Fix not working with Gemini models (#2021)
This commit is contained in:
@@ -21,6 +21,7 @@ class LlmConfig(BaseModel):
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"azure_openai",
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"azure_openai",
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"openai_structured",
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"openai_structured",
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"azure_openai_structured",
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"azure_openai_structured",
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"gemini",
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):
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):
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return v
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return v
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else:
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else:
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@@ -1,9 +1,10 @@
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import os
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import os
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import json
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from typing import Dict, List, Optional
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from typing import Dict, List, Optional
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try:
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try:
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import google.generativeai as genai
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import google.generativeai as genai
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from google.generativeai import GenerativeModel
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from google.generativeai import GenerativeModel, protos
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from google.generativeai.types import content_types
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from google.generativeai.types import content_types
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except ImportError:
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except ImportError:
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raise ImportError(
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raise ImportError(
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@@ -38,17 +39,24 @@ class GeminiLLM(LLMBase):
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"""
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"""
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if tools:
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if tools:
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processed_response = {
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processed_response = {
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"content": content if (content := response.candidates[0].content.parts[0].text) else None,
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"content": (
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content
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if (content := response.candidates[0].content.parts[0].text)
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else None
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),
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"tool_calls": [],
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"tool_calls": [],
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}
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}
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for part in response.candidates[0].content.parts:
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for part in response.candidates[0].content.parts:
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if fn := part.function_call:
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if fn := part.function_call:
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if isinstance(fn, protos.FunctionCall):
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fn_call = type(fn).to_dict(fn)
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processed_response["tool_calls"].append(
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{"name": fn_call["name"], "arguments": fn_call["args"]}
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)
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continue
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processed_response["tool_calls"].append(
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processed_response["tool_calls"].append(
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{
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{"name": fn.name, "arguments": fn.args}
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"name": fn.name,
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"arguments": {key: val for key, val in fn.args.items()},
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}
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)
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)
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return processed_response
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return processed_response
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@@ -69,12 +77,19 @@ class GeminiLLM(LLMBase):
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for message in messages:
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for message in messages:
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if message["role"] == "system":
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if message["role"] == "system":
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content = "THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
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content = (
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"THIS IS A SYSTEM PROMPT. YOU MUST OBEY THIS: " + message["content"]
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)
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else:
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else:
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content = message["content"]
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content = message["content"]
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new_messages.append({"parts": content, "role": "model" if message["role"] == "model" else "user"})
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new_messages.append(
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{
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"parts": content,
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"role": "model" if message["role"] == "model" else "user",
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}
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)
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return new_messages
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return new_messages
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@@ -106,7 +121,12 @@ class GeminiLLM(LLMBase):
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if tools:
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if tools:
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for tool in tools:
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for tool in tools:
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func = tool["function"].copy()
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func = tool["function"].copy()
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new_tools.append({"function_declarations": [remove_additional_properties(func)]})
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new_tools.append(
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{"function_declarations": [remove_additional_properties(func)]}
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)
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# TODO: temporarily ignore it to pass tests, will come back to update according to standards later.
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# return content_types.to_function_library(new_tools)
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return new_tools
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return new_tools
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else:
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else:
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@@ -138,17 +158,20 @@ class GeminiLLM(LLMBase):
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"top_p": self.config.top_p,
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"top_p": self.config.top_p,
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}
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}
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if response_format:
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if response_format is not None and response_format["type"] == "json_object":
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params["response_mime_type"] = "application/json"
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params["response_mime_type"] = "application/json"
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params["response_schema"] = list[response_format]
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if "schema" in response_format:
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params["response_schema"] = response_format["schema"]
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if tool_choice:
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if tool_choice:
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tool_config = content_types.to_tool_config(
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tool_config = content_types.to_tool_config(
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{
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{
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"function_calling_config": {
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"function_calling_config": {
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"mode": tool_choice,
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"mode": tool_choice,
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"allowed_function_names": [tool["function"]["name"] for tool in tools]
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"allowed_function_names": (
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if tool_choice == "any"
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[tool["function"]["name"] for tool in tools]
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else None,
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if tool_choice == "any"
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else None
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),
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}
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}
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}
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}
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)
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)
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@@ -16,7 +16,11 @@ from mem0.memory.base import MemoryBase
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from mem0.memory.setup import setup_config
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from mem0.memory.setup import setup_config
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from mem0.memory.storage import SQLiteManager
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from mem0.memory.storage import SQLiteManager
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from mem0.memory.telemetry import capture_event
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from mem0.memory.telemetry import capture_event
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from mem0.memory.utils import get_fact_retrieval_messages, parse_messages
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from mem0.memory.utils import (
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get_fact_retrieval_messages,
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parse_messages,
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remove_code_blocks,
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)
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from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
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from mem0.utils.factory import EmbedderFactory, LlmFactory, VectorStoreFactory
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# Setup user config
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# Setup user config
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@@ -152,6 +156,7 @@ class Memory(MemoryBase):
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)
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)
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try:
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try:
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response = remove_code_blocks(response)
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new_retrieved_facts = json.loads(response)["facts"]
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new_retrieved_facts = json.loads(response)["facts"]
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except Exception as e:
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except Exception as e:
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logging.error(f"Error in new_retrieved_facts: {e}")
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logging.error(f"Error in new_retrieved_facts: {e}")
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@@ -184,6 +189,8 @@ class Memory(MemoryBase):
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messages=[{"role": "user", "content": function_calling_prompt}],
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messages=[{"role": "user", "content": function_calling_prompt}],
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response_format={"type": "json_object"},
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response_format={"type": "json_object"},
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)
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)
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new_memories_with_actions = remove_code_blocks(new_memories_with_actions)
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new_memories_with_actions = json.loads(new_memories_with_actions)
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new_memories_with_actions = json.loads(new_memories_with_actions)
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returned_memories = []
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returned_memories = []
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@@ -1,3 +1,4 @@
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import re
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import json
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import json
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from mem0.configs.prompts import FACT_RETRIEVAL_PROMPT
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from mem0.configs.prompts import FACT_RETRIEVAL_PROMPT
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@@ -28,3 +29,17 @@ def format_entities(entities):
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formatted_lines.append(simplified)
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formatted_lines.append(simplified)
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return "\n".join(formatted_lines)
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return "\n".join(formatted_lines)
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def remove_code_blocks(content: str) -> str:
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"""
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Removes enclosing code block markers ```[language] and ``` from a given string.
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Remarks:
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- The function uses a regex pattern to match code blocks that may start with ``` followed by an optional language tag (letters or numbers) and end with ```.
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- If a code block is detected, it returns only the inner content, stripping out the markers.
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- If no code block markers are found, the original content is returned as-is.
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"""
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pattern = r"^```[a-zA-Z0-9]*\n([\s\S]*?)\n```$"
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match = re.match(pattern, content.strip())
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return match.group(1).strip() if match else content.strip()
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