DRIFT-Suche
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# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License.
# Copyright (c) 2024 Microsoft Corporation. # Lizenziert unter der MIT-Lizenz.
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import os
from pathlib import Path
import pandas as pd
from graphrag.config.enums import ModelType
from graphrag.config.models.drift_search_config import DRIFTSearchConfig
from graphrag.config.models.language_model_config import LanguageModelConfig
from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
from graphrag.language_model.manager import ModelManager
from graphrag.query.indexer_adapters import (
read_indexer_entities,
read_indexer_relationships,
read_indexer_report_embeddings,
read_indexer_reports,
read_indexer_text_units,
)
from graphrag.query.structured_search.drift_search.drift_context import (
DRIFTSearchContextBuilder,
)
from graphrag.query.structured_search.drift_search.search import DRIFTSearch
from graphrag.tokenizer.get_tokenizer import get_tokenizer
from graphrag.vector_stores.lancedb import LanceDBVectorStore
INPUT_DIR = "./inputs/operation dulce"
LANCEDB_URI = f"{INPUT_DIR}/lancedb"
COMMUNITY_REPORT_TABLE = "community_reports"
COMMUNITY_TABLE = "communities"
ENTITY_TABLE = "entities"
RELATIONSHIP_TABLE = "relationships"
COVARIATE_TABLE = "covariates"
TEXT_UNIT_TABLE = "text_units"
COMMUNITY_LEVEL = 2
# read nodes table to get community and degree data
entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet")
community_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_TABLE}.parquet")
print(f"Entity df columns: {entity_df.columns}")
entities = read_indexer_entities(entity_df, community_df, COMMUNITY_LEVEL)
# load description embeddings to an in-memory lancedb vectorstore
# to connect to a remote db, specify url and port values.
description_embedding_store = LanceDBVectorStore(
vector_store_schema_config=VectorStoreSchemaConfig(
index_name="default-entity-description"
),
)
description_embedding_store.connect(db_uri=LANCEDB_URI)
full_content_embedding_store = LanceDBVectorStore(
vector_store_schema_config=VectorStoreSchemaConfig(
index_name="default-community-full_content"
)
)
full_content_embedding_store.connect(db_uri=LANCEDB_URI)
print(f"Entity count: {len(entity_df)}")
entity_df.head()
relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet")
relationships = read_indexer_relationships(relationship_df)
print(f"Relationship count: {len(relationship_df)}")
relationship_df.head()
text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet")
text_units = read_indexer_text_units(text_unit_df)
print(f"Text unit records: {len(text_unit_df)}")
text_unit_df.head()
import os from pathlib import Path import pandas as pd from graphrag.config.enums import ModelType from graphrag.config.models.drift_search_config import DRIFTSearchConfig from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig from graphrag.language_model.manager import ModelManager from graphrag.query.indexer_adapters import ( read_indexer_entities, read_indexer_relationships, read_indexer_report_embeddings, read_indexer_reports, read_indexer_text_units, ) from graphrag.query.structured_search.drift_search.drift_context import ( DRIFTSearchContextBuilder, ) from graphrag.query.structured_search.drift_search.search import DRIFTSearch from graphrag.tokenizer.get_tokenizer import get_tokenizer from graphrag.vector_stores.lancedb import LanceDBVectorStore INPUT_DIR = "./inputs/operation dulce" LANCEDB_URI = f"{INPUT_DIR}/lancedb" COMMUNITY_REPORT_TABLE = "community_reports" COMMUNITY_TABLE = "communities" ENTITY_TABLE = "entities" RELATIONSHIP_TABLE = "relationships" COVARIATE_TABLE = "covariates" TEXT_UNIT_TABLE = "text_units" COMMUNITY_LEVEL = 2 # Lese die Nodes-Tabelle, um Community- und Grad-Daten zu erhalten entity_df = pd.read_parquet(f"{INPUT_DIR}/{ENTITY_TABLE}.parquet") community_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_TABLE}.parquet") print(f"Entity df columns: {entity_df.columns}") entities = read_indexer_entities(entity_df, community_df, COMMUNITY_LEVEL) # Lade Beschreibungs-Embeddings in einen In-Memory-LanceDB-Vektorspeicher # Um eine Verbindung zu einer Remote-Datenbank herzustellen, geben Sie URL und Port-Werte an. description_embedding_store = LanceDBVectorStore( vector_store_schema_config=VectorStoreSchemaConfig( index_name="default-entity-description" ), ) description_embedding_store.connect(db_uri=LANCEDB_URI) full_content_embedding_store = LanceDBVectorStore( vector_store_schema_config=VectorStoreSchemaConfig( index_name="default-community-full_content" ) ) full_content_embedding_store.connect(db_uri=LANCEDB_URI) print(f"Entity count: {len(entity_df)}") entity_df.head() relationship_df = pd.read_parquet(f"{INPUT_DIR}/{RELATIONSHIP_TABLE}.parquet") relationships = read_indexer_relationships(relationship_df) print(f"Relationship count: {len(relationship_df)}") relationship_df.head() text_unit_df = pd.read_parquet(f"{INPUT_DIR}/{TEXT_UNIT_TABLE}.parquet") text_units = read_indexer_text_units(text_unit_df) print(f"Text unit records: {len(text_unit_df)}") text_unit_df.head()
Entity df columns: Index(['id', 'human_readable_id', 'title', 'type', 'description',
'text_unit_ids', 'frequency', 'degree', 'x', 'y'],
dtype='object')
Entity count: 18
Relationship count: 54
Text unit records: 5
Out[2]
| id | human_readable_id | text | n_tokens | document_ids | entity_ids | relationship_ids | covariate_ids | |
|---|---|---|---|---|---|---|---|---|
| 0 | 8e938693af886bfd081acbbe8384c3671446bff84a134a... | 1 | # Operation: Dulce\n\n## Kapitel 1\n\nDas Durchdringen... | 1200 | [6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8... | [425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f... | [2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838... | [745d28dd-be20-411b-85ff-1c69ca70e7b3, 9cba185... |
| 1 | fd1f46d32e1df6cd429542aeda3d64ddf3745ccb80f443... | 2 | , das hohle Echo der Bucht eine deutliche Erinnerung ... | 1200 | [6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8... | [425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f... | [2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838... | [4f9b461f-5e8f-465d-9586-e2fc81787062, 0f74618... |
| 2 | 7296d9a1f046854d59079dc183de8a054c27c4843d2979... | 3 | anders als Lob von anderen. Dies war... | 1200 | [6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8... | [425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f... | [2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838... | [3ef1be9c-4080-4fac-99bd-c4a636248904, 8730b20... |
| 3 | ac72722a02ac71242a2a91fca323198d04197daf60515d... | 4 | Kontrast zur starren Stille, die das ... umgibt | 1200 | [6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8... | [425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f... | [2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838... | [2c292047-b79a-4958-ab57-7bf7d7a22c92, 3cbd18a... |
| 4 | 4c277337d461a16aaf8f9760ddb8b44ef220e948a2341d... | 5 | eine Maske der Pflicht.\n\nInmitten des Abstiegs... | 35 | [6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8... | [d084d615-3584-4ec8-9931-90aa6075c764, 4b84859... | [6efdc42e-69a2-47c0-97ec-4b296cd16d5e] | [db8da02f-f889-4bb5-8e81-ab2a72e380bb] |
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api_key = os.environ["GRAPHRAG_API_KEY"]
chat_config = LanguageModelConfig(
api_key=api_key,
type=ModelType.Chat,
model_provider="openai",
model="gpt-4.1",
max_retries=20,
)
chat_model = ModelManager().get_or_create_chat_model(
name="local_search",
model_type=ModelType.Chat,
config=chat_config,
)
tokenizer = get_tokenizer(chat_config)
embedding_config = LanguageModelConfig(
api_key=api_key,
type=ModelType.Embedding,
model_provider="openai",
model="text-embedding-3-small",
max_retries=20,
)
text_embedder = ModelManager().get_or_create_embedding_model(
name="local_search_embedding",
model_type=ModelType.Embedding,
config=embedding_config,
)
api_key = os.environ["GRAPHRAG_API_KEY"] chat_config = LanguageModelConfig( api_key=api_key, type=ModelType.Chat, model_provider="openai", model="gpt-4.1", max_retries=20, ) chat_model = ModelManager().get_or_create_chat_model( name="local_search", model_type=ModelType.Chat, config=chat_config, ) tokenizer = get_tokenizer(chat_config) embedding_config = LanguageModelConfig( api_key=api_key, type=ModelType.Embedding, model_provider="openai", model="text-embedding-3-small", max_retries=20, ) text_embedder = ModelManager().get_or_create_embedding_model( name="local_search_embedding", model_type=ModelType.Embedding, config=embedding_config, )
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def read_community_reports(
input_dir: str,
community_report_table: str = COMMUNITY_REPORT_TABLE,
):
"""Embeds the full content of the community reports and saves the DataFrame with embeddings to the output path."""
input_path = Path(input_dir) / f"{community_report_table}.parquet"
return pd.read_parquet(input_path)
report_df = read_community_reports(INPUT_DIR)
reports = read_indexer_reports(
report_df,
community_df,
COMMUNITY_LEVEL,
content_embedding_col="full_content_embeddings",
)
read_indexer_report_embeddings(reports, full_content_embedding_store)
def read_community_reports( input_dir: str, community_report_table: str = COMMUNITY_REPORT_TABLE, ): """Betten Sie den vollständigen Inhalt der Community-Berichte ein und speichern Sie den DataFrame mit den Einbettungen am Ausgabeort.""" input_path = Path(input_dir) / f"{community_report_table}.parquet" return pd.read_parquet(input_path) report_df = read_community_reports(INPUT_DIR) reports = read_indexer_reports( report_df, community_df, COMMUNITY_LEVEL, content_embedding_col="full_content_embeddings", ) read_indexer_report_embeddings(reports, full_content_embedding_store)
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drift_params = DRIFTSearchConfig(
temperature=0,
max_tokens=12_000,
primer_folds=1,
drift_k_followups=3,
n_depth=3,
n=1,
)
context_builder = DRIFTSearchContextBuilder(
model=chat_model,
text_embedder=text_embedder,
entities=entities,
relationships=relationships,
reports=reports,
entity_text_embeddings=description_embedding_store,
text_units=text_units,
tokenizer=tokenizer,
config=drift_params,
)
search = DRIFTSearch(
model=chat_model, context_builder=context_builder, tokenizer=tokenizer
)
drift_params = DRIFTSearchConfig( temperature=0, max_tokens=12_000, primer_folds=1, drift_k_followups=3, n_depth=3, n=1, ) context_builder = DRIFTSearchContextBuilder( model=chat_model, text_embedder=text_embedder, entities=entities, relationships=relationships, reports=reports, entity_text_embeddings=description_embedding_store, text_units=text_units, tokenizer=tokenizer, config=drift_params, ) search = DRIFTSearch( model=chat_model, context_builder=context_builder, tokenizer=tokenizer )
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resp = await search.search("Who is agent Mercer?")
resp = await search.search("Wer ist Agent Mercer?")
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resp.response
resp.response
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"Agent Alex Mercer: Role and Significance in Operation: Dulce\n------------------------------------------------------------\n\nAgent Alex Mercer is a central figure in the Paranormal Military Squad, serving as a key leader and mentor during Operation: Dulce—a mission focused on exploring the mysterious Dulce base and investigating advanced alien technology. Mercer is recognized for his leadership, adaptability, and emphasis on intuition and trust, which he instills in his team members, particularly Sam Rivera, the squad's cybersecurity expert. His mentorship strengthens team cohesion and prepares members for the unpredictable challenges of the mission.\n\nMercer collaborates closely with other prominent agents, such as Taylor Cruz (the authoritative de facto leader) and Dr. Jordan Hayes (a scientist specializing in alien technology). His professional relationship with Cruz balances adaptability with adherence to protocol, reflecting the diverse skills needed for mission success. Mercer is directly involved in the exploration of the Dulce base, engaging in investigative work, supporting scientific analysis, and maintaining team morale.\n\nOverall, Agent Mercer is depicted as a pivotal leader, mentor, and operative whose actions and relationships significantly shape the outcome of Operation: Dulce [Data: Reports (1); Sources (0, 1, 2, 3)]."
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print(resp.context_data)
print(resp.context_data)
{'What specific actions did Agent Mercer take during Operation: Dulce?': {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 4 a mask of duty.\n\nIn the midst of the descen...
1 2 differently than praise from others. This was...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 1 , the hollow echo of the bay a stark reminder ...
4 3 contrast to the rigid silence enveloping the ...}, "How did Agent Mercer's mentorship influence Sam Rivera's performance?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 4 a mask of duty.\n\nIn the midst of the descen...
1 2 differently than praise from others. This was...
2 1 , the hollow echo of the bay a stark reminder ...
3 3 contrast to the rigid silence enveloping the ...
4 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...}, 'How did Agent Mercer interact with alien technology during the mission?': {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 2 differently than praise from others. This was...
1 3 contrast to the rigid silence enveloping the ...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 1 , the hollow echo of the bay a stark reminder ...
4 4 a mask of duty.\n\nIn the midst of the descen...}, 'In what ways did Agent Mercer interact with alien technology during Operation: Dulce?': {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 2 differently than praise from others. This was...
1 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
2 1 , the hollow echo of the bay a stark reminder ...
3 3 contrast to the rigid silence enveloping the ...
4 4 a mask of duty.\n\nIn the midst of the descen...}, "How did Agent Mercer's relationship with other team members evolve throughout the operation?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
1 2 differently than praise from others. This was...
2 1 , the hollow echo of the bay a stark reminder ...
3 3 contrast to the rigid silence enveloping the ...
4 4 a mask of duty.\n\nIn the midst of the descen...}, "What specific skills did Sam Rivera demonstrate as a result of Mercer's mentorship?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 4 a mask of duty.\n\nIn the midst of the descen...
1 2 differently than praise from others. This was...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 1 , the hollow echo of the bay a stark reminder ...
4 3 contrast to the rigid silence enveloping the ...}, "Were there any moments where Rivera's performance directly impacted the outcome of Operation: Dulce?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 2 differently than praise from others. This was...
1 4 a mask of duty.\n\nIn the midst of the descen...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 3 contrast to the rigid silence enveloping the ...
4 1 , the hollow echo of the bay a stark reminder ...}, "Did Mercer's mentorship style differ from that of Taylor Cruz or other leaders on the team?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 4 a mask of duty.\n\nIn the midst of the descen...
1 2 differently than praise from others. This was...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 1 , the hollow echo of the bay a stark reminder ...
4 3 contrast to the rigid silence enveloping the ...}, "Are there specific examples where Mercer's approach led to better outcomes than Cruz's?": {'reports': id title \
0 1 Paranormal Military Squad and Operation: Dulce
content
0 # Paranormal Military Squad and Operation: Dul... , 'entities': Empty DataFrame
Columns: [in_context]
Index: [], 'sources': id text
0 2 differently than praise from others. This was...
1 3 contrast to the rigid silence enveloping the ...
2 0 # Operation: Dulce\n\n## Chapter 1\n\nThe thru...
3 1 , the hollow echo of the bay a stark reminder ...
4 4 a mask of duty.\n\nIn the midst of the descen...}}