diff --git a/CHANGELOG.md b/CHANGELOG.md index f6035221..15d0b229 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -1,23 +1,30 @@ -### v1.1.0 - - **Hardware Acceleration Support**: Added support for CoreML, CUDA, ROCm to cater to diverse deployment environments. - - **Docker-Compose and Install Script Improvements**: Updated `docker-compose` configurations and `install.sh` script for better setup experience and development workflow. Includes more specific version locking for reliability and easier automation. - - **Permission System**: Implemented a fine-grained permission system. - - **README and Usage Examples**: Updated documentation to enhance clarity and user guidance. - - **Secret Management Overhaul**: Redesigned secret management using a factory/provider pattern for Google Secrets and local `.env` configurations. - - **Storage Backend Overhaul**: Redesigned file storage using a factory/provider pattern for GCS or to simply store on local disk and share a volume between webapp & vector-db-proxy. - - **MongoDB Model Enhancements**: Added filename support for MongoDB models and optimized queries. - - **Vector Database Proxy Updates**: Updated Vector DB Proxy for better structure and maintainability, including production build optimizations. - - **Local Storage and Thread Utilization**: Enhanced local storage handling and introduced thread utilization control. - - **Code Quality and Dependency Management**: Addressed ESLint warnings, updated configurations, and removed unused dependencies. - - **Frontend Updates**: Enabled avatars for apps and agents, added markdown preview for app descriptions, and improved UI components. - - **Stripe Subscription and Billing**: Implemented Stripe functionalities for plan management and addon cross-selling. - ---- +### v1.2.0 + - Add support for ollama models. + - Implement more advanced retrieval strategies for data sources. (raw, self query, multi query, time weighted) + - First iteration of implementing plan limits and stripe handling. + - Make new accounts be Enterprise plan by default when no stripe API key set (for OS users). + - Make Airbyte setup handled from within webapp instead of install.sh + - Add human input checkbox on tasks to choose whether a task requires human input + - Update datasource tables and components to have a new embedding status progress bar. + - Improve consistency of avatars in chat and make them work with the local storage provider. + - Improve chat UX and add customer formatting for thoughts, tasks, and tool messages. + - Add ability to input descriptions on datasource fields for self-query retrieval. + - Add Docker volume and make Qdrant data persistent in docker-compose. + - Various bug fixes and optimizations to improve overall performance and reliability. -Legacy changelog: - -### 1.0.0 - - TODO +### v1.1.0 + - Added support for CoreML, CUDA, ROCm to cater to diverse deployment environments. + - Updated `docker-compose` configurations and `install.sh` script for better setup experience and development workflow. Includes more specific version locking for reliability and easier automation. + - Implemented a fine-grained permission system. + - Updated documentation to enhance clarity and user guidance. + - Redesigned secret management using a factory/provider pattern for Google Secrets and local `.env` configurations. + - Redesigned file storage using a factory/provider pattern for GCS or to simply store on local disk and share a volume between webapp & vector-db-proxy. + - Added filename support for MongoDB models and optimized queries. + - Updated Vector DB Proxy for better structure and maintainability, including production build optimizations. + - Enhanced local storage handling and introduced thread utilization control. + - Addressed ESLint warnings, updated configurations, and removed unused dependencies. + - Enabled avatars for apps and agents, added markdown preview for app descriptions, and improved UI components. + - Implemented Stripe functionalities for plan management and addon cross-selling. ### 0.5.0 - Add RAG from file upload or connection to other data source via Airbyte diff --git a/agent-backend/.gitignore b/agent-backend/.gitignore index 0b3e7ba2..6a3040f9 100644 --- a/agent-backend/.gitignore +++ b/agent-backend/.gitignore @@ -167,11 +167,5 @@ keyfile.json *.ipynb src/output output -src/tools/* -!src/tools/global_tools.py -!src/tools/build_request_from_api_spec.py -!src/tools/code_execution_docker_notebook_tool.py -!src/tools/code_execution_tool.py -!src/tools/rag_tool.py - -.run \ No newline at end of file + +.run diff --git a/agent-backend/Dockerfile b/agent-backend/Dockerfile index 032f0922..bcb7a78f 100644 --- a/agent-backend/Dockerfile +++ b/agent-backend/Dockerfile @@ -1,5 +1,5 @@ # Use the official Python image as the base image -FROM python:3.11-slim as builder +FROM python:3.11 as builder # Set the working directory inside the container WORKDIR /app diff --git a/agent-backend/poetry.lock b/agent-backend/poetry.lock index cbc269a4..97cec36d 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{file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"}, ] +[[package]] +name = "wikibase-rest-api-client" +version = "0.1.3" +description = "A client library for accessing the Wikibase REST API" +optional = false +python-versions = ">=3.8,<4.0" +files = [ + {file = "wikibase_rest_api_client-0.1.3-py3-none-any.whl", hash = "sha256:a636be4ff4fb6a43591a5e190a0a773a7261b2d293b66c68871f1f1577dc8939"}, + {file = "wikibase_rest_api_client-0.1.3.tar.gz", hash = "sha256:43c37ce63e26ca45cde851c65265af3a65bd10fb46d454aec5f1049c90ee4a9d"}, +] + +[package.dependencies] +attrs = ">=21.3.0" +httpx = ">=0.20.0,<0.27.0" +python-dateutil = ">=2.8.0,<3.0.0" + +[[package]] +name = "wikipedia" +version = "1.4.0" +description = "Wikipedia API for Python" +optional = false +python-versions = "*" +files = [ + {file = "wikipedia-1.4.0.tar.gz", hash = "sha256:db0fad1829fdd441b1852306e9856398204dc0786d2996dd2e0c8bb8e26133b2"}, +] + +[package.dependencies] +beautifulsoup4 = "*" +requests = ">=2.0.0,<3.0.0" + [[package]] name = "win32-setctime" version = "1.1.0" @@ -4934,6 +5084,20 @@ files = [ idna = ">=2.0" multidict = ">=4.0" +[[package]] +name = "youtube-search" +version = "2.1.2" +description = "Perform YouTube video searches without the API" +optional = false +python-versions = "*" +files = [ + {file = "youtube-search-2.1.2.tar.gz", hash = "sha256:5749a6d8076fda65557c91367f0fab0936290ecdea457c831c9f3f05918a3aa2"}, + {file = "youtube_search-2.1.2-py3-none-any.whl", hash = "sha256:f9cfaaf7c59806777eb29d3b8acee96b50e80f73a0621c2bb220d4e5beb09e4f"}, +] + +[package.dependencies] +requests = "*" + [[package]] name = "zipp" version = "3.18.1" @@ -4952,4 +5116,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "more-itertools", "p [metadata] lock-version = "2.0" python-versions = ">=3.10,<3.12" -content-hash = "ed42b1b08dd32935962b8f15c1585c67286738a79fbc9486f21dbf0b0dd07ac2" +content-hash = "df8b727ad174030eea0bf1bb7139e7dcedcb9382fe2e52377dece2dca265de1d" diff --git a/agent-backend/pyproject.toml b/agent-backend/pyproject.toml index 85ad436e..5e05ebb3 100644 --- a/agent-backend/pyproject.toml +++ b/agent-backend/pyproject.toml @@ -28,6 +28,13 @@ onnxruntime = "1.17.0" fastembed = "^0.2.2" qdrant-client = "^1.8.0" arxiv = "^2.1.0" +lark = "^1.1.9" +wikipedia = "^1.4.0" +mediawikiapi = "^1.2" +wikibase-rest-api-client = "^0.1.3" +duckduckgo-search = "^5.3.0" +stackapi = "^0.3.0" +youtube-search = "^2.1.2" [build-system] diff --git a/agent-backend/src/build/build_crew.py b/agent-backend/src/build/build_crew.py index b4c65efa..8a33eb42 100644 --- a/agent-backend/src/build/build_crew.py +++ b/agent-backend/src/build/build_crew.py @@ -7,19 +7,18 @@ from socketio.exceptions import ConnectionError as ConnError from socketio import SimpleClient +from lang_models import model_factory as language_model_factory import models.mongo from models.mongo import AppType, ToolType from utils.model_helper import get_enum_key_from_value, get_enum_value_from_str_key, in_enums, keyset, match_key, \ search_subordinate_keys from init.env_variables import AGENT_BACKEND_SOCKET_TOKEN, QDRANT_HOST, SOCKET_URL from typing import Dict -from langchain_openai.chat_models import ChatOpenAI, AzureChatOpenAI -from langchain_openai.embeddings import OpenAIEmbeddings -from langchain_community.embeddings.fastembed import FastEmbedEmbeddings from models.sockets import SocketMessage, SocketEvents, Message -from tools import CodeExecutionTool, RagTool # , RagToolFactory +from tools import CodeExecutionTool, RagTool from messaging.send_message_to_socket import send -from tools.global_tools import CustomHumanInput, GlobalBaseTool, get_papers_from_arxiv, openapi_request +from tools.global_tools import CustomHumanInput, GlobalBaseTool +from tools.builtin_tools import BuiltinTools from redisClient.utilities import RedisClass NTH_CHUNK_CANCEL_CHECK = 20 @@ -60,6 +59,7 @@ def __init__( self.crew_tasks = dict() self.crew_chat_tasks = list() self.crew_chat_agents = list() + self.num_tasks = len(tasks) if socket is None: self.socket = SimpleClient() self.init_socket() @@ -67,7 +67,7 @@ def __init__( def init_socket(self): try: # Initialize the socket client and connect - print(f"Socket URL: {SOCKET_URL}") + logging.debug(f"Socket URL: {SOCKET_URL}") custom_headers = {"x-agent-backend-socket-token": AGENT_BACKEND_SOCKET_TOKEN} self.socket.connect(url=SOCKET_URL, headers=custom_headers) self.socket.emit("join_room", f"_{self.session_id}") @@ -75,95 +75,10 @@ def init_socket(self): logging.error(f"Connection error occurred: {ce}") raise - @staticmethod - def fastembed_standard_doc_name_swap(fastembed_model_name: str, from_standard_to_doc: bool): - from_enum = models.mongo.FastEmbedModelsStandardFormat if from_standard_to_doc else models.mongo.FastEmbedModelsDocFormat - to_enum = models.mongo.FastEmbedModelsDocFormat if from_standard_to_doc else models.mongo.FastEmbedModelsStandardFormat - if in_enums(enums=[to_enum], value=fastembed_model_name): - return fastembed_model_name - elif in_enums(enums=[from_enum], value=fastembed_model_name): - return get_enum_value_from_str_key( - to_enum, - get_enum_key_from_value( - from_enum, - fastembed_model_name - ) - ) - - @staticmethod - def _build_openai_model_with_credential(credential, model): - if model.modelType == models.mongo.ModelType.embedding: - return OpenAIEmbeddings( - api_key=credential.credentials.api_key, - model=model.model_name, - **model.model_dump( - exclude_none=True, - exclude_unset=True, - exclude={ - "id", - "credentialId", - "modelType", - "model_name" - } - ) - ) - else: - return ChatOpenAI( - api_key=credential.credentials.api_key, - **model.model_dump( - exclude_none=True, - exclude_unset=True, - exclude={ - "id", - "credentialId", - "embeddingLength", - "modelType" - } - ) - ) - - @staticmethod - def _build_azure_model_with_credential(credential, model): - return AzureChatOpenAI( - api_key=credential.credentials.api_key, - **model.model_dump( - exclude_none=True, - exclude_unset=True, - exclude={ - "id", - "credentialId", - "embeddingLength" - } - ) - ) - - def _build_fastembed_model_with_credential(self, credential, model): - overwrite_model_name = self.fastembed_standard_doc_name_swap( - model.model_name, - from_standard_to_doc=True - ) - return FastEmbedEmbeddings( - **model.model_dump(exclude_none=True, exclude_unset=True, - exclude={ - "id", - "name", - "embeddingLength", - "model_name", - "modelType"}), - model_name=overwrite_model_name) - - def build_models_with_credentials(self): + def build_models(self): for key, model in self.models_models.items(): credential = match_key(self.credentials_models, key) - if credential: - match credential.type: - # todo: need to find a way to load these class dynamically rather than having all these switch statements - case models.mongo.Platforms.ChatOpenAI: - self.crew_models[key] = self._build_openai_model_with_credential(credential, model) - case models.mongo.Platforms.AzureChatOpenAI: - self.crew_models[key] = self._build_azure_model_with_credential(credential, model) - case models.mongo.Platforms.FastEmbed: - self.crew_models[key] = self._build_fastembed_model_with_credential(credential, model) + self.crew_models[key] = language_model_factory(model, credential) def build_tools_and_their_datasources(self): for key, tool in self.tools_models.items(): @@ -182,8 +97,8 @@ def build_tools_and_their_datasources(self): # TODO: use more secure option tools.CodeExecutionUsingDockerNotebookTool tool_class = CodeExecutionTool if tool.data.builtin: - tool_class = globals()[tool.data.name] - print(f"tool_class: {tool_class}") + tool_class = BuiltinTools.get_tool_class(tool.data.name) + logging.debug(f"tool_class: {tool_class}") # Assign tool models and datasources datasources = search_subordinate_keys(self.datasources_models, key) tool_models_objects = search_subordinate_keys(self.crew_models, key) @@ -191,35 +106,40 @@ def build_tools_and_their_datasources(self): for model_key, model_object in tool_models_objects.items(): model_data = match_key(self.models_models, model_key) tool_models_models.append((model_object, model_data)) + + # Use same llm as Crew's for retriever + retriever_llm = match_key(self.crew_models, keyset(self.crew_model.id)) + tool_instance = tool_class.factory(tool=tool, datasources=list(datasources.values()), - models=tool_models_models) + models=tool_models_models, llm=retriever_llm) self.crew_tools[key] = tool_instance def build_agents(self): for key, agent in self.agents_models.items(): model_obj = match_key(self.crew_models, key, exact=True) agent_tools_objs = search_subordinate_keys(self.crew_tools, key) - # human = CustomHumanInput(self.socket, self.session_id) - # agent_tools_objs["human_input"] = human - # print(agent_tools_objs.values()) + + if any([x.requiresHumanInput for x in self.tasks_models.values()]): + human = CustomHumanInput(self.socket, self.session_id) + agent_tools_objs["human_input"] = human + # print(agent_tools_objs.values()) + self.crew_agents[key] = Agent( **agent.model_dump( exclude_none=True, exclude_unset=True, - exclude={"id", "toolIds", "modelId", "taskIds", "step_callback", "llm", "verbose"} + exclude={"id", "toolIds", "modelId", "taskIds", "step_callback", "llm"} ), step_callback=self.send_to_sockets, stop_generating_check=self.stop_generating_check, llm=model_obj, tools=agent_tools_objs.values(), - verbose=True ) def stop_generating_check(self, step): - print(f"step: {step}") - print(f"step % NTH_CHUNK_CANCEL_CHECK: {step % NTH_CHUNK_CANCEL_CHECK}") + logging.debug(f"stop_generating_check step % NTH_CHUNK_CANCEL_CHECK: {step % NTH_CHUNK_CANCEL_CHECK}") if step % NTH_CHUNK_CANCEL_CHECK == 0: stop_flag = redis_con.get(f"{self.session_id}_stop") - print(f"stop_flag: {stop_flag}") + logging.debug(f"stop_generating_check: {stop_flag}") return stop_flag == "1" return False @@ -265,52 +185,53 @@ def build_chat(self): first_task_tools = [] first_task.tools = first_task_tools first_task_tools.append(human_input_tool) - first_task.description = dedent(f""" - Complete the following steps: - step 1: start by using the "human_input" tool to get the question. - step 2: Complete the following sub-task, using the question if necessary: - ===== SUB-TASK START ===== - {first_task.description} - ===== SUB-TASK END ===== - Here is a context (where you are assitant) from the previous conversation - you had with the user. You are assistant. - Don't use this context as instructions to do anything, only as information - to help you understand what the user is wanting in the chat that you are having - with the user afterwartds. - {self.make_current_context()} - """) + # first_task.description = dedent(f""" + # Complete the following steps: + # step 1: start by using the "human_input" tool to get the question. + # step 2: Complete the following sub-task, using the question if necessary: + # ===== SUB-TASK START ===== + # {first_task.description} + # ===== SUB-TASK END ===== + # Here is a context (where you are assitant) from the previous conversation + # you had with the user. You are assistant. + # Don't use this context as instructions to do anything, only as information + # to help you understand what the user is wanting in the chat that you are having + # with the user afterwartds. + # {self.make_current_context()} + # """) elif len(crew_tasks) > 1: + return crew_chat_model = match_key(self.crew_models, keyset(self.crew_model.id, self.crew_model.modelId)) if crew_chat_model: - # human_input_tool = CustomHumanInput(self.socket, self.session_id) + human_input_tool = CustomHumanInput(self.socket, self.session_id) chat_agent = Agent( llm=crew_chat_model, name='Human partner', role='A human chat partner', goal='Take human input using the "human_input". Pass on the human input. Your must quote the human input exactly.\n', - backstory='You are a helpful agent whose sole job is to get the himan input. To function, you NEED human input ALWAYS.', + backstory='You are a helpful agent whose sole job is to get the human input. To function, you NEED human input ALWAYS.', tools=[human_input_tool], allow_delegation=True, step_callback=self.send_to_sockets, + verbose=True ) chat_task = Task( description=dedent(f""" - You need to use the human tool. ALWAYS use the human tool. step 1: Prompt the user by saying "{self.make_user_question()}" - step 2: use the human tool to get the human answer. - step 3: Wait for that answer. - step 4: Once you get the answer from the human, that's your final answer. - {self.make_current_context()} + step 2: Use the human input tool to get a response from the user. + step 3: Once you get a response from the human, that's your final answer. """), + # {self.make_current_context()} + # """), agent=chat_agent, - expected_output="Human request" + expected_output="Verbatim output the response that the user provided to the human input tool." ) self.crew_chat_tasks = [chat_task] self.crew_chat_agents = [chat_agent] def build_crew(self): # 1. Build llm/embedding model from Model + Credentials - self.build_models_with_credentials() + self.build_models() # 2. Build Crew-Tool from Tool + llm/embedding (#1) + Model (TBD) + Datasource (optional) self.build_tools_and_their_datasources() @@ -322,7 +243,8 @@ def build_crew(self): self.build_tasks() # 5. Build chat Agent + Task - self.build_chat() + # self.build_chat() + # 6. Build Crew-Crew from Crew + Crew-Task (#4) + Crew-Agent (#3) self.crew = Crew( agents=self.crew_chat_agents + list(self.crew_agents.values()), @@ -332,13 +254,13 @@ def build_crew(self): exclude={"id", "tasks", "agents"} ), manager_llm=match_key(self.crew_models, keyset(self.crew_model.id)), - verbose=True + verbose=True, ) def send_to_sockets(self, text=None, event=None, first=None, chunk_id=None, timestamp=None, display_type="bubble", - author_name="System"): + author_name="System", overwrite=False): - # test isnt string, its agentaction, etc + # text isnt string, its agentaction, etc if type(text) != str: text = "NON STRING MESSAGE" @@ -368,6 +290,7 @@ def send_to_sockets(self, text=None, event=None, first=None, chunk_id=None, time tokens=1, timestamp=timestamp, displayType=display_type, + overwrite=overwrite, ) ), "both" @@ -375,4 +298,4 @@ def send_to_sockets(self, text=None, event=None, first=None, chunk_id=None, time def run_crew(self): self.crew.kickoff() - print("FINISHED!") + logging.debug("FINISHED!") diff --git a/agent-backend/src/lang_models/__init__.py b/agent-backend/src/lang_models/__init__.py new file mode 100644 index 00000000..598be8d2 --- /dev/null +++ b/agent-backend/src/lang_models/__init__.py @@ -0,0 +1 @@ +from .factory import model_factory diff --git a/agent-backend/src/lang_models/factory.py b/agent-backend/src/lang_models/factory.py new file mode 100644 index 00000000..7e77ec0d --- /dev/null +++ b/agent-backend/src/lang_models/factory.py @@ -0,0 +1,115 @@ +from langchain_community.embeddings import FastEmbedEmbeddings +from langchain_core.embeddings import Embeddings +from langchain_core.language_models import BaseLanguageModel +from langchain_openai import OpenAIEmbeddings, ChatOpenAI, AzureChatOpenAI + +import models.mongo +from utils.model_helper import in_enums, get_enum_value_from_str_key, get_enum_key_from_value + + +def model_factory(agentcloud_model: models.mongo.Model, + credential: models.mongo.Credentials) -> BaseLanguageModel | Embeddings: + """ + Return a (llm or embedding) langchain model based on credentials and model specs in mongo + """ + match agentcloud_model.type: + case models.mongo.Platforms.ChatOpenAI: + return _build_openai_model_with_credential(agentcloud_model, credential) + case models.mongo.Platforms.AzureChatOpenAI: + return _build_azure_model_with_credential(agentcloud_model, credential) + case models.mongo.Platforms.FastEmbed: + return _build_fastembed_model(agentcloud_model) + case models.mongo.Platforms.Ollama: + return _build_openai_compatible_model_with_credential(agentcloud_model) + + +def _build_openai_compatible_model_with_credential(model: models.mongo.Model) -> BaseLanguageModel | Embeddings: + if model.modelType == models.mongo.ModelType.embedding: + raise # figure this out later + else: + return ChatOpenAI( + **model.model_dump( + exclude_none=True, + exclude_unset=True, + ).get('config') # config key will hold the raw openai format arguments + ) + + +def _build_openai_model_with_credential(model: models.mongo.Model, + credential: models.mongo.Credentials) -> BaseLanguageModel | Embeddings: + if model.modelType == models.mongo.ModelType.embedding: + return OpenAIEmbeddings( + api_key=credential.credentials.api_key, + model=model.model_name, + **model.model_dump( + exclude_none=True, + exclude_unset=True, + exclude={ + "id", + "credentialId", + "modelType", + "model_name", + "name", + "embeddingLength", + "type", + "config" + } + ) + ) + else: + return ChatOpenAI( + api_key=credential.credentials.api_key, + **model.model_dump( + exclude_none=True, + exclude_unset=True, + exclude={ + "id", + "credentialId", + "embeddingLength", + "modelType", + "type", + "config" + } + ) + ) + + +def _build_azure_model_with_credential(model: models.mongo.Model, + credential: models.mongo.Credentials) -> BaseLanguageModel: + return AzureChatOpenAI( + api_key=credential.credentials.api_key, + **model.model_dump( + exclude_none=True, + exclude_unset=True, + exclude={ + "id", + "credentialId", + "embeddingLength", + "type", + "config" + } + ) + ) + + +def _build_fastembed_model(model: models.mongo.Model) -> Embeddings: + overwrite_model_name = _fastembed_standard_doc_name_swap( + model.model_name, + from_standard_to_doc=True + ) + return FastEmbedEmbeddings(model_name=overwrite_model_name) + + +def _fastembed_standard_doc_name_swap(fastembed_model_name: str, from_standard_to_doc: bool): + from_enum = models.mongo.FastEmbedModelsStandardFormat if from_standard_to_doc else models.mongo.FastEmbedModelsDocFormat + to_enum = models.mongo.FastEmbedModelsDocFormat if from_standard_to_doc else models.mongo.FastEmbedModelsStandardFormat + if in_enums(enums=[to_enum], value=fastembed_model_name): + return fastembed_model_name + elif in_enums(enums=[from_enum], value=fastembed_model_name): + return get_enum_value_from_str_key( + to_enum, + get_enum_key_from_value( + from_enum, + fastembed_model_name + ) + ) diff --git a/agent-backend/src/models/mongo.py b/agent-backend/src/models/mongo.py index 019bd1d5..e415e915 100644 --- a/agent-backend/src/models/mongo.py +++ b/agent-backend/src/models/mongo.py @@ -1,4 +1,4 @@ -from typing import Dict, List, Optional, Union, Callable, Annotated +from typing import Dict, List, Optional, Union, Callable, Annotated, Literal from random import randint from pydantic import BaseModel, BeforeValidator, Field, ConfigDict, AliasChoices from enum import Enum @@ -7,6 +7,7 @@ # It will be represented as a `str` on the model so that it can be serialized to JSON. PyObjectId = Annotated[str, BeforeValidator(str)] + # Enums class Process(str, Enum): Sequential = "sequential" @@ -24,6 +25,7 @@ class Platforms(str, Enum): ChatOpenAI = "open_ai" AzureChatOpenAI = "azure" FastEmbed = "fastembed" + Ollama = "ollama" class ModelVariant(str, Enum): @@ -71,6 +73,35 @@ class ToolData(BaseModel): builtin: bool +class Retriever(str, Enum): + RAW = "raw" # no structured query formatting + SELF_QUERY = "self_query" + TIME_WEIGHTED = "time_weighted" + MULTI_QUERY = "multi_query" + + +class MetadataFieldInfo(BaseModel): + name: str + description: str + type: Literal["string", "integer", "float"] + + +class SelfQueryRetrieverConfig(BaseModel): + k: Optional[int] = Field(default=4) + metadata_field_info: Optional[List[MetadataFieldInfo]] = Field(default={}) + + +class TimeWeightedRetrieverConfig(BaseModel): + k: Optional[int] = Field(default=4) + decay_rate: Optional[float] = Field(default=0.01) + timeWeightField: Optional[str] = Field(default="last_accessed_at") + + +# Allows me to be lazy in the webapp and include retriever_config keys from multiple types +class CombinedRetrieverConfig(SelfQueryRetrieverConfig, TimeWeightedRetrieverConfig): + pass + + class Tool(BaseModel): id: Optional[PyObjectId] = Field(alias="_id", default=None) model_config = ConfigDict(extra='ignore') @@ -79,6 +110,8 @@ class Tool(BaseModel): type: Optional[str] = "function" datasourceId: Optional[PyObjectId] = None data: Optional[ToolData] = None + retriever_type: Optional[Retriever] = Retriever.SELF_QUERY + retriever_config: Optional[Union[CombinedRetrieverConfig]] = None class ApiCredentials(BaseModel): @@ -112,6 +145,8 @@ class Model(BaseModel): timeout: Optional[int] = 300 max_retries: Optional[int] = 10 stream: Optional[bool] = True + type: Optional[Platforms] = None + config: Optional[Dict] = None class ChatModel(BaseModel): @@ -142,6 +177,7 @@ class Data(BaseModel): class Task(BaseModel): id: Optional[PyObjectId] = Field(alias="_id", default=None) model_config = ConfigDict(extra='ignore') + name: Optional[str] = '' description: str expected_output: Optional[str] = Field(validation_alias=AliasChoices('expectedOutput', 'expected_output')) expectedOutput: Optional[str] = None @@ -154,6 +190,7 @@ class Task(BaseModel): outputPydantic: Optional[BaseModel] = None outputFile: Optional[str] = None callback: Optional[Callable] = None + requiresHumanInput: bool = False class Agent(BaseModel): @@ -187,6 +224,8 @@ class Crew(BaseModel): managerLLM: Optional[Model] = None functionCallingLLM: Optional[Callable] = None verbose: Optional[bool] = False + memory: Optional[bool] = False + cache: Optional[bool] = False config: Optional[Dict] = {} maxRPM: Optional[int] = None language: Optional[str] = "en" @@ -211,7 +250,7 @@ class Datasource(BaseModel): name: str sourceId: PyObjectId sourceType: str - embeddingField : Optional[str] = Field(default="page_content") + embeddingField: Optional[str] = Field(default="page_content") workspaceId: PyObjectId connectionId: PyObjectId destinationId: PyObjectId diff --git a/agent-backend/src/models/sockets.py b/agent-backend/src/models/sockets.py index 38ae76fe..3b61512d 100644 --- a/agent-backend/src/models/sockets.py +++ b/agent-backend/src/models/sockets.py @@ -34,6 +34,7 @@ class Message(BaseModel): type: Optional[MessageType] = MessageType.TEXT.value displayType: Optional[str] = MessageDisplayType.BUBBLE.value timestamp: Optional[float] = datetime.now().timestamp() * 1000 + overwrite: Optional[bool] = False class SocketMessage(BaseModel): diff --git a/agent-backend/src/tools/builtin_tools.py b/agent-backend/src/tools/builtin_tools.py new file mode 100644 index 00000000..8f7b77c6 --- /dev/null +++ b/agent-backend/src/tools/builtin_tools.py @@ -0,0 +1,25 @@ +from typing import Type + +from langchain_core.tools import ToolException + +from .builtins import * +from .builtins.base import BaseBuiltinTool + + +class BuiltinTools: + tools = { + 'get_papers_from_arxiv': ArxivTool, + 'search_wikipedia': WikipediaTool, + 'search_wikidata': WikidataTool, + 'search_duckduckgo': DuckDuckGoSearchTool, + 'search_stackexchange': StackExchangeTool, + 'search_youtube': YoutubeSearchTool + } + + @classmethod + def get_tool_class(cls, tool_name: str) -> Type[BaseBuiltinTool]: + klass = cls.tools.get(tool_name) + if klass is None: + raise ToolException(f"Tool with name '{tool_name}' not found.") + return klass + diff --git a/agent-backend/src/tools/builtins/__init__.py b/agent-backend/src/tools/builtins/__init__.py new file mode 100644 index 00000000..77265fa4 --- /dev/null +++ b/agent-backend/src/tools/builtins/__init__.py @@ -0,0 +1,6 @@ +from .arxiv import ArxivTool +from .wikipedia import WikipediaTool +from .wikidata import WikidataTool +from .duckduckgo import DuckDuckGoSearchTool +from .stackexchange import StackExchangeTool +from .youtube import YoutubeSearchTool diff --git a/agent-backend/src/tools/builtins/arxiv.py b/agent-backend/src/tools/builtins/arxiv.py new file mode 100644 index 00000000..a87aed86 --- /dev/null +++ b/agent-backend/src/tools/builtins/arxiv.py @@ -0,0 +1,27 @@ +import arxiv +from .base import BaseBuiltinTool + + +class ArxivTool(BaseBuiltinTool): + """ + get_papers_from_arxiv + This tool takes a string query as input and fetches related research papers from the arXiv repository. + The run_tool function connects to the arXiv API, submits the query, and retrieves a list of papers matching the query criteria. + The returned data includes essential details like the paper's title, authors, abstract, and arXiv ID. + Args: + query (str): The query to send to arxiv to search for papers with. + """ + + def run_tool(self, query: str) -> str: + try: + self.logger.info(f"{self.__class__.__name__} searching for '{query}'") + search = arxiv.Search( + query=query, max_results=10, sort_by=arxiv.SortCriterion.SubmittedDate + ) + results = [] + for result in arxiv.Client().results(search): + results.append(result.title) + return results + except Exception as e: + print(f"An error occurred: {str(e)}") + return f"An error occurred: {str(e)}" diff --git a/agent-backend/src/tools/builtins/base.py b/agent-backend/src/tools/builtins/base.py new file mode 100644 index 00000000..bd7360ea --- /dev/null +++ b/agent-backend/src/tools/builtins/base.py @@ -0,0 +1,58 @@ +import json +import logging +from abc import ABC, abstractmethod +from typing import Type + +from langchain_core.tools import ToolException +from pydantic import BaseModel, Field + +from models.mongo import Tool +from tools.global_tools import GlobalBaseTool + + +class BuiltinToolArgsSchema(BaseModel): + query: str = Field(description="search string to invoke tool with") + + +class BaseBuiltinTool(GlobalBaseTool): + name: str + description: str + code: str + function_name: str + properties_dict: dict + args_schema: Type[BaseModel] = BuiltinToolArgsSchema + logger: logging.Logger = None + + @classmethod + def factory(cls, tool: Tool, **kwargs): + return cls( + name=tool.name, + description=tool.description, + function_name=tool.data.name, + code=tool.data.code, + properties_dict=tool.data.parameters.properties if tool.data.parameters.properties else [], + verbose=True, + handle_tool_error=True + ) + + def __init__(self, **kwargs): + kwargs["logger"] = logging.getLogger(self.__class__.__name__) + kwargs["logger"].setLevel(logging.DEBUG) + super().__init__(**kwargs) + + @abstractmethod + def run_tool(self, query: str) -> str: + pass + + def _run(self, query: str) -> str: + try: + self.logger.debug(f"{self.__class__.__name__} received {query}") + json_query = json.loads(query) + query_val = json_query["query"] if "query" in json_query else json_query["text"] + self.logger.info(f"{self.__class__.__name__} search string = '{query_val}'") + return self.run_tool(query_val) + except ToolException as te: + raise te + except Exception as e: + raise ToolException(f"An error occurred: {e}") + diff --git a/agent-backend/src/tools/builtins/duckduckgo.py b/agent-backend/src/tools/builtins/duckduckgo.py new file mode 100644 index 00000000..6cba7320 --- /dev/null +++ b/agent-backend/src/tools/builtins/duckduckgo.py @@ -0,0 +1,16 @@ +from langchain_community.tools import DuckDuckGoSearchResults + +from tools.builtins.base import BaseBuiltinTool + + +class DuckDuckGoSearchTool(BaseBuiltinTool): + ddg: DuckDuckGoSearchResults + + def __init__(self, **kwargs): + kwargs["ddg"] = DuckDuckGoSearchResults() + super().__init__(**kwargs) + + def run_tool(self, query: str) -> str: + results = self.ddg.run(query) + self.logger.debug(f"{self.__class__.__name__} search results: {results}") + return results diff --git a/agent-backend/src/tools/builtins/stackexchange.py b/agent-backend/src/tools/builtins/stackexchange.py new file mode 100644 index 00000000..eee8f683 --- /dev/null +++ b/agent-backend/src/tools/builtins/stackexchange.py @@ -0,0 +1,15 @@ +from langchain_community.utilities import StackExchangeAPIWrapper +from tools.builtins.base import BaseBuiltinTool + + +class StackExchangeTool(BaseBuiltinTool): + stackexchange: StackExchangeAPIWrapper + + def __init__(self, **kwargs): + kwargs["stackexchange"] = StackExchangeAPIWrapper() + super().__init__(**kwargs) + + def run_tool(self, query: str) -> str: + results = self.stackexchange.run(query) + self.logger.debug(f"{self.__class__.__name__} search results: {results}") + return results diff --git a/agent-backend/src/tools/builtins/wikidata.py b/agent-backend/src/tools/builtins/wikidata.py new file mode 100644 index 00000000..faa3d972 --- /dev/null +++ b/agent-backend/src/tools/builtins/wikidata.py @@ -0,0 +1,20 @@ +from typing import Type + +from langchain_community.tools.wikidata.tool import WikidataAPIWrapper, WikidataQueryRun +from langchain_core.tools import ToolException, BaseTool + +from tools.builtins.base import BaseBuiltinTool + + +class WikidataTool(BaseBuiltinTool): + wikidata: WikidataQueryRun + + def __init__(self, **kwargs): + kwargs["wikidata"] = WikidataQueryRun(api_wrapper=WikidataAPIWrapper()) + super().__init__(**kwargs) + + def run_tool(self, query: str) -> str: + results = self.wikidata.run(query) + self.logger.debug(f"{self.__class__.__name__} search results: {results}") + return results + diff --git a/agent-backend/src/tools/builtins/wikipedia.py b/agent-backend/src/tools/builtins/wikipedia.py new file mode 100644 index 00000000..c820f0b0 --- /dev/null +++ b/agent-backend/src/tools/builtins/wikipedia.py @@ -0,0 +1,17 @@ +from langchain_community.tools.wikipedia.tool import WikipediaQueryRun, WikipediaAPIWrapper +from langchain_core.tools import ToolException + +from tools.builtins.base import BaseBuiltinTool + + +class WikipediaTool(BaseBuiltinTool): + wikipedia: WikipediaQueryRun + + def __init__(self, **kwargs): + kwargs["wikipedia"] = WikipediaQueryRun(api_wrapper=WikipediaAPIWrapper()) + super().__init__(**kwargs) + + def run_tool(self, query: str) -> str: + results = self.wikipedia.run(query) + self.logger.debug(f"{self.__class__.__name__} search results: {results}") + return results diff --git a/agent-backend/src/tools/builtins/youtube.py b/agent-backend/src/tools/builtins/youtube.py new file mode 100644 index 00000000..85d8f109 --- /dev/null +++ b/agent-backend/src/tools/builtins/youtube.py @@ -0,0 +1,15 @@ +from langchain_community.tools import YouTubeSearchTool as LC_YouTubeSearchTool +from tools.builtins.base import BaseBuiltinTool + + +class YoutubeSearchTool(BaseBuiltinTool): + yt: LC_YouTubeSearchTool + + def __init__(self, **kwargs): + kwargs["yt"] = LC_YouTubeSearchTool() + super().__init__(**kwargs) + + def run_tool(self, query: str) -> str: + results = self.yt.run(query) + self.logger.debug(f"{self.__class__.__name__} search results: {results}") + return results diff --git a/agent-backend/src/tools/global_tools.py b/agent-backend/src/tools/global_tools.py index 7276e542..3cb8641c 100644 --- a/agent-backend/src/tools/global_tools.py +++ b/agent-backend/src/tools/global_tools.py @@ -26,7 +26,7 @@ class CustomHumanInput(BaseTool): This class initializes with a socket client and a session identifier to manage messages for a specific connection session.""" name = "human_input" - description = """Sends input to the user. The parameter of the input is called "text". It then waits for the human to respond. It returns the human response.""" + description = """Sends input to the user. The parameter of the input is called "text". Finish your message and the human will respond. It returns the human response.""" args_schema: Type[BaseModel] = HumanInputParams session_id: str = None socket_client: SimpleClient = None @@ -39,16 +39,28 @@ def __init__(self, socket_client: SimpleClient, session_id: str, **kwargs: Any): @staticmethod def extract_message(text): try: + #NOTE: testing + return text + if isinstance(text, str) and text.startswith('{'): text_json = json.loads(text) if len(text_json) > 1: - return text # not a single key object - return next((value for value in text_json.values() if value is not None), - None) # get the first key, which is usually "question" or "message" - return text + return json.dumps(text_json) # return the stringified JSON object + return next((value for value in text_json.values() if value is not None), None) except Exception as e: - logging.exception(e) - return text + if text.startswith('{'): + if not text.endswith('"}'): + text += '"}' + elif not text.endswith('}'): + text += '}' + try: + text_json = json.loads(text) + if len(text_json) > 1: + return json.dumps(text_json) # return the stringified JSON object + return next((value for value in text_json.values() if value is not None), None) + except Exception as ex: + logging.exception(ex) + return text def _run( self, @@ -123,50 +135,6 @@ def factory(cls, tool: Tool, datasources: List[Datasource], models: List[Tuple[A pass -class get_papers_from_arxiv(GlobalBaseTool): - """ -get_papers_from_arxiv -This function takes a string query as input and fetches related research papers from the arXiv repository. -The function connects to the arXiv API, submits the query, and retrieves a list of papers matching the query criteria. -The returned data includes essential details like the paper's title, authors, abstract, and arXiv ID. - Args: - query (str): The query to send to arxiv to search for papers with. - """ - name: str = "get_papers_from_arxiv" - description: str = "This function takes a string query as input and fetches related research papers from the arXiv repository. The function connects to the arXiv API, submits the query, and retrieves a list of papers matching the query criteria. The returned data includes essential details like the paper's title, authors, abstract, and arXiv ID." - code: str - function_name: str - properties_dict: dict = None - args_schema: Type = None - - @classmethod - def factory(cls, tool: Tool, **kargs): - return get_papers_from_arxiv( - name=tool.name, - description=tool.description, - function_name=tool.data.name, - code=tool.data.code, - properties_dict=tool.data.parameters.properties if tool.data.parameters.properties else [] - ) - - def _run( - self, - query: str - ) -> str: - try: - import arxiv - search = arxiv.Search( - query=query, max_results=10, sort_by=arxiv.SortCriterion.SubmittedDate - ) - results = [] - for result in arxiv.Client().results(search): - results.append(result.title) - return results - except Exception as e: - print(f"An error occurred: {str(e)}") - return f"An error occurred: {str(e)}" - - class openapi_request: @staticmethod @tool("Open AI request") diff --git a/agent-backend/src/tools/rag_tool.py b/agent-backend/src/tools/rag_tool.py index 969324ba..4df7b0a6 100644 --- a/agent-backend/src/tools/rag_tool.py +++ b/agent-backend/src/tools/rag_tool.py @@ -1,4 +1,6 @@ from typing import Any, Callable, List, Tuple, Type + +from langchain_core.language_models import BaseLanguageModel from langchain_core.pydantic_v1 import BaseModel, Field from langchain_community.vectorstores import VectorStore from langchain_core.embeddings import Embeddings @@ -11,6 +13,8 @@ from langchain_community.vectorstores.qdrant import Qdrant from qdrant_client import QdrantClient +from .retrievers import BaseToolRetriever, retriever_factory + ###### INSTANTIATE FROM FACTORY CLASS (AT BOTTOM) UNLESS YOU KNOW REALLY MEAN IT ###### @@ -44,14 +48,12 @@ class RagTool(GlobalBaseTool): DEFAULT_DESCRIPTION = "Returns information from a search" name: str = Field(default=DEFAULT_NAME) description: str = Field(DEFAULT_DESCRIPTION) - vector_store: VectorStore - embedding: Embeddings - pre_processors: List[Callable] = Field(default=list()) - post_processors: List[Callable] = Field(default=list()) args_schema: Type[BaseModel] = RagToolArgsSchema + retriever: BaseToolRetriever @classmethod - def factory(cls, tool: Tool, datasources: List[Datasource], models: List[Tuple[Any, Model]]) -> BaseTool: + def factory(cls, tool: Tool, datasources: List[Datasource], models: List[Tuple[Any, Model]], + llm: BaseLanguageModel = None) -> BaseTool: assert len(datasources) == 1 assert isinstance(datasources[0], Datasource) @@ -66,66 +68,35 @@ def factory(cls, tool: Tool, datasources: List[Datasource], models: List[Tuple[A collection = str(datasource.id) - return RagTool( - vector_store=Qdrant( - client=QdrantClient(QDRANT_HOST), - collection_name=collection, - embeddings=embedding_model, - vector_name=model_data.model_name, - content_payload_key="page_content" - ), - embedding=embedding_model, - name=tool.name, description=tool.description) - - def register_pre_processors(self, functions: Callable | List[Callable]): - if hasattr(functions, '__iter__'): - for func in functions: - self.pre_processors.append(func) - else: - self.pre_processors.append(functions) - - def register_post_processors(self, functions: Callable | List[Callable]): - if hasattr(functions, '__iter__'): - for func in functions: - self.post_processors.append(func) - else: - self.post_processors.append(functions) + vector_store = Qdrant( + client=QdrantClient(QDRANT_HOST), + collection_name=collection, + embeddings=embedding_model, + vector_name=model_data.model_name, + content_payload_key="page_content" + ) + + embedding = embedding_model + + return RagTool(name=tool.name, + description=tool.description, + retriever=retriever_factory(tool, vector_store, embedding, llm)) def _run(self, query): + print(f"{self.__class__.__name__} received {query}") processed_query = query try: - json_obj = json.loads(query) - processed_query = json_obj["query"] + processed_query = json.loads(query) except: - pass - print("query => ", processed_query) - """ Returns search results""" - search_results = [] - pre_data = [processed_query] - for pre_processor in self.pre_processors: - pre_data = pre_processor(pre_data) - if hasattr(pre_data, '__iter__') == False: - pre_data = [pre_data] - for pre_data_question in pre_data: - embedded_question = self.embedding.embed_query(pre_data_question) - # print("SEARCH QUESTION", pre_data_question, embedded_question) - res = self.vector_store.similarity_search_by_vector(embedded_question, k=4) - search_results += res - for post_processor in self.post_processors: - search_results = post_processor(search_results) - print("SEARCH RESULTS", search_results) - return "\n".join(map(lambda x: x if type(x) is str else x.page_content, - search_results)) ## assuming this is langchain_core.documents.Document or containts Document - -# class RagToolFactory: -# """First call init(...) method to create set up the retrieval, then call generate_langchain_tool(...) to convert to a tool ready for Crew""" - -# tool_instance: RagTool = None - -# def init(self, vector_store: VectorStore, embedding: Embeddings, pre_processors=list(), post_processors=list()): -# self.tool_instance = RagTool(vector_store, embedding, pre_processors, post_processors) - -# def generate_langchain_tool(self, tool_name: str = _default_tool_name, -# tool_description: str = _default_tool_description): -# return Tool(name=tool_name, description=tool_description + '. You must use argument "query" as string input', func=self.tool_instance.search -# ,args_schema=RagToolArgsSchema) + if query.startswith("{"): + if not query.endswith('"}'): + query += '"}' + elif not query.endswith('}'): + query += '}' + try: + processed_query = json.loads(query) + except: + processed_query = query + print("processed_query => ", processed_query) + """ Returns search results via configured retriever """ + return self.retriever.run(processed_query) diff --git a/agent-backend/src/tools/retrievers/__init__.py b/agent-backend/src/tools/retrievers/__init__.py new file mode 100644 index 00000000..7bb23d27 --- /dev/null +++ b/agent-backend/src/tools/retrievers/__init__.py @@ -0,0 +1,2 @@ +from .base import BaseToolRetriever +from .factory import retriever_factory diff --git a/agent-backend/src/tools/retrievers/base.py b/agent-backend/src/tools/retrievers/base.py new file mode 100644 index 00000000..6fce22b6 --- /dev/null +++ b/agent-backend/src/tools/retrievers/base.py @@ -0,0 +1,39 @@ +import logging +from abc import ABC + +from langchain_core.retrievers import BaseRetriever + +from models.mongo import Tool +from tools.retrievers.callback_handler import RetrieverCallbackHandler + + +class BaseToolRetriever(ABC): + logger: logging.Logger + tool: Tool + retriever: BaseRetriever + + def __init__(self): + self.init_logger() + + def run(self, query): + # Perform the query and get results + results = self.perform_query(query) + print(f"RAG query results: {results}") + if not results or len(results) == 0: + return f"NO RESULTS FOUND IN DATASOURCE \"{self.tool.name}\" FOR QUERY: \"{query}\"" + formatted_results = self.format_results(results) + print(f"RAG formatted response: {formatted_results}") + return formatted_results + + def perform_query(self, query): + self.logger.debug( + f"{self.__class__.__name__} in tool: '{self.tool.name}', retriever_config: {self.tool.retriever_config}") + return self.retriever.invoke(query, config={'callbacks': [RetrieverCallbackHandler()]}) + + def format_results(self, results): + self.logger.debug(f"{self.__class__.__name__} results: {results}") + return "\n".join(map(lambda x: x if type(x) is str else x.page_content, results)) + + def init_logger(self): + self.logger = logging.getLogger(self.__class__.__name__) + self.logger.setLevel(logging.DEBUG) diff --git a/agent-backend/src/tools/retrievers/callback_handler.py b/agent-backend/src/tools/retrievers/callback_handler.py new file mode 100644 index 00000000..cf743212 --- /dev/null +++ b/agent-backend/src/tools/retrievers/callback_handler.py @@ -0,0 +1,39 @@ +import logging +from typing import Dict, Any, Sequence + +from langchain_core.callbacks import BaseCallbackHandler +from langchain_core.documents import Document + +logger = logging.getLogger(__name__) +logger.setLevel(logging.DEBUG) + + +class RetrieverCallbackHandler(BaseCallbackHandler): + def on_retriever_start( + self, + serialized: Dict[str, Any], + query: str, + **kwargs: Any, + ) -> None: + logger.debug(f"on_retriever_start: \n {serialized} \n query => {query} \n args => {kwargs} \n -----") + + def on_retriever_end( + self, + documents: Sequence[Document], + **kwargs: Any, + ) -> None: + logger.debug(f"on_retriever_end: \n documents => {documents} \n args => {kwargs} \n -----") + + def on_retriever_error( + self, + error: BaseException, + **kwargs: Any, + ) -> Any: + logger.debug(f"on_retriever_error: \n error => {error} \n args => {kwargs} \n -----") + + def on_text( + self, + text: str, + **kwargs: Any, + ) -> None: + logger.debug(f"on_text: \n text => {text} \n args => {kwargs} \n -----") diff --git a/agent-backend/src/tools/retrievers/custom/__init__.py b/agent-backend/src/tools/retrievers/custom/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/agent-backend/src/tools/retrievers/custom/time_weighted_retriever.py b/agent-backend/src/tools/retrievers/custom/time_weighted_retriever.py new file mode 100644 index 00000000..e146c557 --- /dev/null +++ b/agent-backend/src/tools/retrievers/custom/time_weighted_retriever.py @@ -0,0 +1,206 @@ +import datetime +from copy import deepcopy +from typing import Any, Dict, List, Optional, Tuple +from dateutil.parser import parse + +from langchain_core.callbacks import ( + AsyncCallbackManagerForRetrieverRun, + CallbackManagerForRetrieverRun, +) +from langchain_core.documents import Document +from langchain_core.pydantic_v1 import Field +from langchain_core.retrievers import BaseRetriever +from langchain_core.vectorstores import VectorStore + + +def _get_hours_passed(time: datetime.datetime, ref_time: datetime.datetime) -> float: + """Get the hours passed between two datetimes.""" + # Convert both datetimes to UTC if one or both are naive + if time.tzinfo is None: + time = time.replace(tzinfo=datetime.timezone.utc) + if ref_time.tzinfo is None: + ref_time = ref_time.replace(tzinfo=datetime.timezone.utc) + + # Return the difference in hours + return (time - ref_time).total_seconds() / 3600 + + +class CustomTimeWeightedVectorStoreRetriever(BaseRetriever): + """Retriever that combines embedding similarity with + recency in retrieving values.""" + + time_weight_field_name: str = Field(default="last_accessed_at") + """The name of the field used for recency lookup""" + + vectorstore: VectorStore + """The vectorstore to store documents and determine salience.""" + + search_kwargs: dict = Field(default_factory=lambda: dict(k=100)) + """Keyword arguments to pass to the vectorstore similarity search.""" + + # TODO: abstract as a queue + memory_stream: List[Document] = Field(default_factory=list) + """The memory_stream of documents to search through.""" + + decay_rate: float = Field(default=0.01) + """The exponential decay factor used as (1.0-decay_rate)**(hrs_passed).""" + + k: int = 4 + """The maximum number of documents to retrieve in a given call.""" + + other_score_keys: List[str] = [] + """Other keys in the metadata to factor into the score, e.g. 'importance'.""" + + default_salience: Optional[float] = None + """The salience to assign memories not retrieved from the vector store. + + None assigns no salience to documents not fetched from the vector store. + """ + + class Config: + """Configuration for this pydantic object.""" + + arbitrary_types_allowed = True + + def _document_get_date(self, field: str, document: Document) -> datetime.datetime: + """Return the value of the date field of a document.""" + if field in document.metadata: + value = document.metadata[field] + if isinstance(value, float): + return datetime.datetime.fromtimestamp(value) + elif isinstance(value, str): + # Try parsing the string as a datetime + try: + return parse(value) + except (ValueError, TypeError): + pass + return value + # Fallback to current datetime if the field is missing or not a valid date representation + return datetime.datetime.now() + + def _get_combined_score( + self, + document: Document, + vector_relevance: Optional[float], + current_time: datetime.datetime, + ) -> float: + """Return the combined score for a document.""" + hours_passed = _get_hours_passed( + current_time, + self._document_get_date(self.time_weight_field_name, document), + ) + score = (1.0 - self.decay_rate) ** hours_passed + for key in self.other_score_keys: + if key in document.metadata: + score += document.metadata[key] + if vector_relevance is not None: + score += vector_relevance + return score + + def get_salient_docs(self, query: str) -> Dict[int, Tuple[Document, float]]: + """Return documents that are salient to the query.""" + docs_and_scores: List[Tuple[Document, float]] + docs_and_scores = self.vectorstore.similarity_search_with_relevance_scores( + query, **self.search_kwargs + ) + print(f"docs_and_scores: {docs_and_scores}") + results = {} + for idx, (fetched_doc, relevance) in enumerate(docs_and_scores): + if "buffer_idx" in fetched_doc.metadata: + buffer_idx = fetched_doc.metadata["buffer_idx"] + doc = self.memory_stream[buffer_idx] + results[buffer_idx] = (doc, relevance) + else: + results[idx] = (fetched_doc, relevance) + return results + + async def aget_salient_docs(self, query: str) -> Dict[int, Tuple[Document, float]]: + """Return documents that are salient to the query.""" + docs_and_scores: List[Tuple[Document, float]] + docs_and_scores = ( + await self.vectorstore.asimilarity_search_with_relevance_scores( + query, **self.search_kwargs + ) + ) + results = {} + for fetched_doc, relevance in docs_and_scores: + if "buffer_idx" in fetched_doc.metadata: + buffer_idx = fetched_doc.metadata["buffer_idx"] + doc = self.memory_stream[buffer_idx] + results[buffer_idx] = (doc, relevance) + return results + + def _get_rescored_docs( + self, docs_and_scores: Dict[Any, Tuple[Document, Optional[float]]] + ) -> List[Document]: + current_time = datetime.datetime.now() + rescored_docs = [ + (doc, self._get_combined_score(doc, relevance, current_time)) + for doc, relevance in docs_and_scores.values() + ] + rescored_docs.sort(key=lambda x: x[1], reverse=True) + return rescored_docs[: self.k] + # result = [] + # # Ensure frequently accessed memories aren't forgotten + # for doc, _ in rescored_docs[: self.k]: + # # TODO: Update vector store doc once `update` method is exposed. + # buffered_doc = self.memory_stream[doc.metadata["buffer_idx"]] + # buffered_doc.metadata[self.time_weight_field_name] = current_time + # result.append(buffered_doc) + # return result + + def _get_relevant_documents( + self, query: str, *, run_manager: CallbackManagerForRetrieverRun + ) -> List[Document]: + docs_and_scores = { + doc.metadata["buffer_idx"]: (doc, self.default_salience) + for doc in self.memory_stream[-self.k:] + } + # If a doc is considered salient, update the salience score + docs_and_scores.update(self.get_salient_docs(query)) + return self._get_rescored_docs(docs_and_scores) + + async def _aget_relevant_documents( + self, query: str, *, run_manager: AsyncCallbackManagerForRetrieverRun + ) -> List[Document]: + docs_and_scores = { + doc.metadata["buffer_idx"]: (doc, self.default_salience) + for doc in self.memory_stream[-self.k:] + } + # If a doc is considered salient, update the salience score + docs_and_scores.update(await self.aget_salient_docs(query)) + return self._get_rescored_docs(docs_and_scores) + + def add_documents(self, documents: List[Document], **kwargs: Any) -> List[str]: + """Add documents to vectorstore.""" + current_time = kwargs.get("current_time") + if current_time is None: + current_time = datetime.datetime.now() + # Avoid mutating input documents + dup_docs = [deepcopy(d) for d in documents] + for i, doc in enumerate(dup_docs): + if self.time_weight_field_name not in doc.metadata: + doc.metadata[self.time_weight_field_name] = current_time + if "created_at" not in doc.metadata: + doc.metadata["created_at"] = current_time + doc.metadata["buffer_idx"] = len(self.memory_stream) + i + self.memory_stream.extend(dup_docs) + return self.vectorstore.add_documents(dup_docs, **kwargs) + + async def aadd_documents( + self, documents: List[Document], **kwargs: Any + ) -> List[str]: + """Add documents to vectorstore.""" + current_time = kwargs.get("current_time") + if current_time is None: + current_time = datetime.datetime.now() + # Avoid mutating input documents + dup_docs = [deepcopy(d) for d in documents] + for i, doc in enumerate(dup_docs): + if self.time_weight_field_name not in doc.metadata: + doc.metadata[self.time_weight_field_name] = current_time + if "created_at" not in doc.metadata: + doc.metadata["created_at"] = current_time + doc.metadata["buffer_idx"] = len(self.memory_stream) + i + self.memory_stream.extend(dup_docs) + return await self.vectorstore.aadd_documents(dup_docs, **kwargs) diff --git a/agent-backend/src/tools/retrievers/default.py b/agent-backend/src/tools/retrievers/default.py new file mode 100644 index 00000000..5e536ab4 --- /dev/null +++ b/agent-backend/src/tools/retrievers/default.py @@ -0,0 +1,14 @@ +from langchain_core.embeddings import Embeddings +from langchain_core.vectorstores import VectorStore + +from models.mongo import Tool +from .base import BaseToolRetriever +from .callback_handler import RetrieverCallbackHandler +from .similarity_search import SimilaritySearchRetriever + + +class DefaultRetriever(BaseToolRetriever): + def __init__(self, tool: Tool, embedding: Embeddings, vector_store: VectorStore): + self.tool = tool + self.retriever = SimilaritySearchRetriever(embedding=embedding, vector_store=vector_store) + super().__init__() diff --git a/agent-backend/src/tools/retrievers/factory.py b/agent-backend/src/tools/retrievers/factory.py new file mode 100644 index 00000000..c1538e1a --- /dev/null +++ b/agent-backend/src/tools/retrievers/factory.py @@ -0,0 +1,22 @@ +import logging +from langchain_core.embeddings import Embeddings +from langchain_community.vectorstores import VectorStore +from langchain_core.language_models import BaseLanguageModel + +from models.mongo import Retriever, Tool +from .default import DefaultRetriever +from .self_query import SelfQueryRetriever +from .time_weighted import TimeWeightedRetriever +from .multi_query import MultiQueryRetriever + + +def retriever_factory(tool: Tool, vector_store: VectorStore, embedding: Embeddings, llm: BaseLanguageModel): + match tool.retriever_type: + case Retriever.RAW: + return DefaultRetriever(tool, embedding, vector_store) + case Retriever.SELF_QUERY: + return SelfQueryRetriever(tool, llm, vector_store) + case Retriever.TIME_WEIGHTED: + return TimeWeightedRetriever(tool, vector_store) + case Retriever.MULTI_QUERY: + return MultiQueryRetriever(tool, llm, vector_store) diff --git a/agent-backend/src/tools/retrievers/multi_query.py b/agent-backend/src/tools/retrievers/multi_query.py new file mode 100644 index 00000000..852f9d34 --- /dev/null +++ b/agent-backend/src/tools/retrievers/multi_query.py @@ -0,0 +1,16 @@ +import logging + +from langchain.retrievers.multi_query import MultiQueryRetriever as LC_MultiQueryRetriever +from langchain_core.language_models import BaseLanguageModel +from langchain_core.vectorstores import VectorStore + +from .base import BaseToolRetriever +from models.mongo import Tool + + +class MultiQueryRetriever(BaseToolRetriever): + def __init__(self, tool: Tool, llm: BaseLanguageModel, vector_store: VectorStore): + self.tool = tool + self.retriever = LC_MultiQueryRetriever.from_llm(retriever=vector_store.as_retriever(), llm=llm) + logging.getLogger("langchain.retrievers.multi_query").setLevel(logging.DEBUG) + super().__init__() diff --git a/agent-backend/src/tools/retrievers/self_query.py b/agent-backend/src/tools/retrievers/self_query.py new file mode 100644 index 00000000..c479c786 --- /dev/null +++ b/agent-backend/src/tools/retrievers/self_query.py @@ -0,0 +1,93 @@ +from langchain.chains.query_constructor.schema import AttributeInfo +from langchain.retrievers import SelfQueryRetriever as LC_SelfQueryRetriever +from langchain_core.language_models import BaseLanguageModel +from langchain_core.vectorstores import VectorStore +from langchain.retrievers.self_query.qdrant import QdrantTranslator +from langchain.chains.query_constructor.ir import ( + Comparator, +) + +from models.mongo import Tool +from .base import BaseToolRetriever + +# monkey patching langchain for this to work properly with our Qdrant metadata layout (not nested) +from langchain_community.vectorstores.qdrant import Qdrant +from langchain_community.docstore.document import Document + + +def custom_document_from_scored_point(cls, scored_point, collection_name, content_payload_key, metadata_payload_key): + metadata = scored_point.payload or {} + # Check if metadata is a dictionary and handle it appropriately + if isinstance(metadata, dict): + metadata["_id"] = scored_point.id + metadata["_collection_name"] = collection_name + else: + metadata = { + "_id": scored_point.id, + "_collection_name": collection_name, + metadata_payload_key: metadata, + } + return Document( + page_content=scored_point.payload.get(content_payload_key), + metadata=metadata, + ) + + +Qdrant._document_from_scored_point = classmethod(custom_document_from_scored_point) + + +def custom_visit_comparison(self, comparison): + try: + from qdrant_client.http import models as rest + except ImportError as e: + raise ImportError( + "Cannot import qdrant_client. Please install with `pip install qdrant-client`." + ) from e + self._validate_func(comparison.comparator) + + # Use the attribute directly instead of metadata_key + "." + ... + attribute = comparison.attribute + + if comparison.comparator == Comparator.EQ: + return rest.FieldCondition( + key=attribute, match=rest.MatchValue(value=comparison.value) + ) + elif comparison.comparator == Comparator.LIKE: + return rest.FieldCondition( + key=attribute, match=rest.MatchText(text=comparison.value) + ) + else: + kwargs = {comparison.comparator.value: comparison.value} + return rest.FieldCondition(key=attribute, range=rest.Range(**kwargs)) + + +QdrantTranslator.visit_comparison = custom_visit_comparison + + +class SelfQueryRetriever(BaseToolRetriever): + def __init__(self, tool: Tool, llm: BaseLanguageModel, vector_store: VectorStore): + self.tool = tool + self.metadata_field_info = list( + map(lambda x: AttributeInfo(**x.model_dump()), tool.retriever_config.metadata_field_info)) + self.retriever = LC_SelfQueryRetriever.from_llm( + llm=llm, + vectorstore=vector_store, + document_contents=tool.description, + metadata_field_info=self.metadata_field_info, + verbose=True + ) + super().__init__() + + def perform_query(self, query): + self.logger.info(f"metadata_field_info: {self.metadata_field_info}") + query_result = super().perform_query(query) + self.logger.info(f"query result: {query_result}") + return query_result + + def format_results(self, results): + self.logger.debug(f"{self.__class__.__name__} results: {results}") + return "\n".join( + map(lambda x: x if type(x) is str else str({ + 'data': x.page_content, + 'metadata': x.metadata + }), results)) diff --git a/agent-backend/src/tools/retrievers/similarity_search.py b/agent-backend/src/tools/retrievers/similarity_search.py new file mode 100644 index 00000000..75a169b0 --- /dev/null +++ b/agent-backend/src/tools/retrievers/similarity_search.py @@ -0,0 +1,16 @@ +from langchain_core.callbacks import CallbackManagerForRetrieverRun +from langchain_core.embeddings import Embeddings +from langchain_core.retrievers import BaseRetriever +from langchain_core.vectorstores import VectorStore + +from .base import BaseToolRetriever + + +class SimilaritySearchRetriever(BaseRetriever): + embedding: Embeddings + + vector_store: VectorStore + + def _get_relevant_documents(self, query: str, *, run_manager: CallbackManagerForRetrieverRun): + embedded_question = self.embedding.embed_query(query) + return self.vector_store.similarity_search_by_vector(embedded_question, k=4) diff --git a/agent-backend/src/tools/retrievers/time_weighted.py b/agent-backend/src/tools/retrievers/time_weighted.py new file mode 100644 index 00000000..277a3fdf --- /dev/null +++ b/agent-backend/src/tools/retrievers/time_weighted.py @@ -0,0 +1,27 @@ +import datetime + +from langchain_core.vectorstores import VectorStore + +from models.mongo import Tool +from .base import BaseToolRetriever +from .custom.time_weighted_retriever import CustomTimeWeightedVectorStoreRetriever + + +class TimeWeightedRetriever(BaseToolRetriever): + + def __init__(self, tool: Tool, vector_store: VectorStore): + self.tool = tool + self.retriever = CustomTimeWeightedVectorStoreRetriever( + vectorstore=vector_store, + time_weight_field_name=tool.retriever_config.timeWeightField, + decay_rate=tool.retriever_config.decay_rate) + super().__init__() + + def format_results(self, results): + self.logger.debug(f"{self.__class__.__name__} results: {results}") + return "\n".join( + map(lambda x: x if type(x) is str else str({ + 'data': x[0].page_content, + 'metadata': x[0].metadata, + 'time_weight_score': x[1] + }), results)) diff --git a/docker-compose.yml b/docker-compose.yml index 6806f198..714bcc74 100644 --- a/docker-compose.yml +++ b/docker-compose.yml @@ -34,11 +34,12 @@ services: args: - GOOGLE_KEYPATH=keyfile.json - SHORT_COMMIT_HASH=${SHORT_COMMIT_HASH} - - NEXT_PUBLIC_NO_PAYMENT_REQUIRED=1 - - NEXT_PUBLIC_ENABLE_GOOGLE_OAUTH= - - NEXT_PUBLIC_ENABLE_GITHUB_OAUTH= + # - NEXT_PUBLIC_ENABLE_GOOGLE_OAUTH= + # - NEXT_PUBLIC_ENABLE_GITHUB_OAUTH= - NEXT_PUBLIC_GCS_BUCKET_NAME=${GCS_BUCKET_NAME} + - NEXT_PUBLIC_HUBSPOT_MEETING_LINK=https://meetings.hubspot.com/andrew520 - POSTHOG_KEY= + - GOOGLE_APPLICATION_CREDENTIALS=/app/keyfile.json ports: - "127.0.0.1:3000:3000" depends_on: @@ -46,6 +47,7 @@ services: - docker_mongo - docker_redis environment: + - GOOGLE_APPLICATION_CREDENTIALS=/app/keyfile.json - RABBITMQ_HOST=docker_rabbitmq - RABBITMQ_PORT=5672 - RABBITMQ_STREAM=streaming @@ -68,7 +70,6 @@ services: - DEBUG=webapp:* - STRIPE_WEBHOOK_SECRET= - STRIPE_ACCOUNT_SECRET= - - STRIPE_PRICE_ID= - NEXT_PUBLIC_GCS_BUCKET_NAME=${GCS_BUCKET_NAME} - OAUTH_GITHUB_CLIENT_ID= - OAUTH_GITHUB_CLIENT_SECRET= @@ -86,6 +87,8 @@ services: - STRIPE_FREE_PLAN_PRICE_ID=price_1P0zlRDxQ9GZKzvoYUAzWMSv - STRIPE_PRO_PLAN_PRICE_ID=price_1OAMHSDxQ9GZKzvoBbDryhiZ - STRIPE_TEAMS_PLAN_PRICE_ID=price_1P0xEGDxQ9GZKzvoTDfMJK6f + - STRIPE_ADDON_USERS_PRICE_ID=price_1PFnQmDxQ9GZKzvoS4cRlCtt + - STRIPE_ADDON_STORAGE_PRICE_ID=price_1PGClWDxQ9GZKzvoiB8SNOrV - NEXT_PUBLIC_STORAGE_PROVIDER=local - NEXT_PUBLIC_SECRET_PROVIDER=local - UPLOADS_BASE_PATH=/tmp @@ -158,11 +161,16 @@ services: - '127.0.0.1:6333:6333' - '127.0.0.1:6334:6334' image: qdrant/qdrant + environment: + - QDRANT__LOG_LEVEL=DEBUG + volumes: + - qdrant_data:/qdrant_data volumes: mongodb_data_container: redis_data: datasource_files: + qdrant_data: networks: default: diff --git a/install.sh b/install.sh index 82e15bd9..0d1b2524 100755 --- a/install.sh +++ b/install.sh @@ -172,7 +172,6 @@ else cp "$SERVICE_ACCOUNT_JSON_PATH" vector-db-proxy/keyfile.json fi - echo "=> Starting airbyte" # Define the target version @@ -205,71 +204,12 @@ else fi cd airbyte -./run-ab-platform.sh -b +./run-ab-platform.sh -b & +sleep 0.1 +print_logo +wait cd .. -echo "=> Starting rabbitmq and vector_db_proxy" - -# startup rqbbitmq, qdrant, and vector proxy in advance -docker_up vector_db_proxy - -# get instance configuration for setup status -INSTANCE_CONFIGURATION=$(curl -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==' 'http://localhost:8000/api/v1/instance_configuration') -INITIAL_SETUP_COMPLETE=$(echo $INSTANCE_CONFIGURATION | jq -r '.initialSetupComplete') -echo INITIAL_SETUP_COMPLETE $INITIAL_SETUP_COMPLETE -if [ "$INITIAL_SETUP_COMPLETE" != "true" ]; then - # if not setup yet, bypass setup screen - echo Skipping airbyte setup screen... - INSTANCE_CONFIGURATION=$(curl 'http://localhost:8000/api/v1/instance_configuration/setup' -X POST \ - -H 'Content-Type: application/json' \ - -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==' \ - --data-raw '{"email":"example@example.org","anonymousDataCollection":false,"securityCheck":"succeeded","organizationName":"example","initialSetupComplete":true,"displaySetupWizard":false}') -fi - -# get the first workspace from list (default workspace) -WORKSPACES_LIST=$(curl 'http://localhost:8006/v1/workspaces' \ - -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==') -AIRBYTE_ADMIN_WORKSPACE_ID=$(echo $WORKSPACES_LIST | jq -r '.data[0].workspaceId') -export AIRBYTE_ADMIN_WORKSPACE_ID -# NOTE: no need to create if doesnt exist, one is already created/existing by default. -echo AIRBYTE_ADMIN_WORKSPACE_ID $AIRBYTE_ADMIN_WORKSPACE_ID - -# get list of destinations and take first one if already exists -DESTINATIONS_LIST=$(curl 'http://localhost:8000/api/v1/destinations/list' -X POST \ - -H 'Content-Type: application/json' \ - -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==' \ - --data-raw '{"workspaceId":"'"$AIRBYTE_ADMIN_WORKSPACE_ID"'"}') -AIRBYTE_ADMIN_DESTINATION_ID=$(echo $DESTINATIONS_LIST | jq -r '.destinations[0].destinationId') -# else create the destination - -echo AIRBYTE_ADMIN_DESTINATION_ID $AIRBYTE_ADMIN_DESTINATION_ID -if [ "$AIRBYTE_ADMIN_DESTINATION_ID" == "null" ] || [ -z "$AIRBYTE_ADMIN_DESTINATION_ID" ]; then - echo Creating destination - CREATED_DESTINATION=$(curl 'http://localhost:8000/api/v1/destinations/create' --compressed -X POST \ - -H 'Content-Type: application/json' \ - -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==' \ - --data-raw '{"name":"RabbitMQ","destinationDefinitionId":"e06ad785-ad6f-4647-b2e8-3027a5c59454","workspaceId":"'"$AIRBYTE_ADMIN_WORKSPACE_ID"'","connectionConfiguration":{"routing_key":"key","username":"guest","password":"guest","exchange":"agentcloud","port":5672,"host":"0.0.0.0","ssl":false}}') - echo Created destination: $CREATED_DESTINATION - AIRBYTE_ADMIN_DESTINATION_ID=$(echo $CREATED_DESTINATION | jq -r '.destinationId') -fi -export AIRBYTE_ADMIN_DESTINATION_ID - - -# # set the webhook urls for airbyte webhooks back to the webapp -# UPDATED_WEBHOOK_URLS=$(curl 'http://localhost:8000/api/v1/workspaces/update' --compressed -X POST \ -# -H 'content-type: application/json' \ -# -H 'Authorization: Basic YWlyYnl0ZTpwYXNzd29yZA==' \ -# --data-raw '{"workspaceId":"'"$AIRBYTE_ADMIN_WORKSPACE_ID"'","notificationSettings":{"sendOnFailure":{"notificationType":["customerio","slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnSuccess":{"notificationType":["slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnConnectionUpdate":{"notificationType":["customerio","slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnConnectionUpdateActionRequired":{"notificationType":["customerio","slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnSyncDisabled":{"notificationType":["customerio","slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnSyncDisabledWarning":{"notificationType":["customerio","slack"],"slackConfiguration":{"webhook":"http://webapp_next:3000/webhook/sync-successful"}},"sendOnBreakingChangeWarning":{"notificationType":["customerio"]},"sendOnBreakingChangeSyncsDisabled":{"notificationType":["customerio"]}}}') - - -echo Airbyte setup values: -echo Instance configuration: $INSTANCE_CONFIGURATION -echo Workspaces list: $WORKSPACES_LIST -echo Destinations list: $DESTINATIONS_LIST -echo Airbyte Workspace ID: $AIRBYTE_ADMIN_WORKSPACE_ID -echo Airbyte Destination ID: $AIRBYTE_ADMIN_DESTINATION_ID - - echo "=> Starting agentcloud backend..." docker compose up --build -d diff --git a/vector-db-proxy/Dockerfile b/vector-db-proxy/Dockerfile index 29088fbb..d5dd2536 100644 --- a/vector-db-proxy/Dockerfile +++ b/vector-db-proxy/Dockerfile @@ -1,4 +1,4 @@ -FROM rust as builder +FROM rust:1.77.2 as builder ARG RELEASE WORKDIR /usr/local/src/vector-db-proxy RUN mkdir -p src @@ -37,7 +37,7 @@ RUN mkdir /onnxruntime \ && cp lib/libonnxruntime.so.1.17.1 /usr/lib # Final Stage (using rust image to be safe so we have important libs like libssl, etc) -FROM rust +FROM rust:1.77.2 WORKDIR /app ARG RELEASE ENV IS_RELEASE=$RELEASE diff --git a/vector-db-proxy/src/data/processing_incoming_messages.rs b/vector-db-proxy/src/data/processing_incoming_messages.rs index a5679915..9d452d31 100644 --- a/vector-db-proxy/src/data/processing_incoming_messages.rs +++ b/vector-db-proxy/src/data/processing_incoming_messages.rs @@ -5,7 +5,7 @@ use qdrant_client::client::QdrantClient; use serde_json::Value; use crate::llm::models::EmbeddingModels; -use crate::mongo::queries::get_embedding_model_and_embedding_key; +use crate::mongo::queries::{get_embedding_model_and_embedding_key, increment_by_one}; use crate::qdrant::helpers::embed_payload; use crate::qdrant::utils::Qdrant; use crate::utils::conversions::convert_serde_value_to_hashmap_string; @@ -19,7 +19,6 @@ pub async fn process_messages( // initiate variables let mongodb_connection = mongo_conn.read().await; let mut message_count: Vec = vec![]; - // let redis_connection = redis_connection_pool.lock().await; match serde_json::from_str(message.as_str()) { Ok::(message_data) => { message_count.push(1); @@ -32,6 +31,7 @@ pub async fn process_messages( let embedding_model_name = model_parameters.model; let ds_clone = datasource_id.clone(); let qdrant = Qdrant::new(qdrant_conn, datasource_id); + let mut field_path = "recordCount.failure"; if let Value::Object(data_obj) = message_data { let mut metadata = convert_serde_value_to_hashmap_string(data_obj); if let Some(text_field) = embedding_field { @@ -39,16 +39,36 @@ pub async fn process_messages( let text = metadata.remove(text_field.as_str()).unwrap(); metadata.insert("page_content".to_string(), text.to_owned()); let mongo_conn_clone = Arc::clone(&mongo_conn); + let ds_clone = ds_clone.clone(); match embed_payload( mongo_conn_clone, &metadata, &text, - Some(ds_clone), + Some(ds_clone.clone()), EmbeddingModels::from(embedding_model_name)).await { Ok(point_struct) => { - let _ = qdrant.upsert_data_point_blocking(point_struct).await; + match qdrant.upsert_data_point_blocking(point_struct).await { + Ok(result) => { + match result { + true => { + field_path = "recordCount.success"; + let _ = increment_by_one(&mongodb_connection, &*ds_clone, field_path).await.unwrap(); + } + false => { + log::warn!("An error occurred while inserting into vector database"); + let _ = increment_by_one(&mongodb_connection, &*ds_clone, field_path).await.unwrap(); + } + } + } + Err(e) => { + log::warn!("An error occurred while inserting into vector database. Error: {}", e); + let _ = increment_by_one(&mongodb_connection, &*ds_clone, field_path).await.unwrap(); + } + } + let _ = increment_by_one(&mongodb_connection, &*ds_clone, field_path).await.unwrap(); } Err(e) => { + let _ = increment_by_one(&mongodb_connection, &*ds_clone, field_path).await.unwrap(); log::error!("An error occurred while upserting point structs to Qdrant: {}", e); } } diff --git a/vector-db-proxy/src/init/models.rs b/vector-db-proxy/src/init/models.rs index 9d35d55c..d6a66b6a 100644 --- a/vector-db-proxy/src/init/models.rs +++ b/vector-db-proxy/src/init/models.rs @@ -44,8 +44,8 @@ impl GlobalData { redis_host: dotenv::var("REDIS_HOST").unwrap_or("localhost".to_string()), redis_port: dotenv::var("REDIS_PORT").unwrap_or("6379".to_string()), thread_percentage_utilisation: dotenv::var("THREAD_PERCENTAGE_UTILISATION") - .unwrap_or("0.8".to_string()).parse().unwrap_or(0.8), - use_gpu: dotenv::var("USE_GPU").unwrap_or("false".to_string()), + .unwrap_or("1".to_string()).parse().unwrap_or(0.8), + use_gpu: dotenv::var("USE_GPU").unwrap_or("true".to_string()), logging_level: dotenv::var("LOGGING_LEVEL").unwrap_or("debug".to_string()), } } diff --git a/vector-db-proxy/src/mongo/models.rs b/vector-db-proxy/src/mongo/models.rs index ae352d28..384f67db 100644 --- a/vector-db-proxy/src/mongo/models.rs +++ b/vector-db-proxy/src/mongo/models.rs @@ -38,6 +38,7 @@ pub struct DataSources { pub chunkCharacter: Option, pub lastSyncedDate: Option, pub embeddingField: Option, + pub timeWeightField: Option, pub createdDate: Option, pub status: String, } diff --git a/vector-db-proxy/src/mongo/queries.rs b/vector-db-proxy/src/mongo/queries.rs index df7335ad..c4000413 100644 --- a/vector-db-proxy/src/mongo/queries.rs +++ b/vector-db-proxy/src/mongo/queries.rs @@ -147,3 +147,20 @@ pub async fn get_model_credentials( } } } + +pub async fn increment_by_one(db: &Database, datasource_id: &str, field_path: &str) -> Result<()> { + let datasources_collection = db.collection::("datasources"); + let filter = doc! {"_id": ObjectId::from_str(datasource_id).unwrap()}; // This filter applies to all documents, or specify criteria to select a document + let update = doc! { + "$inc": { field_path: 1 }, + }; + let update_options = mongodb::options::UpdateOptions::default(); + + match datasources_collection.update_one(filter, update, update_options).await { + Ok(_) => Ok(()), + Err(e) => { + log::error!("Error: {}", e); + Err(anyhow!("Failed to increment variable. Error: {}", e)) + } + } +} diff --git a/webapp/.env.example b/webapp/.env.example index e2140a4c..8818bd75 100644 --- a/webapp/.env.example +++ b/webapp/.env.example @@ -46,4 +46,5 @@ STRIPE_PRO_PLAN_PRICE_ID= STRIPE_TEAMS_PLAN_PRICE_ID= NEXT_PUBLIC_STORAGE_PROVIDER=local NEXT_PUBLIC_SECRET_PROVIDER=local -UPLOADS_BASE_PATH=./static +NEXT_PUBLIC_HUBSPOT_MEETING_LINK=https://meetings.hubspot.com/andrew520 +UPLOADS_BASE_PATH=/tmp diff --git a/webapp/Dockerfile b/webapp/Dockerfile index 762c34ce..cd1fe535 100644 --- a/webapp/Dockerfile +++ b/webapp/Dockerfile @@ -43,6 +43,8 @@ ARG NEXT_PUBLIC_ENABLE_GOOGLE_OAUTH ARG NEXT_PUBLIC_ENABLE_GITHUB_OAUTH ARG NEXT_PUBLIC_GCS_BUCKET_NAME ARG GOOGLE_KEYPATH +ARG GOOGLE_APPLICATION_CREDENTIALS +ARG NEXT_PUBLIC_HUBSPOT_MEETING_LINK # Set environment variables based on ARGs ENV NEXT_PUBLIC_SHORT_COMMIT_HASH=$SHORT_COMMIT_HASH \ @@ -51,7 +53,7 @@ ENV NEXT_PUBLIC_SHORT_COMMIT_HASH=$SHORT_COMMIT_HASH \ NEXT_PUBLIC_ENABLE_GOOGLE_OAUTH=$NEXT_PUBLIC_ENABLE_GOOGLE_OAUTH \ NEXT_PUBLIC_ENABLE_GITHUB_OAUTH=$NEXT_PUBLIC_ENABLE_GITHUB_OAUTH \ NEXT_PUBLIC_GCS_BUCKET_NAME=$NEXT_PUBLIC_GCS_BUCKET_NAME \ - GOOGLE_APPLICATION_CREDENTIALS=/app/keyfile.json + NEXT_PUBLIC_HUBSPOT_MEETING_LINK=$NEXT_PUBLIC_HUBSPOT_MEETING_LINK # Copy the Google credentials file from the build context COPY $GOOGLE_KEYPATH /app/keyfile.json diff --git a/webapp/public/agentcloud-mark-white-bg-black-Enterprise.png b/webapp/public/agentcloud-mark-white-bg-black-Enterprise.png new file mode 100644 index 00000000..c6630687 Binary files /dev/null and b/webapp/public/agentcloud-mark-white-bg-black-Enterprise.png differ diff --git a/webapp/public/agentcloud-mark-white-bg-black-Free.png b/webapp/public/agentcloud-mark-white-bg-black-Free.png new file mode 100644 index 00000000..6e52a3f2 Binary files /dev/null and b/webapp/public/agentcloud-mark-white-bg-black-Free.png differ diff --git a/webapp/public/agentcloud-mark-white-bg-black-Pro.png b/webapp/public/agentcloud-mark-white-bg-black-Pro.png new file mode 100644 index 00000000..c9cdd7df Binary files /dev/null and b/webapp/public/agentcloud-mark-white-bg-black-Pro.png differ diff --git a/webapp/public/agentcloud-mark-white-bg-black-Teams.png b/webapp/public/agentcloud-mark-white-bg-black-Teams.png new file mode 100644 index 00000000..17e1cc42 Binary files /dev/null and b/webapp/public/agentcloud-mark-white-bg-black-Teams.png differ diff --git a/webapp/public/images/agentcloud-mark-white-bg-black-Enterprise.png b/webapp/public/images/agentcloud-mark-white-bg-black-Enterprise.png new file mode 100644 index 00000000..c6630687 Binary files /dev/null and b/webapp/public/images/agentcloud-mark-white-bg-black-Enterprise.png differ diff --git a/webapp/public/images/agentcloud-mark-white-bg-black-Free.png b/webapp/public/images/agentcloud-mark-white-bg-black-Free.png new file mode 100644 index 00000000..6e52a3f2 Binary files /dev/null and b/webapp/public/images/agentcloud-mark-white-bg-black-Free.png differ diff --git a/webapp/public/images/agentcloud-mark-white-bg-black-Pro.png b/webapp/public/images/agentcloud-mark-white-bg-black-Pro.png new file mode 100644 index 00000000..c9cdd7df Binary files /dev/null and b/webapp/public/images/agentcloud-mark-white-bg-black-Pro.png differ diff --git a/webapp/public/images/agentcloud-mark-white-bg-black-Teams.png b/webapp/public/images/agentcloud-mark-white-bg-black-Teams.png new file mode 100644 index 00000000..17e1cc42 Binary files /dev/null and b/webapp/public/images/agentcloud-mark-white-bg-black-Teams.png differ diff --git a/webapp/src/api.ts b/webapp/src/api.ts index 8836b3eb..2940d1ca 100644 --- a/webapp/src/api.ts +++ b/webapp/src/api.ts @@ -3,8 +3,8 @@ import NProgress from 'nprogress'; // Account export function getAccount(body, dispatch, errorCallback, router) { const queryString = new URLSearchParams({ - memberId: body?.memberId, - resourceSlug: body?.resourceSlug, + ...(body?.memberId ? { memberId: body.memberId } : {}), + ...(body?.resourceSlug ? { resourceSlug: body.resourceSlug } : {}), }).toString(); return ApiCall(`/account.json?${queryString}`, 'GET', null, dispatch, errorCallback, router); } @@ -29,18 +29,9 @@ export function verifyToken(body, dispatch, errorCallback, router) { export function switchTeam(body, dispatch, errorCallback, router) { return ApiCall('/forms/account/switch', 'POST', body, dispatch, errorCallback, router); } -export function getPaymentLink(body, dispatch, errorCallback, router) { - return ApiCall('/stripe-paymentlink', 'POST', body, dispatch, errorCallback, router); -} export function getPortalLink(body, dispatch, errorCallback, router) { return ApiCall('/stripe-portallink', 'POST', body, dispatch, errorCallback, router); } -export function changePlan(body, dispatch, errorCallback, router) { - return ApiCall('/stripe-plan', 'POST', body, dispatch, errorCallback, router); -} -export function createPortalSession(body, dispatch, errorCallback, router) { - return ApiCall('/stripe-portal', 'POST', body, dispatch, errorCallback, router); -} export function adminEditAccount(body, dispatch, errorCallback, router) { return ApiCall('/forms/account/admin', 'POST', body, dispatch, errorCallback, router); } diff --git a/webapp/src/components/AgentAvatar.tsx b/webapp/src/components/AgentAvatar.tsx index b0337c0a..954e8cfc 100644 --- a/webapp/src/components/AgentAvatar.tsx +++ b/webapp/src/components/AgentAvatar.tsx @@ -1,11 +1,11 @@ import Blockies from 'react-blockies'; import StorageProviderFactory from 'storage/index'; -export default function AgentAvatar({ agent, fill=false, size=16 }) { +export default function AgentAvatar({ agent, fill=false, size=10 }) { const storageProvider = StorageProviderFactory.getStorageProvider(); return {agent?.icon?.filename ? - : } + : agent?.name && } ; } diff --git a/webapp/src/components/AgentForm.tsx b/webapp/src/components/AgentForm.tsx index b4bdc80d..ede0c07c 100644 --- a/webapp/src/components/AgentForm.tsx +++ b/webapp/src/components/AgentForm.tsx @@ -319,7 +319,7 @@ export default function AgentForm({ agent = {}, models = [], tools=[], groups=[]
Process App (multi-agent)
-
+ */}
-
+ {/*
@@ -324,17 +330,16 @@ export default function AppForm({ agentChoices = [], taskChoices = [], /*toolCho onChange={(e) => { e.target.checked && setCrew(previousCrew => { return { ...previousCrew, process: ProcessImpl.HIERARCHICAL }; }); }} className='form-radio' /> - Hirarchial + Flexible
-
+ */} {(appTypeState === AppType.CHAT || crewState.process === ProcessImpl.HIERARCHICAL) &&
setAppCache(e.target.checked)} + className='mr-2 h-4 w-4 rounded border-gray-300 text-indigo-600 focus:ring-indigo-500' + /> + Cache Tool Results + +
+
+ +
+
+ +
+
+ diff --git a/webapp/src/components/AvatarUploader.tsx b/webapp/src/components/AvatarUploader.tsx index f4053683..204a753a 100644 --- a/webapp/src/components/AvatarUploader.tsx +++ b/webapp/src/components/AvatarUploader.tsx @@ -4,7 +4,6 @@ import { CameraIcon, } from '@heroicons/react/24/outline'; import AgentAvatar from 'components/AgentAvatar'; -import ButtonSpinner from 'components/ButtonSpinner'; import ErrorAlert from 'components/ErrorAlert'; import { useAccountContext } from 'context/account'; import { useRouter } from 'next/router'; @@ -80,7 +79,6 @@ export default function AvatarUploader({ callback, existingAvatar }) { ? : } - TODO: button to remove avatar and a modal of aset library and click to pick one ); } diff --git a/webapp/src/components/BillingBanner.tsx b/webapp/src/components/BillingBanner.tsx new file mode 100644 index 00000000..5746c2f3 --- /dev/null +++ b/webapp/src/components/BillingBanner.tsx @@ -0,0 +1,22 @@ +import Link from 'next/link'; +import React from 'react'; + +export default function BillingBanner({ stripePlan, stripeEndsAt }) { + const now = Date.now(); + const daysRemaining = Math.floor((stripeEndsAt - now) / 86400000); + + if (!stripePlan || !stripeEndsAt || stripeEndsAt <= now/* || daysRemaining > 14*/) { + return null; + } + + return ( +
+ + Your free trial of {stripePlan} ends in {daysRemaining} days ({new Date(stripeEndsAt).toDateString()}). + + + Manage Subscription + +
+ ); +} diff --git a/webapp/src/components/ButtonSpinner.tsx b/webapp/src/components/ButtonSpinner.tsx index 1c96d8fb..bb6d83cd 100644 --- a/webapp/src/components/ButtonSpinner.tsx +++ b/webapp/src/components/ButtonSpinner.tsx @@ -1,6 +1,6 @@ export default function ButtonSpinner({ size=16, className='' }) { - return + return ; } diff --git a/webapp/src/components/CreateDatasourceForm.tsx b/webapp/src/components/CreateDatasourceForm.tsx index 15298107..5d9d9761 100644 --- a/webapp/src/components/CreateDatasourceForm.tsx +++ b/webapp/src/components/CreateDatasourceForm.tsx @@ -6,6 +6,7 @@ import ButtonSpinner from 'components/ButtonSpinner'; import CreateModelModal from 'components/CreateModelModal'; import DropZone from 'components/DropZone'; import ErrorAlert from 'components/ErrorAlert'; +import RetrievalStrategyComponent from 'components/RetrievalStrategyComponent'; import SubscriptionModal from 'components/SubscriptionModal'; import { useAccountContext } from 'context/account'; import dynamic from 'next/dynamic'; @@ -14,8 +15,10 @@ import { useRouter } from 'next/router'; import React, { useEffect, useMemo, useState } from 'react'; import Select from 'react-tailwindcss-select'; import { toast } from 'react-toastify'; +import { pricingMatrix } from 'struct/billing'; import { ModelEmbeddingLength, ModelList } from 'struct/model'; import { DatasourceScheduleType } from 'struct/schedule'; +import { Retriever } from 'struct/tool'; import SelectClassNames from 'styles/SelectClassNames'; const TailwindForm = dynamic(() => import('components/rjsf'), { ssr: false, @@ -45,6 +48,7 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD const [step, setStep] = useState(initialStep); const [accountContext]: any = useAccountContext(); const { account, csrf, teamName } = accountContext as any; + const { stripePlan } = (account?.stripe||{}); const router = useRouter(); const { resourceSlug } = router.query; const [error, setError] = useState(null); @@ -61,11 +65,22 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD const foundModel = models && models.find(m => m._id === modelId); const [cronTimezone, setCronTimezone] = useState(Intl.DateTimeFormat().resolvedOptions().timeZone || 'UTC'); const [scheduleType, setScheduleType] = useState(DatasourceScheduleType.MANUAL); + + //TODO: move into RetrievalStrategyComponent, keep the setters passed as props + const [toolRetriever, setToolRetriever] = useState(Retriever.SELF_QUERY); + const [toolDecayRate, setToolDecayRate] = useState(0.5); + useEffect(() => { + if (toolRetriever !== Retriever.TIME_WEIGHTED) { + setToolDecayRate(0.5); + } + }, [toolRetriever]); + const [toolTimeWeightField, setToolTimeWeightField] = useState(null); + const [datasourceId, setDatasourceId] = useState(null); const [discoveredSchema, setDiscoveredSchema] = useState(null); const [loading, setLoading] = useState(false); const [submitting, setSubmitting] = useState(false); - const [streamState, setStreamState] = useState({ streams: [], selectedFieldsMap: {} }); + const [streamState, setStreamState] = useState({ streams: [], selectedFieldsMap: {}, descriptionsMap: {} }); const [formData, setFormData] = useState(null); const SubmitButton = (props: SubmitButtonProps) => { const { uiSchema } = props; @@ -115,14 +130,17 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD setConnector(null); }; }, []); - const connectorOptions = connectors ? Object.keys(connectors) - .filter(key => connectors[key]?.connector_type === 'source') - .map(key => ({ - value: connectors[key]?.definitionId, - label: connectors[key]?.name_oss || 'test', - icon: connectors[key]?.iconUrl_oss, - supportLevel: connectors[key]?.supportLevel_oss, - })) : []; + let connectorOptions: any = connectors ? Object.keys(connectors).filter(key => connectors[key]?.connector_type === 'source') : []; + //Note: uncomment if you want to hide unavailable due to plan connectors + // if (pricingMatrix && pricingMatrix[stripePlan]?.allowedConnectors?.length > 0) { + // connectorOptions = connectorOptions.filter(co => pricingMatrix[stripePlan].allowedConnectors.includes(connectors[co]?.definitionId)); + // } + connectorOptions = connectorOptions.map(key => ({ + value: connectors[key]?.definitionId, + label: connectors[key]?.name_oss || 'test', + icon: connectors[key]?.iconUrl_oss, + supportLevel: connectors[key]?.supportLevel_oss, + })); const modelCallback = async (addedModelId) => { await fetchDatasourceFormData && fetchDatasourceFormData(); @@ -152,7 +170,6 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD }; //step 2, getting schema and testing connection await API.testDatasource(body, (stagedDatasource) => { - console.log('stagedDatasource', stagedDatasource); if (stagedDatasource) { setDatasourceId(stagedDatasource.datasourceId); setDiscoveredSchema(stagedDatasource.discoveredSchema); @@ -162,7 +179,6 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD } // nothing to toast here }, (res) => { - console.log(res); setError(res); }, compact ? null : router); // callback && stagedDatasource && callback(stagedDatasource._id); @@ -181,9 +197,19 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD cronTimezone, streams: streamState.streams, selectedFieldsMap: streamState.selectedFieldsMap, + descriptionsMap: streamState.descriptionsMap, datasourceName, datasourceDescription, embeddingField, + retriever: toolRetriever, + retriever_config: { + timeWeightField: toolTimeWeightField, + decay_rate: toolDecayRate, + metadata_field_info: Object.entries(discoveredSchema.catalog.streams[0].stream.jsonSchema.properties) + .map(([ek, ev]) => { + return { name: ek, description: streamState.descriptionsMap[ek], type: ev['airbyte_type'] || ev['type'] }; + }), + }, }; const addedDatasource: any = await API.addDatasource(body, () => { toast.success('Added datasource'); @@ -232,6 +258,7 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD modelId={modelId} name={datasourceName} description={datasourceDescription} + retriever={toolRetriever} callback={() => { //jank form reset (for now) setStep(initialStep); @@ -302,7 +329,17 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD }} /> - + ; case 2: @@ -317,6 +354,12 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD classNames={SelectClassNames} value={connector} onChange={(v: any) => { + if (!stripePlan + || !pricingMatrix[stripePlan].dataConnections + || (pricingMatrix[stripePlan].allowedConnectors.length > 0 + && !pricingMatrix[stripePlan].allowedConnectors.includes(v.value))) { + return setSubscriptionModalOpen(true); + } setLoading(v != null); setConnector(v); if (v) { @@ -384,6 +427,17 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD className='block w-full rounded-md border-0 py-1.5 text-gray-900 shadow-sm ring-1 ring-inset ring-gray-300 placeholder:text-gray-400 focus:ring-2 focus:ring-inset focus:ring-indigo-600 sm:text-sm sm:leading-6 dark:bg-slate-800 dark:ring-slate-600 dark:text-white' /> + { e.preventDefault(); + //todo: make the streamlist fully controlled const streams = Array.from(e.target.elements) .filter(x => x['checked'] === true) .filter(x => !x['dataset']['parent']) @@ -437,7 +492,16 @@ export default function CreateDatasourceForm({ models, compact, callback, fetchD acc[x['dataset']['parent']] = (acc[x['dataset']['parent']]||[]).concat([x['name']]); return acc; }, {}); - setStreamState({ streams, selectedFieldsMap}); + const descriptionsMap = Array.from(e.target.elements) + .filter(x => x['type'] === 'text') + .filter(x => x['dataset']['checked'] === 'true') + .reduce((acc, x) => { + if (streams.some(s => selectedFieldsMap[s].includes(x['name']))) { + acc[x['name']] = x['value']; + } + return acc; + }, {}); + setStreamState({ streams, selectedFieldsMap, descriptionsMap }); setStep(4); }}> + {toolRetriever === Retriever.TIME_WEIGHTED &&
+ +
+ +
+
}
); } -export function StreamsList({ streams, existingStreams, readonly } - : { streams?: any, existingStreams?: any, readonly?: boolean }) { +export function StreamsList({ streams, existingStreams, readonly, descriptionsMap } +: { streams?: any, existingStreams?: any, readonly?: boolean, descriptionsMap?: any }) { return (
{streams.map((stream, index) => ( @@ -94,6 +125,7 @@ export function StreamsList({ streams, existingStreams, readonly } key={index} stream={stream} existingStream={existingStreams?.find(es => es.stream.name === stream?.stream?.name)} + descriptionsMap={descriptionsMap} /> ))}
diff --git a/webapp/src/components/DatasourceTable.tsx b/webapp/src/components/DatasourceTable.tsx index d4e23624..04685396 100644 --- a/webapp/src/components/DatasourceTable.tsx +++ b/webapp/src/components/DatasourceTable.tsx @@ -9,6 +9,7 @@ import { TrashIcon, } from '@heroicons/react/20/solid'; import ButtonSpinner from 'components/ButtonSpinner'; +import DatasourceStatusIndicator from 'components/DatasourceStatusIndicator'; // Update this path as necessary import { useAccountContext } from 'context/account'; import { useNotificationContext } from 'context/notifications'; import Link from 'next/link'; @@ -28,7 +29,7 @@ export default function DatasourceTable({ datasources, fetchDatasources }: { dat const [syncing, setSyncing] = useReducer(submittingReducer, {}); const [deleting, setDeleting] = useReducer(submittingReducer, {}); const [deletingMap, setDeletingMap] = useState({}); - + async function deleteDatasource(datasourceId) { setDeleting({ [datasourceId]: true }); try { @@ -102,69 +103,70 @@ export default function DatasourceTable({ datasources, fetchDatasources }: { dat - {datasources.map((datasource) => ( - - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> - - - {datasource.sourceType} - - - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> -
-
{datasource.name}
-
- - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> - - {datasource.status || 'Unknown'}{[DatasourceStatus.PROCESSING, DatasourceStatus.EMBEDDING].includes(datasource.status) && } - - - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> - - {datasource?.connectionSettings?.scheduleType || '-'} - - - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> -
- {datasource.sourceType === 'file' ? 'N/A' : (datasource.lastSyncedDate ? new Date(datasource.lastSyncedDate).toLocaleString() : 'Never')} -
- - router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> - - {new Date(datasource.createdDate).toLocaleString()} - - - - {datasource.sourceType !== 'file' && } - {/* - */} - - - - ))} + {datasources.map((datasource) => { + const processingOrEmbedding = [DatasourceStatus.PROCESSING, DatasourceStatus.EMBEDDING].includes(datasource?.status); + return ( + + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> + + + {datasource.sourceType} + + + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> +
+
{datasource.name}
+
+ + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> + + + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> + + {datasource?.connectionSettings?.scheduleType || '-'} + + + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> +
+ {datasource.sourceType === 'file' ? 'N/A' : (datasource.lastSyncedDate ? new Date(datasource.lastSyncedDate).toLocaleString() : 'Never')} +
+ + router.push(`/${resourceSlug}/datasource/${datasource._id}`)}> + + {new Date(datasource.createdDate).toLocaleString()} + + + + {datasource.sourceType !== 'file' && } + {/* + */} + + + + ); + })} diff --git a/webapp/src/components/DropZone.tsx b/webapp/src/components/DropZone.tsx index ec35c9a3..26c9f2d6 100644 --- a/webapp/src/components/DropZone.tsx +++ b/webapp/src/components/DropZone.tsx @@ -2,6 +2,7 @@ import { Dialog, Transition } from '@headlessui/react'; import { TrashIcon } from '@heroicons/react/20/solid'; import ButtonSpinner from 'components/ButtonSpinner'; import DatasourceChunkingForm from 'components/DatasourceChunkingForm'; +import SubscriptionModal from 'components/SubscriptionModal'; import { useAccountContext } from 'context/account'; import getFileFormat from 'misc/getfileformat'; import { useRouter } from 'next/router'; @@ -9,18 +10,22 @@ import path from 'path'; import React, { Fragment, useCallback, useRef,useState } from 'react'; import { useDropzone } from 'react-dropzone'; import { toast } from 'react-toastify'; +import { pricingMatrix,SubscriptionPlan } from 'struct/billing'; +import { Retriever } from 'struct/tool'; import formatSize from 'utils/formatsize'; import * as API from '../api'; -export default function DropZone({ modalOpen, children, setFiles, files, modelId, name, callback, description }: - { modalOpen: boolean, children: any, setFiles: any, files: any[], modelId: string, name: string, description: string, callback?: Function }) { +export default function DropZone({ modalOpen, children, setFiles, files, modelId, name, callback, description, retriever }: + { modalOpen: boolean, children: any, setFiles: any, files: any[], modelId: string, name: string, description: string, retriever?: Retriever, callback?: Function }) { const [accountContext]: any = useAccountContext(); - const { csrf } = accountContext as any; + const { csrf, account } = accountContext as any; + const { stripePlan } = account?.stripe || {}; + const [subscriptionModalOpen, setSubscriptionModalOpen] = useState(false); const router = useRouter(); const { resourceSlug } = router.query; - const maxSize = 10*1024*1024;//10MB + const maxSize = pricingMatrix[stripePlan].maxFileUploadBytes; const [loading, setLoading] = useState(false); const [chunkStrategy, setChunkStrategy] = useState('semantic'); const [chunkCharacter, setChunkCharacter] = useState(''); @@ -37,6 +42,7 @@ export default function DropZone({ modalOpen, children, setFiles, files, modelId formData.set('modelId', modelId as string); formData.set('datasourceDescription', description as string); formData.set('name', name as string); + formData.set('retriever', retriever as string); formData.set('_csrf', csrf as string); acceptedFiles.forEach(file => { formData.append('file', file); @@ -58,17 +64,28 @@ export default function DropZone({ modalOpen, children, setFiles, files, modelId setFiles(newAcceptedFiles); }, [files]); + const onDropRejected = useCallback((fileRejection) => { + if ([SubscriptionPlan.ENTERPRISE].includes(stripePlan)) { + toast.error(`Maximum file size exceeded (${formatSize(maxSize)}).`); + } else { + setSubscriptionModalOpen(true); + } + }, []); + // @ts-ignore let { isDragActive, getRootProps, getInputProps, isDragReject, acceptedFiles, rejectedFiles } = useDropzone({ + onDropRejected, onDrop, minSize: 0, maxSize, + maxFiles: 1, }); const isFileTooLarge = rejectedFiles && rejectedFiles.length > 0 && rejectedFiles[0].size > maxSize; - const open = files != null && files.length > 0; + const open = (files != null && files.length > 0); return ( + ; + return
  • + {sessions?.length > 0 &&
    Session History
    } + {(!sessions || loading) + ?
    + +
    + :
      + {sessions.map(s => (
    • + +

      {s.previewLabel || s.name || New Session}

      + + + + + Open options + + + + + {({ active }) => ( + + View + + )} + + + {({ active }) => ( + + )} + + + + +
      • ))} +
    } +
    • ; }; diff --git a/webapp/src/components/ProgressBar.tsx b/webapp/src/components/ProgressBar.tsx new file mode 100644 index 00000000..583caa3d --- /dev/null +++ b/webapp/src/components/ProgressBar.tsx @@ -0,0 +1,32 @@ +import ButtonSpinner from 'components/ButtonSpinner'; +import React from 'react'; + +interface ProgressBarProps { + total?: number + success?: number; + failure?: number; + text?: string; +} + +const ProgressBar: React.FC = function ({ total=null, success=0, failure=0, text='Embedding' }) { + const successPercentage = (total != null ? (success/total)*100 : 0) || 0; + const failurePercentage = (total != null ? (failure/total)*100 : 0) || 0; + return (
    +
    + + {text}{' '} + ({successPercentage.toFixed(1).endsWith('0') ? successPercentage : successPercentage.toFixed(1)}%) + {(successPercentage > 0 || failurePercentage > 0) && + {total && `${(success||0)+(failure||0)}/${total} (${successPercentage.toFixed(1)}%)\nsuccess: ${success||0}\nfailure: ${failure||0}`} + } + + +
    +
    + + +
    +
    ); +}; + +export default ProgressBar; diff --git a/webapp/src/components/RetrievalStrategyComponent.tsx b/webapp/src/components/RetrievalStrategyComponent.tsx new file mode 100644 index 00000000..d53f8cdd --- /dev/null +++ b/webapp/src/components/RetrievalStrategyComponent.tsx @@ -0,0 +1,125 @@ +import InfoAlert from 'components/InfoAlert'; +import React, { useEffect } from 'react'; +import { Retriever, ToolType } from 'struct/tool'; + +interface RetrievalStrategyProps { + toolRetriever: Retriever; + setToolRetriever: (value: Retriever) => void; + toolDecayRate?: number; + setToolDecayRate?: (value: number) => void; + currentDatasource?: { sourceType: string }; + toolTimeWeightField?: string; + setToolTimeWeightField?: (value: string) => void; + schema?: any; + defaultRetriever?: Retriever; +} + +const RetrievalStrategyComponent: React.FC = ({ + toolRetriever, + setToolRetriever, + toolDecayRate, + setToolDecayRate, + currentDatasource, + toolTimeWeightField, + setToolTimeWeightField, + schema, + defaultRetriever, +}) => { + useEffect(() => { + if (defaultRetriever) { + setToolRetriever(defaultRetriever || Retriever.SELF_QUERY); + } + }, []); + return ( +
    +
    + +
    + +
    +
    + + {toolRetriever === Retriever.TIME_WEIGHTED && ( + <> +
    + +
    + setToolDecayRate(parseFloat(e.target.value))} + className='w-full h-2 bg-gray-200 rounded-lg appearance-none cursor-pointer dark:bg-gray-700' + /> +
    + 0 + +
    {toolDecayRate}
    + {(toolDecayRate === 1 || toolDecayRate === 0) && } +     + 1 +
    +
    +
    + {setToolTimeWeightField &&
    + +
    + +
    +
    } + + )} + + {/*toolRetriever === Retriever.SELF_QUERY && ... */} + +
    + ); +}; + +export default RetrievalStrategyComponent; diff --git a/webapp/src/components/SessionChatbox.tsx b/webapp/src/components/SessionChatbox.tsx index 55274ce9..8e33d66c 100644 --- a/webapp/src/components/SessionChatbox.tsx +++ b/webapp/src/components/SessionChatbox.tsx @@ -1,14 +1,14 @@ import { PaperAirplaneIcon, + StopIcon, } from '@heroicons/react/20/solid'; import React, { useState } from 'react'; -import Blockies from 'react-blockies'; import { useAccountContext } from '../context/account'; import handleShiftNewlines from '../lib/misc/handleshiftnewlines'; import classNames from './ClassNames'; -export default function SessionChatbox({ lastMessageFeedback, chatBusyState, onSubmit, scrollToBottom }) { //TODO: just get scrolltobottom from chatcontext +export default function SessionChatbox({ lastMessageFeedback, chatBusyState, onSubmit, scrollToBottom, stopGenerating }) { //TODO: just get scrolltobottom from chatcontext const [accountContext]: any = useAccountContext(); const { account, csrf } = accountContext as any; @@ -53,7 +53,17 @@ export default function SessionChatbox({ lastMessageFeedback, chatBusyState, onS */}
    - +
    : + } ; diff --git a/webapp/src/components/StartSessionChatbox.tsx b/webapp/src/components/StartSessionChatbox.tsx deleted file mode 100644 index f460c4b6..00000000 --- a/webapp/src/components/StartSessionChatbox.tsx +++ /dev/null @@ -1,173 +0,0 @@ -import { - PaperAirplaneIcon, -} from '@heroicons/react/20/solid'; -import Link from 'next/link'; -import { useRouter } from 'next/router'; -import React, { useEffect, useState } from 'react'; -import Select from 'react-tailwindcss-select'; -import { toast } from 'react-toastify'; - -import * as API from '../api'; -// import CreateAgentModal from '../components/CreateAgentModal'; -// import CreateCrewModal from '../components/CreateCrewModal'; -import { useAccountContext } from '../context/account'; -import handleShiftNewlines from '../lib/misc/handleshiftnewlines'; -import SelectClassNames, { SelectClassNamesInverted } from '../lib/styles/SelectClassNames'; - -export default function StartSessionForm({ agents = [], crews = [], setOpen, fetchSessions, inverted = false }) { - - const [accountContext]: any = useAccountContext(); - const { account, csrf } = accountContext as any; - const router = useRouter(); - const { resourceSlug } = router.query; - const { stripeCustomerId } = account?.stripe || {}; - const [error, setError] = useState(); - const [promptValue, setPromptValue] = useState(''); - const [selected, setSelected] = useState(null); - const [addedId, setAddedId] = useState(null); - const [modalOpen, setModalOpen]: any = useState(false); - - async function addSession(e) { - e.preventDefault(); - if (!selected?.value && !selected?.value) { - toast.error('Please select an agent or crew from the dropdown'); - return null; - } - const target = e.target.form ? e.target.form : e.target; - await API.addSession({ - _csrf: target._csrf.value, - resourceSlug, - prompt: target.prompt.value, - id: selected?.value, - }, null, setError, router); - } - - async function callback(id: string) { - setAddedId(id); - await fetchSessions(); - } - - //When agents or crews change set the selected crew from the callback - useEffect(() => { - if (!addedId) { return; } - const foundAgent = agents.find(a => a._id === addedId); - foundAgent && setSelected({ label: foundAgent.name, value: foundAgent._id, tools: foundAgent.toolIds?.length > 0, rag: foundAgent.datasourceIds?.length > 0 }); - const foundCrew = crews.find(g => g._id === addedId); - foundCrew && setSelected({ label: foundCrew.name, value: foundCrew._id }); - setAddedId(null); - setModalOpen(false); - }, [agents, crews]); - - const crewOptions: any[] = crews - .map(g => ({ - label: g.name, - value: g._id, - //TODO: rag true/false capability - })).concat([{ - label: '+ New crew', - value: null, - crew: true, - } as any]); - - const agentOptions: any[] = agents - .map(a => ({ - label: a.name, - value: a._id, - rag: a.datasourceIds?.length > 0, - tools: a.toolIds?.length > 0, - } as any)).concat([{ - label: '+ New agent', - value: null, - crew: false, - } as any]); - - return (
    - - {/*modalOpen === 'crew' - ? - : */} - -
    -
    -
    -
    - -