity specification, and a claim description, extract all entities that match the entity specification and all claims against those entities. + +-Steps- +1. Extract all named entities that match the predefined entity specification. Entity specification can either be a list of entity names or a list of entity types. +2. For each entity identified in step 1, extract all claims associated with the entity. Claims need to match the specified claim description, and the entity should be the subject of the claim. +For each claim, extract the following information: +- Subject: name of the entity that is subject of the claim, capitalized. The subject entity is one that committed the action described in the claim. Subject needs to be one of the named entities identified in step 1. +- Object: name of the entity that is object of the claim, capitalized. The object entity is one that either reports/handles or is affected by the action described in the claim. If object entity is unknown, use **NONE**. +- Claim Type: overall category of the claim, capitalized. Name it in a way that can be repeated across multiple text inputs, so that similar claims share the same claim type +- Claim Status: **TRUE**, **FALSE**, or **SUSPECTED**. TRUE means the claim is confirmed, FALSE means the claim is found to be False, SUSPECTED means the claim is not verified. +- Claim Description: Detailed description explaining the reasoning behind the claim, together with all the related evidence and references. +- Claim Date: Period (start_date, end_date) when the claim was made. Both start_date and end_date should be in ISO-8601 format. If the claim was made on a single date rather than a date range, set the same date for both start_date and end_date. If date is unknown, return **NONE**. +- Claim Source Text: List of **all** quotes from the original text that are relevant to the claim. + +Format each claim as ({tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +3. Return output in English as a single list of all the claims identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. + +4. When finished, output {completion_delimiter} + +-Examples- +Example 1: +Entity specification: organization +Claim description: red flags associated with an entity +Text: According to an article on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B. The company is owned by Person C who was suspected of engaging in corruption activities in 2015. +Output: + +(COMPANY A{tuple_delimiter}GOVERNMENT AGENCY B{tuple_delimiter}ANTI-COMPETITIVE PRACTICES{tuple_delimiter}TRUE{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10{tuple_delimiter}According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.) +{completion_delimiter} + +Example 2: +Entity specification: Company A, Person C +Claim description: red flags associated with an entity +Text: According to an article on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B. The company is owned by Person C who was suspected of engaging in corruption activities in 2015. +Output: + +(COMPANY A{tuple_delimiter}GOVERNMENT AGENCY B{tuple_delimiter}ANTI-COMPETITIVE PRACTICES{tuple_delimiter}TRUE{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}2022-01-10T00:00:00{tuple_delimiter}Company A was found to engage in anti-competitive practices because it was fined for bid rigging in multiple public tenders published by Government Agency B according to an article published on 2022/01/10{tuple_delimiter}According to an article published on 2022/01/10, Company A was fined for bid rigging while participating in multiple public tenders published by Government Agency B.) +{record_delimiter} +(PERSON C{tuple_delimiter}NONE{tuple_delimiter}CORRUPTION{tuple_delimiter}SUSPECTED{tuple_delimiter}2015-01-01T00:00:00{tuple_delimiter}2015-12-30T00:00:00{tuple_delimiter}Person C was suspected of engaging in corruption activities in 2015{tuple_delimiter}The company is owned by Person C who was suspected of engaging in corruption activities in 2015) +{completion_delimiter} + +-Real Data- +Use the following input for your answer. +Entity specification: {entity_specs} +Claim description: {claim_description} +Text: {input_text} +Output: \ No newline at end of file diff --git a/prompts/extract_graph.txt b/prompts/extract_graph.txt new file mode 100644 index 0000000000..c0edeba3e4 --- /dev/null +++ b/prompts/extract_graph.txt @@ -0,0 +1,121 @@ + +-Goal- +Given a text document that is potentially relevant to this activity and a list of entity types, identify all entities of those types from the text and all relationships among the identified entities. + +-Steps- +1. Identify all entities. For each identified entity, extract the following information: +- entity_name: Name of the entity, capitalized +- entity_type: One of the following types: [{entity_types}] +- entity_description: Comprehensive description of the entity's attributes and activities +Format each entity as ("entity"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +2. From the entities identified in step 1, identify all pairs of (source_entity, target_entity) that are *clearly related* to each other. +For each pair of related entities, extract the following information: +- source_entity: name of the source entity, as identified in step 1 +- target_entity: name of the target entity, as identified in step 1 +- relationship_description: explanation as to why you think the source entity and the target entity are related to each other +- relationship_strength: a numeric score indicating strength of the relationship between the source entity and target entity + Format each relationship as ("relationship"{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}{tuple_delimiter}) + +3. Return output in English as a single list of all the entities and relationships identified in steps 1 and 2. Use **{record_delimiter}** as the list delimiter. + +4. When finished, output {completion_delimiter} + +###################### +-Examples- +###################### +Example 1: +Entity_types: ORGANIZATION,PERSON +Text: +The Verdantis's Central Institution is scheduled to meet on Monday and Thursday, with the institution planning to release its latest policy decision on Thursday at 1:30 p.m. PDT, followed by a press conference where Central Institution Chair Martin Smith will take questions. Investors expect the Market Strategy Committee to hold its benchmark interest rate steady in a range of 3.5%-3.75%. +###################### +Output: +("entity"{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution is the Federal Reserve of Verdantis, which is setting interest rates on Monday and Thursday) +{record_delimiter} +("entity"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}PERSON{tuple_delimiter}Martin Smith is the chair of the Central Institution) +{record_delimiter} +("entity"{tuple_delimiter}MARKET STRATEGY COMMITTEE{tuple_delimiter}ORGANIZATION{tuple_delimiter}The Central Institution committee makes key decisions about interest rates and the growth of Verdantis's money supply) +{record_delimiter} +("relationship"{tuple_delimiter}MARTIN SMITH{tuple_delimiter}CENTRAL INSTITUTION{tuple_delimiter}Martin Smith is the Chair of the Central Institution and will answer questions at a press conference{tuple_delimiter}9) +{completion_delimiter} + +###################### +Example 2: +Entity_types: ORGANIZATION +Text: +TechGlobal's (TG) stock skyrocketed in its opening day on the Global Exchange Thursday. But IPO experts warn that the semiconductor corporation's debut on the public markets isn't indicative of how other newly listed companies may perform. + +TechGlobal, a formerly public company, was taken private by Vision Holdings in 2014. The well-established chip designer says it powers 85% of premium smartphones. +###################### +Output: +("entity"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}ORGANIZATION{tuple_delimiter}TechGlobal is a stock now listed on the Global Exchange which powers 85% of premium smartphones) +{record_delimiter} +("entity"{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}ORGANIZATION{tuple_delimiter}Vision Holdings is a firm that previously owned TechGlobal) +{record_delimiter} +("relationship"{tuple_delimiter}TECHGLOBAL{tuple_delimiter}VISION HOLDINGS{tuple_delimiter}Vision Holdings formerly owned TechGlobal from 2014 until present{tuple_delimiter}5) +{completion_delimiter} + +###################### +Example 3: +Entity_types: ORGANIZATION,GEO,PERSON +Text: +Five Aurelians jailed for 8 years in Firuzabad and widely regarded as hostages are on their way home to Aurelia. + +The swap orchestrated by Quintara was finalized when $8bn of Firuzi funds were transferred to financial institutions in Krohaara, the capital of Quintara. + +The exchange initiated in Firuzabad's capital, Tiruzia, led to the four men and one woman, who are also Firuzi nationals, boarding a chartered flight to Krohaara. + +They were welcomed by senior Aurelian officials and are now on their way to Aurelia's capital, Cashion. + +The Aurelians include 39-year-old businessman Samuel Namara, who has been held in Tiruzia's Alhamia Prison, as well as journalist Durke Bataglani, 59, and environmentalist Meggie Tazbah, 53, who also holds Bratinas nationality. +###################### +Output: +("entity"{tuple_delimiter}FIRUZABAD{tuple_delimiter}GEO{tuple_delimiter}Firuzabad held Aurelians as hostages) +{record_delimiter} +("entity"{tuple_delimiter}AURELIA{tuple_delimiter}GEO{tuple_delimiter}Country seeking to release hostages) +{record_delimiter} +("entity"{tuple_delimiter}QUINTARA{tuple_delimiter}GEO{tuple_delimiter}Country that negotiated a swap of money in exchange for hostages) +{record_delimiter} +{record_delimiter} +("entity"{tuple_delimiter}TIRUZIA{tuple_delimiter}GEO{tuple_delimiter}Capital of Firuzabad where the Aurelians were being held) +{record_delimiter} +("entity"{tuple_delimiter}KROHAARA{tuple_delimiter}GEO{tuple_delimiter}Capital city in Quintara) +{record_delimiter} +("entity"{tuple_delimiter}CASHION{tuple_delimiter}GEO{tuple_delimiter}Capital city in Aurelia) +{record_delimiter} +("entity"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}PERSON{tuple_delimiter}Aurelian who spent time in Tiruzia's Alhamia Prison) +{record_delimiter} +("entity"{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}GEO{tuple_delimiter}Prison in Tiruzia) +{record_delimiter} +("entity"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}PERSON{tuple_delimiter}Aurelian journalist who was held hostage) +{record_delimiter} +("entity"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}PERSON{tuple_delimiter}Bratinas national and environmentalist who was held hostage) +{record_delimiter} +("relationship"{tuple_delimiter}FIRUZABAD{tuple_delimiter}AURELIA{tuple_delimiter}Firuzabad negotiated a hostage exchange with Aurelia{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}AURELIA{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}QUINTARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Quintara brokered the hostage exchange between Firuzabad and Aurelia{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}ALHAMIA PRISON{tuple_delimiter}Samuel Namara was a prisoner at Alhamia prison{tuple_delimiter}8) +{record_delimiter} +("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}Samuel Namara and Meggie Tazbah were exchanged in the same hostage release{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Samuel Namara and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}Meggie Tazbah and Durke Bataglani were exchanged in the same hostage release{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}SAMUEL NAMARA{tuple_delimiter}FIRUZABAD{tuple_delimiter}Samuel Namara was a hostage in Firuzabad{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}MEGGIE TAZBAH{tuple_delimiter}FIRUZABAD{tuple_delimiter}Meggie Tazbah was a hostage in Firuzabad{tuple_delimiter}2) +{record_delimiter} +("relationship"{tuple_delimiter}DURKE BATAGLANI{tuple_delimiter}FIRUZABAD{tuple_delimiter}Durke Bataglani was a hostage in Firuzabad{tuple_delimiter}2) +{completion_delimiter} + +###################### +-Real Data- +###################### +Entity_types: {entity_types} +Text: {input_text} +###################### +Output: \ No newline at end of file diff --git a/prompts/global_search_knowledge_system_prompt.txt b/prompts/global_search_knowledge_system_prompt.txt new file mode 100644 index 0000000000..e69a829f4d --- /dev/null +++ b/prompts/global_search_knowledge_system_prompt.txt @@ -0,0 +1,3 @@ + +The response may also include relevant real-world knowledge outside the dataset, but it must be explicitly annotated with a verification tag [LLM: verify]. For example: +"This is an example sentence supported by real-world knowledge [LLM: verify]." diff --git a/prompts/global_search_map_system_prompt.txt b/prompts/global_search_map_system_prompt.txt new file mode 100644 index 0000000000..5fc61f6093 --- /dev/null +++ b/prompts/global_search_map_system_prompt.txt @@ -0,0 +1,79 @@ + +---Role--- + +You are a helpful assistant responding to questions about data in the tables provided. + + +---Goal--- + +Generate a response consisting of a list of key points that responds to the user's question, summarizing all relevant information in the input data tables. + +You should use the data provided in the data tables below as the primary context for generating the response. +If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +Each key point in the response should have the following element: +- Description: A comprehensive description of the point. +- Importance Score: An integer score between 0-100 that indicates how important the point is in answering the user's question. An 'I don't know' type of response should have a score of 0. + +The response should be JSON formatted as follows: +{{ + "points": [ + {{"description": "Description of point 1 [Data: Reports (report ids)]", "score": score_value}}, + {{"description": "Description of point 2 [Data: Reports (report ids)]", "score": score_value}} + ] +}} + +The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will". + +Points supported by data should list the relevant reports as references as follows: +"This is an example sentence supported by data references [Data: Reports (report ids)]" + +**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Reports (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data report in the provided tables. + +Do not include information where the supporting evidence for it is not provided. + +Limit your response length to {max_length} words. + +---Data tables--- + +{context_data} + +---Goal--- + +Generate a response consisting of a list of key points that responds to the user's question, summarizing all relevant information in the input data tables. + +You should use the data provided in the data tables below as the primary context for generating the response. +If you don't know the answer or if the input data tables do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +Each key point in the response should have the following element: +- Description: A comprehensive description of the point. +- Importance Score: An integer score between 0-100 that indicates how important the point is in answering the user's question. An 'I don't know' type of response should have a score of 0. + +The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will". + +Points supported by data should list the relevant reports as references as follows: +"This is an example sentence supported by data references [Data: Reports (report ids)]" + +**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 64, 46, 34, +more)]. He is also CEO of company X [Data: Reports (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data report in the provided tables. + +Do not include information where the supporting evidence for it is not provided. + +Limit your response length to {max_length} words. + +The response should be JSON formatted as follows: +{{ + "points": [ + {{"description": "Description of point 1 [Data: Reports (report ids)]", "score": score_value}}, + {{"description": "Description of point 2 [Data: Reports (report ids)]", "score": score_value}} + ] +}} diff --git a/prompts/global_search_reduce_system_prompt.txt b/prompts/global_search_reduce_system_prompt.txt new file mode 100644 index 0000000000..4718ee90ef --- /dev/null +++ b/prompts/global_search_reduce_system_prompt.txt @@ -0,0 +1,75 @@ + +---Role--- + +You are a helpful assistant responding to questions about a dataset by synthesizing perspectives from multiple analysts. + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarize all the reports from multiple analysts who focused on different parts of the dataset. + +Note that the analysts' reports provided below are ranked in the **descending order of importance**. + +If you don't know the answer or if the provided reports do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +The final response should remove all irrelevant information from the analysts' reports and merge the cleaned information into a comprehensive answer that provides explanations of all the key points and implications appropriate for the response length and format. + +Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown. + +The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will". + +The response should also preserve all the data references previously included in the analysts' reports, but do not mention the roles of multiple analysts in the analysis process. + +**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 34, 46, 64, +more)]. He is also CEO of company X [Data: Reports (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + +Limit your response length to {max_length} words. + +---Target response length and format--- + +{response_type} + + +---Analyst Reports--- + +{report_data} + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarize all the reports from multiple analysts who focused on different parts of the dataset. + +Note that the analysts' reports provided below are ranked in the **descending order of importance**. + +If you don't know the answer or if the provided reports do not contain sufficient information to provide an answer, just say so. Do not make anything up. + +The final response should remove all irrelevant information from the analysts' reports and merge the cleaned information into a comprehensive answer that provides explanations of all the key points and implications appropriate for the response length and format. + +The response shall preserve the original meaning and use of modal verbs such as "shall", "may" or "will". + +The response should also preserve all the data references previously included in the analysts' reports, but do not mention the roles of multiple analysts in the analysis process. + +**Do not list more than 5 record ids in a single reference**. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Reports (2, 7, 34, 46, 64, +more)]. He is also CEO of company X [Data: Reports (1, 3)]" + +where 1, 2, 3, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + +Limit your response length to {max_length} words. + +---Target response length and format--- + +{response_type} + +Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown. diff --git a/prompts/local_search_system_prompt.txt b/prompts/local_search_system_prompt.txt new file mode 100644 index 0000000000..0c20dd8ad6 --- /dev/null +++ b/prompts/local_search_system_prompt.txt @@ -0,0 +1,63 @@ + +---Role--- + +You are a helpful assistant responding to questions about data in the tables provided. + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input data tables appropriate for the response length and format, and incorporating any relevant general knowledge. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (15, 16), Reports (1), Entities (5, 7); Relationships (23); Claims (2, 7, 34, 46, 64, +more)]." + +where 15, 16, 1, 5, 7, 23, 2, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + + +---Target response length and format--- + +{response_type} + + +---Data tables--- + +{context_data} + + +---Goal--- + +Generate a response of the target length and format that responds to the user's question, summarizing all information in the input data tables appropriate for the response length and format, and incorporating any relevant general knowledge. + +If you don't know the answer, just say so. Do not make anything up. + +Points supported by data should list their data references as follows: + +"This is an example sentence supported by multiple data references [Data: (record ids); (record ids)]." + +Do not list more than 5 record ids in a single reference. Instead, list the top 5 most relevant record ids and add "+more" to indicate that there are more. + +For example: + +"Person X is the owner of Company Y and subject to many allegations of wrongdoing [Data: Sources (15, 16), Reports (1), Entities (5, 7); Relationships (23); Claims (2, 7, 34, 46, 64, +more)]." + +where 15, 16, 1, 5, 7, 23, 2, 7, 34, 46, and 64 represent the id (not the index) of the relevant data record. + +Do not include information where the supporting evidence for it is not provided. + + +---Target response length and format--- + +{response_type} + +Add sections and commentary to the response as appropriate for the length and format. Style the response in markdown. diff --git a/prompts/question_gen_system_prompt.txt b/prompts/question_gen_system_prompt.txt new file mode 100644 index 0000000000..f87287d5f5 --- /dev/null +++ b/prompts/question_gen_system_prompt.txt @@ -0,0 +1,22 @@ + +---Role--- + +You are a helpful assistant generating a bulleted list of {question_count} questions about data in the tables provided. + + +---Data tables--- + +{context_data} + + +---Goal--- + +Given a series of example questions provided by the user, generate a bulleted list of {question_count} candidates for the next question. Use - marks as bullet points. + +These candidate questions should represent the most important or urgent information content or themes in the data tables. + +The candidate questions should be answerable using the data tables provided, but should not mention any specific data fields or data tables in the question text. + +If the user's questions reference several named entities, then each candidate question should reference all named entities. + +---Example questions--- diff --git a/prompts/summarize_descriptions.txt b/prompts/summarize_descriptions.txt new file mode 100644 index 0000000000..ab0262fedb --- /dev/null +++ b/prompts/summarize_descriptions.txt @@ -0,0 +1,14 @@ + +You are a helpful assistant responsible for generating a comprehensive summary of the data provided below. +Given one or more 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. +Make sure it is written in third person, and include the entity names so we have the full context. +Limit the final description length to {max_length} words. + +####### +-Data- +Entities: {entity_name} +Description List: {description_list} +####### +Output: diff --git a/settings.yaml b/settings.yaml new file mode 100644 index 0000000000..02c0beddfe --- /dev/null +++ b/settings.yaml @@ -0,0 +1,146 @@ +### This config file contains required core defaults that must be set, along with a handful of common optional settings. +### For a full list of available settings, see https://microsoft.github.io/graphrag/config/yaml/ + +### LLM settings ### +## There are a number of settings to tune the threading and token limits for LLM calls - check the docs. + +models: + default_chat_model: + type: chat + model_provider: gemini + auth_type: api_key # or azure_managed_identity + api_key: ${GEMINI_API_KEY} # set this in the generated .env file, or remove if managed identity + model: gemini-2.5-flash-lite + # api_base: https://.openai.azure.com + # api_version: 2024-05-01-preview + model_supports_json: true # recommended if this is available for your model. + concurrent_requests: 25 + async_mode: threaded # or asyncio + retry_strategy: exponential_backoff + max_retries: 10 + tokens_per_minute: null + requests_per_minute: null + default_embedding_model: + type: embedding + model_provider: gemini + auth_type: api_key + api_key: ${GEMINI_API_KEY} + model: gemini-embedding-001 + # api_base: https://.openai.azure.com + # api_version: 2024-05-01-preview + concurrent_requests: 25 + async_mode: threaded # or asyncio + retry_strategy: exponential_backoff + max_retries: 10 + tokens_per_minute: null + requests_per_minute: null + +### Input settings ### + +input: + storage: + type: file # or blob + base_dir: "input" + file_type: text # [csv, text, json] + +chunks: + size: 1200 + overlap: 100 + group_by_columns: [id] + +### Output/storage settings ### +## If blob storage is specified in the following four sections, +## connection_string and container_name must be provided + +output: + type: file # [file, blob, cosmosdb] + base_dir: "output" + +cache: + type: file # [file, blob, cosmosdb] + base_dir: "cache" + +reporting: + type: file # [file, blob] + base_dir: "logs" + +vector_store: + default_vector_store: + type: lancedb + db_uri: output\lancedb + container_name: default + +### Workflow settings ### + +embed_text: + model_id: default_embedding_model + vector_store_id: default_vector_store + +extract_graph: + model_id: default_chat_model + prompt: "prompts/extract_graph.txt" + entity_types: [organization,person,geo,event] + max_gleanings: 1 + +summarize_descriptions: + model_id: default_chat_model + prompt: "prompts/summarize_descriptions.txt" + max_length: 500 + +extract_graph_nlp: + text_analyzer: + extractor_type: regex_english # [regex_english, syntactic_parser, cfg] + async_mode: threaded # or asyncio + +cluster_graph: + max_cluster_size: 10 + +extract_claims: + enabled: false + model_id: default_chat_model + prompt: "prompts/extract_claims.txt" + description: "Any claims or facts that could be relevant to information discovery." + max_gleanings: 1 + +community_reports: + model_id: default_chat_model + graph_prompt: "prompts/community_report_graph.txt" + text_prompt: "prompts/community_report_text.txt" + max_length: 2000 + max_input_length: 8000 + +embed_graph: + enabled: false # if true, will generate node2vec embeddings for nodes + +umap: + enabled: false # if true, will generate UMAP embeddings for nodes (embed_graph must also be enabled) + +snapshots: + graphml: false + embeddings: false + +### Query settings ### +## The prompt locations are required here, but each search method has a number of optional knobs that can be tuned. +## See the config docs: https://microsoft.github.io/graphrag/config/yaml/#query + +local_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/local_search_system_prompt.txt" + +global_search: + chat_model_id: default_chat_model + map_prompt: "prompts/global_search_map_system_prompt.txt" + reduce_prompt: "prompts/global_search_reduce_system_prompt.txt" + knowledge_prompt: "prompts/global_search_knowledge_system_prompt.txt" + +drift_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/drift_search_system_prompt.txt" + reduce_prompt: "prompts/drift_search_reduce_prompt.txt" + +basic_search: + chat_model_id: default_chat_model + embedding_model_id: default_embedding_model + prompt: "prompts/basic_search_system_prompt.txt"