fix: fix OPENAI_API_KEY bug in retrieval

api/routes/telephony.py

@@ -1263,7 +1263,9 @@ async def handle_inbound_telephony(
 
     try:
         webhook_data, data_source = await parse_webhook_request(request)
-        logger.info(f"Inbound call data with data source: {data_source} and data :{dict(webhook_data)}")
+        logger.info(
+            f"Inbound call data with data source: {data_source} and data :{dict(webhook_data)}"
+        )
         headers = dict(request.headers)
 
         # Detect provider and normalize data

api/services/gen_ai/__init__.py

@@ -6,10 +6,12 @@ from .embedding import (
     OpenAIEmbeddingService,
     SentenceTransformerEmbeddingService,
 )
+from .json_parser import parse_llm_json
 
 __all__ = [
     "BaseEmbeddingService",
     "EmbeddingAPIKeyNotConfiguredError",
     "SentenceTransformerEmbeddingService",
     "OpenAIEmbeddingService",
+    "parse_llm_json",
 ]

api/services/gen_ai/json_parser.py

@@ -0,0 +1,154 @@
+"""Robust JSON parser for handling common LLM output mistakes."""
+
+from __future__ import annotations
+
+import json
+import re
+from typing import Any
+
+
+def parse_llm_json(raw_content: str) -> dict[str, Any]:
+    """Parse JSON from LLM output, handling common formatting issues.
+
+    Handles the following common LLM mistakes:
+    1. JSON wrapped in markdown code blocks (```json ... ``` or ``` ... ```)
+    2. Extra whitespace or newlines around JSON
+    3. Text before/after the JSON object
+
+    Args:
+        raw_content: The raw string output from the LLM.
+
+    Returns:
+        Parsed JSON as a dictionary. If parsing fails, returns {"raw": raw_content}.
+    """
+    if not raw_content or not raw_content.strip():
+        return {}
+
+    content = raw_content.strip()
+
+    # Attempt 1: Direct parse (ideal case)
+    parsed = _try_parse_json(content)
+    if parsed is not None:
+        return parsed
+
+    # Attempt 2: Remove markdown code block wrappers
+    # Matches ```json ... ``` or ``` ... ```
+    code_block_pattern = r"```(?:json)?\s*([\s\S]*?)\s*```"
+    code_block_match = re.search(code_block_pattern, content)
+    if code_block_match:
+        extracted = code_block_match.group(1).strip()
+        parsed = _try_parse_json(extracted)
+        if parsed is not None:
+            return parsed
+
+    # Attempt 3: Find JSON object by matching braces
+    parsed = _extract_json_object(content)
+    if parsed is not None:
+        return parsed
+
+    # Attempt 4: Find JSON array by matching brackets
+    parsed = _extract_json_array(content)
+    if parsed is not None:
+        return parsed
+
+    # All attempts failed - return raw content
+    return {"raw": raw_content}
+
+
+def _try_parse_json(content: str) -> dict[str, Any] | list | None:
+    """Attempt to parse JSON, returning None on failure."""
+    try:
+        result = json.loads(content)
+        if isinstance(result, (dict, list)):
+            return result
+        return None
+    except json.JSONDecodeError:
+        return None
+
+
+def _extract_json_object(content: str) -> dict[str, Any] | None:
+    """Extract a JSON object from text by finding matching braces."""
+    # Find the first opening brace
+    start = content.find("{")
+    if start == -1:
+        return None
+
+    # Find matching closing brace by counting braces
+    depth = 0
+    in_string = False
+    escape_next = False
+    end = -1
+
+    for i, char in enumerate(content[start:], start=start):
+        if escape_next:
+            escape_next = False
+            continue
+
+        if char == "\\":
+            escape_next = True
+            continue
+
+        if char == '"' and not escape_next:
+            in_string = not in_string
+            continue
+
+        if in_string:
+            continue
+
+        if char == "{":
+            depth += 1
+        elif char == "}":
+            depth -= 1
+            if depth == 0:
+                end = i
+                break
+
+    if end == -1:
+        return None
+
+    json_str = content[start : end + 1]
+    return _try_parse_json(json_str)
+
+
+def _extract_json_array(content: str) -> list | None:
+    """Extract a JSON array from text by finding matching brackets."""
+    # Find the first opening bracket
+    start = content.find("[")
+    if start == -1:
+        return None
+
+    # Find matching closing bracket by counting brackets
+    depth = 0
+    in_string = False
+    escape_next = False
+    end = -1
+
+    for i, char in enumerate(content[start:], start=start):
+        if escape_next:
+            escape_next = False
+            continue
+
+        if char == "\\":
+            escape_next = True
+            continue
+
+        if char == '"' and not escape_next:
+            in_string = not in_string
+            continue
+
+        if in_string:
+            continue
+
+        if char == "[":
+            depth += 1
+        elif char == "]":
+            depth -= 1
+            if depth == 0:
+                end = i
+                break
+
+    if end == -1:
+        return None
+
+    json_str = content[start : end + 1]
+    return _try_parse_json(json_str)

api/services/pipecat/run_pipeline.py

@@ -29,7 +29,6 @@ from api.services.pipecat.service_factory import (
     create_llm_service,
     create_stt_service,
     create_tts_service,
-    create_voicemail_classification_llm,
 )
 from api.services.pipecat.tracing_config import setup_pipeline_tracing
 from api.services.pipecat.transport_setup import (
@@ -501,12 +500,21 @@ async def _run_pipeline(
 
         node_transition_callback = send_node_transition
 
+    # Extract embeddings configuration from user config
+    embeddings_api_key = None
+    embeddings_model = None
+    if user_config and user_config.embeddings:
+        embeddings_api_key = user_config.embeddings.api_key
+        embeddings_model = user_config.embeddings.model
+
     engine = PipecatEngine(
         llm=llm,
         workflow=workflow_graph,
         call_context_vars=merged_call_context_vars,
         workflow_run_id=workflow_run_id,
         node_transition_callback=node_transition_callback,
+        embeddings_api_key=embeddings_api_key,
+        embeddings_model=embeddings_model,
     )
 
     # Create pipeline components with audio configuration and engine
@@ -562,24 +570,23 @@ async def _run_pipeline(
     voicemail_detector = None
     start_node = workflow_graph.nodes.get(workflow_graph.start_node_id)
     if start_node and start_node.detect_voicemail:
-        classification_llm = create_voicemail_classification_llm()
+        logger.info(f"Voicemail detection enabled for workflow run {workflow_run_id}")
-        if classification_llm:
+        # Create a separate LLM instance for the voicemail sub-pipeline
-            logger.info(
+        # (can't share with main pipeline as it would mess up frame linking)
-                f"Voicemail detection enabled for workflow run {workflow_run_id}"
+        voicemail_llm = create_llm_service(user_config)
-            )
+        voicemail_detector = VoicemailDetector(
-            voicemail_detector = VoicemailDetector(
+            llm=voicemail_llm,
-                llm=classification_llm,
+            voicemail_response_delay=2.0,
-                voicemail_response_delay=2.0,
+        )
-            )
 
-            # Register event handler to end task when voicemail is detected
+        # Register event handler to end task when voicemail is detected
-            @voicemail_detector.event_handler("on_voicemail_detected")
+        @voicemail_detector.event_handler("on_voicemail_detected")
-            async def _on_voicemail_detected(_processor):
+        async def _on_voicemail_detected(_processor):
-                logger.info(f"Voicemail detected for workflow run {workflow_run_id}")
+            logger.info(f"Voicemail detected for workflow run {workflow_run_id}")
-                await engine.send_end_task_frame(
+            await engine.send_end_task_frame(
-                    reason=EndTaskReason.VOICEMAIL_DETECTED.value,
+                reason=EndTaskReason.VOICEMAIL_DETECTED.value,
-                    abort_immediately=True,
+                abort_immediately=True,
-                )
+            )
 
     # Build the pipeline with the STT mute filter and context controller
     pipeline = build_pipeline(

api/services/pipecat/service_factory.py

@@ -1,4 +1,3 @@
-import os
 from typing import TYPE_CHECKING
 
 from fastapi import HTTPException
@@ -242,24 +241,3 @@ def create_llm_service(user_config):
         )
     else:
         raise HTTPException(status_code=400, detail="Invalid LLM provider")
-
-
-def create_voicemail_classification_llm():
-    """Create a fast, lightweight LLM service for voicemail classification.
-
-    Uses gpt-4o-mini which is fast and cost-effective for simple classification tasks.
-    The model only needs to output "CONVERSATION" or "VOICEMAIL" based on transcriptions.
-
-    Returns:
-        OpenAILLMService instance, or None if OPENAI_API_KEY is not set.
-    """
-    api_key = os.environ.get("OPENAI_API_KEY")
-    if not api_key:
-        logger.warning("OPENAI_API_KEY not set - voicemail detection will be disabled")
-        return None
-
-    return OpenAILLMService(
-        api_key=api_key,
-        model="gpt-4o",
-        params=OpenAILLMService.InputParams(temperature=0.0),
-    )

api/services/telephony/base.py

@@ -278,7 +278,9 @@ class TelephonyProvider(ABC):
 
     @staticmethod
     @abstractmethod
-    async def generate_inbound_response(websocket_url: str, workflow_run_id: int = None) -> tuple:
+    async def generate_inbound_response(
+        websocket_url: str, workflow_run_id: int = None
+    ) -> tuple:
         """
         Generate the appropriate response for an inbound webhook.
 

api/services/telephony/providers/cloudonix_provider.py

@@ -436,13 +436,21 @@ class CloudonixProvider(TelephonyProvider):
             return True
 
         # 2: Check for Cloudonix-specific headers
-        cloudonix_headers = ["x-cx-apikey", "x-cx-domain", "x-cx-session", "x-cx-source"]
+        cloudonix_headers = [
+            "x-cx-apikey",
+            "x-cx-domain",
+            "x-cx-session",
+            "x-cx-source",
+        ]
         if any(header in headers for header in cloudonix_headers):
             return True
 
         # 3: Check data structure for Cloudonix-specific fields
-        if ("SessionData" in webhook_data and "Domain" in webhook_data and 
+        if (
-            webhook_data.get("Domain", "").endswith(".cloudonix.net")):
+            "SessionData" in webhook_data
+            and "Domain" in webhook_data
+            and webhook_data.get("Domain", "").endswith(".cloudonix.net")
+        ):
             return True
 
         # Check if AccountSid is a Cloudonix domain
@@ -468,11 +476,9 @@ class CloudonixProvider(TelephonyProvider):
         session_data = webhook_data.get("SessionData", {})
         token = session_data.get("token", "") if isinstance(session_data, dict) else ""
 
-        call_id = (webhook_data.get("Session") or 
+        call_id = webhook_data.get("Session") or webhook_data.get("CallSid") or token
-                  webhook_data.get("CallSid") or 
-                  token)
 
-        account_id = (webhook_data.get("Domain") or webhook_data.get("AccountSid", ""))
+        account_id = webhook_data.get("Domain") or webhook_data.get("AccountSid", "")
 
         # Extract underlying provider information from SessionData if available
         session_data = webhook_data.get("SessionData", {})
@@ -570,11 +576,11 @@ class CloudonixProvider(TelephonyProvider):
         if is_valid:
             logger.info("Cloudonix x-cx-apikey validation successful")
         else:
-            logger.warning(f"Cloudonix x-cx-apikey validation failed. Expected key ending with ...{self.bearer_token[-8:] if len(self.bearer_token) > 8 else 'SHORT_KEY'}")
+            logger.warning(
-            
+                f"Cloudonix x-cx-apikey validation failed. Expected key ending with ...{self.bearer_token[-8:] if len(self.bearer_token) > 8 else 'SHORT_KEY'}"
-        return True #TODO: update this post clarification from cloudonix
+            )
-
 
+        return True  # TODO: update this post clarification from cloudonix
 
     @staticmethod
     async def generate_inbound_response(
@@ -599,10 +605,7 @@ class CloudonixProvider(TelephonyProvider):
         logger.info(f"Cloudonix inbound CXML response content:")
         logger.info(cxml_content)
 
-        response = Response(
+        response = Response(content=cxml_content, media_type="application/xml")
-            content=cxml_content, 
-            media_type="application/xml"
-        )
 
         logger.info(f"Cloudonix inbound response object: {response}")
         logger.info(f"Response headers: {response.headers}")

api/services/telephony/providers/twilio_provider.py

@@ -310,14 +310,20 @@ class TwilioProvider(TelephonyProvider):
             return True
 
         # 2: Check for Twilio-specific headers
-        twilio_headers = ["x-twilio-signature", "i-twilio-idempotency-token", "x-home-region"]
+        twilio_headers = [
+            "x-twilio-signature",
+            "i-twilio-idempotency-token",
+            "x-home-region",
+        ]
         if any(header in headers for header in twilio_headers):
             return True
 
         # 3: Check data structure - CallSid + AccountSid with AC prefix + ApiVersion
-        if ("CallSid" in webhook_data and 
+        if (
-            "AccountSid" in webhook_data and 
+            "CallSid" in webhook_data
-            "ApiVersion" in webhook_data):
+            and "AccountSid" in webhook_data
+            and "ApiVersion" in webhook_data
+        ):
             # Ensure AccountSid looks like Twilio (starts with AC, not a domain)
             account_sid = webhook_data.get("AccountSid", "")
             if account_sid.startswith("AC") and not "." in account_sid:

api/services/workflow/pipecat_engine.py

@@ -69,6 +69,8 @@ class PipecatEngine:
         node_transition_callback: Optional[
             Callable[[str, Optional[str]], Awaitable[None]]
         ] = None,
+        embeddings_api_key: Optional[str] = None,
+        embeddings_model: Optional[str] = None,
     ):
         self.task = task
         self.llm = llm
@@ -103,6 +105,10 @@ class PipecatEngine:
         # Custom tool manager (initialized in initialize())
         self._custom_tool_manager: Optional[CustomToolManager] = None
 
+        # Embeddings configuration (passed from run_pipeline.py)
+        self._embeddings_api_key: Optional[str] = embeddings_api_key
+        self._embeddings_model: Optional[str] = embeddings_model
+
     async def _get_organization_id(self) -> Optional[int]:
         """Get and cache the organization ID from workflow run."""
         if self._custom_tool_manager:
@@ -318,11 +324,19 @@ class PipecatEngine:
                         "Organization ID not available for knowledge base retrieval"
                     )
 
+                if not self._embeddings_api_key:
+                    raise ValueError(
+                        "Embeddings API key not configured. Please set your API key in "
+                        "Model Configurations > Embedding."
+                    )
+
                 result = await retrieve_from_knowledge_base(
                     query=query,
                     organization_id=organization_id,
                     document_uuids=document_uuids,
                     limit=3,  # Return top 3 most relevant chunks
+                    embeddings_api_key=self._embeddings_api_key,
+                    embeddings_model=self._embeddings_model,
                 )
 
                 await function_call_params.result_callback(result)

api/services/workflow/pipecat_engine_variable_extractor.py

@@ -1,13 +1,11 @@
 from __future__ import annotations
 
-import json
-import os
 from typing import TYPE_CHECKING, Any, List
 
 from loguru import logger
-from openai import AsyncOpenAI
 from opentelemetry import trace
 
+from api.services.gen_ai.json_parser import parse_llm_json
 from api.services.pipecat.tracing_config import is_tracing_enabled
 from api.services.workflow.dto import ExtractionVariableDTO
 from pipecat.processors.aggregators.llm_context import LLMContext
@@ -32,7 +30,6 @@ class VariableExtractionManager:
         # and update internal counters / extracted variable state.
         self._engine = engine
         self._context = engine.context
-        self._model = "gpt-4o"
 
     # ------------------------------------------------------------------
     # Internal helpers
@@ -147,46 +144,43 @@ class VariableExtractionManager:
         extraction_context.set_messages(extraction_messages)
 
         # ------------------------------------------------------------------
-        # Use independent OpenAI client for LLM call
+        # Use engine's LLM for out-of-band inference (no pipeline frames)
         # ------------------------------------------------------------------
-        client = AsyncOpenAI(api_key=os.environ.get("OPENAI_API_KEY"))
+        llm_response = await self._engine.llm.run_inference(extraction_context)
 
-        # Direct API call - no pipeline involvement
+        # Get model name for tracing
-        response = await client.chat.completions.create(
+        model_name = getattr(self._engine.llm, "model_name", "unknown")
-            model=self._model,
-            messages=extraction_messages,
-            temperature=0.0,
-            response_format={"type": "json_object"},
-        )
-
-        llm_response = response.choices[0].message.content
 
         if is_tracing_enabled():
             tracer = trace.get_tracer("pipecat")
             with tracer.start_as_current_span(
-                "variable_extraction", context=parent_ctx
+                "llm-variable-extraction", context=parent_ctx
             ) as span:
                 add_llm_span_attributes(
                     span,
-                    service_name="OpenAILLMService",
+                    service_name=self._engine.llm.__class__.__name__,
-                    model=self._model,
+                    model=model_name,
-                    operation_name="variable_extraction",
+                    operation_name="llm-variable-extraction",
                     messages=extraction_messages,
                     output=llm_response,
                     stream=False,
-                    parameters={"temperature": 0.0, "response_format": "json_object"},
+                    parameters={},
                 )
 
         # ------------------------------------------------------------------
         # Parse the assistant output – fall back to raw text if it is not valid JSON.
+        # Uses parse_llm_json which handles common LLM mistakes like markdown
+        # code blocks (```json ... ```) and extra text around the JSON.
         # ------------------------------------------------------------------
-        try:
+        if llm_response is None:
-            extracted = json.loads(llm_response)
+            logger.warning("Extractor returned no response; returning empty result.")
-        except json.JSONDecodeError:
+            extracted = {}
-            logger.warning(
+        else:
-                "Extractor returned invalid JSON; storing raw content instead."
+            extracted = parse_llm_json(llm_response)
-            )
+            if "raw" in extracted and len(extracted) == 1:
-            extracted = {"raw": llm_response}
+                logger.warning(
+                    "Extractor returned invalid JSON; storing raw content instead."
+                )
 
         logger.debug(f"Extracted variables: {extracted}")
         return extracted

api/tests/test_json_parser.py

@@ -0,0 +1,231 @@
+from api.services.gen_ai.json_parser import (
+    _extract_json_array,
+    _extract_json_object,
+    _try_parse_json,
+    parse_llm_json,
+)
+
+
+class TestParseLlmJson:
+    """Tests for the main parse_llm_json function."""
+
+    def test_empty_string(self):
+        """Empty string returns empty dict."""
+        assert parse_llm_json("") == {}
+
+    def test_whitespace_only(self):
+        """Whitespace-only string returns empty dict."""
+        assert parse_llm_json("   \n\t  ") == {}
+
+    def test_none_handling(self):
+        """None input returns empty dict."""
+        assert parse_llm_json(None) == {}
+
+    def test_valid_json_direct(self):
+        """Valid JSON is parsed directly."""
+        result = parse_llm_json('{"name": "John", "age": 30}')
+        assert result == {"name": "John", "age": 30}
+
+    def test_valid_json_with_whitespace(self):
+        """Valid JSON with surrounding whitespace is parsed."""
+        result = parse_llm_json('  \n{"key": "value"}\n  ')
+        assert result == {"key": "value"}
+
+    def test_markdown_json_code_block(self):
+        """JSON wrapped in ```json ... ``` is extracted and parsed."""
+        input_str = """```json
+{
+  "occupation_of_the_user": "software engineer"
+}
+```"""
+        result = parse_llm_json(input_str)
+        assert result == {"occupation_of_the_user": "software engineer"}
+
+    def test_markdown_generic_code_block(self):
+        """JSON wrapped in ``` ... ``` (no language) is extracted and parsed."""
+        input_str = """```
+{"status": "success", "count": 42}
+```"""
+        result = parse_llm_json(input_str)
+        assert result == {"status": "success", "count": 42}
+
+    def test_markdown_with_surrounding_text(self):
+        """Markdown code block with text before/after is handled."""
+        input_str = """Here is the extracted data:
+```json
+{"name": "Alice"}
+```
+I hope this helps!"""
+        result = parse_llm_json(input_str)
+        assert result == {"name": "Alice"}
+
+    def test_json_with_text_before(self):
+        """JSON with explanatory text before is extracted."""
+        input_str = 'The result is: {"answer": 42}'
+        result = parse_llm_json(input_str)
+        assert result == {"answer": 42}
+
+    def test_json_with_text_after(self):
+        """JSON with text after is extracted."""
+        input_str = '{"found": true} - extraction complete'
+        result = parse_llm_json(input_str)
+        assert result == {"found": True}
+
+    def test_json_with_text_before_and_after(self):
+        """JSON with text on both sides is extracted."""
+        input_str = 'Based on the conversation: {"mood": "happy"} is my assessment.'
+        result = parse_llm_json(input_str)
+        assert result == {"mood": "happy"}
+
+    def test_nested_json_object(self):
+        """Nested JSON objects are parsed correctly."""
+        input_str = '{"user": {"name": "Bob", "address": {"city": "NYC"}}}'
+        result = parse_llm_json(input_str)
+        assert result == {"user": {"name": "Bob", "address": {"city": "NYC"}}}
+
+    def test_json_with_string_containing_braces(self):
+        """JSON with braces inside strings is parsed correctly."""
+        input_str = '{"code": "function() { return {}; }"}'
+        result = parse_llm_json(input_str)
+        assert result == {"code": "function() { return {}; }"}
+
+    def test_json_with_escaped_quotes(self):
+        """JSON with escaped quotes is parsed correctly."""
+        input_str = '{"message": "He said \\"hello\\""}'
+        result = parse_llm_json(input_str)
+        assert result == {"message": 'He said "hello"'}
+
+    def test_json_array_direct(self):
+        """JSON array is parsed directly."""
+        result = parse_llm_json("[1, 2, 3]")
+        assert result == [1, 2, 3]
+
+    def test_json_array_with_objects(self):
+        """JSON array of objects is parsed correctly."""
+        input_str = '[{"id": 1}, {"id": 2}]'
+        result = parse_llm_json(input_str)
+        assert result == [{"id": 1}, {"id": 2}]
+
+    def test_json_array_in_markdown(self):
+        """JSON array in markdown code block is extracted."""
+        input_str = """```json
+["apple", "banana", "cherry"]
+```"""
+        result = parse_llm_json(input_str)
+        assert result == ["apple", "banana", "cherry"]
+
+    def test_invalid_json_returns_raw(self):
+        """Invalid JSON returns raw content in 'raw' key."""
+        input_str = "This is not JSON at all"
+        result = parse_llm_json(input_str)
+        assert result == {"raw": "This is not JSON at all"}
+
+    def test_malformed_json_returns_raw(self):
+        """Malformed JSON returns raw content."""
+        input_str = '{"key": "value"'  # Missing closing brace
+        result = parse_llm_json(input_str)
+        assert result == {"raw": '{"key": "value"'}
+
+    def test_complex_real_world_example(self):
+        """Test with a realistic LLM output example."""
+        input_str = """Based on our conversation, I've extracted the following information:
+
+```json
+{
+  "user_name": "John Smith",
+  "email": "john@example.com",
+  "preferences": {
+    "notifications": true,
+    "theme": "dark"
+  }
+}
+```
+
+Let me know if you need anything else!"""
+        result = parse_llm_json(input_str)
+        assert result == {
+            "user_name": "John Smith",
+            "email": "john@example.com",
+            "preferences": {"notifications": True, "theme": "dark"},
+        }
+
+    def test_json_with_newlines_inside(self):
+        """JSON with newlines inside values is handled."""
+        input_str = '{"text": "line1\\nline2"}'
+        result = parse_llm_json(input_str)
+        assert result == {"text": "line1\nline2"}
+
+    def test_json_with_unicode(self):
+        """JSON with unicode characters is parsed correctly."""
+        input_str = '{"greeting": "こんにちは", "emoji": "🎉"}'
+        result = parse_llm_json(input_str)
+        assert result == {"greeting": "こんにちは", "emoji": "🎉"}
+
+    def test_multiple_code_blocks_uses_first(self):
+        """When multiple code blocks exist, the first is used."""
+        input_str = """```json
+{"first": true}
+```
+Some text
+```json
+{"second": true}
+```"""
+        result = parse_llm_json(input_str)
+        assert result == {"first": True}
+
+
+class TestTryParseJson:
+    """Tests for the _try_parse_json helper."""
+
+    def test_valid_dict(self):
+        assert _try_parse_json('{"a": 1}') == {"a": 1}
+
+    def test_valid_list(self):
+        assert _try_parse_json("[1, 2]") == [1, 2]
+
+    def test_invalid_returns_none(self):
+        assert _try_parse_json("not json") is None
+
+    def test_primitive_returns_none(self):
+        """Primitive values (not dict/list) return None."""
+        assert _try_parse_json('"just a string"') is None
+        assert _try_parse_json("42") is None
+        assert _try_parse_json("true") is None
+
+
+class TestExtractJsonObject:
+    """Tests for the _extract_json_object helper."""
+
+    def test_extracts_from_text(self):
+        result = _extract_json_object('prefix {"key": "value"} suffix')
+        assert result == {"key": "value"}
+
+    def test_no_object_returns_none(self):
+        assert _extract_json_object("no json here") is None
+
+    def test_nested_braces(self):
+        result = _extract_json_object('{"outer": {"inner": 1}}')
+        assert result == {"outer": {"inner": 1}}
+
+    def test_braces_in_strings(self):
+        result = _extract_json_object('{"code": "{ }"}')
+        assert result == {"code": "{ }"}
+
+
+class TestExtractJsonArray:
+    """Tests for the _extract_json_array helper."""
+
+    def test_extracts_from_text(self):
+        result = _extract_json_array("here is the list: [1, 2, 3] done")
+        assert result == [1, 2, 3]
+
+    def test_no_array_returns_none(self):
+        assert _extract_json_array("no array here") is None
+
+    def test_nested_arrays(self):
+        result = _extract_json_array("[[1, 2], [3, 4]]")
+        assert result == [[1, 2], [3, 4]]
+
+    def test_brackets_in_strings(self):
+        result = _extract_json_array('["a[b]c"]')
+        assert result == ["a[b]c"]