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https://github.com/khoj-ai/khoj.git
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Merge branch 'master' of github.com:khoj-ai/khoj into improve-debug-reasoning-and-other-misc-fixes
This commit is contained in:
commit
2b35790165
8 changed files with 125 additions and 111 deletions
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@ -189,7 +189,7 @@ def converse_anthropic(
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n"
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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# Setup Prompt with Primer or Conversation History
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@ -116,8 +116,10 @@ def gemini_send_message_to_model(
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messages, system_prompt = format_messages_for_gemini(messages)
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messages, system_prompt = format_messages_for_gemini(messages)
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model_kwargs = {}
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model_kwargs = {}
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if response_type == "json_object":
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model_kwargs["response_mime_type"] = "application/json"
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# Sometimes, this causes unwanted behavior and terminates response early. Disable for now while it's flaky.
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# if response_type == "json_object":
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# model_kwargs["response_mime_type"] = "application/json"
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# Get Response from Gemini
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# Get Response from Gemini
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return gemini_completion_with_backoff(
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return gemini_completion_with_backoff(
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@ -193,7 +195,7 @@ def converse_gemini(
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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if ConversationCommand.Online in conversation_commands or ConversationCommand.Webpage in conversation_commands:
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n"
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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# Setup Prompt with Primer or Conversation History
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@ -160,9 +160,7 @@ def converse_offline(
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# Initialize Variables
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# Initialize Variables
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assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
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assert loaded_model is None or isinstance(loaded_model, Llama), "loaded_model must be of type Llama, if configured"
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offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
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offline_chat_model = loaded_model or download_model(model, max_tokens=max_prompt_size)
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compiled_references = "\n\n".join({f"# File: {item['file']}\n## {item['compiled']}\n" for item in references})
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tracer["chat_model"] = model
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tracer["chat_model"] = model
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current_date = datetime.now()
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current_date = datetime.now()
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if agent and agent.personality:
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if agent and agent.personality:
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@ -204,7 +202,7 @@ def converse_offline(
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context_message += f"{prompts.online_search_conversation_offline.format(online_results=yaml_dump(simplified_online_results))}\n\n"
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context_message += f"{prompts.online_search_conversation_offline.format(online_results=yaml_dump(simplified_online_results))}\n\n"
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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if ConversationCommand.Code in conversation_commands and not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n"
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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# Setup Prompt with Primer or Conversation History
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@ -191,7 +191,7 @@ def converse(
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if not is_none_or_empty(online_results):
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if not is_none_or_empty(online_results):
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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context_message += f"{prompts.online_search_conversation.format(online_results=yaml_dump(online_results))}\n\n"
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if not is_none_or_empty(code_results):
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if not is_none_or_empty(code_results):
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context_message += f"{prompts.code_executed_context.format(code_results=yaml_dump(code_results))}\n\n"
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context_message += f"{prompts.code_executed_context.format(code_results=str(code_results))}\n\n"
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context_message = context_message.strip()
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context_message = context_message.strip()
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# Setup Prompt with Primer or Conversation History
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# Setup Prompt with Primer or Conversation History
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@ -22,7 +22,7 @@ from langchain.schema import ChatMessage
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from llama_cpp.llama import Llama
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from llama_cpp.llama import Llama
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from transformers import AutoTokenizer
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from transformers import AutoTokenizer
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from khoj.database.adapters import ConversationAdapters, ais_user_subscribed
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from khoj.database.adapters import ConversationAdapters
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from khoj.database.models import ChatModelOptions, ClientApplication, KhojUser
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from khoj.database.models import ChatModelOptions, ClientApplication, KhojUser
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.offline.utils import download_model, infer_max_tokens
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from khoj.processor.conversation.offline.utils import download_model, infer_max_tokens
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@ -457,6 +457,11 @@ def clean_json(response: str):
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return response.strip().replace("\n", "").removeprefix("```json").removesuffix("```")
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return response.strip().replace("\n", "").removeprefix("```json").removesuffix("```")
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def clean_code_python(code: str):
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"""Remove any markdown codeblock and newline formatting if present. Useful for non schema enforceable models"""
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return code.strip().removeprefix("```python").removesuffix("```")
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def defilter_query(query: str):
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def defilter_query(query: str):
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"""Remove any query filters in query"""
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"""Remove any query filters in query"""
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defiltered_query = query
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defiltered_query = query
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@ -12,6 +12,7 @@ from khoj.database.models import Agent, KhojUser
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation import prompts
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from khoj.processor.conversation.utils import (
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from khoj.processor.conversation.utils import (
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ChatEvent,
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ChatEvent,
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clean_code_python,
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clean_json,
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clean_json,
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construct_chat_history,
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construct_chat_history,
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)
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)
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@ -126,13 +127,18 @@ async def execute_sandboxed_python(code: str, sandbox_url: str = SANDBOX_URL) ->
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Returns the result of the code execution as a dictionary.
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Returns the result of the code execution as a dictionary.
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"""
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"""
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headers = {"Content-Type": "application/json"}
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headers = {"Content-Type": "application/json"}
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data = {"code": code}
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cleaned_code = clean_code_python(code)
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data = {"code": cleaned_code}
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async with aiohttp.ClientSession() as session:
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async with aiohttp.ClientSession() as session:
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async with session.post(sandbox_url, json=data, headers=headers) as response:
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async with session.post(sandbox_url, json=data, headers=headers) as response:
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if response.status == 200:
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if response.status == 200:
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result: dict[str, Any] = await response.json()
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result: dict[str, Any] = await response.json()
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result["code"] = code
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result["code"] = cleaned_code
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return result
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return result
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else:
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else:
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return {"code": code, "success": False, "std_err": f"Failed to execute code with {response.status}"}
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return {
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"code": cleaned_code,
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"success": False,
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"std_err": f"Failed to execute code with {response.status}",
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}
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@ -710,7 +710,6 @@ async def chat(
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meta_log = conversation.conversation_log
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meta_log = conversation.conversation_log
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is_automated_task = conversation_commands == [ConversationCommand.AutomatedTask]
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is_automated_task = conversation_commands == [ConversationCommand.AutomatedTask]
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pending_research = True
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researched_results = ""
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researched_results = ""
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online_results: Dict = dict()
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online_results: Dict = dict()
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code_results: Dict = dict()
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code_results: Dict = dict()
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@ -730,6 +729,16 @@ async def chat(
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tracer=tracer,
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tracer=tracer,
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)
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)
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# If we're doing research, we don't want to do anything else
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if ConversationCommand.Research in conversation_commands:
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conversation_commands = [ConversationCommand.Research]
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conversation_commands_str = ", ".join([cmd.value for cmd in conversation_commands])
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async for result in send_event(
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ChatEvent.STATUS, f"**Chose Data Sources to Search:** {conversation_commands_str}"
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):
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yield result
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mode = await aget_relevant_output_modes(
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mode = await aget_relevant_output_modes(
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q, meta_log, is_automated_task, user, uploaded_images, agent, tracer=tracer
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q, meta_log, is_automated_task, user, uploaded_images, agent, tracer=tracer
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)
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)
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@ -759,7 +768,6 @@ async def chat(
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):
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):
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if isinstance(research_result, InformationCollectionIteration):
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if isinstance(research_result, InformationCollectionIteration):
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if research_result.summarizedResult:
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if research_result.summarizedResult:
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pending_research = False
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if research_result.onlineContext:
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if research_result.onlineContext:
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online_results.update(research_result.onlineContext)
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online_results.update(research_result.onlineContext)
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if research_result.codeContext:
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if research_result.codeContext:
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@ -773,10 +781,11 @@ async def chat(
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yield research_result
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yield research_result
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# researched_results = await extract_relevant_info(q, researched_results, agent)
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# researched_results = await extract_relevant_info(q, researched_results, agent)
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logger.info(f"Researched Results: {researched_results}")
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logger.info(f"Researched Results: {researched_results}")
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pending_research = False
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for cmd in conversation_commands:
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await conversation_command_rate_limiter.update_and_check_if_valid(request, cmd)
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q = q.replace(f"/{cmd.value}", "").strip()
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used_slash_summarize = conversation_commands == [ConversationCommand.Summarize]
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used_slash_summarize = conversation_commands == [ConversationCommand.Summarize]
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file_filters = conversation.file_filters if conversation else []
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file_filters = conversation.file_filters if conversation else []
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@ -788,11 +797,9 @@ async def chat(
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and not used_slash_summarize
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and not used_slash_summarize
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# but we can't actually summarize
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# but we can't actually summarize
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and len(file_filters) != 1
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and len(file_filters) != 1
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# not pending research
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and not pending_research
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):
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):
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conversation_commands.remove(ConversationCommand.Summarize)
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conversation_commands.remove(ConversationCommand.Summarize)
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elif ConversationCommand.Summarize in conversation_commands and pending_research:
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elif ConversationCommand.Summarize in conversation_commands:
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response_log = ""
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response_log = ""
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agent_has_entries = await EntryAdapters.aagent_has_entries(agent)
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agent_has_entries = await EntryAdapters.aagent_has_entries(agent)
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if len(file_filters) == 0 and not agent_has_entries:
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if len(file_filters) == 0 and not agent_has_entries:
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@ -886,7 +893,7 @@ async def chat(
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# Gather Context
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# Gather Context
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## Extract Document References
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## Extract Document References
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if pending_research:
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if not ConversationCommand.Research in conversation_commands:
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try:
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try:
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async for result in extract_references_and_questions(
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async for result in extract_references_and_questions(
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request,
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request,
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@ -933,7 +940,6 @@ async def chat(
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if ConversationCommand.Notes in conversation_commands and is_none_or_empty(compiled_references):
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if ConversationCommand.Notes in conversation_commands and is_none_or_empty(compiled_references):
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conversation_commands.remove(ConversationCommand.Notes)
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conversation_commands.remove(ConversationCommand.Notes)
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if pending_research:
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## Gather Online References
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## Gather Online References
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if ConversationCommand.Online in conversation_commands:
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if ConversationCommand.Online in conversation_commands:
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try:
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try:
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@ -960,7 +966,6 @@ async def chat(
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):
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):
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yield result
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yield result
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if pending_research:
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## Gather Webpage References
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## Gather Webpage References
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if ConversationCommand.Webpage in conversation_commands:
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if ConversationCommand.Webpage in conversation_commands:
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try:
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try:
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@ -999,9 +1004,8 @@ async def chat(
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):
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):
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yield result
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yield result
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if pending_research:
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## Gather Code Results
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## Gather Code Results
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if ConversationCommand.Code in conversation_commands and pending_research:
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if ConversationCommand.Code in conversation_commands:
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try:
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try:
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context = f"# Iteration 1:\n#---\nNotes:\n{compiled_references}\n\nOnline Results:{online_results}"
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context = f"# Iteration 1:\n#---\nNotes:\n{compiled_references}\n\nOnline Results:{online_results}"
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async for result in run_code(
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async for result in run_code(
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@ -364,7 +364,6 @@ tool_descriptions_for_llm = {
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ConversationCommand.Webpage: "To use if the user has directly provided the webpage urls or you are certain of the webpage urls to read.",
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ConversationCommand.Webpage: "To use if the user has directly provided the webpage urls or you are certain of the webpage urls to read.",
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ConversationCommand.Code: "To run Python code in a Pyodide sandbox with no network access. Helpful when need to parse information, run complex calculations, create documents and charts for user. Matplotlib, bs4, pandas, numpy, etc. are available.",
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ConversationCommand.Code: "To run Python code in a Pyodide sandbox with no network access. Helpful when need to parse information, run complex calculations, create documents and charts for user. Matplotlib, bs4, pandas, numpy, etc. are available.",
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ConversationCommand.Summarize: "To retrieve an answer that depends on the entire document or a large text.",
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ConversationCommand.Summarize: "To retrieve an answer that depends on the entire document or a large text.",
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ConversationCommand.Research: "To use when you need to do DEEP research on a topic. This will take longer than usual, but give a more detailed, comprehensive answer.",
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}
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}
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function_calling_description_for_llm = {
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function_calling_description_for_llm = {
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Loading…
Reference in a new issue