UN-3810 [FEAT] Prompt caching: SDK master switch + document-context caching (answer_prompt)

unstract/sdk1/src/unstract/sdk1/adapters/base1.py

@@ -1096,7 +1096,7 @@
     guardrailConfig: dict | None = None  # noqa: N815
 
     @staticmethod
     def validate(adapter_metadata: dict[str, "Any"]) -> dict[str, "Any"]:
         adapter_metadata["model"] = AWSBedrockLLMParameters.validate_model(
             adapter_metadata
         )
@@ -1136,6 +1136,24 @@
                 result_metadata["thinking"] = thinking_config
                 result_metadata["temperature"] = 1
 
+        # Prompt caching is opt-in and applied on the message payload (a
+        # `cache_control` block on the stable system prompt), not as a LiteLLM
+        # completion param, so it is excluded from Pydantic validation and
+        # carried through on the validated dict for the LLM layer to read.
+        # Only Anthropic/Claude models on Bedrock support prompt caching, so
+        # don't advertise the flag for other Bedrock families (Titan, Llama,
+        # etc.). The LLM layer enforces the same model gate; this just keeps the
+        # validated metadata honest.
+        # Check both ``model`` and ``model_id`` so callers routing through a
+        # Bedrock Application Inference Profile (opaque ARN in ``model``, Claude
+        # id in ``model_id``) still qualify.
+        bedrock_model_ids = " ".join(
+            str(result_metadata.get(field, "")) for field in _MODEL_ID_FIELDS
+        ).lower()
+        enable_prompt_caching = bool(
+            adapter_metadata.get("enable_prompt_caching", False)
+        ) and ("anthropic" in bedrock_model_ids or "claude" in bedrock_model_ids)
+
         _pack_bedrock_guardrail_config(result_metadata)
 
         # Create validation metadata excluding control fields. `auth_type` is
@@ -1152,6 +1170,7 @@
                 "guardrail_identifier",
                 "guardrail_version",
                 "guardrail_trace",
+                "enable_prompt_caching",
             )
         }
 
@@ -1170,6 +1189,7 @@
         # lenient. Reads auth_type from result_metadata since validation_
         # metadata strips it before Pydantic.
         validated = _resolve_bedrock_aws_credentials(result_metadata, validated)
+        validated["enable_prompt_caching"] = enable_prompt_caching
         return _strip_deprecated_sampling_params(validated)
 
     @staticmethod
@@ -1237,13 +1257,20 @@
                 result_metadata["thinking"] = thinking_config
                 result_metadata["temperature"] = 1
 
+        # Prompt caching is opt-in and applied on the message payload (a
+        # `cache_control` block on the stable system prompt), not as a LiteLLM
+        # completion param, so it is excluded from Pydantic validation and
+        # carried through on the validated dict for the LLM layer to read.
+        enable_prompt_caching = bool(adapter_metadata.get("enable_prompt_caching", False))
+
         # Create validation metadata excluding control fields
         exclude_fields = (
             "enable_thinking",
             "budget_tokens",
             "thinking",
             "enable_extended_context",
             "extra_headers",
+            "enable_prompt_caching",
         )
         validation_metadata = {
             k: v for k, v in result_metadata.items() if k not in exclude_fields
@@ -1259,6 +1286,8 @@
         if enable_extended_context:
             validated["extra_headers"] = {"anthropic-beta": "context-1m-2025-08-07"}
 
+        validated["enable_prompt_caching"] = enable_prompt_caching
+
         return _strip_deprecated_sampling_params(validated)
 
     @staticmethod