fix: Ollama Truncation

.secrets.baseline

@@ -124,6 +124,12 @@
     },
     {
       "path": "detect_secrets.filters.heuristic.is_templated_secret"
+    },
+    {
+      "path": "detect_secrets.filters.regex.should_exclude_file",
+      "pattern": [
+        "examples/*"
+      ]
     }
   ],
   "results": {
@@ -133,14 +139,14 @@
         "filename": "docs/topics/generators.mdx",
         "hashed_secret": "ef5225a03e4f9cc953ab3c4dd41f5c4db7dc2e5b",
         "is_verified": false,
-        "line_number": 342
+        "line_number": 360
       },
       {
         "type": "Secret Keyword",
         "filename": "docs/topics/generators.mdx",
         "hashed_secret": "eb6256c862c356b375aafa760fa1851e33aa62a9",
         "is_verified": false,
-        "line_number": 366
+        "line_number": 384
       }
     ],
     "tests/test_http_spec.py": [
@@ -153,5 +159,5 @@
       }
     ]
   },
-  "generated_at": "2025-07-23T00:26:51Z"
+  "generated_at": "2025-09-03T21:56:13Z"
 }

docs/api/error.mdx

@@ -68,6 +68,14 @@ max_rounds = max_rounds
 
 The number of rounds which was exceeded.
 
+GeneratorWarning
+----------------
+
+Base class for all generator warnings.
+
+This is used to indicate that something unexpected happened during the generator execution,
+but it is not critical enough to stop the execution.
+
 InvalidGeneratorError
 ---------------------
 

docs/api/generator.mdx

@@ -1934,7 +1934,9 @@ get\_generator
 
 ```python
 get_generator(
-    identifier: str, *, params: GenerateParams | None = None
+    identifier: str,
+    *,
+    params: GenerateParams | dict[str, Any] | None = None,
 ) -> Generator
 ```
 
@@ -1964,7 +1966,7 @@ You can also specify arguments to the generator by comma-separating them:
   (`str`)
   –The identifier string to use to get a generator.
 * **`params`**
-  (`GenerateParams | None`, default:
+  (`GenerateParams | dict[str, Any] | None`, default:
   `None`
   )
   –The generation parameters to use for the generator.
@@ -1982,8 +1984,9 @@ You can also specify arguments to the generator by comma-separating them:
 
 <Accordion title="Source code in rigging/generator/base.py" icon="code">
 ```python
-@lru_cache(maxsize=128)
-def get_generator(identifier: str, *, params: GenerateParams | None = None) -> Generator:
+def get_generator(
+    identifier: str, *, params: GenerateParams | dict[str, t.Any] | None = None
+) -> Generator:
     """
     Get a generator by an identifier string. Uses LiteLLM by default.
 
@@ -2080,6 +2083,8 @@ def get_generator(identifier: str, *, params: GenerateParams | None = None) -> G
         if isinstance(v, str) and v.lower() in ["true", "false"]:
             init_kwargs[k] = v.lower() == "true"
 
+    params = GenerateParams(**params) if isinstance(params, dict) else params
+
     try:
         merged_params = GenerateParams(**kwargs).merge_with(params)
     except Exception as e:

docs/api/model.mdx

@@ -237,15 +237,15 @@ def from_text(
             # Walk through any fields which are strings, and dedent them
 
             for field_name, field_info in cls.model_fields.items():
-                if isinstance(field_info, XmlEntityInfo) and field_info.annotation == str:  # noqa: E721
+                if isinstance(field_info, XmlEntityInfo) and field_info.annotation is str:
                     model.__dict__[field_name] = textwrap.dedent(
                         model.__dict__[field_name]
                     ).strip()
 
             extracted.append((model, slice_))
         except Exception as e:  # noqa: BLE001
             extracted.append((e, slice_))
-            continue
+        continue
 
     # sort back to original order
     extracted.sort(key=lambda x: x[1].start)
@@ -471,11 +471,7 @@ def preprocess_with_cdata(cls, content: str) -> str:
         }
     else:
         field_map = {
-            (
-                field_info.path
-                if isinstance(field_info, XmlEntityInfo) and field_info.path
-                else field_name
-            ): field_info
+            cls._get_field_xml_name(field_name, field_info): field_info
             for field_name, field_info in cls.model_fields.items()
             if isinstance(field_info, XmlEntityInfo)
             and field_info.location == EntityLocation.ELEMENT
@@ -722,7 +718,7 @@ def xml_example(cls) -> str:
             isinstance(field_info, XmlEntityInfo)
             and field_info.location == EntityLocation.ATTRIBUTE
         ):
-            path = field_info.path or field_name
+            path = cls._get_field_xml_name(field_name, field_info)
             example = str(next(iter(field_info.examples or []), "")).replace('"', """)
             attribute_parts.append(f'{path}="{example}"')
         else:
@@ -732,7 +728,7 @@ def xml_example(cls) -> str:
     lines.append(f"<{cls.__xml_tag__}{attr_string}>")
 
     for field_name, field_info in element_fields.items():
-        path = (isinstance(field_info, XmlEntityInfo) and field_info.path) or field_name
+        path = cls._get_field_xml_name(field_name, field_info)
         description = field_info.description
         example = str(next(iter(field_info.examples or []), ""))
 
@@ -912,12 +908,16 @@ def make_from_schema(
     for field_name, field_schema in properties.items():
         field_type, field_info = _process_field(field_name, field_schema)
 
+        # Use the field name as alias if it differs from python naming conventions
+        alias = field_name if field_name != field_name.replace("-", "_") else None
+
         fields[field_name] = (
             field_type,
             field_cls(
                 default=... if field_name in required else None,
                 description=field_schema.get("description", ""),
                 title=field_schema.get("title", ""),
+                alias=alias,
                 **field_info,
             )
             if isinstance(field_info, dict)

docs/topics/generators.mdx

@@ -22,7 +22,7 @@ openai/o3-mini
 gemini/gemini-2.5-pro
 claude-4-sonnet-latest
 vllm_hosted/meta-llama/Llama-3.1-8B-Instruct
-ollama/qwen3
+ollama_chat/qwen3
 
 openai/gpt-4,api_key=sk-1234
 anthropic/claude-3-7-haiku-latest,stop=output:;---,seed=1337
@@ -184,12 +184,30 @@ NAME          ID              SIZE      PROCESSOR    UNTIL
 qwen3:0.6b    7df6b6e09427    2.3 GB    100% GPU     4 minutes from now
 ```
 
-Using this model in Rigging is as simple as using the `ollama/` or `ollama_chat/` prefixes:
+<Warning>
+Ollama is configured with a maximum context length on the server, by default 4096 tokens. This does not change depending on model and requires configuration to update.
+
+If the input messages to the API would exceed this length, Ollama will silently truncate them to fit in the context window. This behavior can cause unexpected generation results due to missing context and is very difficult to detect in Rigging.
+
+We make a best effort by monitoring model responses and checking if the reported input tokens is far less than the input messages we just sent. If observed, the following warning will be emitted.
+
+```
+GeneratorWarning: Input messages may have been truncated ...
+```
+
+When in doubt, monitor the Ollama server logs for the following:
+
+```bash
+... msg="truncating input prompt" limit=4096 prompt=6767 keep=4 new=409
+```
+</Warning>
+
+Using this model in Rigging is as simple as using the `ollama_chat/` (recommended) or `ollama/` prefixes:
 
 ```python
 import rigging as rg
 
-qwen = rg.get_generator("ollama/qwen3:0.6b")
+qwen = rg.get_generator("ollama_chat/qwen3:0.6b")
 
 chat = await qwen.chat("Hello!").run()
 print(chat.conversation)
@@ -211,7 +229,7 @@ If you are running the Ollama server somewhere besides localhost, just pass the
 
 ```python
 qwen = rg.get_generator(
-  "ollama/qwen3:0.6b,api_base=http://remote-server:11434"
+  "ollama_chat/qwen3:0.6b,api_base=http://remote-server:11434"
 )
 ```
 </Note>

pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "rigging"
-version = "3.3.2"
+version = "3.3.3"
 description = "LLM Interaction Framework"
 authors = ["Nick Landers <monoxgas@gmail.com>"]
 license = "MIT"

rigging/error.py

@@ -82,6 +82,15 @@ class TokenizerWarning(Warning):
     """
 
 
+class GeneratorWarning(Warning):
+    """
+    Base class for all generator warnings.
+
+    This is used to indicate that something unexpected happened during the generator execution,
+    but it is not critical enough to stop the execution.
+    """
+
+
 # System Exceptions
 
 

rigging/generator/base.py

@@ -4,7 +4,6 @@
 import functools
 import inspect
 import typing as t
-from functools import lru_cache
 
 from loguru import logger
 from pydantic import (
@@ -738,8 +737,9 @@ def encode_value(val: t.Any) -> t.Any:
     return identifier
 
 
-@lru_cache(maxsize=128)
-def get_generator(identifier: str, *, params: GenerateParams | None = None) -> Generator:
+def get_generator(
+    identifier: str, *, params: GenerateParams | dict[str, t.Any] | None = None
+) -> Generator:
     """
     Get a generator by an identifier string. Uses LiteLLM by default.
 
@@ -836,6 +836,8 @@ def decode_value(value: str) -> t.Any:
         if isinstance(v, str) and v.lower() in ["true", "false"]:
             init_kwargs[k] = v.lower() == "true"
 
+    params = GenerateParams(**params) if isinstance(params, dict) else params
+
     try:
         merged_params = GenerateParams(**kwargs).merge_with(params)
     except Exception as e:

rigging/generator/litellm_.py

@@ -3,9 +3,11 @@
 import datetime
 import re
 import typing as t
+import warnings
 
 from loguru import logger
 
+from rigging.error import GeneratorWarning
 from rigging.generator.base import (
     Fixup,
     GeneratedMessage,
@@ -206,7 +208,7 @@ async def supports_function_calling(self) -> bool | None:
                     ):
                         self._supports_function_calling = True
             except Exception as e:  # noqa: BLE001
-                logger.warning(f"Failed to check for function calling support: {e}")
+                logger.warning(f"Error checking for function calling support: {e}")
                 span.set_attribute("error", str(e))
 
             span.set_attribute("supports_function_calling", self._supports_function_calling)
@@ -236,6 +238,37 @@ async def _ensure_delay_between_requests(self) -> None:
     # This seems like a brittle feature at the moment, so we'll
     # leave it out for now.
 
+    def _warn_on_input_truncation(
+        self, messages: list[Message], response: "GeneratedMessage"
+    ) -> None:
+        # Ollama has a known behavior where it performs silent truncation
+        # of input messages rather than return an error or any API indication.
+        #
+        # This code attempts to detect such truncation by comparing the expected
+        # input length with the reported usage - but it's not foolproof.
+        #
+        # See:
+        # - https://github.com/ollama/ollama/issues/7043
+        # - https://github.com/ollama/ollama/issues/7987
+        # - https://github.com/ollama/ollama/issues/4967
+
+        # We can't check with usage info
+        if not response.usage:
+            return
+
+        # Get a general view of how long we might expect the input prompt to
+        # We'll use a gracious 4 char per token estimate
+        input_tokens_estimate = int(sum(len(message.content) for message in messages) / 4)
+
+        # Check if the response reports that accepted input tokens are less than this
+        if response.usage.input_tokens < input_tokens_estimate:
+            warnings.warn(
+                f"Input messages may have been truncated - see https://github.com/ollama/ollama/issues/7043 "
+                f"(input tokens: {response.usage.input_tokens} < estimate: {input_tokens_estimate})",
+                GeneratorWarning,
+                stacklevel=2,
+            )
+
     def _parse_model_response(
         self,
         response: "ModelResponse",
@@ -359,7 +392,9 @@ async def _generate_message(
             )
 
             self._last_request_time = datetime.datetime.now(tz=datetime.timezone.utc)
-            return self._parse_model_response(response)
+            generated = self._parse_model_response(response)
+            self._warn_on_input_truncation(list(messages), generated)
+            return generated
 
     async def _generate_text(self, text: str, params: GenerateParams) -> GeneratedText:
         import litellm