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GraphRAG

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GraphRAG

Guide 27 ended with local_graph_search: vector-match a seed entity, walk its neighbourhood, hand the subgraph to a generator. That answers entity-centric questions ("who does Alice collaborate with?") because everything relevant sits within a few hops of one node.

It cannot answer corpus-centric questions: "what are the main research areas across the whole lab?". No single neighbourhood contains that answer; it is spread over the entire graph. Retrieving more hops doesn't help: you'd end up stuffing the whole graph into the prompt.

GraphRAG (Edge et al., 2024) solves this with a divide-and-conquer scheme borrowed from MapReduce:

  1. Index time: cluster the graph into communities (groups of densely-connected entities, found with the Louvain algorithm) and score every node's importance with PageRank. Done once, persisted on the nodes.
  2. Query time (map): answer the question against each community subgraph independently, in parallel, scoring how relevant each community is.
  3. Query time (reduce): combine the partial answers, best first, into one final answer.
graph LR
    subgraph "Index time (once)"
        A[("Graph store")] --> B["build_communities()<br/>Louvain + PageRank"]
    end
    subgraph "Query time"
        Q["Question"] --> G["GlobalGraphSearch<br/>top-k communities"]
        G --> M1["Map: answer vs community 1"]
        G --> M2["Map: answer vs community 2"]
        G --> M3["Map: answer vs community k"]
        M1 --> R["Reduce: combine<br/>partial answers"]
        M2 --> R
        M3 --> R
    end

Synalinks packages this as three modules, from lowest to highest level:

  • LocalGraphSearch: the entity-centric side, as a trainable module: an LM turns your inputs into seed queries, the store expands their neighbourhood, the subgraph flows out.
  • GlobalGraphSearch: retrieval only: the top-k communities as subgraphs, no LM involved.
  • GlobalGraphMapReduce: the full query-time pipeline: GlobalGraphSearch + a parallel map generator + a reduce generator.

This guide assumes a populated graph store; extraction is Guide 27's job. To keep this guide deterministic we hand-author a small collaboration graph instead of extracting one.

The Graph: a Research Lab

Six researchers, two tightly-knit teams, one cross-team collaboration:

  • Vision team: Alice, Bob, Carol, collaborating with each other.
  • Language team: Dan, Eve, Frank, collaborating with each other.
  • One bridge: Alice also collaborates with Dan.

Louvain will recover the two teams as communities. Note one backend constraint worth knowing up front: Louvain runs on a single node label at a time (here everything is a Researcher). Graphs with several entity types can either pass node_labels=[...] to cluster one type, or use algorithm="weakly_connected_components" which supports any number of labels.

Because a Relation carries its endpoint entities in full, seeding the store takes only the edge list (the relations-only strategy from Guide 27): UpdateKnowledge unpacks each edge into its two nodes plus the link.

class Researcher(synalinks.Entity):
    label: Literal["Researcher"]
    name: str = synalinks.Field(description="The researcher's name.")
    focus: str = synalinks.Field(description="Their research focus.")

class CollaboratesWith(synalinks.Relation):
    label: Literal["CollaboratesWith"]
    subj: Researcher
    obj: Researcher

Index Time: build_communities

After the graph is stored (and embedded: the local side needs the vectors), one call materializes the GraphRAG index:

stamped = await knowledge_base.build_communities(algorithm="louvain")

This clusters the graph with Louvain, scores every node with PageRank, and persists both as reserved community / rank properties on the nodes. It is idempotent: re-run it whenever the graph has changed enough to matter, exactly like rebuilding any index. Everything at query time just reads these properties; no clustering happens per query.

Local Search: LocalGraphSearch

The entity-centric retriever, now as a module you can drop into a program (rather than the raw kb.local_graph_search() call from Guide 27). Its embedded generator reads your inputs and emits the seed queries; the store vector-matches k seeds of label, expands max_hops around them, and returns the deduped union as one subgraph:

inputs = synalinks.Input(data_model=Query)
subgraph = await synalinks.LocalGraphSearch(
    knowledge_base=knowledge_base,
    language_model=language_model,
    label="Researcher",
    max_hops=2,
    k=1,
)(inputs)

label is optional: when omitted, the LM infers it per call, constrained to an enum of the labels actually present in the store: it cannot pick a nonexistent one. The generated queries (and the inputs) are concatenated onto the output by default (return_query / return_inputs), so a downstream generator sees question + queries + subgraph together.

Global Search: GlobalGraphSearch

The retrieval-only half of the global side. It reads the communities build_communities stamped and returns the k most important ones, each rebuilt as a full subgraph (member entities plus the relations internal to the community; edges that straddle two communities belong to neither), ordered by aggregate PageRank:

communities = await synalinks.GlobalGraphSearch(
    knowledge_base=knowledge_base,
    k=3,
)(inputs)

Two things distinguish it from every other retriever: it runs no language model, and it ignores the content of its inputs: global search is whole-graph by definition, so there is no query to build. Relevance to a specific question is decided downstream, which is exactly the map step's job.

Map-Reduce: GlobalGraphMapReduce

The full GraphRAG query-time pipeline in one module:

answer = await synalinks.GlobalGraphMapReduce(
    knowledge_base=knowledge_base,
    language_model=language_model,
    k=3,
)(inputs)

Under the hood it:

  1. fetches the top-k communities via an embedded GlobalGraphSearch;
  2. maps: one generator call per community, in parallel, each answering the inputs against that community's subgraph alone and scoring the community's relevance 0-100;
  3. filters (score_threshold, default 0 = keep everything; raise it to drop irrelevant communities like the original GraphRAG) and sorts the partial answers best-first;
  4. reduces: one final generator call that combines them.

By default the reduce step returns a plain ChatMessage, the same convention as a bare Generator. Pass schema= or data_model= to get a structured final answer instead, and return_community_answers=True to keep the scored partial answers in the output (useful for debugging, or as a reward signal). The map and reduce prompts are regular trainable generators: map_instructions / reduce_instructions set their starting instructions, and the whole module optimizes like any other (see the Training guide).

Choosing Local vs Global

Question shape Example Module
Entity-centric ("around here") "Who does Alice collaborate with?" LocalGraphSearch
Corpus-centric ("across everything") "What are the lab's main research areas?" GlobalGraphMapReduce
Need the raw communities Custom summarization pipeline GlobalGraphSearch
Exact traversal "Capitals and their countries" kb.cypher(...)

The two sides compose: nothing stops a program from running both retrievers and letting a final generator weigh the neighbourhood against the big picture.

Key Takeaways

  • Local search answers entity-centric questions; global search answers corpus-centric ones. Neighbourhood expansion cannot summarize a whole graph; that is what communities are for.
  • build_communities is the index-time half: Louvain communities
  • PageRank ranks, persisted on the nodes, rebuilt on demand. Louvain needs a single node label; WCC handles many.
  • LocalGraphSearch wraps seed-and-expand as a trainable module: the LM writes the seed queries and can even infer the entity label, constrained to labels that exist.
  • GlobalGraphSearch returns the top-k communities as subgraphs. No LM, input content ignored; relevance is the map step's job.
  • GlobalGraphMapReduce = search + parallel scored map + best-first reduce. Chat-message output by default, structured via schema/data_model, filterable via score_threshold.

API References

Source

import asyncio
from typing import List
from typing import Literal

from dotenv import load_dotenv

import synalinks

# =============================================================================
# Graph Schema + Input
# =============================================================================


class Researcher(synalinks.Entity):
    label: Literal["Researcher"]
    name: str = synalinks.Field(description="The researcher's name, e.g. 'Alice'.")
    focus: str = synalinks.Field(description="Their research focus.")


class CollaboratesWith(synalinks.Relation):
    label: Literal["CollaboratesWith"]
    subj: Researcher
    obj: Researcher


# Relations-only seeding (Guide 27, strategy 4): each edge carries its two
# endpoint entities in full, so the edge list alone populates the graph.
class LabCollaborations(synalinks.Relations):
    relations: List[CollaboratesWith] = synalinks.Field(
        description="Every collaboration in the lab.",
    )


class Query(synalinks.DataModel):
    question: str = synalinks.Field(description="The user question")


# =============================================================================
# The Lab Graph: two teams + one bridge
# =============================================================================

ALICE = Researcher(
    label="Researcher", name="Alice", focus="diffusion models for image generation"
)
BOB = Researcher(label="Researcher", name="Bob", focus="real-time image segmentation")
CAROL = Researcher(label="Researcher", name="Carol", focus="video understanding")
DAN = Researcher(label="Researcher", name="Dan", focus="low-resource machine translation")
EVE = Researcher(label="Researcher", name="Eve", focus="speech recognition")
FRANK = Researcher(
    label="Researcher", name="Frank", focus="task-oriented dialogue systems"
)

COLLABORATIONS = LabCollaborations(
    relations=[
        # Vision team: a triangle.
        CollaboratesWith(label="CollaboratesWith", subj=ALICE, obj=BOB),
        CollaboratesWith(label="CollaboratesWith", subj=BOB, obj=CAROL),
        CollaboratesWith(label="CollaboratesWith", subj=CAROL, obj=ALICE),
        # Language team: a triangle.
        CollaboratesWith(label="CollaboratesWith", subj=DAN, obj=EVE),
        CollaboratesWith(label="CollaboratesWith", subj=EVE, obj=FRANK),
        CollaboratesWith(label="CollaboratesWith", subj=FRANK, obj=DAN),
        # The bridge between the two teams.
        CollaboratesWith(label="CollaboratesWith", subj=ALICE, obj=DAN),
    ],
)


# =============================================================================
# Main Demonstration
# =============================================================================


async def main():
    load_dotenv()
    synalinks.clear_session()
    synalinks.enable_logging(log_level="info")

    language_model = synalinks.LanguageModel(model="ollama/mistral:latest")
    embedding_model = synalinks.EmbeddingModel(model="ollama/mxbai-embed-large")

    knowledge_base = synalinks.KnowledgeBase(
        graph_uri="ladybug://:memory:",
        entity_models=[Researcher],
        relation_models=[CollaboratesWith],
        embedding_model=embedding_model,
        metric="cosine",
        wipe_on_start=True,
    )

    # -------------------------------------------------------------------------
    # Index time 1/2: embed + store the graph
    # -------------------------------------------------------------------------
    print("=" * 60)
    print("Index time: store the graph")
    print("=" * 60)

    inputs = synalinks.Input(data_model=LabCollaborations)
    embedded = await synalinks.EmbedKnowledge(
        embedding_model=embedding_model,
        in_mask=["name"],
    )(inputs)
    stored = await synalinks.UpdateKnowledge(
        knowledge_base=knowledge_base,
    )(embedded)

    loader = synalinks.Program(
        inputs=inputs,
        outputs=stored,
        name="lab_graph_loader",
        description="Embed and store the lab collaboration graph.",
    )
    await loader(COLLABORATIONS)

    # -------------------------------------------------------------------------
    # Index time 2/2: build the communities (the GraphRAG index)
    # -------------------------------------------------------------------------
    print("\n" + "=" * 60)
    print("Index time: build_communities")
    print("=" * 60)

    stamped = await knowledge_base.build_communities(algorithm="louvain")
    print(f"\nNodes stamped with community + rank: {stamped}")

    # -------------------------------------------------------------------------
    # Local search: entity-centric questions
    # -------------------------------------------------------------------------
    print("\n" + "=" * 60)
    print("LocalGraphSearch: around Alice")
    print("=" * 60)

    inputs = synalinks.Input(data_model=Query)
    subgraph = await synalinks.LocalGraphSearch(
        knowledge_base=knowledge_base,
        language_model=language_model,
        label="Researcher",
        max_hops=2,
        k=1,
    )(inputs)

    local_search = synalinks.Program(
        inputs=inputs,
        outputs=subgraph,
        name="local_graph_search",
        description="Retrieve the neighbourhood around the entities in question.",
    )

    result = await local_search(
        Query(question="Who does Alice collaborate with, and what do they work on?")
    )
    print("\nNeighbourhood entities:")
    for entity in result.get("entities"):
        print(f"  - {entity['name']}: {entity['focus']}")
    print("\nNeighbourhood relations:")
    for relation in result.get("relations"):
        print(f"  - {relation['subj']['name']} -> {relation['obj']['name']}")

    # -------------------------------------------------------------------------
    # Global search (retrieval only): the top communities
    # -------------------------------------------------------------------------
    print("\n" + "=" * 60)
    print("GlobalGraphSearch: the communities")
    print("=" * 60)

    inputs = synalinks.Input(data_model=Query)
    communities = await synalinks.GlobalGraphSearch(
        knowledge_base=knowledge_base,
        k=3,
        return_inputs=False,
    )(inputs)

    global_search = synalinks.Program(
        inputs=inputs,
        outputs=communities,
        name="global_graph_search",
        description="Retrieve the graph's most important communities.",
    )

    result = await global_search(Query(question="ignored: global search is whole-graph"))
    for i, community in enumerate(result.get("knowledge_graphs")):
        members = ", ".join(entity["name"] for entity in community["entities"])
        print(f"\nCommunity #{i} ({len(community['relations'])} internal edges):")
        print(f"  {members}")

    # -------------------------------------------------------------------------
    # Map-reduce: corpus-centric questions
    # -------------------------------------------------------------------------
    print("\n" + "=" * 60)
    print("GlobalGraphMapReduce: the whole-lab question")
    print("=" * 60)

    inputs = synalinks.Input(data_model=Query)
    answer = await synalinks.GlobalGraphMapReduce(
        knowledge_base=knowledge_base,
        language_model=language_model,
        k=3,
        return_community_answers=True,
    )(inputs)

    global_qa = synalinks.Program(
        inputs=inputs,
        outputs=answer,
        name="global_graph_qa",
        description="Answer corpus-level questions by community map-reduce.",
    )
    global_qa.summary()

    result = await global_qa(
        Query(question="What are the main research areas in the lab?")
    )
    print("\nScored community answers (map step):")
    for community_answer in result.get("community_answers"):
        score = community_answer["relevance_score"]
        print(f"  [{score:5.1f}] {community_answer['partial_answer']}")
    print("\nFinal answer (reduce step):")
    print(result.get("content"))

    print("\nDone!")


if __name__ == "__main__":
    asyncio.run(main())

Run log

This guide calls synalinks.enable_logging(log_level="info"), so a full run traces every module call: the graph load, community building, both retrievers, and the map-reduce generators. The log below is the unedited output of running the guide above with local models.

Full run log: guides/32_graphrag.log 
[Synalinks]
Call ID: 8641d349-811e-42e9-ae97-34a67b9b2771
Parent call ID: None
Module: Functional
Module Name: lab_graph_loader
Module Description: Embed and store the lab collaboration graph.
Data Model JSON:
[
  {
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 0b491a25-9918-4926-8803-a60a953116a0
Parent call ID: 8641d349-811e-42e9-ae97-34a67b9b2771
Module: EmbedKnowledge
Module Name: embed_knowledge
Module Description: Extracts a field of interest and generate the corresponding embedding vector.
Data Model JSON:
[
  {
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 6f0d18a1-f17c-4f29-b493-f8b5a6d93373
Parent call ID: 0b491a25-9918-4926-8803-a60a953116a0
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
  {
    "texts": [
      "Alice",
      "Bob",
      "Bob",
      "Carol",
      "Carol",
      "Alice",
      "Dan",
      "Eve",
      "Eve",
      "Frank",
      "Frank",
      "Dan",
      "Alice",
      "Dan"
    ]
  }
]

[Synalinks]
Call ID: 6f0d18a1-f17c-4f29-b493-f8b5a6d93373
Parent call ID: 0b491a25-9918-4926-8803-a60a953116a0
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
  {
    "embeddings": [
      [
        -0.010673877,
        -0.022871714,
        0.00012302384,
        -0.033362903,
        -0.028919492,
        -0.0452797,
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        0.010972375,
        0.012764523,
        0.00051369215,
        0.017513867,
        0.017243847,
        0.032729845,
        -0.017869355,
        -0.016447678,
        0.025104795,
        -0.022927446,
        -0.042765453,
        -0.039486766,
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        0.0016271377,
        0.029306786,
        -0.061951976,
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        0.00024548764,
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]

[Synalinks]
Call ID: 0b491a25-9918-4926-8803-a60a953116a0
Parent call ID: 8641d349-811e-42e9-ae97-34a67b9b2771
Module: EmbedKnowledge
Module Name: embed_knowledge
Module Description: Extracts a field of interest and generate the corresponding embedding vector.
Data Model JSON:
[
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          "name": "Alice",
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        "subj": {
          "label": "Researcher",
          "name": "Eve",
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]

[Synalinks]
Call ID: 7ac9e563-2097-4a4c-a6cb-71725c7dd17d
Parent call ID: 6f0d18a1-f17c-4f29-b493-f8b5a6d93373
Module: UpdateKnowledge
Module Name: update_knowledge
Module Description: Update (insert or upsert) data models in the given knowledge base.
Data Model JSON:
[
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]

[Synalinks]
Call ID: 7ac9e563-2097-4a4c-a6cb-71725c7dd17d
Parent call ID: 6f0d18a1-f17c-4f29-b493-f8b5a6d93373
Module: UpdateKnowledge
Module Name: update_knowledge
Module Description: Update (insert or upsert) data models in the given knowledge base.
Data Model JSON:
[
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[Synalinks]
Call ID: 8641d349-811e-42e9-ae97-34a67b9b2771
Parent call ID: None
Module: Functional
Module Name: lab_graph_loader
Module Description: Embed and store the lab collaboration graph.
Data Model JSON:
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        }
      }
    ]
  }
]

[Synalinks]
Call ID: fbc54a68-4b53-4008-97ed-79fe2adcaa7d
Parent call ID: None
Module: Functional
Module Name: local_graph_search
Module Description: Retrieve the neighbourhood around the entities in question.
Data Model JSON:
[
  {
    "question": "Who does Alice collaborate with, and what do they work on?"
  }
]

[Synalinks]
Call ID: 91bfd2a6-671c-47de-b793-a09e11473e91
Parent call ID: fbc54a68-4b53-4008-97ed-79fe2adcaa7d
Module: LocalGraphSearch
Module Name: local_graph_search
Module Description: GraphRAG-style *local* search: seed by vector, expand, return a subgraph.
Data Model JSON:
[
  {
    "question": "Who does Alice collaborate with, and what do they work on?"
  }
]

[Synalinks]
Call ID: 1333437a-6c61-42de-bb6b-e3b3b2327ecd
Parent call ID: 91bfd2a6-671c-47de-b793-a09e11473e91
Module: Generator
Module Name: local_graph_search_query_generator_local_graph_search
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "question": "Who does Alice collaborate with, and what do they work on?"
  }
]

[Synalinks]
Call ID: cda54acf-e85b-453b-8d56-b9f0a3b085cf
Parent call ID: 1333437a-6c61-42de-bb6b-e3b3b2327ecd
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nYour task is to answer with a JSON containing the following keys: ['local_graph_search']\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "<input>\n{'question': 'Who does Alice collaborate with, and what do they work on?'}\n</input>\n<output>\n"
      }
    ]
  }
]

[Synalinks]
Call ID: cda54acf-e85b-453b-8d56-b9f0a3b085cf
Parent call ID: 1333437a-6c61-42de-bb6b-e3b3b2327ecd
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "local_graph_search": [
      "Alice's collaborators might include Bob, Carol, Dave, or Eve. To determine the specific collaboration between Alice and each of them, you would need to perform separate graph searches in a knowledge base that includes information about their collaborations."
    ]
  }
]

[Synalinks]
Call ID: 1333437a-6c61-42de-bb6b-e3b3b2327ecd
Parent call ID: 91bfd2a6-671c-47de-b793-a09e11473e91
Module: Generator
Module Name: local_graph_search_query_generator_local_graph_search
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "local_graph_search": [
      "Alice's collaborators might include Bob, Carol, Dave, or Eve. To determine the specific collaboration between Alice and each of them, you would need to perform separate graph searches in a knowledge base that includes information about their collaborations."
    ]
  }
]

[Synalinks]
Call ID: 076e6813-4525-4205-9b9b-79d1384b7567
Parent call ID: cda54acf-e85b-453b-8d56-b9f0a3b085cf
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
  {
    "texts": [
      "Alice's collaborators might include Bob, Carol, Dave, or Eve. To determine the specific collaboration between Alice and each of them, you would need to perform separate graph searches in a knowledge base that includes information about their collaborations."
    ]
  }
]

[Synalinks]
Call ID: 076e6813-4525-4205-9b9b-79d1384b7567
Parent call ID: cda54acf-e85b-453b-8d56-b9f0a3b085cf
Module: EmbeddingModel
Module Name: embedding_model
Module Description: An embedding model API wrapper.
Data Model JSON:
[
  {
    "embeddings": [
      [
        0.0475376,
        -0.0016463723,
        -0.0020196806,
        -0.031977385,
        0.017251529,
        -0.030340228,
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        0.027655322,
        0.011566436,
        0.0062843794,
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        0.04039105,
        -0.03312338,
        -0.0058973897,
        0.004806335,
        -0.034185585,
        0.007016263,
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        0.037154198,
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        0.036131166,
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        0.11669014,
        0.07171826,
        0.0010615834,
        -0.012710593,
        0.0054783463,
        -0.023016347,
        -0.027228136,
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        0.06329427,
        -0.018693928,
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    ]
  }
]

[Synalinks]
Call ID: 91bfd2a6-671c-47de-b793-a09e11473e91
Parent call ID: fbc54a68-4b53-4008-97ed-79fe2adcaa7d
Module: LocalGraphSearch
Module Name: local_graph_search
Module Description: GraphRAG-style *local* search: seed by vector, expand, return a subgraph.
Data Model JSON:
[
  {
    "question": "Who does Alice collaborate with, and what do they work on?",
    "local_graph_search": [
      "Alice's collaborators might include Bob, Carol, Dave, or Eve. To determine the specific collaboration between Alice and each of them, you would need to perform separate graph searches in a knowledge base that includes information about their collaborations."
    ],
    "entities": [
      {
        "label": "Researcher",
        "name": "Alice",
        "focus": "diffusion models for image generation"
      },
      {
        "label": "Researcher",
        "name": "Bob",
        "focus": "real-time image segmentation"
      },
      {
        "label": "Researcher",
        "name": "Carol",
        "focus": "video understanding"
      },
      {
        "label": "Researcher",
        "name": "Dan",
        "focus": "low-resource machine translation"
      },
      {
        "label": "Researcher",
        "name": "Eve",
        "focus": "speech recognition"
      },
      {
        "label": "Researcher",
        "name": "Frank",
        "focus": "task-oriented dialogue systems"
      }
    ],
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: fbc54a68-4b53-4008-97ed-79fe2adcaa7d
Parent call ID: None
Module: Functional
Module Name: local_graph_search
Module Description: Retrieve the neighbourhood around the entities in question.
Data Model JSON:
[
  {
    "question": "Who does Alice collaborate with, and what do they work on?",
    "local_graph_search": [
      "Alice's collaborators might include Bob, Carol, Dave, or Eve. To determine the specific collaboration between Alice and each of them, you would need to perform separate graph searches in a knowledge base that includes information about their collaborations."
    ],
    "entities": [
      {
        "label": "Researcher",
        "name": "Alice",
        "focus": "diffusion models for image generation"
      },
      {
        "label": "Researcher",
        "name": "Bob",
        "focus": "real-time image segmentation"
      },
      {
        "label": "Researcher",
        "name": "Carol",
        "focus": "video understanding"
      },
      {
        "label": "Researcher",
        "name": "Dan",
        "focus": "low-resource machine translation"
      },
      {
        "label": "Researcher",
        "name": "Eve",
        "focus": "speech recognition"
      },
      {
        "label": "Researcher",
        "name": "Frank",
        "focus": "task-oriented dialogue systems"
      }
    ],
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 0f36221f-8de9-4b43-8ac3-4e2cfd838cdf
Parent call ID: None
Module: Functional
Module Name: global_graph_search
Module Description: Retrieve the graph's most important communities.
Data Model JSON:
[
  {
    "question": "ignored: global search is whole-graph"
  }
]

[Synalinks]
Call ID: c25f314d-cd2a-4743-8383-34b0fa682ee5
Parent call ID: 0f36221f-8de9-4b43-8ac3-4e2cfd838cdf
Module: GlobalGraphSearch
Module Name: global_graph_search
Module Description: GraphRAG-style *global* search: return the graph's top communities.
Data Model JSON:
[
  {
    "question": "ignored: global search is whole-graph"
  }
]

[Synalinks]
Call ID: c25f314d-cd2a-4743-8383-34b0fa682ee5
Parent call ID: 0f36221f-8de9-4b43-8ac3-4e2cfd838cdf
Module: GlobalGraphSearch
Module Name: global_graph_search
Module Description: GraphRAG-style *global* search: return the graph's top communities.
Data Model JSON:
[
  {
    "knowledge_graphs": [
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Dan",
            "focus": "low-resource machine translation"
          },
          {
            "label": "Researcher",
            "name": "Eve",
            "focus": "speech recognition"
          },
          {
            "label": "Researcher",
            "name": "Frank",
            "focus": "task-oriented dialogue systems"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            }
          }
        ]
      },
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Alice",
            "focus": "diffusion models for image generation"
          },
          {
            "label": "Researcher",
            "name": "Bob",
            "focus": "real-time image segmentation"
          },
          {
            "label": "Researcher",
            "name": "Carol",
            "focus": "video understanding"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            }
          }
        ]
      }
    ]
  }
]

[Synalinks]
Call ID: 0f36221f-8de9-4b43-8ac3-4e2cfd838cdf
Parent call ID: None
Module: Functional
Module Name: global_graph_search
Module Description: Retrieve the graph's most important communities.
Data Model JSON:
[
  {
    "knowledge_graphs": [
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Dan",
            "focus": "low-resource machine translation"
          },
          {
            "label": "Researcher",
            "name": "Eve",
            "focus": "speech recognition"
          },
          {
            "label": "Researcher",
            "name": "Frank",
            "focus": "task-oriented dialogue systems"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            }
          }
        ]
      },
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Alice",
            "focus": "diffusion models for image generation"
          },
          {
            "label": "Researcher",
            "name": "Bob",
            "focus": "real-time image segmentation"
          },
          {
            "label": "Researcher",
            "name": "Carol",
            "focus": "video understanding"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            }
          }
        ]
      }
    ]
  }
]

============================================================
Index time: store the graph
============================================================

============================================================
Index time: build_communities
============================================================

Nodes stamped with community + rank: 6

============================================================
LocalGraphSearch: around Alice
============================================================

Neighbourhood entities:
  - Alice: diffusion models for image generation
  - Bob: real-time image segmentation
  - Carol: video understanding
  - Dan: low-resource machine translation
  - Eve: speech recognition
  - Frank: task-oriented dialogue systems

Neighbourhood relations:
  - Alice -> Bob
  - Carol -> Alice
  - Bob -> Carol
  - Alice -> Dan
  - Dan -> Eve
  - Frank -> Dan

============================================================
GlobalGraphSearch: the communities
============================================================

Community #0 (3 internal edges):
  Dan, Eve, Frank

Community #1 (3 internal edges):
  Alice, Bob, Carol

============================================================
GlobalGraphMapReduce: the whole-lab question
============================================================
Program: global_graph_qa
description: 'Answer corpus-level questions by community map-reduce.'
┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━┓
┃ Module (type)               ┃ Output Schema                    ┃  Vars # ┃
┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━┩
│ input_module_3              │ Query:                           │       0 │
│ (InputModule)               │   question: str                  │         │
├─────────────────────────────┼──────────────────────────────────┼─────────┤
│ global_graph_map_reduce     │ CommunityAnswer:                 │       2 │
│ (GlobalGraphMapReduce)      │   partial_answer: str            │         │
│                             │   relevance_score: float         │         │
│                             │                                  │         │
│                             │ ChatRole: Literal['system',      │         │
│                             │ 'developer', 'user',             │         │
│                             │ 'assistant', 'tool', 'function'] │         │
│                             │                                  │         │
│                             │ ToolCallFunction:                │         │
│                             │   name: str                      │         │
│                             │   arguments: dict | str          │         │
│                             │                                  │         │
│                             │ ToolCall:                        │         │
│                             │   id: str                        │         │
│                             │   type: Literal['function'] =    │         │
│                             │ 'function'                       │         │
│                             │   function: ToolCallFunction     │         │
│                             │                                  │         │
│                             │ CommunityAnswers:                │         │
│                             │   community_answers:             │         │
│                             │ list[CommunityAnswer]            │         │
│                             │   role: ChatRole                 │         │
│                             │   reasoning_content: str | None  │         │
│                             │ = None                           │         │
│                             │   thinking_blocks: list | None = │         │
│                             │ None                             │         │
│                             │   content: str | list | dict |   │         │
│                             │ None = None                      │         │
│                             │   tool_call_id: str | None =     │         │
│                             │ None                             │         │
│                             │   tool_calls: list[ToolCall] |   │         │
│                             │ None = None                      │         │
│                             │   name: str | None = None        │         │
│                             │   refusal: str | None = None     │         │
│                             │   audio: dict | None = None      │         │
└─────────────────────────────┴──────────────────────────────────┴─────────┘
[Synalinks]
Call ID: 943a3860-5b1f-4ee8-b5dc-2431f87f8735
Parent call ID: None
Module: Functional
Module Name: global_graph_qa
Module Description: Answer corpus-level questions by community map-reduce.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?"
  }
]

[Synalinks]
Call ID: 8044c38b-0a76-4412-82f6-43f7f9b171c6
Parent call ID: 943a3860-5b1f-4ee8-b5dc-2431f87f8735
Module: GlobalGraphMapReduce
Module Name: global_graph_map_reduce
Module Description: GraphRAG-style *global* answering: map over communities, then reduce.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?"
  }
]

[Synalinks]
Call ID: c7e9c0ac-1a53-4c51-b5da-1f9578e18e8d
Parent call ID: 8044c38b-0a76-4412-82f6-43f7f9b171c6
Module: GlobalGraphSearch
Module Name: global_graph_search_global_graph_map_reduce
Module Description: GraphRAG-style *global* search: return the graph's top communities.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?"
  }
]

[Synalinks]
Call ID: c7e9c0ac-1a53-4c51-b5da-1f9578e18e8d
Parent call ID: 8044c38b-0a76-4412-82f6-43f7f9b171c6
Module: GlobalGraphSearch
Module Name: global_graph_search_global_graph_map_reduce
Module Description: GraphRAG-style *global* search: return the graph's top communities.
Data Model JSON:
[
  {
    "knowledge_graphs": [
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Dan",
            "focus": "low-resource machine translation"
          },
          {
            "label": "Researcher",
            "name": "Eve",
            "focus": "speech recognition"
          },
          {
            "label": "Researcher",
            "name": "Frank",
            "focus": "task-oriented dialogue systems"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Eve",
              "focus": "speech recognition"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Frank",
              "focus": "task-oriented dialogue systems"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Dan",
              "focus": "low-resource machine translation"
            }
          }
        ]
      },
      {
        "entities": [
          {
            "label": "Researcher",
            "name": "Alice",
            "focus": "diffusion models for image generation"
          },
          {
            "label": "Researcher",
            "name": "Bob",
            "focus": "real-time image segmentation"
          },
          {
            "label": "Researcher",
            "name": "Carol",
            "focus": "video understanding"
          }
        ],
        "relations": [
          {
            "subj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Bob",
              "focus": "real-time image segmentation"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            }
          },
          {
            "subj": {
              "label": "Researcher",
              "name": "Carol",
              "focus": "video understanding"
            },
            "label": "CollaboratesWith",
            "obj": {
              "label": "Researcher",
              "name": "Alice",
              "focus": "diffusion models for image generation"
            }
          }
        ]
      }
    ]
  }
]

[Synalinks]
Call ID: aa13eef3-d9aa-481c-95f9-efdaddf072ff
Parent call ID: c7e9c0ac-1a53-4c51-b5da-1f9578e18e8d
Module: Generator
Module Name: map_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?",
    "entities": [
      {
        "label": "Researcher",
        "name": "Dan",
        "focus": "low-resource machine translation"
      },
      {
        "label": "Researcher",
        "name": "Eve",
        "focus": "speech recognition"
      },
      {
        "label": "Researcher",
        "name": "Frank",
        "focus": "task-oriented dialogue systems"
      }
    ],
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Eve",
          "focus": "speech recognition"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Frank",
          "focus": "task-oriented dialogue systems"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Dan",
          "focus": "low-resource machine translation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: cdca2dfc-c8d1-4e98-af55-664c3819e08a
Parent call ID: aa13eef3-d9aa-481c-95f9-efdaddf072ff
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nAnswer the inputs using ONLY the provided community subgraph (its entities and relations). Then rate the community's relevance to the inputs with `relevance_score` between 0 and 100 (0 = nothing in this community helps).\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "<input>\n{'question': 'What are the main research areas in the lab?', 'entities': [{'label': 'Researcher', 'name': 'Dan', 'focus': 'low-resource machine translation'}, {'label': 'Researcher', 'name': 'Eve', 'focus': 'speech recognition'}, {'label': 'Researcher', 'name': 'Frank', 'focus': 'task-oriented dialogue systems'}], 'relations': [{'subj': {'label': 'Researcher', 'name': 'Dan', 'focus': 'low-resource machine translation'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Eve', 'focus': 'speech recognition'}}, {'subj': {'label': 'Researcher', 'name': 'Eve', 'focus': 'speech recognition'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Frank', 'focus': 'task-oriented dialogue systems'}}, {'subj': {'label': 'Researcher', 'name': 'Frank', 'focus': 'task-oriented dialogue systems'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Dan', 'focus': 'low-resource machine translation'}}]}\n</input>\n<output>\n"
      }
    ]
  }
]

[Synalinks]
Call ID: eb936ce9-6c7d-455a-bf2a-1b66d6cb7f35
Parent call ID: cdca2dfc-c8d1-4e98-af55-664c3819e08a
Module: Generator
Module Name: map_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?",
    "entities": [
      {
        "label": "Researcher",
        "name": "Alice",
        "focus": "diffusion models for image generation"
      },
      {
        "label": "Researcher",
        "name": "Bob",
        "focus": "real-time image segmentation"
      },
      {
        "label": "Researcher",
        "name": "Carol",
        "focus": "video understanding"
      }
    ],
    "relations": [
      {
        "subj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Bob",
          "focus": "real-time image segmentation"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        }
      },
      {
        "subj": {
          "label": "Researcher",
          "name": "Carol",
          "focus": "video understanding"
        },
        "label": "CollaboratesWith",
        "obj": {
          "label": "Researcher",
          "name": "Alice",
          "focus": "diffusion models for image generation"
        }
      }
    ]
  }
]

[Synalinks]
Call ID: 5b941682-80ad-4caa-aa90-306ce686d0ed
Parent call ID: eb936ce9-6c7d-455a-bf2a-1b66d6cb7f35
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nAnswer the inputs using ONLY the provided community subgraph (its entities and relations). Then rate the community's relevance to the inputs with `relevance_score` between 0 and 100 (0 = nothing in this community helps).\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "<input>\n{'question': 'What are the main research areas in the lab?', 'entities': [{'label': 'Researcher', 'name': 'Alice', 'focus': 'diffusion models for image generation'}, {'label': 'Researcher', 'name': 'Bob', 'focus': 'real-time image segmentation'}, {'label': 'Researcher', 'name': 'Carol', 'focus': 'video understanding'}], 'relations': [{'subj': {'label': 'Researcher', 'name': 'Alice', 'focus': 'diffusion models for image generation'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Bob', 'focus': 'real-time image segmentation'}}, {'subj': {'label': 'Researcher', 'name': 'Bob', 'focus': 'real-time image segmentation'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Carol', 'focus': 'video understanding'}}, {'subj': {'label': 'Researcher', 'name': 'Carol', 'focus': 'video understanding'}, 'label': 'CollaboratesWith', 'obj': {'label': 'Researcher', 'name': 'Alice', 'focus': 'diffusion models for image generation'}}]}\n</input>\n<output>\n"
      }
    ]
  }
]

[Synalinks]
Call ID: cdca2dfc-c8d1-4e98-af55-664c3819e08a
Parent call ID: aa13eef3-d9aa-481c-95f9-efdaddf072ff
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "partial_answer": "The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.",
    "relevance_score": 100
  }
]

[Synalinks]
Call ID: aa13eef3-d9aa-481c-95f9-efdaddf072ff
Parent call ID: c7e9c0ac-1a53-4c51-b5da-1f9578e18e8d
Module: Generator
Module Name: map_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "partial_answer": "The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.",
    "relevance_score": 100
  }
]

[Synalinks]
Call ID: 5b941682-80ad-4caa-aa90-306ce686d0ed
Parent call ID: eb936ce9-6c7d-455a-bf2a-1b66d6cb7f35
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "partial_answer": "The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).",
    "relevance_score": 100
  }
]

[Synalinks]
Call ID: eb936ce9-6c7d-455a-bf2a-1b66d6cb7f35
Parent call ID: cdca2dfc-c8d1-4e98-af55-664c3819e08a
Module: Generator
Module Name: map_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "partial_answer": "The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).",
    "relevance_score": 100
  }
]

[Synalinks]
Call ID: 6922427b-9add-43ba-93f0-e34eb37722f6
Parent call ID: 5b941682-80ad-4caa-aa90-306ce686d0ed
Module: Generator
Module Name: reduce_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "question": "What are the main research areas in the lab?",
    "community_answers": [
      {
        "partial_answer": "The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.",
        "relevance_score": 100
      },
      {
        "partial_answer": "The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).",
        "relevance_score": 100
      }
    ]
  }
]

[Synalinks]
Call ID: 2d9aa255-98b2-4dab-9abe-9e4d02a55027
Parent call ID: 6922427b-9add-43ba-93f0-e34eb37722f6
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "messages": [
      {
        "role": "system",
        "content": "<instructions>\nCombine the community partial answers into one comprehensive answer to the inputs. Weigh each partial answer by its relevance score and ignore the irrelevant ones.\n</instructions>\n"
      },
      {
        "role": "user",
        "content": "<input>\n{'question': 'What are the main research areas in the lab?', 'community_answers': [{'partial_answer': 'The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.', 'relevance_score': 100}, {'partial_answer': 'The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).', 'relevance_score': 100}]}\n</input>\n<output>\n"
      }
    ]
  }
]

[Synalinks]
Call ID: 2d9aa255-98b2-4dab-9abe-9e4d02a55027
Parent call ID: 6922427b-9add-43ba-93f0-e34eb37722f6
Module: LanguageModel
Module Name: language_model
Module Description: A language model API wrapper.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The main research areas in the lab are low-resource machine translation, speech recognition, task-oriented dialogue systems, diffusion models for image generation, real-time image segmentation, and video understanding. These areas are collaborated on by various members including Alice and Carol."
  }
]

[Synalinks]
Call ID: 6922427b-9add-43ba-93f0-e34eb37722f6
Parent call ID: 5b941682-80ad-4caa-aa90-306ce686d0ed
Module: Generator
Module Name: reduce_generator_global_graph_map_reduce
Module Description: Use a `LanguageModel` to generate a data model from an arbitrary input data model.
Data Model JSON:
[
  {
    "role": "assistant",
    "content": "The main research areas in the lab are low-resource machine translation, speech recognition, task-oriented dialogue systems, diffusion models for image generation, real-time image segmentation, and video understanding. These areas are collaborated on by various members including Alice and Carol."
  }
]

[Synalinks]
Call ID: 8044c38b-0a76-4412-82f6-43f7f9b171c6
Parent call ID: 943a3860-5b1f-4ee8-b5dc-2431f87f8735
Module: GlobalGraphMapReduce
Module Name: global_graph_map_reduce
Module Description: GraphRAG-style *global* answering: map over communities, then reduce.
Data Model JSON:
[
  {
    "community_answers": [
      {
        "partial_answer": "The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.",
        "relevance_score": 100
      },
      {
        "partial_answer": "The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).",
        "relevance_score": 100
      }
    ],
    "role": "assistant",
    "content": "The main research areas in the lab are low-resource machine translation, speech recognition, task-oriented dialogue systems, diffusion models for image generation, real-time image segmentation, and video understanding. These areas are collaborated on by various members including Alice and Carol."
  }
]

[Synalinks]
Call ID: 943a3860-5b1f-4ee8-b5dc-2431f87f8735
Parent call ID: None
Module: Functional
Module Name: global_graph_qa
Module Description: Answer corpus-level questions by community map-reduce.
Data Model JSON:
[
  {
    "community_answers": [
      {
        "partial_answer": "The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.",
        "relevance_score": 100
      },
      {
        "partial_answer": "The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).",
        "relevance_score": 100
      }
    ],
    "role": "assistant",
    "content": "The main research areas in the lab are low-resource machine translation, speech recognition, task-oriented dialogue systems, diffusion models for image generation, real-time image segmentation, and video understanding. These areas are collaborated on by various members including Alice and Carol."
  }
]


Scored community answers (map step):
  [100.0] The main research areas in the lab that are mentioned include low-resource machine translation, speech recognition, and task-oriented dialogue systems.
  [100.0] The main research areas in the lab are diffusion models for image generation (collaborated by Alice), real-time image segmentation (collaborated by Bob and Carol), and video understanding (collaborated by Carol and Alice).

Final answer (reduce step):
The main research areas in the lab are low-resource machine translation, speech recognition, task-oriented dialogue systems, diffusion models for image generation, real-time image segmentation, and video understanding. These areas are collaborated on by various members including Alice and Carol.

Done!