feat: LLM-native structured output via JSON schema enforcement (#1037)

Thread existing JSON schemas from prompt definitions through the
text-completion service to LLM backends' native structured output
APIs. When a prompt has response-type "json" and a strict-mode
compatible schema, the LLM constrains token selection at the logit
level to guarantee schema-valid output.

Wire-level changes:
- Add response_format and schema fields to TextCompletionRequest
- Update translator to encode/decode new fields
- Pass new fields through LlmService, TextCompletionClient, and
  PromptManager

Runtime schema compatibility checker:
- New is_strict_mode_compatible() utility validates schemas against
  LLM provider constraints (additionalProperties, required fields,
  no unsupported constraints, no open-ended objects)
- Per-prompt eligibility decision: compliant schemas use structured
  output, non-compliant schemas fall back to free-text + post-hoc
  validation

LLM backend implementations:
- OpenAI: response_format with json_schema, variant-aware top-level
  array rejection (openai variant blocks, llama/vllm variants allow)
- New vllm variant for the OpenAI backend
- vLLM (dedicated): response_format in raw HTTP body
- Ollama: format=<schema> parameter
- Claude: tool-use trick (forced tool call with schema as input_schema)
- Mistral: native json_schema response_format
- Llamafile, LM Studio: OpenAI SDK response_format
- Azure OpenAI: AzureOpenAI SDK response_format
- Azure serverless: response_format in raw HTTP body
- TGI: response_format in raw HTTP body
- VertexAI Gemini: response_mime_type + response_schema
- VertexAI Claude: tool-use trick
- Google AI Studio: response_mime_type + response_schema
- Bedrock, Cohere: signature-only (no structured output yet)

Post-hoc jsonschema.validate() retained as defence-in-depth.

Tech spec added: docs/tech-specs/structured-output.md

Update tests

trustgraph-bedrock/trustgraph/model/text_completion/bedrock/llm.py

@@ -247,7 +247,10 @@ class Processor(LlmService):
 
         return self.model_variants[cache_key]
 
-    async def generate_content(self, system, prompt, model=None, temperature=None):
+    async def generate_content(
+        self, system, prompt, model=None, temperature=None,
+        response_format=None, schema=None,
+    ):
 
         # Use provided model or fall back to default
         model_name = model or self.default_model
@@ -311,7 +314,10 @@ class Processor(LlmService):
         """Bedrock supports streaming"""
         return True
 
-    async def generate_content_stream(self, system, prompt, model=None, temperature=None):
+    async def generate_content_stream(
+        self, system, prompt, model=None, temperature=None,
+        response_format=None, schema=None,
+    ):
         """Stream content generation from Bedrock"""
         model_name = model or self.default_model
         effective_temperature = temperature if temperature is not None else self.temperature