Don't choke on new (or old) versions of langchain_anthropic

.gitignore

@@ -15,4 +15,4 @@ __pycache__/
 .pytest_cache/
 htmlcov/
 .coverage
-
+*.swp

langdspy/prompt_runners.py

@@ -39,13 +39,15 @@ def __init__(self, **kwargs):
 class PromptHistory(BaseModel):
     history: List[Dict[str, Any]] = Field(default_factory=list)
 
-    def add_entry(self, prompt, llm_response, parsed_output, success, timestamp):
+    def add_entry(self, llm, prompt, llm_response, parsed_output, error, start_time, end_time):
         self.history.append({
-            "prompt": prompt,
+            "duration_ms": round((end_time - start_time) * 1000),
+            "llm": llm,
             "llm_response": llm_response,
             "parsed_output": parsed_output,
-            "success": success,
-            "timestamp": timestamp
+            "prompt": prompt,
+            "error": error,
+            "timestamp": end_time,
         })
 
 
@@ -101,6 +103,7 @@ def _invoke_with_retries(self, chain, input, max_tries=1, config: Optional[Runna
         formatted_prompt = None
 
         while max_tries >= 1:
+            start_time = time.time()
             try:
                 kwargs = {**self.model_kwargs, **self.kwargs}
                 # logger.debug(f"PromptRunner invoke with input {input} and kwargs {kwargs} and config {config}")
@@ -137,7 +140,7 @@ def _invoke_with_retries(self, chain, input, max_tries=1, config: Optional[Runna
                 continue
 
 
-            validation = True
+            validation_err = None
 
             # logger.debug(f"Raw output for prompt runner {self.template.__class__.__name__}: {res}")
             if print_prompt:
@@ -150,50 +153,58 @@ def _invoke_with_retries(self, chain, input, max_tries=1, config: Optional[Runna
             except Exception as e:
                 import traceback
                 traceback.print_exc()
-                logger.error(f"Failed to parse output for prompt runner {self.template.__class__.__name__}")
-                validation = False
+                validation_err = f"Failed to parse output for prompt runner {self.template.__class__.__name__}"
+                logger.error(validation_err)
             # logger.debug(f"Parsed output: {parsed_output}")
 
             len_parsed_output = len(parsed_output.keys())
             len_output_variables = len(self.template.output_variables.keys())
             # logger.debug(f"Parsed output keys: {parsed_output.keys()} [{len_parsed_output}] Expected output keys: {self.template.output_variables.keys()} [{len_output_variables}]")
 
             if len(parsed_output.keys()) != len(self.template.output_variables.keys()):
-                logger.error(f"Output keys do not match expected output keys for prompt runner {self.template.__class__.__name__}")
-                validation = False
+                validation_err = f"Output keys do not match expected output keys for prompt runner {self.template.__class__.__name__}"
+                logger.error(validation_err)
 
-            self.prompt_history.add_entry(formatted_prompt, res, parsed_output, validation, time.time())
-
-            if validation:
+            if validation_err is None:
                 # Transform and validate the outputs
                 for attr_name, output_field in self.template.output_variables.items():
                     output_value = parsed_output.get(attr_name)
                     if not output_value:
-                        logger.error(f"Failed to get output value for field {attr_name} for prompt runner {self.template.__class__.__name__}")
-                        validation = False
+                        validation_err = f"Failed to get output value for field {attr_name} for prompt runner {self.template.__class__.__name__}"
+                        logger.error(validation_err)
                         continue
 
                     # Validate the transformed value
                     if not output_field.validate_value(input, output_value):
-                        validation = False
-                        logger.error(f"Failed to validate field {attr_name} value {output_value} for prompt runner {self.template.__class__.__name__}")
+                        validation_err = f"Failed to validate field {attr_name} value {output_value} for prompt runner {self.template.__class__.__name__}"
+                        logger.error(validation_err)
 
                     # Get the transformed value
                     try:
                         transformed_val = output_field.transform_value(output_value)
                     except Exception as e:
                         import traceback
                         traceback.print_exc()
-                        logger.error(f"Failed to transform field {attr_name} value {output_value} for prompt runner {self.template.__class__.__name__}")
-                        validation = False
+                        validation_err = f"Failed to transform field {attr_name} value {output_value} for prompt runner {self.template.__class__.__name__}"
+                        logger.error(validation_err)
                         continue
 
                     # Update the output with the transformed value
                     parsed_output[attr_name] = transformed_val
 
+            end_time = time.time()
+
+            # The model named may be called model_name or model depending on version of langchain_anthropic
+            try:
+                model_name = config["llm"].model_name
+            except AttributeError:
+                model_name = config["llm"].model
+
+            self.prompt_history.add_entry(model_name, formatted_prompt, res, parsed_output, validation_err, start_time, end_time)
+
             res = {attr_name: parsed_output.get(attr_name, None) for attr_name in self.template.output_variables.keys()}
 
-            if validation:
+            if validation_err is None:
                 return res
 
             logger.error(f"Output validation failed for prompt runner {self.template.__class__.__name__}, pausing before we retry")
@@ -263,4 +274,4 @@ def run_task():
 
 
         # logger.debug(f"MultiPromptRunner predictions: {self.predictions}")
-        return predictions
+        return predictions

pyproject.toml

@@ -45,11 +45,11 @@ joblib = "1.3.2"
 jsonpatch = "1.33"
 jsonpointer = "2.4"
 keyring = "24.3.1"
-langchain = "0.1.7"
-langchain-anthropic = "0.1.3"
-langchain-community = "0.0.20"
-langchain-core = "0.1.23"
-langchain-openai = "0.0.6"
+langchain = "^0.1.12"
+langchain-anthropic = "^0.1.3"
+langchain-community = "^0.0.28"
+langchain-core = "^0.1.32"
+langchain-openai = "^0.0.6"
 markdown-it-py = "3.0.0"
 marshmallow = "3.20.2"
 mdurl = "0.1.2"
@@ -107,4 +107,4 @@ build-backend = "poetry.core.masonry.api"
 
 [tool.poetry.scripts]
 test = "scripts:test"
-coverage = "scripts:coverage"
+coverage = "scripts:coverage"