Implemented a way for Google Model to analyze JSON file links using DocumentUrl

pydantic_ai_slim/pydantic_ai/messages.py

@@ -458,6 +458,27 @@ def format(self) -> DocumentFormat:
         except KeyError as e:
             raise ValueError(f'Unknown document media type: {media_type}') from e
 
+    @staticmethod
+    def is_text_like_media_type(media_type: str) -> bool:
+        return (
+            media_type.startswith('text/')
+            or media_type == 'application/json'
+            or media_type.endswith('+json')
+            or media_type == 'application/xml'
+            or media_type.endswith('+xml')
+            or media_type in ('application/x-yaml', 'application/yaml')
+        )
+
+    @staticmethod
+    def inline_text_file_part(text: str, *, media_type: str, identifier: str):
+        text = '\n'.join(
+            [
+                f'-----BEGIN FILE id="{identifier}" type="{media_type}"-----',
+                text,
+                f'-----END FILE id="{identifier}"-----',
+            ]
+        )
+        return {'text': text}
 
 @dataclass(init=False, repr=False)
 class BinaryContent:
@@ -513,8 +534,16 @@ def narrow_type(bc: BinaryContent) -> BinaryContent | BinaryImage:
                 identifier=bc.identifier,
                 vendor_metadata=bc.vendor_metadata,
             )
-        else:
-            return bc
+        else:          
+            return bc  # pragma: no cover
+
+    @staticmethod
+    def is_text_like_media_type(mediatype: str) -> bool:
+      return DocumentUrl.is_text_like_media_type(mediatype)
+
+    @staticmethod
+    def inline_text_file_part(text: str, *, media_type: str, identifier: str):
+      return DocumentUrl.inline_text_file_part(text, media_type=media_type, identifier=identifier)
 
     @classmethod
     def from_data_uri(cls, data_uri: str) -> BinaryContent:

pydantic_ai_slim/pydantic_ai/models/google.py

@@ -19,6 +19,7 @@
     BinaryContent,
     BuiltinToolCallPart,
     BuiltinToolReturnPart,
+    DocumentUrl,
     FilePart,
     FileUrl,
     FinishReason,
@@ -565,17 +566,46 @@ async def _map_user_prompt(self, part: UserPromptPart) -> list[PartDict]:
                 if isinstance(item, str):
                     content.append({'text': item})
                 elif isinstance(item, BinaryContent):
-                    inline_data_dict: BlobDict = {'data': item.data, 'mime_type': item.media_type}
-                    part_dict: PartDict = {'inline_data': inline_data_dict}
-                    if item.vendor_metadata:
-                        part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
-                    content.append(part_dict)
+                    if BinaryContent.is_text_like_media_type(item.media_type):
+                        content.append(
+                            BinaryContent.inline_text_file_part(
+                                item.data.decode('utf-8'),
+                                media_type=item.media_type,
+                                identifier=item.identifier,
+                            )
+                        )
+                    else:
+                        inline_data_dict: BlobDict = {'data': item.data, 'mime_type': item.media_type}
+                        part_dict: PartDict = {'inline_data': inline_data_dict}
+                        if item.vendor_metadata:
+                            part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
+                        content.append(part_dict)
+
+                elif isinstance(item, DocumentUrl):
+                    if DocumentUrl.is_text_like_media_type(item.media_type):
+                        downloaded_text = await download_item(item, data_format='text')
+                        content.append(
+                            DocumentUrl.inline_text_file_part(
+                                downloaded_text['data'],
+                                media_type=item.media_type,
+                                identifier=item.identifier,
+                            )
+                        )
+                    else:
+                        downloaded_item = await download_item(item, data_format='bytes')
+                        inline_data_dict: BlobDict = {
+                            'data': downloaded_item['data'],
+                            'mime_type': downloaded_item['data_type'],
+                        }
+                        content.append({'inline_data': inline_data_dict})
+
                 elif isinstance(item, VideoUrl) and item.is_youtube:
                     file_data_dict: FileDataDict = {'file_uri': item.url, 'mime_type': item.media_type}
                     part_dict: PartDict = {'file_data': file_data_dict}
                     if item.vendor_metadata:  # pragma: no branch
                         part_dict['video_metadata'] = cast(VideoMetadataDict, item.vendor_metadata)
                     content.append(part_dict)
+
                 elif isinstance(item, FileUrl):
                     if item.force_download or (
                         # google-gla does not support passing file urls directly, except for youtube videos
@@ -594,7 +624,8 @@ async def _map_user_prompt(self, part: UserPromptPart) -> list[PartDict]:
                         content.append({'file_data': file_data_dict})  # pragma: lax no cover
                 else:
                     assert_never(item)
-        return content
+
+            return content
 
     def _map_response_schema(self, o: OutputObjectDefinition) -> dict[str, Any]:
         response_schema = o.json_schema.copy()

tests/models/cassettes/test_google/test_google_model_json_document_url_input.yaml

@@ -0,0 +1,176 @@
+interactions:
+- request:
+    body: ''
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      host:
+      - kamalscraping-collab.github.io
+    method: GET
+    uri: https://kamalscraping-collab.github.io/sample-data/sample_transcript.json
+  response:
+    body:
+      string: |-
+          {
+      "items": [
+        {
+          "id": "GR_ad8d2a461fc5",
+          "type": "message",
+          "role": "assistant",
+          "content": [
+            "Hello, how can I help you today?"
+          ],
+          "interrupted": false
+        },
+        {
+          "id": "item_13ecd51e0dcc",
+          "type": "function_call",
+          "call_id": "function-call-18124021183837676163",
+          "arguments": "{\"location\": \"Kampala, Uganda\"}",
+          "name": "lookup_weather"
+        },
+        {
+          "id": "GI_14a70e7c2d20",
+          "type": "message",
+          "role": "user",
+          "content": [
+            "Haide, can you please tell me the weather in compiler Uganda"
+          ],
+          "interrupted": false
+        },
+        {
+          "id": "item_000f739d4414",
+          "type": "function_call_output",
+          "name": "lookup_weather",
+          "call_id": "function-call-18124021183837676163",
+          "output": "{'weather': 'sunny', 'temperature_f': 70}",
+          "is_error": false
+        },
+        {
+          "id": "GR_95c91db6b975",
+          "type": "message",
+          "role": "assistant",
+          "content": [
+            "The weather in Kampala, Uganda is sunny with a temperature of 70 degrees Fahrenheit."
+          ],
+          "interrupted": false
+        },
+        {
+          "id": "GI_c8cc9177073f",
+          "type": "message",
+          "role": "user",
+          "content": [
+            "what can you please tell me what are the best things to do in compiler you're"
+          ],
+          "interrupted": false
+        },
+        {
+          "id": "GR_792c5f6fbc89",
+          "type": "message",
+          "role": "assistant",
+          "content": [
+            "While I can tell you the weather, I'm not able to provide information on the best things to do in a specific location. Is there anything else I can help you with?"
+          ],
+          "interrupted": false
+        }
+      ]
+    }
+    headers:
+      cache-control:
+      - max-age=604800
+      - public
+      connection:
+      - keep-alive
+      content-length:
+      - '2574'
+      content-type:
+      - text/plain; charset=UTF-8
+      etag:
+      - W/"61efea10-a0e"
+      expires:
+      - Fri, 26 Dec 2025 16:42:28 GMT
+      last-modified:
+      - Tue, 25 Jan 2022 12:16:16 GMT
+      strict-transport-security:
+      - max-age=15552000; includeSubDomains
+      transfer-encoding:
+      - chunked
+      vary:
+      - Accept-Encoding
+    status:
+      code: 200
+      message: OK
+- request:
+    headers:
+      accept:
+      - '*/*'
+      accept-encoding:
+      - gzip, deflate
+      connection:
+      - keep-alive
+      content-length:
+      - '3701'
+      content-type:
+      - application/json
+      host:
+      - generativelanguage.googleapis.com
+    method: POST
+    parsed_body:
+      contents:
+      - parts:
+        - text: What is the main content on this document?
+        - inlineData:
+            data: 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
+            mimeType: application/json
+        role: user
+      generationConfig: {}
+      systemInstruction:
+        parts:
+        - text: You are a helpful chatbot.
+        role: user
+    uri: https://generativelanguage.googleapis.com/v1beta/models/gemini-2.5-pro:generateContent
+  response:
+    headers:
+      alt-svc:
+      - h3=":443"; ma=2592000,h3-29=":443"; ma=2592000
+      content-length:
+      - '985'
+      content-type:
+      - application/json; charset=UTF-8
+      server-timing:
+      - gfet4t7; dur=888
+      transfer-encoding:
+      - chunked
+      vary:
+      - Origin
+      - X-Origin
+      - Referer
+    parsed_body:
+      candidates:
+      - avgLogprobs: -0.5004191543116714
+        content:
+          parts:
+          - text: |
+              Based on the JSON data provided, the document contains the log of a conversation between a user and an AI assistant.
+          role: model
+        finishReason: STOP
+      modelVersion: gemini-2.5-pro
+      responseId: 9YfNaLGGDuOmqtsPoLXu4AQ
+      usageMetadata:
+        candidatesTokenCount: 66
+        candidatesTokensDetails:
+        - modality: TEXT
+          tokenCount: 66
+        promptTokenCount: 614
+        promptTokensDetails:
+        - modality: TEXT
+          tokenCount: 614
+        totalTokenCount: 680
+    status:
+      code: 200
+      message: OK
+version: 1

tests/models/test_google.py

@@ -835,6 +835,18 @@ async def test_google_model_text_document_url_input(allow_model_requests: None,
     )
 
 
+async def test_google_model_json_document_url_input(allow_model_requests: None, google_provider: GoogleProvider):
+    m = GoogleModel('gemini-2.5-pro', provider=google_provider)
+    agent = Agent(m, system_prompt='You are a helpful chatbot.')
+
+    json_document_url = DocumentUrl(url='https://kamalscraping-collab.github.io/sample-data/sample_transcript.json')
+
+    result = await agent.run(['What is the main content of this document?', json_document_url])
+    assert result.output == snapshot(
+        'Based on the JSON data provided, the document contains the log of a conversation between a user and an AI assistant.\n'
+    )
+
+
 async def test_google_model_text_as_binary_content_input(allow_model_requests: None, google_provider: GoogleProvider):
     m = GoogleModel('gemini-2.0-flash', provider=google_provider)
     agent = Agent(m, system_prompt='You are a helpful chatbot.')