Merge 9177a0ad73 into b7ee43788d

private_gpt/components/ingest/ingest_component.py

@@ -40,7 +40,12 @@ class BaseIngestComponent(abc.ABC):
         self.transformations = transformations
 
     @abc.abstractmethod
-    def ingest(self, file_name: str, file_data: Path) -> list[Document]:
+    def ingest(
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, Any] | None = None,
+    ) -> list[Document]:
         pass
 
     @abc.abstractmethod
@@ -117,9 +122,16 @@ class SimpleIngestComponent(BaseIngestComponentWithIndex):
     ) -> None:
         super().__init__(storage_context, embed_model, transformations, *args, **kwargs)
 
-    def ingest(self, file_name: str, file_data: Path) -> list[Document]:
+    def ingest(
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, Any] | None = None,
+    ) -> list[Document]:
         logger.info("Ingesting file_name=%s", file_name)
-        documents = IngestionHelper.transform_file_into_documents(file_name, file_data)
+        documents = IngestionHelper.transform_file_into_documents(
+            file_name, file_data, file_metadata
+        )
         logger.info(
             "Transformed file=%s into count=%s documents", file_name, len(documents)
         )
@@ -175,9 +187,16 @@ class BatchIngestComponent(BaseIngestComponentWithIndex):
             processes=self.count_workers
         )
 
-    def ingest(self, file_name: str, file_data: Path) -> list[Document]:
+    def ingest(
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, Any] | None = None,
+    ) -> list[Document]:
         logger.info("Ingesting file_name=%s", file_name)
-        documents = IngestionHelper.transform_file_into_documents(file_name, file_data)
+        documents = IngestionHelper.transform_file_into_documents(
+            file_name, file_data, file_metadata
+        )
         logger.info(
             "Transformed file=%s into count=%s documents", file_name, len(documents)
         )
@@ -185,6 +204,7 @@ class BatchIngestComponent(BaseIngestComponentWithIndex):
         return self._save_docs(documents)
 
     def bulk_ingest(self, files: list[tuple[str, Path]]) -> list[Document]:
+
         documents = list(
             itertools.chain.from_iterable(
                 self._file_to_documents_work_pool.starmap(
@@ -257,12 +277,18 @@ class ParallelizedIngestComponent(BaseIngestComponentWithIndex):
             processes=self.count_workers
         )
 
-    def ingest(self, file_name: str, file_data: Path) -> list[Document]:
+    def ingest(
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, Any] | None = None,
+    ) -> list[Document]:
         logger.info("Ingesting file_name=%s", file_name)
         # Running in a single (1) process to release the current
         # thread, and take a dedicated CPU core for computation
         documents = self._file_to_documents_work_pool.apply(
-            IngestionHelper.transform_file_into_documents, (file_name, file_data)
+            IngestionHelper.transform_file_into_documents,
+            (file_name, file_data, file_metadata),
         )
         logger.info(
             "Transformed file=%s into count=%s documents", file_name, len(documents)
@@ -271,9 +297,9 @@ class ParallelizedIngestComponent(BaseIngestComponentWithIndex):
         return self._save_docs(documents)
 
     def bulk_ingest(self, files: list[tuple[str, Path]]) -> list[Document]:
+
         # Lightweight threads, used for parallelize the
         # underlying IO calls made in the ingestion
-
         documents = list(
             itertools.chain.from_iterable(
                 self._ingest_work_pool.starmap(self.ingest, files)
@@ -459,8 +485,15 @@ class PipelineIngestComponent(BaseIngestComponentWithIndex):
         self.node_q.put(("flush", None, None, None))
         self.node_q.join()
 
-    def ingest(self, file_name: str, file_data: Path) -> list[Document]:
+    def ingest(
-        documents = IngestionHelper.transform_file_into_documents(file_name, file_data)
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, Any] | None = None,
+    ) -> list[Document]:
+        documents = IngestionHelper.transform_file_into_documents(
+            file_name, file_data, file_metadata
+        )
         self.doc_q.put(("process", file_name, documents))
         self._flush()
         return documents

private_gpt/components/ingest/ingest_helper.py

@@ -1,5 +1,6 @@
 import logging
 from pathlib import Path
+from typing import Any
 
 from llama_index.core.readers import StringIterableReader
 from llama_index.core.readers.base import BaseReader
@@ -69,11 +70,13 @@ class IngestionHelper:
 
     @staticmethod
     def transform_file_into_documents(
-        file_name: str, file_data: Path
+        file_name: str, file_data: Path, file_metadata: dict[str, Any] | None = None
     ) -> list[Document]:
         documents = IngestionHelper._load_file_to_documents(file_name, file_data)
         for document in documents:
+            document.metadata.update(file_metadata or {})
             document.metadata["file_name"] = file_name
+
         IngestionHelper._exclude_metadata(documents)
         return documents
 

private_gpt/server/ingest/ingest_router.py

@@ -1,6 +1,7 @@
-from typing import Literal
+import json
+from typing import Any, Literal
 
-from fastapi import APIRouter, Depends, HTTPException, Request, UploadFile
+from fastapi import APIRouter, Depends, Form, HTTPException, Request, UploadFile
 from pydantic import BaseModel, Field
 
 from private_gpt.server.ingest.ingest_service import IngestService
@@ -20,6 +21,16 @@ class IngestTextBody(BaseModel):
             "Chinese martial arts."
         ]
     )
+    metadata: dict[str, Any] = Field(
+        None,
+        examples=[
+            {
+                "title": "Avatar: The Last Airbender",
+                "author": "Michael Dante DiMartino, Bryan Konietzko",
+                "year": "2005",
+            }
+        ],
+    )
 
 
 class IngestResponse(BaseModel):
@@ -38,9 +49,16 @@ def ingest(request: Request, file: UploadFile) -> IngestResponse:
 
 
 @ingest_router.post("/ingest/file", tags=["Ingestion"])
-def ingest_file(request: Request, file: UploadFile) -> IngestResponse:
+def ingest_file(
+    request: Request, file: UploadFile, metadata: str = Form(None)
+) -> IngestResponse:
     """Ingests and processes a file, storing its chunks to be used as context.
 
+    metadata: Optional metadata to be associated with the file.
+    You do not have to specify this field if not needed.
+    The metadata needs to be in JSON format.
+    e.g. {"title": "Avatar: The Last Airbender", "year": "2005"}
+
     The context obtained from files is later used in
     `/chat/completions`, `/completions`, and `/chunks` APIs.
 
@@ -57,7 +75,11 @@ def ingest_file(request: Request, file: UploadFile) -> IngestResponse:
     service = request.state.injector.get(IngestService)
     if file.filename is None:
         raise HTTPException(400, "No file name provided")
-    ingested_documents = service.ingest_bin_data(file.filename, file.file)
+
+    metadata_dict = None if metadata is None else json.loads(metadata)
+    ingested_documents = service.ingest_bin_data(
+        file.filename, file.file, metadata_dict
+    )
     return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
 
 
@@ -73,11 +95,12 @@ def ingest_text(request: Request, body: IngestTextBody) -> IngestResponse:
     extracted Metadata (which is later used to improve context retrieval). That ID
     can be used to filter the context used to create responses in
     `/chat/completions`, `/completions`, and `/chunks` APIs.
+
     """
     service = request.state.injector.get(IngestService)
     if len(body.file_name) == 0:
         raise HTTPException(400, "No file name provided")
-    ingested_documents = service.ingest_text(body.file_name, body.text)
+    ingested_documents = service.ingest_text(body.file_name, body.text, body.metadata)
     return IngestResponse(object="list", model="private-gpt", data=ingested_documents)
 
 

private_gpt/server/ingest/ingest_service.py

@@ -48,7 +48,12 @@ class IngestService:
             settings=settings(),
         )
 
-    def _ingest_data(self, file_name: str, file_data: AnyStr) -> list[IngestedDoc]:
+    def _ingest_data(
+        self,
+        file_name: str,
+        file_data: AnyStr,
+        file_metadata: dict[str, str] | None = None,
+    ) -> list[IngestedDoc]:
         logger.debug("Got file data of size=%s to ingest", len(file_data))
         # llama-index mainly supports reading from files, so
         # we have to create a tmp file to read for it to work
@@ -60,27 +65,37 @@ class IngestService:
                     path_to_tmp.write_bytes(file_data)
                 else:
                     path_to_tmp.write_text(str(file_data))
-                return self.ingest_file(file_name, path_to_tmp)
+                return self.ingest_file(file_name, path_to_tmp, file_metadata)
             finally:
                 tmp.close()
                 path_to_tmp.unlink()
 
-    def ingest_file(self, file_name: str, file_data: Path) -> list[IngestedDoc]:
+    def ingest_file(
+        self,
+        file_name: str,
+        file_data: Path,
+        file_metadata: dict[str, str] | None = None,
+    ) -> list[IngestedDoc]:
         logger.info("Ingesting file_name=%s", file_name)
-        documents = self.ingest_component.ingest(file_name, file_data)
+        documents = self.ingest_component.ingest(file_name, file_data, file_metadata)
         logger.info("Finished ingestion file_name=%s", file_name)
         return [IngestedDoc.from_document(document) for document in documents]
 
-    def ingest_text(self, file_name: str, text: str) -> list[IngestedDoc]:
+    def ingest_text(
+        self, file_name: str, text: str, metadata: dict[str, str] | None = None
+    ) -> list[IngestedDoc]:
         logger.debug("Ingesting text data with file_name=%s", file_name)
-        return self._ingest_data(file_name, text)
+        return self._ingest_data(file_name, text, metadata)
 
     def ingest_bin_data(
-        self, file_name: str, raw_file_data: BinaryIO
+        self,
+        file_name: str,
+        raw_file_data: BinaryIO,
+        file_metadata: dict[str, str] | None = None,
     ) -> list[IngestedDoc]:
         logger.debug("Ingesting binary data with file_name=%s", file_name)
         file_data = raw_file_data.read()
-        return self._ingest_data(file_name, file_data)
+        return self._ingest_data(file_name, file_data, file_metadata)
 
     def bulk_ingest(self, files: list[tuple[str, Path]]) -> list[IngestedDoc]:
         logger.info("Ingesting file_names=%s", [f[0] for f in files])

tests/fixtures/ingest_helper.py

@@ -1,4 +1,6 @@
+import json
 from pathlib import Path
+from typing import Any
 
 import pytest
 from fastapi.testclient import TestClient
@@ -18,6 +20,20 @@ class IngestHelper:
         ingest_result = IngestResponse.model_validate(response.json())
         return ingest_result
 
+    def ingest_file_with_metadata(
+        self, path: Path, metadata: dict[str, Any]
+    ) -> IngestResponse:
+        files = {
+            "file": (path.name, path.open("rb")),
+            "metadata": (None, json.dumps(metadata)),
+        }
+
+        response = self.test_client.post("/v1/ingest/file", files=files)
+
+        assert response.status_code == 200
+        ingest_result = IngestResponse.model_validate(response.json())
+        return ingest_result
+
 
 @pytest.fixture
 def ingest_helper(test_client: TestClient) -> IngestHelper:

tests/server/ingest/test_ingest_routes.py

@@ -44,3 +44,25 @@ def test_ingest_plain_text(test_client: TestClient) -> None:
     assert response.status_code == 200
     ingest_result = IngestResponse.model_validate(response.json())
     assert len(ingest_result.data) == 1
+
+
+def test_ingest_text_with_metadata(test_client: TestClient):
+    response = test_client.post(
+        "/v1/ingest/text",
+        json={"file_name": "file_name", "text": "text", "metadata": {"foo": "bar"}},
+    )
+    assert response.status_code == 200
+    ingest_result = IngestResponse.model_validate(response.json())
+    assert len(ingest_result.data) == 1
+
+    assert ingest_result.data[0].doc_metadata == {
+        "file_name": "file_name",
+        "foo": "bar",
+    }
+
+
+def test_ingest_accepts_txt_files_with_metadata(ingest_helper: IngestHelper) -> None:
+    path = Path(__file__).parents[0] / "test.txt"
+    ingest_result = ingest_helper.ingest_file_with_metadata(path, {"foo": "bar"})
+    assert len(ingest_result.data) == 1
+    assert ingest_result.data[0].doc_metadata == {"file_name": "test.txt", "foo": "bar"}