Metadata & Applying Filters

models/file.py

@@ -37,6 +37,7 @@ def suffix(self) -> str:
 class File(BaseModel):
     url: str
     name: str | None = None
+    metadata: dict | None = None
 
     @property
     def type(self) -> FileType | None:

models/query.py

@@ -1,10 +1,13 @@
-from typing import List, Optional
-
 from pydantic import BaseModel
+from typing import List, Optional, Union
 
 from models.document import BaseDocumentChunk
 from models.ingest import EncoderConfig
 from models.vector_database import VectorDatabase
+from qdrant_client.http.models import Filter as QdrantFilter
+
+
+Filter = Union[QdrantFilter, dict]
 
 
 class RequestPayload(BaseModel):
@@ -15,6 +18,7 @@ class RequestPayload(BaseModel):
     session_id: Optional[str] = None
     interpreter_mode: Optional[bool] = False
     exclude_fields: List[str] = None
+    filter: Optional[Filter] = None
 
 
 class ResponseData(BaseModel):

service/embedding.py

@@ -156,6 +156,7 @@ async def generate_chunks(
     ) -> List[BaseDocumentChunk]:
         doc_chunks = []
         for file in tqdm(self.files, desc="Generating chunks"):
+            file_metadata = file.metadata or {}
             logger.info(f"Splitting method: {config.splitter.name}")
             try:
                 chunks = []
@@ -168,7 +169,10 @@ async def generate_chunks(
                         chunk_data = {
                             "content": element.get("text"),
                             "metadata": self._sanitize_metadata(
-                                element.get("metadata")
+                                {
+                                    **file_metadata,
+                                    **element.get("metadata"),
+                                }
                             ),
                         }
                         chunks.append(chunk_data)

service/router.py

@@ -40,7 +40,9 @@ def create_route_layer() -> RouteLayer:
 async def get_documents(
     *, vector_service: BaseVectorDatabase, payload: RequestPayload
 ) -> list[BaseDocumentChunk]:
-    chunks = await vector_service.query(input=payload.input, top_k=5)
+    chunks = await vector_service.query(
+        input=payload.input, filter=payload.filter, top_k=5
+    )
     # filter out documents with empty content
     chunks = [chunk for chunk in chunks if chunk.content.strip()]
     if not len(chunks):

vectordbs/astra.py

@@ -5,6 +5,7 @@
 from tqdm import tqdm
 
 from models.document import BaseDocumentChunk
+from models.query import Filter
 from vectordbs.base import BaseVectorDatabase
 
 
@@ -54,12 +55,13 @@ async def upsert(self, chunks: List[BaseDocumentChunk]) -> None:
         for i in range(0, len(documents), 5):
             self.collection.insert_many(documents=documents[i : i + 5])
 
-    async def query(self, input: str, top_k: int = 4) -> List:
+    async def query(self, input: str, filter: Filter = None, top_k: int = 4) -> List:
         vectors = await self._generate_vectors(input=input)
         results = self.collection.vector_find(
             vector=vectors[0],
             limit=top_k,
             fields={"text", "page_number", "source", "document_id"},
+            filter=filter,
         )
         return [
             BaseDocumentChunk(

vectordbs/base.py

@@ -7,6 +7,7 @@
 
 from models.delete import DeleteResponse
 from models.document import BaseDocumentChunk
+from models.query import Filter
 from utils.logger import logger
 
 
@@ -24,7 +25,9 @@ async def upsert(self, chunks: List[BaseDocumentChunk]):
         pass
 
     @abstractmethod
-    async def query(self, input: str, top_k: int = 25) -> List[BaseDocumentChunk]:
+    async def query(
+        self, input: str, filter: Filter, top_k: int = 25
+    ) -> List[BaseDocumentChunk]:
         pass
 
     @abstractmethod

vectordbs/pgvector.py

@@ -7,6 +7,7 @@
 from qdrant_client.http import models as rest
 from models.delete import DeleteResponse
 from models.document import BaseDocumentChunk
+from models.query import Filter
 from vectordbs.base import BaseVectorDatabase
 
 MAX_QUERY_TOP_K = 5
@@ -58,14 +59,17 @@ async def upsert(self, chunks: List[BaseDocumentChunk]) -> None:
         self.collection.upsert(records)
         self.collection.create_index()
 
-    async def query(self, input: str, top_k: int = MAX_QUERY_TOP_K) -> List:
+    async def query(
+        self, input: str, filter: Filter = None, top_k: int = MAX_QUERY_TOP_K
+    ) -> List:
         vectors = await self._generate_vectors(input=input)
 
         results = self.collection.query(
             data=vectors[0],
             limit=top_k,
             include_metadata=True,
             include_value=False,
+            filters=filter.model_dump() if filter else {},
         )
 
         chunks = []

vectordbs/pinecone.py

@@ -6,6 +6,7 @@
 
 from models.delete import DeleteResponse
 from models.document import BaseDocumentChunk
+from models.query import Filter
 from utils.logger import logger
 from vectordbs.base import BaseVectorDatabase
 
@@ -52,7 +53,11 @@ async def upsert(self, chunks: List[BaseDocumentChunk], batch_size: int = 100):
             raise
 
     async def query(
-        self, input: str, top_k: int = 25, include_metadata: bool = True
+        self,
+        input: str,
+        filter: Filter = None,
+        top_k: int = 25,
+        include_metadata: bool = True,
     ) -> list[BaseDocumentChunk]:
         if self.index is None:
             raise ValueError(f"Pinecone index {self.index_name} is not initialized.")
@@ -61,6 +66,7 @@ async def query(
             vector=query_vectors[0],
             top_k=top_k,
             include_metadata=include_metadata,
+            filter=filter,
         )
         chunks = []
         if results.get("matches"):

vectordbs/qdrant.py

@@ -7,6 +7,7 @@
 
 from models.delete import DeleteResponse
 from models.document import BaseDocumentChunk
+from models.query import Filter
 from vectordbs.base import BaseVectorDatabase
 
 MAX_QUERY_TOP_K = 5
@@ -69,11 +70,14 @@ async def upsert(self, chunks: List[BaseDocumentChunk]) -> None:
 
         self.client.upsert(collection_name=self.index_name, wait=True, points=points)
 
-    async def query(self, input: str, top_k: int = MAX_QUERY_TOP_K) -> List:
+    async def query(
+        self, input: str, filter: Filter, top_k: int = MAX_QUERY_TOP_K
+    ) -> List:
         vectors = await self._generate_vectors(input=input)
         search_result = self.client.search(
             collection_name=self.index_name,
             query_vector=("content", vectors[0]),
+            query_filter=filter,
             limit=top_k,
             with_payload=True,
         )

vectordbs/weaviate.py

@@ -10,6 +10,8 @@
 from utils.logger import logger
 from vectordbs.base import BaseVectorDatabase
 
+from models.query import Filter
+
 
 class WeaviateService(BaseVectorDatabase):
     def __init__(
@@ -72,7 +74,9 @@ async def upsert(self, chunks: List[BaseDocumentChunk]) -> None:
                 batch.add_data_object(**vector_data)
             batch.flush()
 
-    async def query(self, input: str, top_k: int = 25) -> list[BaseDocumentChunk]:
+    async def query(
+        self, input: str, filter: Filter = {}, top_k: int = 25
+    ) -> list[BaseDocumentChunk]:
         vectors = await self._generate_vectors(input=input)
         vector = {"vector": vectors[0]}
 
@@ -84,6 +88,7 @@ async def query(self, input: str, top_k: int = 25) -> list[BaseDocumentChunk]:
                 )
                 .with_near_vector(vector)
                 .with_limit(top_k)
+                .with_where(filter)
                 .do()
             )
             if "data" not in response: