reduce schema fields

graphrag/config/models/vector_store_schema_config.py

@@ -30,16 +30,6 @@ class VectorStoreSchemaConfig(BaseModel):
         default="vector",
     )
 
-    text_field: str = Field(
-        description="The text field to use.",
-        default="text",
-    )
-
-    attributes_field: str = Field(
-        description="The attributes field to use.",
-        default="attributes",
-    )
-
     vector_size: int = Field(
         description="The vector size to use.",
         default=DEFAULT_VECTOR_SIZE,
@@ -52,8 +42,6 @@ def _validate_schema(self) -> None:
         for field in [
             self.id_field,
             self.vector_field,
-            self.text_field,
-            self.attributes_field,
         ]:
             if not is_valid_field_name(field):
                 msg = f"Unsafe or invalid field name: {field}"

graphrag/index/operations/embed_text/embed_text.py

@@ -152,7 +152,6 @@ async def _text_embed_with_vector_store(
         )
         batch = input.iloc[insert_batch_size * i : insert_batch_size * (i + 1)]
         texts: list[str] = batch[embed_column].tolist()
-        titles: list[str] = batch[title].tolist()
         ids: list[str] = batch[id_column].tolist()
         result = await strategy_exec(texts, callbacks, cache, strategy_config)
         if result.embeddings:
@@ -163,16 +162,12 @@ async def _text_embed_with_vector_store(
 
         vectors = result.embeddings or []
         documents: list[VectorStoreDocument] = []
-        for doc_id, doc_text, doc_title, doc_vector in zip(
-            ids, texts, titles, vectors, strict=True
-        ):
+        for doc_id, doc_vector in zip(ids, vectors, strict=True):
             if type(doc_vector) is np.ndarray:
                 doc_vector = doc_vector.tolist()
             document = VectorStoreDocument(
                 id=doc_id,
-                text=doc_text,
                 vector=doc_vector,
-                attributes={"title": doc_title},
             )
             documents.append(document)
 

graphrag/query/structured_search/basic_search/basic_context.py

@@ -59,14 +59,15 @@ def build_context(
                 text_embedder=lambda t: self.text_embedder.embed(t),
                 k=k,
             )
-            related_text_list = [
-                {
-                    text_id_col: self.text_id_map[f"{chunk.document.id}"],
-                    text_col: chunk.document.text,
-                }
-                for chunk in related_texts
+
+            text_unit_ids = {t.document.id for t in related_texts}
+            text_units_filtered = []
+            text_units_filtered = [
+                {text_id_col: t.id, text_col: t.text}
+                for t in self.text_units or []
+                if t.id in text_unit_ids
             ]
-            related_text_df = pd.DataFrame(related_text_list)
+            related_text_df = pd.DataFrame(text_units_filtered)
         else:
             related_text_df = pd.DataFrame({
                 text_id_col: [],

graphrag/vector_stores/azure_ai_search.py

@@ -3,7 +3,6 @@
 
 """A package containing the Azure AI Search  vector store implementation."""
 
-import json
 from typing import Any
 
 from azure.core.credentials import AzureKeyCredential
@@ -13,7 +12,6 @@
 from azure.search.documents.indexes.models import (
     HnswAlgorithmConfiguration,
     HnswParameters,
-    SearchableField,
     SearchField,
     SearchFieldDataType,
     SearchIndex,
@@ -121,13 +119,6 @@ def load_documents(
                         vector_search_dimensions=self.vector_size,
                         vector_search_profile_name=self.vector_search_profile_name,
                     ),
-                    SearchableField(
-                        name=self.text_field, type=SearchFieldDataType.String
-                    ),
-                    SimpleField(
-                        name=self.attributes_field,
-                        type=SearchFieldDataType.String,
-                    ),
                 ],
                 vector_search=vector_search,
             )
@@ -139,8 +130,6 @@ def load_documents(
             {
                 self.id_field: doc.id,
                 self.vector_field: doc.vector,
-                self.text_field: doc.text,
-                self.attributes_field: json.dumps(doc.attributes),
             }
             for doc in documents
             if doc.vector is not None
@@ -165,9 +154,7 @@ def similarity_search_by_vector(
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=doc.get(self.id_field, ""),
-                    text=doc.get(self.text_field, ""),
                     vector=doc.get(self.vector_field, []),
-                    attributes=(json.loads(doc.get(self.attributes_field, "{}"))),
                 ),
                 # Cosine similarity between 0.333 and 1.000
                 # https://learn.microsoft.com/en-us/azure/search/hybrid-search-ranking#scores-in-a-hybrid-search-results
@@ -192,7 +179,5 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         response = self.db_connection.get_document(id)
         return VectorStoreDocument(
             id=response.get(self.id_field, ""),
-            text=response.get(self.text_field, ""),
             vector=response.get(self.vector_field, []),
-            attributes=(json.loads(response.get(self.attributes_field, "{}"))),
         )

graphrag/vector_stores/base.py

@@ -4,7 +4,7 @@
 """Base classes for vector stores."""
 
 from abc import ABC, abstractmethod
-from dataclasses import dataclass, field
+from dataclasses import dataclass
 from typing import Any
 
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
@@ -18,12 +18,8 @@ class VectorStoreDocument:
     id: str | int
     """unique id for the document"""
 
-    text: str | None
     vector: list[float] | None
 
-    attributes: dict[str, Any] = field(default_factory=dict)
-    """store any additional metadata, e.g. title, date ranges, etc"""
-
 
 @dataclass
 class VectorStoreSearchResult:
@@ -54,9 +50,7 @@ def __init__(
 
         self.index_name = vector_store_schema_config.index_name
         self.id_field = vector_store_schema_config.id_field
-        self.text_field = vector_store_schema_config.text_field
         self.vector_field = vector_store_schema_config.vector_field
-        self.attributes_field = vector_store_schema_config.attributes_field
         self.vector_size = vector_store_schema_config.vector_size
 
     @abstractmethod

graphrag/vector_stores/cosmosdb.py

@@ -3,7 +3,6 @@
 
 """A package containing the CosmosDB vector store implementation."""
 
-import json
 from typing import Any
 
 from azure.cosmos import ContainerProxy, CosmosClient, DatabaseProxy
@@ -171,8 +170,6 @@ def load_documents(
                 doc_json = {
                     self.id_field: doc.id,
                     self.vector_field: doc.vector,
-                    self.text_field: doc.text,
-                    self.attributes_field: json.dumps(doc.attributes),
                 }
                 print("Storing document in CosmosDB:")  # noqa: T201
                 print(doc_json)  # noqa: T201
@@ -187,7 +184,7 @@ def similarity_search_by_vector(
             raise ValueError(msg)
 
         try:
-            query = f"SELECT TOP {k} c.{self.id_field}, c.{self.text_field}, c.{self.vector_field}, c.{self.attributes_field}, VectorDistance(c.{self.vector_field}, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.{self.vector_field}, @embedding)"  # noqa: S608
+            query = f"SELECT TOP {k} c.{self.id_field}, c.{self.vector_field}, VectorDistance(c.{self.vector_field}, @embedding) AS SimilarityScore FROM c ORDER BY VectorDistance(c.{self.vector_field}, @embedding)"  # noqa: S608
             query_params = [{"name": "@embedding", "value": query_embedding}]
             items = list(
                 self._container_client.query_items(
@@ -199,7 +196,7 @@ def similarity_search_by_vector(
         except (CosmosHttpResponseError, ValueError):
             # Currently, the CosmosDB emulator does not support the VectorDistance function.
             # For emulator or test environments - fetch all items and calculate distance locally
-            query = f"SELECT c.{self.id_field}, c.{self.text_field}, c.{self.vector_field}, c.{self.attributes_field} FROM c"  # noqa: S608
+            query = f"SELECT c.{self.id_field}, c.{self.vector_field} FROM c"  # noqa: S608
             items = list(
                 self._container_client.query_items(
                     query=query,
@@ -231,9 +228,7 @@ def cosine_similarity(a, b):
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=item.get(self.id_field, ""),
-                    text=item.get(self.text_field, ""),
                     vector=item.get(self.vector_field, []),
-                    attributes=(json.loads(item.get(self.attributes_field, "{}"))),
                 ),
                 score=item.get("SimilarityScore", 0.0),
             )
@@ -261,8 +256,6 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         return VectorStoreDocument(
             id=item.get(self.id_field, ""),
             vector=item.get(self.vector_field, []),
-            text=item.get(self.text_field, ""),
-            attributes=(json.loads(item.get(self.attributes_field, "{}"))),
         )
 
     def clear(self) -> None:

graphrag/vector_stores/lancedb.py

@@ -3,19 +3,19 @@
 
 """The LanceDB vector storage implementation package."""
 
-import json  # noqa: I001
 from typing import Any
-import pyarrow as pa
+
+import lancedb
 import numpy as np
+import pyarrow as pa
+
 from graphrag.config.models.vector_store_schema_config import VectorStoreSchemaConfig
 from graphrag.data_model.types import TextEmbedder
-
 from graphrag.vector_stores.base import (
     BaseVectorStore,
     VectorStoreDocument,
     VectorStoreSearchResult,
 )
-import lancedb
 
 
 class LanceDBVectorStore(BaseVectorStore):
@@ -41,19 +41,15 @@ def load_documents(
         """Load documents into vector storage."""
         # Step 1: Prepare data columns manually
         ids = []
-        texts = []
         vectors = []
-        attributes = []
 
         for document in documents:
             self.vector_size = (
                 len(document.vector) if document.vector else self.vector_size
             )
             if document.vector is not None and len(document.vector) == self.vector_size:
                 ids.append(document.id)
-                texts.append(document.text)
                 vectors.append(np.array(document.vector, dtype=np.float32))
-                attributes.append(json.dumps(document.attributes))
 
         # Step 2: Handle empty case
         if len(ids) == 0:
@@ -69,9 +65,7 @@ def load_documents(
             # Step 4: Create PyArrow table (let schema be inferred)
             data = pa.table({
                 self.id_field: pa.array(ids, type=pa.string()),
-                self.text_field: pa.array(texts, type=pa.string()),
                 self.vector_field: vector_column,
-                self.attributes_field: pa.array(attributes, type=pa.string()),
             })
 
         # NOTE: If modifying the next section of code, ensure that the schema remains the same.
@@ -127,9 +121,7 @@ def similarity_search_by_vector(
             VectorStoreSearchResult(
                 document=VectorStoreDocument(
                     id=doc[self.id_field],
-                    text=doc[self.text_field],
                     vector=doc[self.vector_field],
-                    attributes=json.loads(doc[self.attributes_field]),
                 ),
                 score=1 - abs(float(doc["_distance"])),
             )
@@ -155,8 +147,6 @@ def search_by_id(self, id: str) -> VectorStoreDocument:
         if doc:
             return VectorStoreDocument(
                 id=doc[0][self.id_field],
-                text=doc[0][self.text_field],
                 vector=doc[0][self.vector_field],
-                attributes=json.loads(doc[0][self.attributes_field]),
             )
-        return VectorStoreDocument(id=id, text=None, vector=None)
+        return VectorStoreDocument(id=id, vector=None)

tests/integration/vector_stores/test_azure_ai_search.py

@@ -63,8 +63,6 @@ def vector_store_custom(self, mock_search_client, mock_index_client):
             vector_store_schema_config=VectorStoreSchemaConfig(
                 index_name="test_vectors",
                 id_field="id_custom",
-                text_field="text_custom",
-                attributes_field="attributes_custom",
                 vector_field="vector_custom",
                 vector_size=5,
             ),
@@ -86,15 +84,11 @@ def sample_documents(self):
         return [
             VectorStoreDocument(
                 id="doc1",
-                text="This is document 1",
                 vector=[0.1, 0.2, 0.3, 0.4, 0.5],
-                attributes={"title": "Doc 1", "category": "test"},
             ),
             VectorStoreDocument(
                 id="doc2",
-                text="This is document 2",
                 vector=[0.2, 0.3, 0.4, 0.5, 0.6],
-                attributes={"title": "Doc 2", "category": "test"},
             ),
         ]
 
@@ -110,26 +104,20 @@ async def test_vector_store_operations(
         search_results = [
             {
                 "id": "doc1",
-                "text": "This is document 1",
                 "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
-                "attributes": '{"title": "Doc 1", "category": "test"}',
                 "@search.score": 0.9,
             },
             {
                 "id": "doc2",
-                "text": "This is document 2",
                 "vector": [0.2, 0.3, 0.4, 0.5, 0.6],
-                "attributes": '{"title": "Doc 2", "category": "test"}',
                 "@search.score": 0.8,
             },
         ]
         mock_search_client.search.return_value = search_results
 
         mock_search_client.get_document.return_value = {
             "id": "doc1",
-            "text": "This is document 1",
             "vector": [0.1, 0.2, 0.3, 0.4, 0.5],
-            "attributes": '{"title": "Doc 1", "category": "test"}',
         }
 
         vector_store.load_documents(sample_documents)
@@ -154,8 +142,6 @@ def mock_embedder(text: str) -> list[float]:
 
         doc = vector_store.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
-        assert doc.attributes["title"] == "Doc 1"
 
     async def test_empty_embedding(self, vector_store, mock_search_client):
         """Test similarity search by text with empty embedding."""
@@ -186,26 +172,20 @@ async def test_vector_store_customization(
         search_results = [
             {
                 vector_store_custom.id_field: "doc1",
-                vector_store_custom.text_field: "This is document 1",
                 vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
-                vector_store_custom.attributes_field: '{"title": "Doc 1", "category": "test"}',
                 "@search.score": 0.9,
             },
             {
                 vector_store_custom.id_field: "doc2",
-                vector_store_custom.text_field: "This is document 2",
                 vector_store_custom.vector_field: [0.2, 0.3, 0.4, 0.5, 0.6],
-                vector_store_custom.attributes_field: '{"title": "Doc 2", "category": "test"}',
                 "@search.score": 0.8,
             },
         ]
         mock_search_client.search.return_value = search_results
 
         mock_search_client.get_document.return_value = {
             vector_store_custom.id_field: "doc1",
-            vector_store_custom.text_field: "This is document 1",
             vector_store_custom.vector_field: [0.1, 0.2, 0.3, 0.4, 0.5],
-            vector_store_custom.attributes_field: '{"title": "Doc 1", "category": "test"}',
         }
 
         vector_store_custom.load_documents(sample_documents)
@@ -230,5 +210,3 @@ def mock_embedder(text: str) -> list[float]:
 
         doc = vector_store_custom.search_by_id("doc1")
         assert doc.id == "doc1"
-        assert doc.text == "This is document 1"
-        assert doc.attributes["title"] == "Doc 1"