Local Search

In [1]:

# Copyright (c) 2024 Microsoft Corporation.
# Licensed under the MIT License.

# Copyright (c) 2024 Microsoft Corporation. # Licensed under the MIT License.

In [2]:

import os

import pandas as pd

from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey
from graphrag.query.indexer_adapters import (
    read_indexer_covariates,
    read_indexer_entities,
    read_indexer_relationships,
    read_indexer_reports,
    read_indexer_text_units,
)
from graphrag.query.question_gen.local_gen import LocalQuestionGen
from graphrag.query.structured_search.local_search.mixed_context import (
    LocalSearchMixedContext,
)
from graphrag.query.structured_search.local_search.search import LocalSearch
from graphrag.vector_stores.lancedb import LanceDBVectorStore

import os import pandas as pd from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig from graphrag.query.context_builder.entity_extraction import EntityVectorStoreKey from graphrag.query.indexer_adapters import ( read_indexer_covariates, read_indexer_entities, read_indexer_relationships, read_indexer_reports, read_indexer_text_units, ) from graphrag.query.question_gen.local_gen import LocalQuestionGen from graphrag.query.structured_search.local_search.mixed_context import ( LocalSearchMixedContext, ) from graphrag.query.structured_search.local_search.search import LocalSearch from graphrag.vector_stores.lancedb import LanceDBVectorStore

Local Search Example¶

Local search method generates answers by combining relevant data from the AI-extracted knowledge-graph with text chunks of the raw documents. This method is suitable for questions that require an understanding of specific entities mentioned in the documents (e.g. What are the healing properties of chamomile?).

Load text units and graph data tables as context for local search¶

• In this test we first load indexing outputs from parquet files to dataframes, then convert these dataframes into collections of data objects aligning with the knowledge model.

Load tables to dataframes¶

In [3]:

INPUT_DIR = "./inputs/operation dulce"
LANCEDB_URI = f"{INPUT_DIR}/lancedb"

COMMUNITY_REPORT_TABLE = "community_reports"
ENTITY_TABLE = "entities"
COMMUNITY_TABLE = "communities"
RELATIONSHIP_TABLE = "relationships"
COVARIATE_TABLE = "covariates"
TEXT_UNIT_TABLE = "text_units"
COMMUNITY_LEVEL = 2

INPUT_DIR = "./inputs/operation dulce" LANCEDB_URI = f"{INPUT_DIR}/lancedb" COMMUNITY_REPORT_TABLE = "community_reports" ENTITY_TABLE = "entities" COMMUNITY_TABLE = "communities" RELATIONSHIP_TABLE = "relationships" COVARIATE_TABLE = "covariates" TEXT_UNIT_TABLE = "text_units" COMMUNITY_LEVEL = 2

Read entities¶

In [4]:

# 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")

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)

print(f"Entity count: {len(entity_df)}")
entity_df.head()

# 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") 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) print(f"Entity count: {len(entity_df)}") entity_df.head()

Entity count: 18

Out[4]:

	id	human_readable_id	title	type	description	text_unit_ids	frequency	degree	x	y
0	425a7862-0aef-4f69-a4c8-8bd42151c9d4	0	ALEX MERCER	PERSON	Agent Alex Mercer is a determined individual w...	[8e938693af886bfd081acbbe8384c3671446bff84a134...	4	9	0	0
1	bcdbf1fc-0dc1-460f-bc71-2781729c96ba	1	TAYLOR CRUZ	PERSON	Agent Taylor Cruz is a commanding and authorit...	[8e938693af886bfd081acbbe8384c3671446bff84a134...	4	8	0	0
2	ef02ef24-5762-46ce-93ce-7dea6fc86595	2	JORDAN HAYES	PERSON	Dr. Jordan Hayes is a scientist and a member o...	[8e938693af886bfd081acbbe8384c3671446bff84a134...	4	9	0	0
3	8b163d27-e43a-4a2c-a26f-866778d8720e	3	SAM RIVERA	PERSON	Sam Rivera is a cybersecurity expert and a tal...	[8e938693af886bfd081acbbe8384c3671446bff84a134...	4	8	0	0
4	542aa5bd-ba2d-400a-8488-c52d50bc300d	4	PARANORMAL MILITARY SQUAD	ORGANIZATION	The PARANORMAL MILITARY SQUAD is an elite grou...	[8e938693af886bfd081acbbe8384c3671446bff84a134...	2	6	0	0

Read relationships¶

In [5]:

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()

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()

Relationship count: 54

Out[5]:

	id	human_readable_id	source	target	description	weight	combined_degree	text_unit_ids
0	2bfad9f4-5abd-48d0-8db3-a9cad9120413	0	ALEX MERCER	TAYLOR CRUZ	Alex Mercer and Taylor Cruz are both agents wo...	37.0	17	[8e938693af886bfd081acbbe8384c3671446bff84a134...
1	6cbb838f-9e83-4086-a684-15c8ed709e52	1	ALEX MERCER	JORDAN HAYES	Alex Mercer and Jordan Hayes are both agents w...	42.0	18	[8e938693af886bfd081acbbe8384c3671446bff84a134...
2	bfdc25f1-80ca-477b-a304-94465b69e680	2	ALEX MERCER	SAM RIVERA	Alex Mercer and Sam Rivera are both agents and...	26.0	17	[8e938693af886bfd081acbbe8384c3671446bff84a134...
3	7a7e943d-a4f5-487b-9625-5d0907c4c26d	3	ALEX MERCER	PARANORMAL MILITARY SQUAD	Alex Mercer is a member of the Paranormal Mili...	17.0	15	[8e938693af886bfd081acbbe8384c3671446bff84a134...
4	5e00bcb9-a17e-4c27-8241-6ebb286a7fc6	4	ALEX MERCER	DULCE	Alex Mercer is preparing to lead the team into...	15.0	14	[8e938693af886bfd081acbbe8384c3671446bff84a134...

In [6]:

# NOTE: covariates are turned off by default, because they generally need prompt tuning to be valuable
# Please see the GRAPHRAG_CLAIM_* settings
covariate_df = pd.read_parquet(f"{INPUT_DIR}/{COVARIATE_TABLE}.parquet")

claims = read_indexer_covariates(covariate_df)

print(f"Claim records: {len(claims)}")
covariates = {"claims": claims}

# NOTE: covariates are turned off by default, because they generally need prompt tuning to be valuable # Please see the GRAPHRAG_CLAIM_* settings covariate_df = pd.read_parquet(f"{INPUT_DIR}/{COVARIATE_TABLE}.parquet") claims = read_indexer_covariates(covariate_df) print(f"Claim records: {len(claims)}") covariates = {"claims": claims}

Claim records: 17

Read community reports¶

In [7]:

report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet")
reports = read_indexer_reports(report_df, community_df, COMMUNITY_LEVEL)

print(f"Report records: {len(report_df)}")
report_df.head()

report_df = pd.read_parquet(f"{INPUT_DIR}/{COMMUNITY_REPORT_TABLE}.parquet") reports = read_indexer_reports(report_df, community_df, COMMUNITY_LEVEL) print(f"Report records: {len(report_df)}") report_df.head()

Report records: 2

Out[7]:

	id	human_readable_id	community	level	parent	children	title	summary	full_content	rank	rating_explanation	findings	full_content_json	period	size
0	6c3a555680d647ac8be866a129c7b0ea	0	0	0	-1	[]	Operation: Dulce and Dulce Base Exploration	The community revolves around 'Operation: Dulc...	# Operation: Dulce and Dulce Base Exploration\...	8.5	The impact severity rating is high due to the ...	[{'explanation': 'Operation: Dulce is a signif...	{\n "title": "Operation: Dulce and Dulce Ba...	2025-03-04	7
1	0127331a1ea34b8ba19de2c2a4cb3bc9	1	1	0	-1	[]	Paranormal Military Squad and Operation: Dulce	The community centers around the Paranormal Mi...	# Paranormal Military Squad and Operation: Dul...	8.5	The impact severity rating is high due to the ...	[{'explanation': 'Agent Alex Mercer is a key f...	{\n "title": "Paranormal Military Squad and...	2025-03-04	9

Read text units¶

In [8]:

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()

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()

Text unit records: 5

Out[8]:

	id	human_readable_id	text	n_tokens	document_ids	entity_ids	relationship_ids	covariate_ids
0	8e938693af886bfd081acbbe8384c3671446bff84a134a...	1	# Operation: Dulce\n\n## Chapter 1\n\nThe thru...	1200	[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...	[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...	[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...	[745d28dd-be20-411b-85ff-1c69ca70e7b3, 9cba185...
1	fd1f46d32e1df6cd429542aeda3d64ddf3745ccb80f443...	2	, the hollow echo of the bay a stark reminder ...	1200	[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...	[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...	[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...	[4f9b461f-5e8f-465d-9586-e2fc81787062, 0f74618...
2	7296d9a1f046854d59079dc183de8a054c27c4843d2979...	3	differently than praise from others. This was...	1200	[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...	[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...	[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...	[3ef1be9c-4080-4fac-99bd-c4a636248904, 8730b20...
3	ac72722a02ac71242a2a91fca323198d04197daf60515d...	4	contrast to the rigid silence enveloping the ...	1200	[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...	[425a7862-0aef-4f69-a4c8-8bd42151c9d4, bcdbf1f...	[2bfad9f4-5abd-48d0-8db3-a9cad9120413, 6cbb838...	[2c292047-b79a-4958-ab57-7bf7d7a22c92, 3cbd18a...
4	4c277337d461a16aaf8f9760ddb8b44ef220e948a2341d...	5	a mask of duty.\n\nIn the midst of the descen...	35	[6e81f882f89dd5596e1925dd3ae8a4f0a0edcb55b35a8...	[d084d615-3584-4ec8-9931-90aa6075c764, 4b84859...	[6efdc42e-69a2-47c0-97ec-4b296cd16d5e]	[db8da02f-f889-4bb5-8e81-ab2a72e380bb]

In [9]:

from graphrag.config.enums import ModelType
from graphrag.config.models.language_model_config import LanguageModelConfig
from graphrag.language_model.manager import ModelManager
from graphrag.tokenizer.get_tokenizer import get_tokenizer

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,
)

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,
)

tokenizer = get_tokenizer(chat_config)

from graphrag.config.enums import ModelType from graphrag.config.models.language_model_config import LanguageModelConfig from graphrag.language_model.manager import ModelManager from graphrag.tokenizer.get_tokenizer import get_tokenizer 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, ) 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, ) tokenizer = get_tokenizer(chat_config)

Create local search context builder¶

In [10]:

context_builder = LocalSearchMixedContext(
    community_reports=reports,
    text_units=text_units,
    entities=entities,
    relationships=relationships,
    # if you did not run covariates during indexing, set this to None
    covariates=covariates,
    entity_text_embeddings=description_embedding_store,
    embedding_vectorstore_key=EntityVectorStoreKey.ID,  # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE
    text_embedder=text_embedder,
    tokenizer=tokenizer,
)

context_builder = LocalSearchMixedContext( community_reports=reports, text_units=text_units, entities=entities, relationships=relationships, # if you did not run covariates during indexing, set this to None covariates=covariates, entity_text_embeddings=description_embedding_store, embedding_vectorstore_key=EntityVectorStoreKey.ID, # if the vectorstore uses entity title as ids, set this to EntityVectorStoreKey.TITLE text_embedder=text_embedder, tokenizer=tokenizer, )

Create local search engine¶

In [11]:

# text_unit_prop: proportion of context window dedicated to related text units
# community_prop: proportion of context window dedicated to community reports.
# The remaining proportion is dedicated to entities and relationships. Sum of text_unit_prop and community_prop should be <= 1
# conversation_history_max_turns: maximum number of turns to include in the conversation history.
# conversation_history_user_turns_only: if True, only include user queries in the conversation history.
# top_k_mapped_entities: number of related entities to retrieve from the entity description embedding store.
# top_k_relationships: control the number of out-of-network relationships to pull into the context window.
# include_entity_rank: if True, include the entity rank in the entity table in the context window. Default entity rank = node degree.
# include_relationship_weight: if True, include the relationship weight in the context window.
# include_community_rank: if True, include the community rank in the context window.
# return_candidate_context: if True, return a set of dataframes containing all candidate entity/relationship/covariate records that
# could be relevant. Note that not all of these records will be included in the context window. The "in_context" column in these
# dataframes indicates whether the record is included in the context window.
# max_tokens: maximum number of tokens to use for the context window.


local_context_params = {
    "text_unit_prop": 0.5,
    "community_prop": 0.1,
    "conversation_history_max_turns": 5,
    "conversation_history_user_turns_only": True,
    "top_k_mapped_entities": 10,
    "top_k_relationships": 10,
    "include_entity_rank": True,
    "include_relationship_weight": True,
    "include_community_rank": False,
    "return_candidate_context": False,
    "embedding_vectorstore_key": EntityVectorStoreKey.ID,  # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids
    "max_tokens": 12_000,  # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000)
}

model_params = {
    "max_tokens": 2_000,  # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500)
    "temperature": 0.0,
}

# text_unit_prop: proportion of context window dedicated to related text units # community_prop: proportion of context window dedicated to community reports. # The remaining proportion is dedicated to entities and relationships. Sum of text_unit_prop and community_prop should be <= 1 # conversation_history_max_turns: maximum number of turns to include in the conversation history. # conversation_history_user_turns_only: if True, only include user queries in the conversation history. # top_k_mapped_entities: number of related entities to retrieve from the entity description embedding store. # top_k_relationships: control the number of out-of-network relationships to pull into the context window. # include_entity_rank: if True, include the entity rank in the entity table in the context window. Default entity rank = node degree. # include_relationship_weight: if True, include the relationship weight in the context window. # include_community_rank: if True, include the community rank in the context window. # return_candidate_context: if True, return a set of dataframes containing all candidate entity/relationship/covariate records that # could be relevant. Note that not all of these records will be included in the context window. The "in_context" column in these # dataframes indicates whether the record is included in the context window. # max_tokens: maximum number of tokens to use for the context window. local_context_params = { "text_unit_prop": 0.5, "community_prop": 0.1, "conversation_history_max_turns": 5, "conversation_history_user_turns_only": True, "top_k_mapped_entities": 10, "top_k_relationships": 10, "include_entity_rank": True, "include_relationship_weight": True, "include_community_rank": False, "return_candidate_context": False, "embedding_vectorstore_key": EntityVectorStoreKey.ID, # set this to EntityVectorStoreKey.TITLE if the vectorstore uses entity title as ids "max_tokens": 12_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 5000) } model_params = { "max_tokens": 2_000, # change this based on the token limit you have on your model (if you are using a model with 8k limit, a good setting could be 1000=1500) "temperature": 0.0, }

In [12]:

search_engine = LocalSearch(
    model=chat_model,
    context_builder=context_builder,
    tokenizer=tokenizer,
    model_params=model_params,
    context_builder_params=local_context_params,
    response_type="multiple paragraphs",  # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report
)

search_engine = LocalSearch( model=chat_model, context_builder=context_builder, tokenizer=tokenizer, model_params=model_params, context_builder_params=local_context_params, response_type="multiple paragraphs", # free form text describing the response type and format, can be anything, e.g. prioritized list, single paragraph, multiple paragraphs, multiple-page report )

Run local search on sample queries¶

In [13]:

result = await search_engine.search("Tell me about Agent Mercer")
print(result.response)

result = await search_engine.search("Tell me about Agent Mercer") print(result.response)

Warning: No community records added when building community context.

Reached token limit - reverting to previous context state

## Overview of Agent Alex Mercer

Agent Alex Mercer is a central figure within the Paranormal Military Squad, playing a crucial role in Operation: Dulce, the mission to explore and uncover the secrets of the mysterious Dulce base. Mercer is characterized by his determination, leadership qualities, and a nuanced internal conflict between strict adherence to protocol and a natural inclination to question and explore beyond the surface. He is respected by his peers for his expertise and is often seen as a guiding force within the team [Data: Entities (0); Relationships (3, 17, 24, 23, 4, +more)].

## Role in Operation: Dulce

Mercer is directly involved in Operation: Dulce, both as a leader and as an active participant in the exploration of the Dulce base. He is responsible for guiding the team, making critical decisions, and ensuring the mission's objectives are met. His leadership is not without its challenges, as he must balance the demands of protocol—often emphasized by Agent Taylor Cruz—with his own investigative instincts. This internal struggle is highlighted in his interactions with Cruz and his reflective moments during mission briefings [Data: Relationships (0, 3, 4, 5, 17, 24, 23, +more); Claims (3, 5)].

## Relationships and Team Dynamics

Mercer maintains strong professional relationships with other key members of the squad. He shares a mutual respect and understanding with Dr. Jordan Hayes, particularly admiring Hayes's analytical abilities and scientific expertise. Their collaboration is marked by a shared commitment to discovery and a willingness to challenge the status quo when necessary [Data: Relationships (1, 10, 25, 41, +more)]. Mercer also acts as a mentor to Sam Rivera, providing guidance and support, and recognizing Rivera's technical skills as vital to the mission's success [Data: Relationships (2, 15, 16, 30, +more)].

His relationship with Taylor Cruz is more complex, defined by a professional rivalry and a competitive undercurrent. While Mercer acknowledges Cruz's authority and often follows their lead, he is not afraid to question decisions or advocate for a more flexible approach when the situation demands it [Data: Relationships (0, 5, 6, 19, +more)].

## Personality and Motivations

Mercer is depicted as someone who feels the weight of responsibility deeply, often reflecting on the broader implications of their mission for humanity. He is not content with simply following orders; instead, he seeks to understand the true significance of their discoveries and the potential impact on the world. This sense of duty, combined with his curiosity and willingness to challenge established norms, makes him a dynamic and sometimes conflicted leader [Data: Claims (3, 5); Sources (0, 3)].

## Key Contributions

- **Leadership:** Mercer is often at the forefront of mission planning and execution, guiding the team through high-stakes situations [Data: Relationships (4, 17, 24, 23, +more)].
- **Mentorship:** He provides mentorship to Sam Rivera, helping to develop Rivera's skills and confidence [Data: Relationships (2, 15)].
- **Collaboration:** Mercer works closely with Dr. Jordan Hayes, leveraging their combined expertise to solve complex problems related to alien technology [Data: Relationships (1, 10, 48, +more)].
- **Adaptability:** Despite his respect for protocol, Mercer is willing to adapt and question orders when necessary, demonstrating flexibility in the face of the unknown [Data: Claims (3); Sources (0, 3)].

## Conclusion

Agent Alex Mercer stands out as a determined, thoughtful, and adaptable leader within the Paranormal Military Squad. His ability to balance protocol with critical thinking, foster strong team relationships, and maintain a sense of duty makes him indispensable to Operation: Dulce and the ongoing efforts to unravel the mysteries of the Dulce base [Data: Entities (0, 4, 5, 7, 8); Relationships (1, 2, 3, 4, 5, 6, 17, 23, 24, +more); Claims (3, 5)].

In [14]:

question = "Tell me about Dr. Jordan Hayes"
result = await search_engine.search(question)
print(result.response)

question = "Tell me about Dr. Jordan Hayes" result = await search_engine.search(question) print(result.response)

Warning: No community records added when building community context.

Reached token limit - reverting to previous context state

## Overview of Dr. Jordan Hayes

Dr. Jordan Hayes is a scientist and a key member of the Paranormal Military Squad, recognized for their expertise in physics and their composed, analytical demeanor. Hayes is deeply involved in Operation: Dulce, a mission focused on investigating alien technology and its implications for humanity. Their role is characterized by a reflective and skeptical approach, often advocating for adaptability and critical thinking in the face of unknown variables [Data: Entities (2, 4, 5); Relationships (10, 27, 48, 51, +more)].

## Role in Operation: Dulce

Dr. Hayes is directly engaged in working with alien technology, often found in the lab meticulously analyzing enigmatic circuitry retrieved from the Dulce base. Their scientific insights are crucial to the team's understanding of the potential paradigm shifts that such technology could bring. Hayes is not only responsible for providing analytical assessments but also for identifying hidden elements within the base, such as a suspicious panel that suggested concealed secrets [Data: Entities (2, 13, 8); Relationships (48, 51, 26, 11, +more); Claims (10, 6)].

## Team Dynamics and Relationships

Within the Paranormal Military Squad, Hayes is respected for their analytical mind and is seen as a voice of reason, often challenging the strict adherence to protocols favored by Agent Taylor Cruz. This skepticism and emphasis on adaptability create a dynamic tension within the team, particularly during mission briefings and critical decision points. Hayes shares a mutual respect and understanding with Agent Alex Mercer, with both agents valuing each other's expertise and commitment to the mission. Their interactions are marked by a blend of professional camaraderie and a shared drive to uncover the truth behind the Dulce base [Data: Entities (2, 0, 1, 7); Relationships (1, 5, 25, 9, +more); Claims (2, 6)].

## Notable Characteristics and Contributions

Hayes is portrayed as someone who balances scientific rigor with a willingness to question established procedures. They are reflective, often contemplating the broader implications of their discoveries for humanity. Their recent actions have shown newfound assertiveness, particularly as the team prepares to enter and explore the Dulce base. Hayes's ability to identify anomalies and provide critical insights has made them an indispensable asset to the squad [Data: Entities (2); Claims (10, 6, 2)].

## Summary

In summary, Dr. Jordan Hayes stands out as a thoughtful, analytical, and adaptable scientist within the Paranormal Military Squad. Their expertise in physics, commitment to understanding alien technology, and willingness to challenge protocol make them a central figure in Operation: Dulce and a vital contributor to the team's efforts to navigate the mysteries of the Dulce base [Data: Entities (2, 4, 5); Relationships (10, 27, 48, 51, +more); Claims (10, 6, 2)].

Inspecting the context data used to generate the response¶

In [15]:

result.context_data["entities"].head()

result.context_data["entities"].head()

Out[15]:

	id	entity	description	number of relationships	in_context
0	15	BRIEFING ROOM		2	True
1	6	DULCE		5	True
2	7	THE TEAM	The team is a group of individuals preparing t...	5	True
3	3	SAM RIVERA	Sam Rivera is a cybersecurity expert and a tal...	8	True
4	2	JORDAN HAYES	Dr. Jordan Hayes is a scientist and a member o...	9	True

In [16]:

result.context_data["relationships"].head()

result.context_data["relationships"].head()

Out[16]:

	id	source	target	description	weight	links	in_context
0	24	ALEX MERCER	OPERATION: DULCE	Alex Mercer is involved in and is part of Oper...	24.0	7	True
1	27	JORDAN HAYES	OPERATION: DULCE	Jordan Hayes is part of Operation: Dulce	8.0	7	True
2	48	OPERATION: DULCE	JORDAN HAYES	Jordan Hayes is working on alien technology as...	16.0	7	True
3	1	ALEX MERCER	JORDAN HAYES	Alex Mercer and Jordan Hayes are both agents w...	42.0	6	True
4	31	SAM RIVERA	OPERATION: DULCE	Sam Rivera is part of Operation: Dulce	1.0	7	True

In [17]:

if "reports" in result.context_data:
    result.context_data["reports"].head()

if "reports" in result.context_data: result.context_data["reports"].head()

In [18]:

result.context_data["sources"].head()

result.context_data["sources"].head()

Out[18]:

	id	text
0	3	contrast to the rigid silence enveloping the ...
1	0	# Operation: Dulce\n\n## Chapter 1\n\nThe thru...
2	1	, the hollow echo of the bay a stark reminder ...

In [19]:

if "claims" in result.context_data:
    print(result.context_data["claims"].head())

if "claims" in result.context_data: print(result.context_data["claims"].head())

   id        entity               object_id     status start_date end_date  \
0   4    SAM RIVERA                    NONE  SUSPECTED       NONE     NONE   
1   9    SAM RIVERA                    NONE       TRUE       NONE     NONE   
2  15    SAM RIVERA  SIGNAL INCONSISTENCIES       TRUE       NONE     NONE   
3   2  JORDAN HAYES                    NONE  SUSPECTED       NONE     NONE   
4   6  JORDAN HAYES                    NONE  SUSPECTED       NONE     NONE   

                                         description  in_context  
0  Sam Rivera is engaged in a fervent quest for a...        True  
1  Sam Rivera demonstrated technical expertise by...        True  
2  Sam Rivera identifies structured anomalies in ...        True  
3  Jordan Hayes is portrayed as skeptical of Tayl...        True  
4  Jordan Hayes is providing analytical insights ...        True  

Question Generation¶

This function takes a list of user queries and generates the next candidate questions.

In [20]:

question_generator = LocalQuestionGen(
    model=chat_model,
    context_builder=context_builder,
    tokenizer=tokenizer,
    model_params=model_params,
    context_builder_params=local_context_params,
)

question_generator = LocalQuestionGen( model=chat_model, context_builder=context_builder, tokenizer=tokenizer, model_params=model_params, context_builder_params=local_context_params, )

In [21]:

question_history = [
    "Tell me about Agent Mercer",
    "What happens in Dulce military base?",
]
candidate_questions = await question_generator.agenerate(
    question_history=question_history, context_data=None, question_count=5
)
print(candidate_questions.response)

question_history = [ "Tell me about Agent Mercer", "What happens in Dulce military base?", ] candidate_questions = await question_generator.agenerate( question_history=question_history, context_data=None, question_count=5 ) print(candidate_questions.response)

Warning: No community records added when building community context.

Reached token limit - reverting to previous context state

['- What role does Agent Mercer play in the exploration of Dulce base?', '- How does Agent Mercer interact with other members of the team during Operation: Dulce?', '- What challenges does Agent Mercer face while investigating the secrets of Dulce base?', '- In what ways does Agent Mercer contribute to the success of the mission at Dulce base?', '- How does Agent Mercer’s relationship with the team influence the outcome of the operation at Dulce base?']