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harrison/p
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harrison/a
| Author | SHA1 | Date | |
|---|---|---|---|
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80bb3206da | ||
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a0cd0175a8 | ||
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fde13c9e95 |
@@ -1,87 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"id": "66a7777e",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Bilibili\n",
|
||||
"\n",
|
||||
"This loader utilizes the `bilibili-api` to fetch the text transcript from Bilibili, one of the most beloved long-form video sites in China.\n",
|
||||
"\n",
|
||||
"With this BiliBiliLoader, users can easily obtain the transcript of their desired video content on the platform."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 11,
|
||||
"id": "9ec8a3b3",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.document_loaders.bilibili import BiliBiliLoader"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 12,
|
||||
"id": "43128d8d",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#!pip install bilibili-api"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 13,
|
||||
"id": "35d6809a",
|
||||
"metadata": {
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
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"loader = BiliBiliLoader(\n",
|
||||
" [\"https://www.bilibili.com/video/BV1xt411o7Xu/\"]\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"loader.load()"
|
||||
],
|
||||
"metadata": {
|
||||
"collapsed": false,
|
||||
"pycharm": {
|
||||
"name": "#%%\n"
|
||||
}
|
||||
}
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
||||
}
|
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@@ -139,7 +139,7 @@
|
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}
|
||||
],
|
||||
"source": [
|
||||
"llm_chain.predict(human_input=\"Not too bad - how are you?\")"
|
||||
"llm_chain.predict(human_input=\"Not to bad - how are you?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
|
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@@ -1,62 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"id": "91c6a7ef",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# Postgres Chat Message History\n",
|
||||
"\n",
|
||||
"This notebook goes over how to use Postgres to store chat message history."
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "d15e3302",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from langchain.memory import PostgresChatMessageHistory\n",
|
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"\n",
|
||||
"history = PostgresChatMessageHistory(connection_string=\"postgresql://postgres:mypassword@localhost/chat_history\", session_id=\"foo\")\n",
|
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"\n",
|
||||
"history.add_user_message(\"hi!\")\n",
|
||||
"\n",
|
||||
"history.add_ai_message(\"whats up?\")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"id": "64fc465e",
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"history.messages"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "Python 3 (ipykernel)",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
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"version": "3.11.2"
|
||||
}
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 5
|
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}
|
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@@ -314,7 +314,7 @@
|
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"source": [
|
||||
"## Saving Message History\n",
|
||||
"\n",
|
||||
"You may often have to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that."
|
||||
"You may often to save messages, and then load them to use again. This can be done easily by first converting the messages to normal python dictionaries, saving those (as json or something) and then loading those. Here is an example of doing that."
|
||||
]
|
||||
},
|
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{
|
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|
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@@ -5,7 +5,7 @@
|
||||
"id": "517a9fd4",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# BabyAGI with Tools\n",
|
||||
"# BabyAGI User Guide\n",
|
||||
"\n",
|
||||
"This notebook builds on top of [baby agi](baby_agi.ipynb), but shows how you can swap out the execution chain. The previous execution chain was just an LLM which made stuff up. By swapping it out with an agent that has access to tools, we can hopefully get real reliable information"
|
||||
]
|
||||
@@ -54,7 +54,9 @@
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"%pip install faiss-cpu > /dev/null%pip install google-search-results > /dev/nullfrom langchain.vectorstores import FAISS\n",
|
||||
"%pip install faiss-cpu > /dev/null",
|
||||
"%pip install google-search-results > /dev/null",
|
||||
"from langchain.vectorstores import FAISS\n",
|
||||
"from langchain.docstore import InMemoryDocstore"
|
||||
]
|
||||
},
|
||||
|
||||
@@ -1,693 +0,0 @@
|
||||
{
|
||||
"cells": [
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"# CAMEL Role-Playing Autonomous Cooperative Agents\n",
|
||||
"\n",
|
||||
"This is a langchain implementation of paper: \"CAMEL: Communicative Agents for “Mind” Exploration of Large Scale Language Model Society\".\n",
|
||||
"\n",
|
||||
"Overview:\n",
|
||||
"\n",
|
||||
"The rapid advancement of conversational and chat-based language models has led to remarkable progress in complex task-solving. However, their success heavily relies on human input to guide the conversation, which can be challenging and time-consuming. This paper explores the potential of building scalable techniques to facilitate autonomous cooperation among communicative agents and provide insight into their \"cognitive\" processes. To address the challenges of achieving autonomous cooperation, we propose a novel communicative agent framework named role-playing. Our approach involves using inception prompting to guide chat agents toward task completion while maintaining consistency with human intentions. We showcase how role-playing can be used to generate conversational data for studying the behaviors and capabilities of chat agents, providing a valuable resource for investigating conversational language models. Our contributions include introducing a novel communicative agent framework, offering a scalable approach for studying the cooperative behaviors and capabilities of multi-agent systems, and open-sourcing our library to support research on communicative agents and beyond.\n",
|
||||
"\n",
|
||||
"The original implementation: https://github.com/lightaime/camel\n",
|
||||
"\n",
|
||||
"Project website: https://www.camel-ai.org/\n",
|
||||
"\n",
|
||||
"Arxiv paper: https://arxiv.org/abs/2303.17760\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Import LangChain related modules "
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 1,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"from typing import List\n",
|
||||
"from langchain.chat_models import ChatOpenAI\n",
|
||||
"from langchain.prompts.chat import (\n",
|
||||
" SystemMessagePromptTemplate,\n",
|
||||
" HumanMessagePromptTemplate,\n",
|
||||
")\n",
|
||||
"from langchain.schema import (\n",
|
||||
" AIMessage,\n",
|
||||
" HumanMessage,\n",
|
||||
" SystemMessage,\n",
|
||||
" BaseMessage,\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Define a CAMEL agent helper class"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 2,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"class CAMELAgent:\n",
|
||||
"\n",
|
||||
" def __init__(\n",
|
||||
" self,\n",
|
||||
" system_message: SystemMessage,\n",
|
||||
" model: ChatOpenAI,\n",
|
||||
" ) -> None:\n",
|
||||
" self.system_message = system_message\n",
|
||||
" self.model = model\n",
|
||||
" self.init_messages()\n",
|
||||
"\n",
|
||||
" def reset(self) -> None:\n",
|
||||
" self.init_messages()\n",
|
||||
" return self.stored_messages\n",
|
||||
"\n",
|
||||
" def init_messages(self) -> None:\n",
|
||||
" self.stored_messages = [self.system_message]\n",
|
||||
"\n",
|
||||
" def update_messages(self, message: BaseMessage) -> List[BaseMessage]:\n",
|
||||
" self.stored_messages.append(message)\n",
|
||||
" return self.stored_messages\n",
|
||||
"\n",
|
||||
" def step(\n",
|
||||
" self,\n",
|
||||
" input_message: HumanMessage,\n",
|
||||
" ) -> AIMessage:\n",
|
||||
" messages = self.update_messages(input_message)\n",
|
||||
"\n",
|
||||
" output_message = self.model(messages)\n",
|
||||
" self.update_messages(output_message)\n",
|
||||
"\n",
|
||||
" return output_message\n"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Setup OpenAI API key and roles and task for role-playing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 3,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"import os\n",
|
||||
"\n",
|
||||
"os.environ[\"OPENAI_API_KEY\"] = \"\"\n",
|
||||
"\n",
|
||||
"assistant_role_name = \"Python Programmer\"\n",
|
||||
"user_role_name = \"Stock Trader\"\n",
|
||||
"task = \"Develop a trading bot for the stock market\"\n",
|
||||
"word_limit = 50 # word limit for task brainstorming"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a task specify agent for brainstorming and get the specified task"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 4,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Specified task: Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"task_specifier_sys_msg = SystemMessage(content=\"You can make a task more specific.\")\n",
|
||||
"task_specifier_prompt = (\n",
|
||||
"\"\"\"Here is a task that {assistant_role_name} will help {user_role_name} to complete: {task}.\n",
|
||||
"Please make it more specific. Be creative and imaginative.\n",
|
||||
"Please reply with the specified task in {word_limit} words or less. Do not add anything else.\"\"\"\n",
|
||||
")\n",
|
||||
"task_specifier_template = HumanMessagePromptTemplate.from_template(template=task_specifier_prompt)\n",
|
||||
"task_specify_agent = CAMELAgent(task_specifier_sys_msg, ChatOpenAI(temperature=1.0))\n",
|
||||
"task_specifier_msg = task_specifier_template.format_messages(assistant_role_name=assistant_role_name,\n",
|
||||
" user_role_name=user_role_name,\n",
|
||||
" task=task, word_limit=word_limit)[0]\n",
|
||||
"specified_task_msg = task_specify_agent.step(task_specifier_msg)\n",
|
||||
"print(f\"Specified task: {specified_task_msg.content}\")\n",
|
||||
"specified_task = specified_task_msg.content"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create inception prompts for AI assistant and AI user for role-playing"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 5,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"assistant_inception_prompt = (\n",
|
||||
"\"\"\"Never forget you are a {assistant_role_name} and I am a {user_role_name}. Never flip roles! Never instruct me!\n",
|
||||
"We share a common interest in collaborating to successfully complete a task.\n",
|
||||
"You must help me to complete the task.\n",
|
||||
"Here is the task: {task}. Never forget our task!\n",
|
||||
"I must instruct you based on your expertise and my needs to complete the task.\n",
|
||||
"\n",
|
||||
"I must give you one instruction at a time.\n",
|
||||
"You must write a specific solution that appropriately completes the requested instruction.\n",
|
||||
"You must decline my instruction honestly if you cannot perform the instruction due to physical, moral, legal reasons or your capability and explain the reasons.\n",
|
||||
"Do not add anything else other than your solution to my instruction.\n",
|
||||
"You are never supposed to ask me any questions you only answer questions.\n",
|
||||
"You are never supposed to reply with a flake solution. Explain your solutions.\n",
|
||||
"Your solution must be declarative sentences and simple present tense.\n",
|
||||
"Unless I say the task is completed, you should always start with:\n",
|
||||
"\n",
|
||||
"Solution: <YOUR_SOLUTION>\n",
|
||||
"\n",
|
||||
"<YOUR_SOLUTION> should be specific and provide preferable implementations and examples for task-solving.\n",
|
||||
"Always end <YOUR_SOLUTION> with: Next request.\"\"\"\n",
|
||||
")\n",
|
||||
"\n",
|
||||
"user_inception_prompt = (\n",
|
||||
"\"\"\"Never forget you are a {user_role_name} and I am a {assistant_role_name}. Never flip roles! You will always instruct me.\n",
|
||||
"We share a common interest in collaborating to successfully complete a task.\n",
|
||||
"I must help you to complete the task.\n",
|
||||
"Here is the task: {task}. Never forget our task!\n",
|
||||
"You must instruct me based on my expertise and your needs to complete the task ONLY in the following two ways:\n",
|
||||
"\n",
|
||||
"1. Instruct with a necessary input:\n",
|
||||
"Instruction: <YOUR_INSTRUCTION>\n",
|
||||
"Input: <YOUR_INPUT>\n",
|
||||
"\n",
|
||||
"2. Instruct without any input:\n",
|
||||
"Instruction: <YOUR_INSTRUCTION>\n",
|
||||
"Input: None\n",
|
||||
"\n",
|
||||
"The \"Instruction\" describes a task or question. The paired \"Input\" provides further context or information for the requested \"Instruction\".\n",
|
||||
"\n",
|
||||
"You must give me one instruction at a time.\n",
|
||||
"I must write a response that appropriately completes the requested instruction.\n",
|
||||
"I must decline your instruction honestly if I cannot perform the instruction due to physical, moral, legal reasons or my capability and explain the reasons.\n",
|
||||
"You should instruct me not ask me questions.\n",
|
||||
"Now you must start to instruct me using the two ways described above.\n",
|
||||
"Do not add anything else other than your instruction and the optional corresponding input!\n",
|
||||
"Keep giving me instructions and necessary inputs until you think the task is completed.\n",
|
||||
"When the task is completed, you must only reply with a single word <CAMEL_TASK_DONE>.\n",
|
||||
"Never say <CAMEL_TASK_DONE> unless my responses have solved your task.\"\"\"\n",
|
||||
")"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create a helper helper to get system messages for AI assistant and AI user from role names and the task"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 6,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"def get_sys_msgs(assistant_role_name: str, user_role_name: str, task: str):\n",
|
||||
" \n",
|
||||
" assistant_sys_template = SystemMessagePromptTemplate.from_template(template=assistant_inception_prompt)\n",
|
||||
" assistant_sys_msg = assistant_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]\n",
|
||||
" \n",
|
||||
" user_sys_template = SystemMessagePromptTemplate.from_template(template=user_inception_prompt)\n",
|
||||
" user_sys_msg = user_sys_template.format_messages(assistant_role_name=assistant_role_name, user_role_name=user_role_name, task=task)[0]\n",
|
||||
" \n",
|
||||
" return assistant_sys_msg, user_sys_msg"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Create AI assistant agent and AI user agent from obtained system messages"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 7,
|
||||
"metadata": {},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"assistant_sys_msg, user_sys_msg = get_sys_msgs(assistant_role_name, user_role_name, specified_task)\n",
|
||||
"assistant_agent = CAMELAgent(assistant_sys_msg, ChatOpenAI(temperature=0.2))\n",
|
||||
"user_agent = CAMELAgent(user_sys_msg, ChatOpenAI(temperature=0.2))\n",
|
||||
"\n",
|
||||
"# Reset agents\n",
|
||||
"assistant_agent.reset()\n",
|
||||
"user_agent.reset()\n",
|
||||
"\n",
|
||||
"# Initialize chats \n",
|
||||
"assistant_msg = HumanMessage(\n",
|
||||
" content=(f\"{user_sys_msg.content}. \"\n",
|
||||
" \"Now start to give me introductions one by one. \"\n",
|
||||
" \"Only reply with Instruction and Input.\"))\n",
|
||||
"\n",
|
||||
"user_msg = HumanMessage(content=f\"{assistant_sys_msg.content}\")\n",
|
||||
"user_msg = assistant_agent.step(user_msg)"
|
||||
]
|
||||
},
|
||||
{
|
||||
"attachments": {},
|
||||
"cell_type": "markdown",
|
||||
"metadata": {},
|
||||
"source": [
|
||||
"## Start role-playing session to solve the task!"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": 8,
|
||||
"metadata": {},
|
||||
"outputs": [
|
||||
{
|
||||
"name": "stdout",
|
||||
"output_type": "stream",
|
||||
"text": [
|
||||
"Original task prompt:\n",
|
||||
"Develop a trading bot for the stock market\n",
|
||||
"\n",
|
||||
"Specified task prompt:\n",
|
||||
"Develop a Python-based swing trading bot that scans market trends, monitors stocks, and generates trading signals to help a stock trader to place optimal buy and sell orders with defined stop losses and profit targets.\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Install the necessary Python libraries for data analysis and trading.\n",
|
||||
"Input: None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can install the necessary Python libraries using pip, a package installer for Python. We can install pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following command to install these libraries:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"pip install pandas numpy matplotlib ta-lib\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Import the necessary libraries in the Python script.\n",
|
||||
"Input: None\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can import the necessary libraries in the Python script using the import statement. We need to import pandas, numpy, matplotlib, and ta-lib for data analysis and trading. We can use the following code to import these libraries:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import talib as ta\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Load historical stock data into a pandas DataFrame.\n",
|
||||
"Input: The path to the CSV file containing the historical stock data.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can load historical stock data into a pandas DataFrame using the `read_csv()` function from pandas. We need to pass the path to the CSV file containing the historical stock data as an argument to this function. We can use the following code to load the historical stock data:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"df = pd.read_csv('path/to/csv/file.csv')\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will load the historical stock data into a pandas DataFrame called `df`. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can preprocess the historical stock data by setting the date column as the index and sorting the DataFrame in ascending order by date using the `set_index()` and `sort_index()` functions from pandas. We can use the following code to preprocess the historical stock data:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"df = df.set_index('date')\n",
|
||||
"df = df.sort_index(ascending=True)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will set the date column as the index and sort the DataFrame in ascending order by date. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib.\n",
|
||||
"Input: The period for the short-term moving average and the period for the long-term moving average.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can calculate the short-term and long-term moving averages for the stock data using the `ta.SMA()` function from ta-lib. We need to pass the period for the short-term moving average and the period for the long-term moving average as arguments to this function. We can use the following code to calculate the short-term and long-term moving averages:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"short_ma = ta.SMA(df['close'], timeperiod=short_period)\n",
|
||||
"long_ma = ta.SMA(df['close'], timeperiod=long_period)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will calculate the short-term and long-term moving averages for the stock data and store them in the `short_ma` and `long_ma` variables, respectively. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages using the `concat()` function from pandas. We need to pass the historical stock data, the short-term moving average, and the long-term moving average as arguments to this function. We can use the following code to create the new DataFrame:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"new_df = pd.concat([df, short_ma, long_ma], axis=1)\n",
|
||||
"new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will create a new DataFrame called `new_df` that combines the historical stock data with the short-term and long-term moving averages. The columns of the new DataFrame are named 'open', 'high', 'low', 'close', 'volume', 'short_ma', and 'long_ma'. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. We can use the following code to create the new column:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will create a new column called 'signal' in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages. If the short-term moving average is greater than the long-term moving average, the signal is 1 (buy), otherwise the signal is -1 (sell). Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target.\n",
|
||||
"Input: The stop loss and profit target as percentages.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. We need to pass the stop loss and profit target as percentages as arguments to this function. We can use the following code to create the new column:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"stop_loss = stop_loss_percent / 100\n",
|
||||
"profit_target = profit_target_percent / 100\n",
|
||||
"\n",
|
||||
"new_df['pnl'] = 0.0\n",
|
||||
"buy_price = 0.0\n",
|
||||
"for i in range(1, len(new_df)):\n",
|
||||
" if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:\n",
|
||||
" buy_price = new_df['close'][i]\n",
|
||||
" elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:\n",
|
||||
" sell_price = new_df['close'][i]\n",
|
||||
" if sell_price <= buy_price * (1 - stop_loss):\n",
|
||||
" new_df['pnl'][i] = -stop_loss\n",
|
||||
" elif sell_price >= buy_price * (1 + profit_target):\n",
|
||||
" new_df['pnl'][i] = profit_target\n",
|
||||
" else:\n",
|
||||
" new_df['pnl'][i] = (sell_price - buy_price) / buy_price\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will create a new column called 'pnl' in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target. The stop loss and profit target are calculated based on the stop_loss_percent and profit_target_percent variables, respectively. The buy and sell prices are stored in the buy_price and sell_price variables, respectively. If the sell price is less than or equal to the stop loss, the profit or loss is set to -stop_loss. If the sell price is greater than or equal to the profit target, the profit or loss is set to profit_target. Otherwise, the profit or loss is calculated as (sell_price - buy_price) / buy_price. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Calculate the total profit or loss for all trades.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can calculate the total profit or loss for all trades by summing the values in the 'pnl' column of the DataFrame. We can use the following code to calculate the total profit or loss:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"total_pnl = new_df['pnl'].sum()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will calculate the total profit or loss for all trades and store it in the total_pnl variable. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Visualize the stock data, short-term moving average, and long-term moving average using a line chart.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can visualize the stock data, short-term moving average, and long-term moving average using a line chart using the `plot()` function from pandas. We can use the following code to visualize the data:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"plt.figure(figsize=(12,6))\n",
|
||||
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
|
||||
"plt.plot(new_df.index, new_df['short_ma'], label='Short MA')\n",
|
||||
"plt.plot(new_df.index, new_df['long_ma'], label='Long MA')\n",
|
||||
"plt.xlabel('Date')\n",
|
||||
"plt.ylabel('Price')\n",
|
||||
"plt.title('Stock Data with Moving Averages')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will create a line chart that visualizes the stock data, short-term moving average, and long-term moving average. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each line. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Visualize the buy and sell signals using a scatter plot.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can visualize the buy and sell signals using a scatter plot using the `scatter()` function from matplotlib. We can use the following code to visualize the signals:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"buy_signals = new_df[new_df['signal'] == 1]\n",
|
||||
"sell_signals = new_df[new_df['signal'] == -1]\n",
|
||||
"\n",
|
||||
"plt.figure(figsize=(12,6))\n",
|
||||
"plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')\n",
|
||||
"plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')\n",
|
||||
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
|
||||
"plt.xlabel('Date')\n",
|
||||
"plt.ylabel('Price')\n",
|
||||
"plt.title('Buy and Sell Signals')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will create a scatter plot that visualizes the buy and sell signals. The x-axis represents the date and the y-axis represents the price. The chart also includes a legend that labels each point. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Print the total profit or loss for all trades.\n",
|
||||
"Input: None.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: We can print the total profit or loss for all trades using the `print()` function. We can use the following code to print the total profit or loss:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"print('Total Profit/Loss: {:.2%}'.format(total_pnl))\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"This will print the total profit or loss for all trades in percentage format with two decimal places. Next request.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"Instruction: Finalize the code by putting all the code snippets together in a single Python script.\n",
|
||||
"Input: The necessary inputs for each code snippet.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Solution: Here is the complete Python script that puts all the code snippets together:\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"import pandas as pd\n",
|
||||
"import numpy as np\n",
|
||||
"import matplotlib.pyplot as plt\n",
|
||||
"import talib as ta\n",
|
||||
"\n",
|
||||
"# Load historical stock data into a pandas DataFrame\n",
|
||||
"df = pd.read_csv('path/to/csv/file.csv')\n",
|
||||
"\n",
|
||||
"# Preprocess the historical stock data\n",
|
||||
"df = df.set_index('date')\n",
|
||||
"df = df.sort_index(ascending=True)\n",
|
||||
"\n",
|
||||
"# Calculate the short-term and long-term moving averages\n",
|
||||
"short_period = 10\n",
|
||||
"long_period = 30\n",
|
||||
"short_ma = ta.SMA(df['close'], timeperiod=short_period)\n",
|
||||
"long_ma = ta.SMA(df['close'], timeperiod=long_period)\n",
|
||||
"\n",
|
||||
"# Create a new DataFrame that combines the historical stock data with the short-term and long-term moving averages\n",
|
||||
"new_df = pd.concat([df, short_ma, long_ma], axis=1)\n",
|
||||
"new_df.columns = ['open', 'high', 'low', 'close', 'volume', 'short_ma', 'long_ma']\n",
|
||||
"\n",
|
||||
"# Create a new column in the DataFrame that indicates when to buy or sell the stock based on the crossover of the short-term and long-term moving averages\n",
|
||||
"new_df['signal'] = np.where(new_df['short_ma'] > new_df['long_ma'], 1, -1)\n",
|
||||
"\n",
|
||||
"# Create a new column in the DataFrame that indicates the profit or loss for each trade based on the buy and sell signals and the defined stop loss and profit target\n",
|
||||
"stop_loss_percent = 5\n",
|
||||
"profit_target_percent = 10\n",
|
||||
"stop_loss = stop_loss_percent / 100\n",
|
||||
"profit_target = profit_target_percent / 100\n",
|
||||
"new_df['pnl'] = 0.0\n",
|
||||
"buy_price = 0.0\n",
|
||||
"for i in range(1, len(new_df)):\n",
|
||||
" if new_df['signal'][i] == 1 and new_df['signal'][i-1] == -1:\n",
|
||||
" buy_price = new_df['close'][i]\n",
|
||||
" elif new_df['signal'][i] == -1 and new_df['signal'][i-1] == 1:\n",
|
||||
" sell_price = new_df['close'][i]\n",
|
||||
" if sell_price <= buy_price * (1 - stop_loss):\n",
|
||||
" new_df['pnl'][i] = -stop_loss\n",
|
||||
" elif sell_price >= buy_price * (1 + profit_target):\n",
|
||||
" new_df['pnl'][i] = profit_target\n",
|
||||
" else:\n",
|
||||
" new_df['pnl'][i] = (sell_price - buy_price) / buy_price\n",
|
||||
"\n",
|
||||
"# Calculate the total profit or loss for all trades\n",
|
||||
"total_pnl = new_df['pnl'].sum()\n",
|
||||
"\n",
|
||||
"# Visualize the stock data, short-term moving average, and long-term moving average using a line chart\n",
|
||||
"plt.figure(figsize=(12,6))\n",
|
||||
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
|
||||
"plt.plot(new_df.index, new_df['short_ma'], label='Short MA')\n",
|
||||
"plt.plot(new_df.index, new_df['long_ma'], label='Long MA')\n",
|
||||
"plt.xlabel('Date')\n",
|
||||
"plt.ylabel('Price')\n",
|
||||
"plt.title('Stock Data with Moving Averages')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# Visualize the buy and sell signals using a scatter plot\n",
|
||||
"buy_signals = new_df[new_df['signal'] == 1]\n",
|
||||
"sell_signals = new_df[new_df['signal'] == -1]\n",
|
||||
"plt.figure(figsize=(12,6))\n",
|
||||
"plt.scatter(buy_signals.index, buy_signals['close'], label='Buy', marker='^', color='green')\n",
|
||||
"plt.scatter(sell_signals.index, sell_signals['close'], label='Sell', marker='v', color='red')\n",
|
||||
"plt.plot(new_df.index, new_df['close'], label='Close')\n",
|
||||
"plt.xlabel('Date')\n",
|
||||
"plt.ylabel('Price')\n",
|
||||
"plt.title('Buy and Sell Signals')\n",
|
||||
"plt.legend()\n",
|
||||
"plt.show()\n",
|
||||
"\n",
|
||||
"# Print the total profit or loss for all trades\n",
|
||||
"print('Total Profit/Loss: {:.2%}'.format(total_pnl))\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"You need to replace the path/to/csv/file.csv with the actual path to the CSV file containing the historical stock data. You can also adjust the short_period, long_period, stop_loss_percent, and profit_target_percent variables to suit your needs.\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI User (Stock Trader):\n",
|
||||
"\n",
|
||||
"<CAMEL_TASK_DONE>\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"AI Assistant (Python Programmer):\n",
|
||||
"\n",
|
||||
"Great! Let me know if you need any further assistance.\n",
|
||||
"\n",
|
||||
"\n"
|
||||
]
|
||||
}
|
||||
],
|
||||
"source": [
|
||||
"print(f\"Original task prompt:\\n{task}\\n\")\n",
|
||||
"print(f\"Specified task prompt:\\n{specified_task}\\n\")\n",
|
||||
"\n",
|
||||
"chat_turn_limit, n = 30, 0\n",
|
||||
"while n < chat_turn_limit:\n",
|
||||
" n += 1\n",
|
||||
" user_ai_msg = user_agent.step(assistant_msg)\n",
|
||||
" user_msg = HumanMessage(content=user_ai_msg.content)\n",
|
||||
" print(f\"AI User ({user_role_name}):\\n\\n{user_msg.content}\\n\\n\")\n",
|
||||
" \n",
|
||||
" assistant_ai_msg = assistant_agent.step(user_msg)\n",
|
||||
" assistant_msg = HumanMessage(content=assistant_ai_msg.content)\n",
|
||||
" print(f\"AI Assistant ({assistant_role_name}):\\n\\n{assistant_msg.content}\\n\\n\")\n",
|
||||
" if \"<CAMEL_TASK_DONE>\" in user_msg.content:\n",
|
||||
" break"
|
||||
]
|
||||
}
|
||||
],
|
||||
"metadata": {
|
||||
"kernelspec": {
|
||||
"display_name": "camel",
|
||||
"language": "python",
|
||||
"name": "python3"
|
||||
},
|
||||
"language_info": {
|
||||
"codemirror_mode": {
|
||||
"name": "ipython",
|
||||
"version": 3
|
||||
},
|
||||
"file_extension": ".py",
|
||||
"mimetype": "text/x-python",
|
||||
"name": "python",
|
||||
"nbconvert_exporter": "python",
|
||||
"pygments_lexer": "ipython3",
|
||||
"version": "3.10.9"
|
||||
},
|
||||
"orig_nbformat": 4
|
||||
},
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 2
|
||||
}
|
||||
@@ -20,5 +20,3 @@ Highlighting specific parts:
|
||||
Specific examples of this include:
|
||||
|
||||
- [Baby AGI](agents/baby_agi.ipynb): a notebook implementing [BabyAGI](https://github.com/yoheinakajima/babyagi) by Yohei Nakajima as LLM Chains
|
||||
- [Baby AGI with Tools](agents/baby_agi_with_agent.ipynb): building off the above notebook, this example substitutes in an agent with tools as the execution tools, allowing it to actually take actions.
|
||||
- [CAMEL](agents/camel_role_playing.ipynb): an implementation of the CAMEL (Communicative Agents for “Mind” Exploration of Large Scale Language Model Society) paper, where two agents communicate with eachother.
|
||||
|
||||
@@ -14,13 +14,9 @@ from langchain.agents.agent_toolkits.openapi.planner_prompt import (
|
||||
API_PLANNER_PROMPT,
|
||||
API_PLANNER_TOOL_DESCRIPTION,
|
||||
API_PLANNER_TOOL_NAME,
|
||||
PARSING_DELETE_PROMPT,
|
||||
PARSING_GET_PROMPT,
|
||||
PARSING_PATCH_PROMPT,
|
||||
PARSING_POST_PROMPT,
|
||||
REQUESTS_DELETE_TOOL_DESCRIPTION,
|
||||
REQUESTS_GET_TOOL_DESCRIPTION,
|
||||
REQUESTS_PATCH_TOOL_DESCRIPTION,
|
||||
REQUESTS_POST_TOOL_DESCRIPTION,
|
||||
)
|
||||
from langchain.agents.agent_toolkits.openapi.spec import ReducedOpenAPISpec
|
||||
@@ -28,7 +24,6 @@ from langchain.agents.mrkl.base import ZeroShotAgent
|
||||
from langchain.agents.tools import Tool
|
||||
from langchain.chains.llm import LLMChain
|
||||
from langchain.llms.openai import OpenAI
|
||||
from langchain.memory import ReadOnlySharedMemory
|
||||
from langchain.prompts import PromptTemplate
|
||||
from langchain.requests import RequestsWrapper
|
||||
from langchain.schema import BaseLanguageModel
|
||||
@@ -58,8 +53,7 @@ class RequestsGetToolWithParsing(BaseRequestsTool, BaseTool):
|
||||
data = json.loads(text)
|
||||
except json.JSONDecodeError as e:
|
||||
raise e
|
||||
data_params = data.get("params")
|
||||
response = self.requests_wrapper.get(data["url"], params=data_params)
|
||||
response = self.requests_wrapper.get(data["url"])
|
||||
response = response[: self.response_length]
|
||||
return self.llm_chain.predict(
|
||||
response=response, instructions=data["output_instructions"]
|
||||
@@ -94,56 +88,6 @@ class RequestsPostToolWithParsing(BaseRequestsTool, BaseTool):
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class RequestsPatchToolWithParsing(BaseRequestsTool, BaseTool):
|
||||
name = "requests_patch"
|
||||
description = REQUESTS_PATCH_TOOL_DESCRIPTION
|
||||
|
||||
response_length: Optional[int] = MAX_RESPONSE_LENGTH
|
||||
llm_chain = LLMChain(
|
||||
llm=OpenAI(),
|
||||
prompt=PARSING_PATCH_PROMPT,
|
||||
)
|
||||
|
||||
def _run(self, text: str) -> str:
|
||||
try:
|
||||
data = json.loads(text)
|
||||
except json.JSONDecodeError as e:
|
||||
raise e
|
||||
response = self.requests_wrapper.patch(data["url"], data["data"])
|
||||
response = response[: self.response_length]
|
||||
return self.llm_chain.predict(
|
||||
response=response, instructions=data["output_instructions"]
|
||||
).strip()
|
||||
|
||||
async def _arun(self, text: str) -> str:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
class RequestsDeleteToolWithParsing(BaseRequestsTool, BaseTool):
|
||||
name = "requests_delete"
|
||||
description = REQUESTS_DELETE_TOOL_DESCRIPTION
|
||||
|
||||
response_length: Optional[int] = MAX_RESPONSE_LENGTH
|
||||
llm_chain = LLMChain(
|
||||
llm=OpenAI(),
|
||||
prompt=PARSING_DELETE_PROMPT,
|
||||
)
|
||||
|
||||
def _run(self, text: str) -> str:
|
||||
try:
|
||||
data = json.loads(text)
|
||||
except json.JSONDecodeError as e:
|
||||
raise e
|
||||
response = self.requests_wrapper.delete(data["url"])
|
||||
response = response[: self.response_length]
|
||||
return self.llm_chain.predict(
|
||||
response=response, instructions=data["output_instructions"]
|
||||
).strip()
|
||||
|
||||
async def _arun(self, text: str) -> str:
|
||||
raise NotImplementedError()
|
||||
|
||||
|
||||
#
|
||||
# Orchestrator, planner, controller.
|
||||
#
|
||||
@@ -211,7 +155,7 @@ def _create_api_controller_tool(
|
||||
base_url = api_spec.servers[0]["url"] # TODO: do better.
|
||||
|
||||
def _create_and_run_api_controller_agent(plan_str: str) -> str:
|
||||
pattern = r"\b(GET|POST|PATCH|DELETE)\s+(/\S+)*"
|
||||
pattern = r"\b(GET|POST)\s+(/\S+)*"
|
||||
matches = re.findall(pattern, plan_str)
|
||||
endpoint_names = [
|
||||
"{method} {route}".format(method=method, route=route.split("?")[0])
|
||||
@@ -239,8 +183,6 @@ def create_openapi_agent(
|
||||
api_spec: ReducedOpenAPISpec,
|
||||
requests_wrapper: RequestsWrapper,
|
||||
llm: BaseLanguageModel,
|
||||
shared_memory: Optional[ReadOnlySharedMemory] = None,
|
||||
verbose: bool = True,
|
||||
) -> AgentExecutor:
|
||||
"""Instantiate API planner and controller for a given spec.
|
||||
|
||||
@@ -265,7 +207,7 @@ def create_openapi_agent(
|
||||
},
|
||||
)
|
||||
agent = ZeroShotAgent(
|
||||
llm_chain=LLMChain(llm=llm, prompt=prompt, memory=shared_memory),
|
||||
llm_chain=LLMChain(llm=llm, prompt=prompt),
|
||||
allowed_tools=[tool.name for tool in tools],
|
||||
)
|
||||
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=verbose)
|
||||
return AgentExecutor.from_agent_and_tools(agent=agent, tools=tools, verbose=True)
|
||||
|
||||
@@ -2,16 +2,13 @@
|
||||
|
||||
from langchain.prompts.prompt import PromptTemplate
|
||||
|
||||
|
||||
API_PLANNER_PROMPT = """You are a planner that plans a sequence of API calls to assist with user queries against an API.
|
||||
|
||||
You should:
|
||||
1) evaluate whether the user query can be solved by the API documentated below. If no, say why.
|
||||
2) if yes, generate a plan of API calls and say what they are doing step by step.
|
||||
3) If the plan includes a DELETE call, you should always return an ask from the User for authorization first unless the User has specifically asked to delete something.
|
||||
|
||||
You should only use API endpoints documented below ("Endpoints you can use:").
|
||||
You can only use the DELETE tool if the User has specifically asked to delete something. Otherwise, you should return a request authorization from the User first.
|
||||
Some user queries can be resolved in a single API call, but some will require several API calls.
|
||||
The plan will be passed to an API controller that can format it into web requests and return the responses.
|
||||
|
||||
@@ -23,31 +20,15 @@ Fake endpoints for examples:
|
||||
GET /user to get information about the current user
|
||||
GET /products/search search across products
|
||||
POST /users/{{id}}/cart to add products to a user's cart
|
||||
PATCH /users/{{id}}/cart to update a user's cart
|
||||
DELETE /users/{{id}}/cart to delete a user's cart
|
||||
|
||||
User query: tell me a joke
|
||||
Plan: Sorry, this API's domain is shopping, not comedy.
|
||||
|
||||
Usery query: I want to buy a couch
|
||||
Plan: 1. GET /products with a query param to search for couches
|
||||
Plan: 1. GET /products/search to search for couches
|
||||
2. GET /user to find the user's id
|
||||
3. POST /users/{{id}}/cart to add a couch to the user's cart
|
||||
|
||||
User query: I want to add a lamp to my cart
|
||||
Plan: 1. GET /products with a query param to search for lamps
|
||||
2. GET /user to find the user's id
|
||||
3. PATCH /users/{{id}}/cart to add a lamp to the user's cart
|
||||
|
||||
User query: I want to delete my cart
|
||||
Plan: 1. GET /user to find the user's id
|
||||
2. DELETE required. Did user specify DELETE or previously authorize? Yes, proceed.
|
||||
3. DELETE /users/{{id}}/cart to delete the user's cart
|
||||
|
||||
User query: I want to start a new cart
|
||||
Plan: 1. GET /user to find the user's id
|
||||
2. DELETE required. Did user specify DELETE or previously authorize? No, ask for authorization.
|
||||
3. Are you sure you want to delete your cart?
|
||||
----
|
||||
|
||||
Here are endpoints you can use. Do not reference any of the endpoints above.
|
||||
@@ -59,7 +40,7 @@ Here are endpoints you can use. Do not reference any of the endpoints above.
|
||||
User query: {query}
|
||||
Plan:"""
|
||||
API_PLANNER_TOOL_NAME = "api_planner"
|
||||
API_PLANNER_TOOL_DESCRIPTION = f"Can be used to generate the right API calls to assist with a user query, like {API_PLANNER_TOOL_NAME}(query). Should always be called before trying to call the API controller."
|
||||
API_PLANNER_TOOL_DESCRIPTION = f"Can be used to generate the right API calls to assist with a user query, like {API_PLANNER_TOOL_NAME}(query). Should always be called before trying to calling the API controller."
|
||||
|
||||
# Execution.
|
||||
API_CONTROLLER_PROMPT = """You are an agent that gets a sequence of API calls and given their documentation, should execute them and return the final response.
|
||||
@@ -100,9 +81,8 @@ API_CONTROLLER_TOOL_DESCRIPTION = f"Can be used to execute a plan of API calls,
|
||||
# The goal is to have an agent at the top-level (e.g. so it can recover from errors and re-plan) while
|
||||
# keeping planning (and specifically the planning prompt) simple.
|
||||
API_ORCHESTRATOR_PROMPT = """You are an agent that assists with user queries against API, things like querying information or creating resources.
|
||||
Some user queries can be resolved in a single API call, particularly if you can find appropriate params from the OpenAPI spec; though some require several API call.
|
||||
Some user queries can be resolved in a single API call though some require several API call.
|
||||
You should always plan your API calls first, and then execute the plan second.
|
||||
If the plan includes a DELETE call, be sure to ask the User for authorization first unless the User has specifically asked to delete something.
|
||||
You should never return information without executing the api_controller tool.
|
||||
|
||||
|
||||
@@ -126,12 +106,12 @@ User query: can you add some trendy stuff to my shopping cart.
|
||||
Thought: I should plan API calls first.
|
||||
Action: api_planner
|
||||
Action Input: I need to find the right API calls to add trendy items to the users shopping cart
|
||||
Observation: 1) GET /items with params 'trending' is 'True' to get trending item ids
|
||||
Observation: 1) GET /items/trending to get trending item ids
|
||||
2) GET /user to get user
|
||||
3) POST /cart to post the trending items to the user's cart
|
||||
Thought: I'm ready to execute the API calls.
|
||||
Action: api_controller
|
||||
Action Input: 1) GET /items params 'trending' is 'True' to get trending item ids
|
||||
Action Input: 1) GET /items/trending to get trending item ids
|
||||
2) GET /user to get user
|
||||
3) POST /cart to post the trending items to the user's cart
|
||||
...
|
||||
@@ -143,12 +123,8 @@ Thought: I should generate a plan to help with this query and then copy that pla
|
||||
{agent_scratchpad}"""
|
||||
|
||||
REQUESTS_GET_TOOL_DESCRIPTION = """Use this to GET content from a website.
|
||||
Input to the tool should be a json string with 3 keys: "url", "params" and "output_instructions".
|
||||
The value of "url" should be a string.
|
||||
The value of "params" should be a dict of the needed and available parameters from the OpenAPI spec related to the endpoint.
|
||||
If parameters are not needed, or not available, leave it empty.
|
||||
The value of "output_instructions" should be instructions on what information to extract from the response,
|
||||
for example the id(s) for a resource(s) that the GET request fetches.
|
||||
Input to the tool should be a json string with 2 keys: "url" and "output_instructions".
|
||||
The value of "url" should be a string. The value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the GET request fetches.
|
||||
"""
|
||||
|
||||
PARSING_GET_PROMPT = PromptTemplate(
|
||||
@@ -165,7 +141,7 @@ REQUESTS_POST_TOOL_DESCRIPTION = """Use this when you want to POST to a website.
|
||||
Input to the tool should be a json string with 3 keys: "url", "data", and "output_instructions".
|
||||
The value of "url" should be a string.
|
||||
The value of "data" should be a dictionary of key-value pairs you want to POST to the url.
|
||||
The value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the POST request creates.
|
||||
The value of "summary_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the POST request creates.
|
||||
Always use double quotes for strings in the json string."""
|
||||
|
||||
PARSING_POST_PROMPT = PromptTemplate(
|
||||
@@ -177,37 +153,3 @@ If the response indicates an error, you should instead output a summary of the e
|
||||
Output:""",
|
||||
input_variables=["response", "instructions"],
|
||||
)
|
||||
|
||||
REQUESTS_PATCH_TOOL_DESCRIPTION = """Use this when you want to PATCH content on a website.
|
||||
Input to the tool should be a json string with 3 keys: "url", "data", and "output_instructions".
|
||||
The value of "url" should be a string.
|
||||
The value of "data" should be a dictionary of key-value pairs of the body params available in the OpenAPI spec you want to PATCH the content with at the url.
|
||||
The value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the PATCH request creates.
|
||||
Always use double quotes for strings in the json string."""
|
||||
|
||||
PARSING_PATCH_PROMPT = PromptTemplate(
|
||||
template="""Here is an API response:\n\n{response}\n\n====
|
||||
Your task is to extract some information according to these instructions: {instructions}
|
||||
When working with API objects, you should usually use ids over names. Do not return any ids or names that are not in the response.
|
||||
If the response indicates an error, you should instead output a summary of the error.
|
||||
|
||||
Output:""",
|
||||
input_variables=["response", "instructions"],
|
||||
)
|
||||
|
||||
REQUESTS_DELETE_TOOL_DESCRIPTION = """ONLY USE THIS TOOL WHEN THE USER HAS SPECIFICALLY REQUESTED TO DELETE CONTENT FROM A WEBSITE.
|
||||
Input to the tool should be a json string with 2 keys: "url", and "output_instructions".
|
||||
The value of "url" should be a string.
|
||||
The value of "output_instructions" should be instructions on what information to extract from the response, for example the id(s) for a resource(s) that the DELETE request creates.
|
||||
Always use double quotes for strings in the json string.
|
||||
ONLY USE THIS TOOL IF THE USER HAS SPECIFICALLY REQUESTED TO DELETE SOMETHING."""
|
||||
|
||||
PARSING_DELETE_PROMPT = PromptTemplate(
|
||||
template="""Here is an API response:\n\n{response}\n\n====
|
||||
Your task is to extract some information according to these instructions: {instructions}
|
||||
When working with API objects, you should usually use ids over names. Do not return any ids or names that are not in the response.
|
||||
If the response indicates an error, you should instead output a summary of the error.
|
||||
|
||||
Output:""",
|
||||
input_variables=["response", "instructions"],
|
||||
)
|
||||
|
||||
@@ -57,7 +57,7 @@ class LLMRequestsChain(Chain):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import bs4 python package. "
|
||||
"Please install it with `pip install bs4`."
|
||||
"Please it install it with `pip install bs4`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -55,7 +55,7 @@ class OpenAIModerationChain(Chain):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
"Please it install it with `pip install openai`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -53,7 +53,7 @@ class Crawler:
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import playwright python package. "
|
||||
"Please install it with `pip install playwright`."
|
||||
"Please it install it with `pip install playwright`."
|
||||
)
|
||||
self.browser: Browser = (
|
||||
sync_playwright().start().chromium.launch(headless=False)
|
||||
|
||||
@@ -87,7 +87,7 @@ class AzureChatOpenAI(ChatOpenAI):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
"Please it install it with `pip install openai`."
|
||||
)
|
||||
try:
|
||||
values["client"] = openai.ChatCompletion
|
||||
|
||||
@@ -167,7 +167,7 @@ class ChatOpenAI(BaseChatModel):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
"Please it install it with `pip install openai`."
|
||||
)
|
||||
try:
|
||||
values["client"] = openai.ChatCompletion
|
||||
@@ -327,8 +327,8 @@ class ChatOpenAI(BaseChatModel):
|
||||
|
||||
def get_num_tokens(self, text: str) -> int:
|
||||
"""Calculate num tokens with tiktoken package."""
|
||||
# tiktoken NOT supported for Python 3.7 or below
|
||||
if sys.version_info[1] <= 7:
|
||||
# tiktoken NOT supported for Python 3.8 or below
|
||||
if sys.version_info[1] <= 8:
|
||||
return super().get_num_tokens(text)
|
||||
try:
|
||||
import tiktoken
|
||||
@@ -336,7 +336,7 @@ class ChatOpenAI(BaseChatModel):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to calculate get_num_tokens. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
# create a GPT-3.5-Turbo encoder instance
|
||||
enc = tiktoken.encoding_for_model(self.model_name)
|
||||
@@ -358,7 +358,7 @@ class ChatOpenAI(BaseChatModel):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to calculate get_num_tokens. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
|
||||
model = self.model_name
|
||||
|
||||
58
langchain/document_loaders/aws_textract.py
Normal file
58
langchain/document_loaders/aws_textract.py
Normal file
@@ -0,0 +1,58 @@
|
||||
from io import BytesIO
|
||||
from typing import List
|
||||
|
||||
import boto3
|
||||
from PIL import Image
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
|
||||
|
||||
class AwsTextractExtraction:
|
||||
def __init__(
|
||||
self,
|
||||
aws_region_name: str,
|
||||
aws_secret_key: str,
|
||||
aws_access_key: str,
|
||||
aws_session_token: str,
|
||||
file_path: str,
|
||||
):
|
||||
self.aws_region_name = aws_region_name
|
||||
self.aws_secret_key = aws_secret_key
|
||||
self.aws_access_key = aws_access_key
|
||||
self.aws_session_token = aws_session_token
|
||||
self.file_path = file_path
|
||||
try:
|
||||
import boto3 # noqa: F401
|
||||
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aws boto3 package. "
|
||||
"Please install it with `pip install boto3`."
|
||||
)
|
||||
|
||||
def get_text_from_image(self) -> List[Document]:
|
||||
output = []
|
||||
page_no = 0
|
||||
|
||||
textract_client = boto3.client(
|
||||
"textract",
|
||||
self.aws_region_name,
|
||||
self.aws_access_key,
|
||||
self.aws_access_key,
|
||||
self.aws_session_token,
|
||||
)
|
||||
pil_image_obj = Image.open(self.file_path)
|
||||
buf = BytesIO()
|
||||
pil_image_obj.save(buf, format="PNG")
|
||||
image_bytes = buf.getvalue()
|
||||
|
||||
response = textract_client.detect_document_text(Document={"Bytes": image_bytes})
|
||||
detected_txt = ""
|
||||
for item in response["Blocks"]:
|
||||
if item["BlockType"] == "LINE":
|
||||
detected_txt += item["Text"] + "\n"
|
||||
|
||||
metadata = {"source": self.file_path, "page": page_no}
|
||||
|
||||
output.append(Document(page_content=detected_txt, metadata=metadata))
|
||||
return output
|
||||
@@ -24,7 +24,7 @@ class AzureBlobStorageContainerLoader(BaseLoader):
|
||||
except ImportError as exc:
|
||||
raise ValueError(
|
||||
"Could not import azure storage blob python package. "
|
||||
"Please install it with `pip install azure-storage-blob`."
|
||||
"Please it install it with `pip install azure-storage-blob`."
|
||||
) from exc
|
||||
|
||||
container = ContainerClient.from_connection_string(
|
||||
|
||||
@@ -24,7 +24,7 @@ class AzureBlobStorageFileLoader(BaseLoader):
|
||||
except ImportError as exc:
|
||||
raise ValueError(
|
||||
"Could not import azure storage blob python package. "
|
||||
"Please install it with `pip install azure-storage-blob`."
|
||||
"Please it install it with `pip install azure-storage-blob`."
|
||||
) from exc
|
||||
|
||||
client = BlobClient.from_connection_string(
|
||||
|
||||
@@ -1,77 +0,0 @@
|
||||
import json
|
||||
import re
|
||||
import warnings
|
||||
from typing import List, Tuple
|
||||
|
||||
import requests
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.document_loaders.base import BaseLoader
|
||||
|
||||
|
||||
class BiliBiliLoader(BaseLoader):
|
||||
"""Loader that loads bilibili transcripts."""
|
||||
|
||||
def __init__(self, video_urls: List[str]):
|
||||
"""Initialize with bilibili url."""
|
||||
self.video_urls = video_urls
|
||||
|
||||
def load(self) -> List[Document]:
|
||||
"""Load from bilibili url."""
|
||||
results = []
|
||||
for url in self.video_urls:
|
||||
transcript, video_info = self._get_bilibili_subs_and_info(url)
|
||||
doc = Document(page_content=transcript, metadata=video_info)
|
||||
results.append(doc)
|
||||
|
||||
return results
|
||||
|
||||
def _get_bilibili_subs_and_info(self, url: str) -> Tuple[str, dict]:
|
||||
try:
|
||||
from bilibili_api import sync, video
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"requests package not found, please install it with "
|
||||
"`pip install bilibili-api`"
|
||||
)
|
||||
|
||||
bvid = re.search(r"BV\w+", url)
|
||||
if bvid is not None:
|
||||
v = video.Video(bvid=bvid.group())
|
||||
else:
|
||||
aid = re.search(r"av[0-9]+", url)
|
||||
if aid is not None:
|
||||
try:
|
||||
v = video.Video(aid=int(aid.group()[2:]))
|
||||
except AttributeError:
|
||||
raise ValueError(f"{url} is not bilibili url.")
|
||||
else:
|
||||
raise ValueError(f"{url} is not bilibili url.")
|
||||
|
||||
video_info = sync(v.get_info())
|
||||
video_info.update({"url": url})
|
||||
|
||||
# Get subtitle url
|
||||
subtitle = video_info.pop("subtitle")
|
||||
sub_list = subtitle["list"]
|
||||
if sub_list:
|
||||
sub_url = sub_list[0]["subtitle_url"]
|
||||
result = requests.get(sub_url)
|
||||
raw_sub_titles = json.loads(result.content)["body"]
|
||||
raw_transcript = " ".join([c["content"] for c in raw_sub_titles])
|
||||
|
||||
raw_transcript_with_meta_info = f"""
|
||||
Video Title: {video_info['title']},
|
||||
description: {video_info['desc']}\n
|
||||
Transcript: {raw_transcript}
|
||||
"""
|
||||
return raw_transcript_with_meta_info, video_info
|
||||
else:
|
||||
raw_transcript = ""
|
||||
warnings.warn(
|
||||
f"""
|
||||
No subtitles found for video: {url}.
|
||||
Return Empty transcript.
|
||||
"""
|
||||
)
|
||||
return raw_transcript, video_info
|
||||
@@ -35,7 +35,7 @@ class DuckDBLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import duckdb python package. "
|
||||
"Please install it with `pip install duckdb`."
|
||||
"Please it install it with `pip install duckdb`."
|
||||
)
|
||||
|
||||
docs = []
|
||||
|
||||
@@ -22,7 +22,7 @@ class GCSDirectoryLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import google-cloud-storage python package. "
|
||||
"Please install it with `pip install google-cloud-storage`."
|
||||
"Please it install it with `pip install google-cloud-storage`."
|
||||
)
|
||||
client = storage.Client(project=self.project_name)
|
||||
docs = []
|
||||
|
||||
@@ -23,7 +23,7 @@ class GCSFileLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import google-cloud-storage python package. "
|
||||
"Please install it with `pip install google-cloud-storage`."
|
||||
"Please it install it with `pip install google-cloud-storage`."
|
||||
)
|
||||
|
||||
# Initialise a client
|
||||
|
||||
@@ -21,7 +21,7 @@ class S3DirectoryLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import boto3 python package. "
|
||||
"Please install it with `pip install boto3`."
|
||||
"Please it install it with `pip install boto3`."
|
||||
)
|
||||
s3 = boto3.resource("s3")
|
||||
bucket = s3.Bucket(self.bucket)
|
||||
|
||||
@@ -23,7 +23,7 @@ class S3FileLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import boto3 python package. "
|
||||
"Please install it with `pip install boto3`."
|
||||
"Please it install it with `pip install boto3`."
|
||||
)
|
||||
s3 = boto3.client("s3")
|
||||
with tempfile.TemporaryDirectory() as temp_dir:
|
||||
|
||||
@@ -118,7 +118,7 @@ class YoutubeLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import youtube_transcript_api python package. "
|
||||
"Please install it with `pip install youtube-transcript-api`."
|
||||
"Please it install it with `pip install youtube-transcript-api`."
|
||||
)
|
||||
|
||||
metadata = {"source": self.video_id}
|
||||
@@ -159,7 +159,7 @@ class YoutubeLoader(BaseLoader):
|
||||
except ImportError:
|
||||
raise ImportError(
|
||||
"Could not import pytube python package. "
|
||||
"Please install it with `pip install pytube`."
|
||||
"Please it install it with `pip install pytube`."
|
||||
)
|
||||
yt = YouTube(f"https://www.youtube.com/watch?v={self.video_id}")
|
||||
video_info = {
|
||||
|
||||
@@ -58,7 +58,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aleph_alpha_client python package. "
|
||||
"Please install it with `pip install aleph_alpha_client`."
|
||||
"Please it install it with `pip install aleph_alpha_client`."
|
||||
)
|
||||
values["client"] = Client(token=aleph_alpha_api_key)
|
||||
return values
|
||||
@@ -81,7 +81,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aleph_alpha_client python package. "
|
||||
"Please install it with `pip install aleph_alpha_client`."
|
||||
"Please it install it with `pip install aleph_alpha_client`."
|
||||
)
|
||||
document_embeddings = []
|
||||
|
||||
@@ -121,7 +121,7 @@ class AlephAlphaAsymmetricSemanticEmbedding(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aleph_alpha_client python package. "
|
||||
"Please install it with `pip install aleph_alpha_client`."
|
||||
"Please it install it with `pip install aleph_alpha_client`."
|
||||
)
|
||||
symmetric_params = {
|
||||
"prompt": Prompt.from_text(text),
|
||||
@@ -166,7 +166,7 @@ class AlephAlphaSymmetricSemanticEmbedding(AlephAlphaAsymmetricSemanticEmbedding
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aleph_alpha_client python package. "
|
||||
"Please install it with `pip install aleph_alpha_client`."
|
||||
"Please it install it with `pip install aleph_alpha_client`."
|
||||
)
|
||||
query_params = {
|
||||
"prompt": Prompt.from_text(text),
|
||||
|
||||
@@ -48,7 +48,7 @@ class CohereEmbeddings(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import cohere python package. "
|
||||
"Please install it with `pip install cohere`."
|
||||
"Please it install it with `pip install cohere`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -73,7 +73,7 @@ class HuggingFaceHubEmbeddings(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please install it with `pip install huggingface_hub`."
|
||||
"Please it install it with `pip install huggingface_hub`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -36,7 +36,7 @@ class JinaEmbeddings(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import `jina` python package. "
|
||||
"Please install it with `pip install jina`."
|
||||
"Please it install it with `pip install jina`."
|
||||
)
|
||||
|
||||
# Setup client
|
||||
|
||||
@@ -188,7 +188,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
"Please it install it with `pip install openai`."
|
||||
)
|
||||
return values
|
||||
|
||||
@@ -242,7 +242,7 @@ class OpenAIEmbeddings(BaseModel, Embeddings):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to for OpenAIEmbeddings. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
|
||||
def _embedding_func(self, text: str, *, engine: str) -> List[float]:
|
||||
|
||||
@@ -131,7 +131,7 @@ class SagemakerEndpointEmbeddings(BaseModel, Embeddings):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import boto3 python package. "
|
||||
"Please install it with `pip install boto3`."
|
||||
"Please it install it with `pip install boto3`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -56,7 +56,7 @@ class NetworkxEntityGraph:
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import networkx python package. "
|
||||
"Please install it with `pip install networkx`."
|
||||
"Please it install it with `pip install networkx`."
|
||||
)
|
||||
if graph is not None:
|
||||
if not isinstance(graph, nx.DiGraph):
|
||||
@@ -72,7 +72,7 @@ class NetworkxEntityGraph:
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import networkx python package. "
|
||||
"Please install it with `pip install networkx`."
|
||||
"Please it install it with `pip install networkx`."
|
||||
)
|
||||
graph = nx.read_gml(gml_path)
|
||||
return cls(graph)
|
||||
|
||||
@@ -149,7 +149,7 @@ class AlephAlpha(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import aleph_alpha_client python package. "
|
||||
"Please install it with `pip install aleph_alpha_client`."
|
||||
"Please it install it with `pip install aleph_alpha_client`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -76,7 +76,7 @@ class Anthropic(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import anthropic python package. "
|
||||
"Please install it with `pip install anthropic`."
|
||||
"Please it install it with `pip install anthropic`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -73,7 +73,7 @@ class Cohere(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import cohere python package. "
|
||||
"Please install it with `pip install cohere`."
|
||||
"Please it install it with `pip install cohere`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -70,7 +70,7 @@ class HuggingFaceEndpoint(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please install it with `pip install huggingface_hub`."
|
||||
"Please it install it with `pip install huggingface_hub`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -66,7 +66,7 @@ class HuggingFaceHub(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import huggingface_hub python package. "
|
||||
"Please install it with `pip install huggingface_hub`."
|
||||
"Please it install it with `pip install huggingface_hub`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -177,8 +177,6 @@ class LlamaCpp(LLM):
|
||||
raise ValueError("`stop` found in both the input and default params.")
|
||||
elif self.stop:
|
||||
params["stop_sequences"] = self.stop
|
||||
elif stop:
|
||||
params["stop_sequences"] = stop
|
||||
else:
|
||||
params["stop_sequences"] = []
|
||||
|
||||
|
||||
@@ -28,7 +28,7 @@ class ManifestWrapper(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import manifest python package. "
|
||||
"Please install it with `pip install manifest-ml`."
|
||||
"Please it install it with `pip install manifest-ml`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -76,7 +76,7 @@ class NLPCloud(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import nlpcloud python package. "
|
||||
"Please install it with `pip install nlpcloud`."
|
||||
"Please it install it with `pip install nlpcloud`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -446,7 +446,7 @@ class BaseOpenAI(BaseLLM):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to calculate get_num_tokens. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
encoder = "gpt2"
|
||||
if self.model_name in ("text-davinci-003", "text-davinci-002"):
|
||||
@@ -611,7 +611,7 @@ class OpenAIChat(BaseLLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import openai python package. "
|
||||
"Please install it with `pip install openai`."
|
||||
"Please it install it with `pip install openai`."
|
||||
)
|
||||
try:
|
||||
values["client"] = openai.ChatCompletion
|
||||
@@ -742,7 +742,7 @@ class OpenAIChat(BaseLLM):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to calculate get_num_tokens. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
# create a GPT-3.5-Turbo encoder instance
|
||||
enc = tiktoken.encoding_for_model("gpt-3.5-turbo")
|
||||
|
||||
@@ -176,7 +176,7 @@ class SagemakerEndpoint(LLM):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import boto3 python package. "
|
||||
"Please install it with `pip install boto3`."
|
||||
"Please it install it with `pip install boto3`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -5,7 +5,6 @@ from langchain.memory.buffer import (
|
||||
from langchain.memory.buffer_window import ConversationBufferWindowMemory
|
||||
from langchain.memory.chat_message_histories.dynamodb import DynamoDBChatMessageHistory
|
||||
from langchain.memory.chat_message_histories.in_memory import ChatMessageHistory
|
||||
from langchain.memory.chat_message_histories.postgres import PostgresChatMessageHistory
|
||||
from langchain.memory.chat_message_histories.redis import RedisChatMessageHistory
|
||||
from langchain.memory.combined import CombinedMemory
|
||||
from langchain.memory.entity import (
|
||||
@@ -37,5 +36,4 @@ __all__ = [
|
||||
"ConversationTokenBufferMemory",
|
||||
"RedisChatMessageHistory",
|
||||
"DynamoDBChatMessageHistory",
|
||||
"PostgresChatMessageHistory",
|
||||
]
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
from langchain.memory.chat_message_histories.dynamodb import DynamoDBChatMessageHistory
|
||||
from langchain.memory.chat_message_histories.postgres import PostgresChatMessageHistory
|
||||
from langchain.memory.chat_message_histories.redis import RedisChatMessageHistory
|
||||
|
||||
__all__ = [
|
||||
"DynamoDBChatMessageHistory",
|
||||
"RedisChatMessageHistory",
|
||||
"PostgresChatMessageHistory",
|
||||
]
|
||||
|
||||
@@ -1,86 +0,0 @@
|
||||
import json
|
||||
import logging
|
||||
from typing import List
|
||||
|
||||
from langchain.schema import (
|
||||
AIMessage,
|
||||
BaseChatMessageHistory,
|
||||
BaseMessage,
|
||||
HumanMessage,
|
||||
_message_to_dict,
|
||||
messages_from_dict,
|
||||
)
|
||||
|
||||
logger = logging.getLogger(__name__)
|
||||
|
||||
DEFAULT_CONNECTION_STRING = "postgresql://postgres:mypassword@localhost/chat_history"
|
||||
|
||||
|
||||
class PostgresChatMessageHistory(BaseChatMessageHistory):
|
||||
def __init__(
|
||||
self,
|
||||
session_id: str,
|
||||
connection_string: str = DEFAULT_CONNECTION_STRING,
|
||||
table_name: str = "message_store",
|
||||
):
|
||||
import psycopg
|
||||
from psycopg.rows import dict_row
|
||||
|
||||
try:
|
||||
self.connection = psycopg.connect(connection_string)
|
||||
self.cursor = self.connection.cursor(row_factory=dict_row)
|
||||
except psycopg.OperationalError as error:
|
||||
logger.error(error)
|
||||
|
||||
self.session_id = session_id
|
||||
self.table_name = table_name
|
||||
|
||||
self._create_table_if_not_exists()
|
||||
|
||||
def _create_table_if_not_exists(self) -> None:
|
||||
create_table_query = f"""CREATE TABLE IF NOT EXISTS {self.table_name} (
|
||||
id SERIAL PRIMARY KEY,
|
||||
session_id TEXT NOT NULL,
|
||||
message JSONB NOT NULL
|
||||
);"""
|
||||
self.cursor.execute(create_table_query)
|
||||
self.connection.commit()
|
||||
|
||||
@property
|
||||
def messages(self) -> List[BaseMessage]: # type: ignore
|
||||
"""Retrieve the messages from PostgreSQL"""
|
||||
query = f"SELECT message FROM {self.table_name} WHERE session_id = %s;"
|
||||
self.cursor.execute(query, (self.session_id,))
|
||||
items = [record["message"] for record in self.cursor.fetchall()]
|
||||
messages = messages_from_dict(items)
|
||||
return messages
|
||||
|
||||
def add_user_message(self, message: str) -> None:
|
||||
self.append(HumanMessage(content=message))
|
||||
|
||||
def add_ai_message(self, message: str) -> None:
|
||||
self.append(AIMessage(content=message))
|
||||
|
||||
def append(self, message: BaseMessage) -> None:
|
||||
"""Append the message to the record in PostgreSQL"""
|
||||
from psycopg import sql
|
||||
|
||||
query = sql.SQL("INSERT INTO {} (session_id, message) VALUES (%s, %s);").format(
|
||||
sql.Identifier(self.table_name)
|
||||
)
|
||||
self.cursor.execute(
|
||||
query, (self.session_id, json.dumps(_message_to_dict(message)))
|
||||
)
|
||||
self.connection.commit()
|
||||
|
||||
def clear(self) -> None:
|
||||
"""Clear session memory from PostgreSQL"""
|
||||
query = f"DELETE FROM {self.table_name} WHERE session_id = %s;"
|
||||
self.cursor.execute(query, (self.session_id,))
|
||||
self.connection.commit()
|
||||
|
||||
def __del__(self) -> None:
|
||||
if self.cursor:
|
||||
self.cursor.close()
|
||||
if self.connection:
|
||||
self.connection.close()
|
||||
@@ -1,6 +1,5 @@
|
||||
"""Lightweight wrapper around requests library, with async support."""
|
||||
from contextlib import asynccontextmanager
|
||||
from typing import Any, AsyncGenerator, Dict, Optional
|
||||
from typing import Any, Dict, Optional
|
||||
|
||||
import aiohttp
|
||||
import requests
|
||||
@@ -43,62 +42,47 @@ class Requests(BaseModel):
|
||||
"""DELETE the URL and return the text."""
|
||||
return requests.delete(url, headers=self.headers, **kwargs)
|
||||
|
||||
@asynccontextmanager
|
||||
async def _arequest(
|
||||
self, method: str, url: str, **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
) -> aiohttp.ClientResponse:
|
||||
"""Make an async request."""
|
||||
if not self.aiosession:
|
||||
async with aiohttp.ClientSession() as session:
|
||||
async with session.request(
|
||||
method, url, headers=self.headers, **kwargs
|
||||
) as response:
|
||||
yield response
|
||||
return response
|
||||
else:
|
||||
async with self.aiosession.request(
|
||||
method, url, headers=self.headers, **kwargs
|
||||
) as response:
|
||||
yield response
|
||||
return response
|
||||
|
||||
@asynccontextmanager
|
||||
async def aget(
|
||||
self, url: str, **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
async def aget(self, url: str, **kwargs: Any) -> aiohttp.ClientResponse:
|
||||
"""GET the URL and return the text asynchronously."""
|
||||
async with self._arequest("GET", url, **kwargs) as response:
|
||||
yield response
|
||||
return await self._arequest("GET", url, **kwargs)
|
||||
|
||||
@asynccontextmanager
|
||||
async def apost(
|
||||
self, url: str, data: Dict[str, Any], **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
) -> aiohttp.ClientResponse:
|
||||
"""POST to the URL and return the text asynchronously."""
|
||||
async with self._arequest("POST", url, **kwargs) as response:
|
||||
yield response
|
||||
return await self._arequest("POST", url, json=data, **kwargs)
|
||||
|
||||
@asynccontextmanager
|
||||
async def apatch(
|
||||
self, url: str, data: Dict[str, Any], **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
) -> aiohttp.ClientResponse:
|
||||
"""PATCH the URL and return the text asynchronously."""
|
||||
async with self._arequest("PATCH", url, **kwargs) as response:
|
||||
yield response
|
||||
return await self._arequest("PATCH", url, json=data, **kwargs)
|
||||
|
||||
@asynccontextmanager
|
||||
async def aput(
|
||||
self, url: str, data: Dict[str, Any], **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
) -> aiohttp.ClientResponse:
|
||||
"""PUT the URL and return the text asynchronously."""
|
||||
async with self._arequest("PUT", url, **kwargs) as response:
|
||||
yield response
|
||||
return await self._arequest("PUT", url, json=data, **kwargs)
|
||||
|
||||
@asynccontextmanager
|
||||
async def adelete(
|
||||
self, url: str, **kwargs: Any
|
||||
) -> AsyncGenerator[aiohttp.ClientResponse, None]:
|
||||
async def adelete(self, url: str, **kwargs: Any) -> aiohttp.ClientResponse:
|
||||
"""DELETE the URL and return the text asynchronously."""
|
||||
async with self._arequest("DELETE", url, **kwargs) as response:
|
||||
yield response
|
||||
return await self._arequest("DELETE", url, **kwargs)
|
||||
|
||||
|
||||
class TextRequestsWrapper(BaseModel):
|
||||
@@ -142,28 +126,28 @@ class TextRequestsWrapper(BaseModel):
|
||||
|
||||
async def aget(self, url: str, **kwargs: Any) -> str:
|
||||
"""GET the URL and return the text asynchronously."""
|
||||
async with self.requests.aget(url, **kwargs) as response:
|
||||
return await response.text()
|
||||
response = await self.requests.aget(url, **kwargs)
|
||||
return await response.text()
|
||||
|
||||
async def apost(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
"""POST to the URL and return the text asynchronously."""
|
||||
async with self.requests.apost(url, **kwargs) as response:
|
||||
return await response.text()
|
||||
response = await self.requests.apost(url, data, **kwargs)
|
||||
return await response.text()
|
||||
|
||||
async def apatch(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
"""PATCH the URL and return the text asynchronously."""
|
||||
async with self.requests.apatch(url, **kwargs) as response:
|
||||
return await response.text()
|
||||
response = await self.requests.apatch(url, data, **kwargs)
|
||||
return await response.text()
|
||||
|
||||
async def aput(self, url: str, data: Dict[str, Any], **kwargs: Any) -> str:
|
||||
"""PUT the URL and return the text asynchronously."""
|
||||
async with self.requests.aput(url, **kwargs) as response:
|
||||
return await response.text()
|
||||
response = await self.requests.aput(url, data, **kwargs)
|
||||
return await response.text()
|
||||
|
||||
async def adelete(self, url: str, **kwargs: Any) -> str:
|
||||
"""DELETE the URL and return the text asynchronously."""
|
||||
async with self.requests.adelete(url, **kwargs) as response:
|
||||
return await response.text()
|
||||
response = await self.requests.adelete(url, **kwargs)
|
||||
return await response.text()
|
||||
|
||||
|
||||
# For backwards compatibility
|
||||
|
||||
@@ -194,7 +194,7 @@ class BaseLanguageModel(BaseModel, ABC):
|
||||
raise ValueError(
|
||||
"Could not import transformers python package. "
|
||||
"This is needed in order to calculate get_num_tokens. "
|
||||
"Please install it with `pip install transformers`."
|
||||
"Please it install it with `pip install transformers`."
|
||||
)
|
||||
# create a GPT-3 tokenizer instance
|
||||
tokenizer = GPT2TokenizerFast.from_pretrained("gpt2")
|
||||
|
||||
@@ -131,7 +131,7 @@ class TextSplitter(ABC):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import transformers python package. "
|
||||
"Please install it with `pip install transformers`."
|
||||
"Please it install it with `pip install transformers`."
|
||||
)
|
||||
return cls(length_function=_huggingface_tokenizer_length, **kwargs)
|
||||
|
||||
@@ -150,7 +150,7 @@ class TextSplitter(ABC):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to calculate max_tokens_for_prompt. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
|
||||
# create a GPT-3 encoder instance
|
||||
@@ -205,7 +205,7 @@ class TokenTextSplitter(TextSplitter):
|
||||
raise ValueError(
|
||||
"Could not import tiktoken python package. "
|
||||
"This is needed in order to for TokenTextSplitter. "
|
||||
"Please install it with `pip install tiktoken`."
|
||||
"Please it install it with `pip install tiktoken`."
|
||||
)
|
||||
# create a GPT-3 encoder instance
|
||||
self._tokenizer = tiktoken.get_encoding(encoding_name)
|
||||
|
||||
@@ -50,7 +50,6 @@ class PythonAstREPLTool(BaseTool):
|
||||
)
|
||||
globals: Optional[Dict] = Field(default_factory=dict)
|
||||
locals: Optional[Dict] = Field(default_factory=dict)
|
||||
sanitize_input: bool = True
|
||||
|
||||
@root_validator(pre=True)
|
||||
def validate_python_version(cls, values: Dict) -> Dict:
|
||||
@@ -66,9 +65,6 @@ class PythonAstREPLTool(BaseTool):
|
||||
def _run(self, query: str) -> str:
|
||||
"""Use the tool."""
|
||||
try:
|
||||
if self.sanitize_input:
|
||||
# Remove the triple backticks from the query.
|
||||
query = query.strip().strip("```")
|
||||
tree = ast.parse(query)
|
||||
module = ast.Module(tree.body[:-1], type_ignores=[])
|
||||
exec(ast.unparse(module), self.globals, self.locals) # type: ignore
|
||||
|
||||
@@ -31,7 +31,7 @@ class WikipediaAPIWrapper(BaseModel):
|
||||
except ImportError:
|
||||
raise ValueError(
|
||||
"Could not import wikipedia python package. "
|
||||
"Please install it with `pip install wikipedia`."
|
||||
"Please it install it with `pip install wikipedia`."
|
||||
)
|
||||
return values
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import uuid
|
||||
from typing import Any, Iterable, List, Optional, Type
|
||||
from typing import Any, Iterable, List, Optional
|
||||
|
||||
import numpy as np
|
||||
|
||||
@@ -210,7 +210,7 @@ class AtlasDB(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[AtlasDB],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Optional[Embeddings] = None,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
@@ -270,7 +270,7 @@ class AtlasDB(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls: Type[AtlasDB],
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Optional[Embeddings] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
|
||||
@@ -1,10 +1,8 @@
|
||||
"""Interface for vector stores."""
|
||||
from __future__ import annotations
|
||||
|
||||
import asyncio
|
||||
from abc import ABC, abstractmethod
|
||||
from functools import partial
|
||||
from typing import Any, Dict, Iterable, List, Optional, Type, TypeVar
|
||||
from typing import Any, Dict, Iterable, List, Optional
|
||||
|
||||
from pydantic import BaseModel, Field, root_validator
|
||||
|
||||
@@ -12,8 +10,6 @@ from langchain.docstore.document import Document
|
||||
from langchain.embeddings.base import Embeddings
|
||||
from langchain.schema import BaseRetriever
|
||||
|
||||
VST = TypeVar("VST", bound="VectorStore")
|
||||
|
||||
|
||||
class VectorStore(ABC):
|
||||
"""Interface for vector stores."""
|
||||
@@ -85,12 +81,7 @@ class VectorStore(ABC):
|
||||
self, query: str, k: int = 4, **kwargs: Any
|
||||
) -> List[Document]:
|
||||
"""Return docs most similar to query."""
|
||||
|
||||
# This is a temporary workaround to make the similarity search
|
||||
# asynchronous. The proper solution is to make the similarity search
|
||||
# asynchronous in the vector store implementations.
|
||||
func = partial(self.similarity_search, query, k, **kwargs)
|
||||
return await asyncio.get_event_loop().run_in_executor(None, func)
|
||||
raise NotImplementedError
|
||||
|
||||
def similarity_search_by_vector(
|
||||
self, embedding: List[float], k: int = 4, **kwargs: Any
|
||||
@@ -110,12 +101,7 @@ class VectorStore(ABC):
|
||||
self, embedding: List[float], k: int = 4, **kwargs: Any
|
||||
) -> List[Document]:
|
||||
"""Return docs most similar to embedding vector."""
|
||||
|
||||
# This is a temporary workaround to make the similarity search
|
||||
# asynchronous. The proper solution is to make the similarity search
|
||||
# asynchronous in the vector store implementations.
|
||||
func = partial(self.similarity_search_by_vector, embedding, k, **kwargs)
|
||||
return await asyncio.get_event_loop().run_in_executor(None, func)
|
||||
raise NotImplementedError
|
||||
|
||||
def max_marginal_relevance_search(
|
||||
self, query: str, k: int = 4, fetch_k: int = 20
|
||||
@@ -139,12 +125,7 @@ class VectorStore(ABC):
|
||||
self, query: str, k: int = 4, fetch_k: int = 20
|
||||
) -> List[Document]:
|
||||
"""Return docs selected using the maximal marginal relevance."""
|
||||
|
||||
# This is a temporary workaround to make the similarity search
|
||||
# asynchronous. The proper solution is to make the similarity search
|
||||
# asynchronous in the vector store implementations.
|
||||
func = partial(self.max_marginal_relevance_search, query, k, fetch_k)
|
||||
return await asyncio.get_event_loop().run_in_executor(None, func)
|
||||
raise NotImplementedError
|
||||
|
||||
def max_marginal_relevance_search_by_vector(
|
||||
self, embedding: List[float], k: int = 4, fetch_k: int = 20
|
||||
@@ -172,11 +153,11 @@ class VectorStore(ABC):
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls: Type[VST],
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
**kwargs: Any,
|
||||
) -> VST:
|
||||
) -> VectorStore:
|
||||
"""Return VectorStore initialized from documents and embeddings."""
|
||||
texts = [d.page_content for d in documents]
|
||||
metadatas = [d.metadata for d in documents]
|
||||
@@ -184,11 +165,11 @@ class VectorStore(ABC):
|
||||
|
||||
@classmethod
|
||||
async def afrom_documents(
|
||||
cls: Type[VST],
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
**kwargs: Any,
|
||||
) -> VST:
|
||||
) -> VectorStore:
|
||||
"""Return VectorStore initialized from documents and embeddings."""
|
||||
texts = [d.page_content for d in documents]
|
||||
metadatas = [d.metadata for d in documents]
|
||||
@@ -197,22 +178,22 @@ class VectorStore(ABC):
|
||||
@classmethod
|
||||
@abstractmethod
|
||||
def from_texts(
|
||||
cls: Type[VST],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
**kwargs: Any,
|
||||
) -> VST:
|
||||
) -> VectorStore:
|
||||
"""Return VectorStore initialized from texts and embeddings."""
|
||||
|
||||
@classmethod
|
||||
async def afrom_texts(
|
||||
cls: Type[VST],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
**kwargs: Any,
|
||||
) -> VST:
|
||||
) -> VectorStore:
|
||||
"""Return VectorStore initialized from texts and embeddings."""
|
||||
raise NotImplementedError
|
||||
|
||||
|
||||
@@ -3,7 +3,7 @@ from __future__ import annotations
|
||||
|
||||
import logging
|
||||
import uuid
|
||||
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
|
||||
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
|
||||
@@ -269,7 +269,7 @@ class Chroma(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[Chroma],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Optional[Embeddings] = None,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
@@ -307,7 +307,7 @@ class Chroma(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls: Type[Chroma],
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Optional[Embeddings] = None,
|
||||
ids: Optional[List[str]] = None,
|
||||
|
||||
@@ -1,10 +1,7 @@
|
||||
"""VectorStore wrapper around a Postgres/PGVector database."""
|
||||
from __future__ import annotations
|
||||
|
||||
import enum
|
||||
import logging
|
||||
import uuid
|
||||
from typing import Any, Dict, Iterable, List, Optional, Tuple, Type
|
||||
from typing import Any, Dict, Iterable, List, Optional, Tuple
|
||||
|
||||
import sqlalchemy
|
||||
from pgvector.sqlalchemy import Vector
|
||||
@@ -349,7 +346,7 @@ class PGVector(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[PGVector],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
@@ -358,7 +355,7 @@ class PGVector(VectorStore):
|
||||
ids: Optional[List[str]] = None,
|
||||
pre_delete_collection: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> PGVector:
|
||||
) -> "PGVector":
|
||||
"""
|
||||
Return VectorStore initialized from texts and embeddings.
|
||||
Postgres connection string is required
|
||||
@@ -398,7 +395,7 @@ class PGVector(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls: Type[PGVector],
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
collection_name: str = _LANGCHAIN_DEFAULT_COLLECTION_NAME,
|
||||
@@ -406,7 +403,7 @@ class PGVector(VectorStore):
|
||||
ids: Optional[List[str]] = None,
|
||||
pre_delete_collection: bool = False,
|
||||
**kwargs: Any,
|
||||
) -> PGVector:
|
||||
) -> "PGVector":
|
||||
"""
|
||||
Return VectorStore initialized from documents and embeddings.
|
||||
Postgres connection string is required
|
||||
|
||||
@@ -2,8 +2,7 @@
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
import warnings
|
||||
from typing import Any, Callable, Iterable, List, Optional, Tuple, Union
|
||||
from typing import Any, Callable, Iterable, List, Optional, Tuple
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.embeddings.base import Embeddings
|
||||
@@ -27,13 +26,13 @@ class Pinecone(VectorStore):
|
||||
pinecone.init(api_key="***", environment="...")
|
||||
index = pinecone.Index("langchain-demo")
|
||||
embeddings = OpenAIEmbeddings()
|
||||
vectorstore = Pinecone(index, embeddings, "text")
|
||||
vectorstore = Pinecone(index, embeddings.embed_query, "text")
|
||||
"""
|
||||
|
||||
def __init__(
|
||||
self,
|
||||
index: Any,
|
||||
embeddings: Union[Embeddings, Callable],
|
||||
embedding_function: Callable,
|
||||
text_key: str,
|
||||
namespace: Optional[str] = None,
|
||||
):
|
||||
@@ -51,17 +50,7 @@ class Pinecone(VectorStore):
|
||||
f"got {type(index)}"
|
||||
)
|
||||
self._index = index
|
||||
if isinstance(embeddings, Embeddings):
|
||||
self._embeddings = embeddings
|
||||
else:
|
||||
# This is for backwards compatibility issues. Previously,
|
||||
# embeddings.embed_query was passed in, not the whole class
|
||||
warnings.warn(
|
||||
"passing a function as embeddings is deprecated, "
|
||||
"you should pass an Embedding object directly. "
|
||||
"If this throws an error, that is why."
|
||||
)
|
||||
self._embeddings = embeddings.__self__ # type: ignore
|
||||
self._embedding_function = embedding_function
|
||||
self._text_key = text_key
|
||||
self._namespace = namespace
|
||||
|
||||
@@ -89,14 +78,13 @@ class Pinecone(VectorStore):
|
||||
if namespace is None:
|
||||
namespace = self._namespace
|
||||
# Embed and create the documents
|
||||
_texts = list(texts)
|
||||
ids = ids or [str(uuid.uuid4()) for _ in _texts]
|
||||
embeddings = self._embeddings.embed_documents(_texts)
|
||||
metadatas = metadatas or [{}] * len(_texts)
|
||||
for metadata, text in zip(metadatas, _texts):
|
||||
docs = []
|
||||
ids = ids or [str(uuid.uuid4()) for _ in texts]
|
||||
for i, text in enumerate(texts):
|
||||
embedding = self._embedding_function(text)
|
||||
metadata = metadatas[i] if metadatas else {}
|
||||
metadata[self._text_key] = text
|
||||
docs = list(zip(ids, embeddings, metadatas))
|
||||
|
||||
docs.append((ids[i], embedding, metadata))
|
||||
# upsert to Pinecone
|
||||
self._index.upsert(vectors=docs, namespace=namespace, batch_size=batch_size)
|
||||
return ids
|
||||
@@ -121,7 +109,7 @@ class Pinecone(VectorStore):
|
||||
"""
|
||||
if namespace is None:
|
||||
namespace = self._namespace
|
||||
query_obj = self._embeddings.embed_query(query)
|
||||
query_obj = self._embedding_function(query)
|
||||
docs = []
|
||||
results = self._index.query(
|
||||
[query_obj],
|
||||
@@ -157,7 +145,7 @@ class Pinecone(VectorStore):
|
||||
"""
|
||||
if namespace is None:
|
||||
namespace = self._namespace
|
||||
query_obj = self._embeddings.embed_query(query)
|
||||
query_obj = self._embedding_function(query)
|
||||
docs = []
|
||||
results = self._index.query(
|
||||
[query_obj],
|
||||
@@ -256,7 +244,7 @@ class Pinecone(VectorStore):
|
||||
|
||||
# upsert to Pinecone
|
||||
index.upsert(vectors=list(to_upsert), namespace=namespace)
|
||||
return cls(index, embedding, text_key, namespace)
|
||||
return cls(index, embedding.embed_query, text_key, namespace)
|
||||
|
||||
@classmethod
|
||||
def from_existing_index(
|
||||
@@ -275,4 +263,6 @@ class Pinecone(VectorStore):
|
||||
"Please install it with `pip install pinecone-client`."
|
||||
)
|
||||
|
||||
return cls(pinecone.Index(index_name), embedding, text_key, namespace)
|
||||
return cls(
|
||||
pinecone.Index(index_name), embedding.embed_query, text_key, namespace
|
||||
)
|
||||
|
||||
@@ -1,9 +1,7 @@
|
||||
"""Wrapper around Qdrant vector database."""
|
||||
from __future__ import annotations
|
||||
|
||||
import uuid
|
||||
from operator import itemgetter
|
||||
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Type, Union
|
||||
from typing import Any, Callable, Dict, Iterable, List, Optional, Tuple, Union, cast
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.embeddings.base import Embeddings
|
||||
@@ -178,9 +176,55 @@ class Qdrant(VectorStore):
|
||||
for i in mmr_selected
|
||||
]
|
||||
|
||||
@classmethod
|
||||
def from_documents(
|
||||
cls,
|
||||
documents: List[Document],
|
||||
embedding: Embeddings,
|
||||
location: Optional[str] = None,
|
||||
url: Optional[str] = None,
|
||||
port: Optional[int] = 6333,
|
||||
grpc_port: int = 6334,
|
||||
prefer_grpc: bool = False,
|
||||
https: Optional[bool] = None,
|
||||
api_key: Optional[str] = None,
|
||||
prefix: Optional[str] = None,
|
||||
timeout: Optional[float] = None,
|
||||
host: Optional[str] = None,
|
||||
path: Optional[str] = None,
|
||||
collection_name: Optional[str] = None,
|
||||
distance_func: str = "Cosine",
|
||||
content_payload_key: str = CONTENT_KEY,
|
||||
metadata_payload_key: str = METADATA_KEY,
|
||||
**kwargs: Any,
|
||||
) -> "Qdrant":
|
||||
return cast(
|
||||
Qdrant,
|
||||
super().from_documents(
|
||||
documents,
|
||||
embedding,
|
||||
location=location,
|
||||
url=url,
|
||||
port=port,
|
||||
grpc_port=grpc_port,
|
||||
prefer_grpc=prefer_grpc,
|
||||
https=https,
|
||||
api_key=api_key,
|
||||
prefix=prefix,
|
||||
timeout=timeout,
|
||||
host=host,
|
||||
path=path,
|
||||
collection_name=collection_name,
|
||||
distance_func=distance_func,
|
||||
content_payload_key=content_payload_key,
|
||||
metadata_payload_key=metadata_payload_key,
|
||||
**kwargs,
|
||||
),
|
||||
)
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[Qdrant],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
@@ -200,7 +244,7 @@ class Qdrant(VectorStore):
|
||||
content_payload_key: str = CONTENT_KEY,
|
||||
metadata_payload_key: str = METADATA_KEY,
|
||||
**kwargs: Any,
|
||||
) -> Qdrant:
|
||||
) -> "Qdrant":
|
||||
"""Construct Qdrant wrapper from raw documents.
|
||||
|
||||
Args:
|
||||
|
||||
@@ -4,7 +4,7 @@ from __future__ import annotations
|
||||
import json
|
||||
import logging
|
||||
import uuid
|
||||
from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple, Type
|
||||
from typing import Any, Callable, Dict, Iterable, List, Mapping, Optional, Tuple
|
||||
|
||||
import numpy as np
|
||||
from pydantic import BaseModel, root_validator
|
||||
@@ -227,7 +227,7 @@ class Redis(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[Redis],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
|
||||
@@ -1,7 +1,7 @@
|
||||
"""Wrapper around weaviate vector database."""
|
||||
from __future__ import annotations
|
||||
|
||||
from typing import Any, Dict, Iterable, List, Optional, Type
|
||||
from typing import Any, Dict, Iterable, List, Optional
|
||||
from uuid import uuid4
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
@@ -104,11 +104,11 @@ class Weaviate(VectorStore):
|
||||
|
||||
@classmethod
|
||||
def from_texts(
|
||||
cls: Type[Weaviate],
|
||||
cls,
|
||||
texts: List[str],
|
||||
embedding: Embeddings,
|
||||
metadatas: Optional[List[dict]] = None,
|
||||
**kwargs: Any,
|
||||
) -> Weaviate:
|
||||
) -> VectorStore:
|
||||
"""Not implemented for Weaviate yet."""
|
||||
raise NotImplementedError("weaviate does not currently support `from_texts`.")
|
||||
|
||||
680
poetry.lock
generated
680
poetry.lock
generated
@@ -562,7 +562,7 @@ name = "backoff"
|
||||
version = "2.2.1"
|
||||
description = "Function decoration for backoff and retry"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7,<4.0"
|
||||
files = [
|
||||
{file = "backoff-2.2.1-py3-none-any.whl", hash = "sha256:63579f9a0628e06278f7e47b7d7d5b6ce20dc65c5e96a6f3ca99a6adca0396e8"},
|
||||
@@ -936,31 +936,6 @@ files = [
|
||||
{file = "charset_normalizer-3.1.0-py3-none-any.whl", hash = "sha256:3d9098b479e78c85080c98e1e35ff40b4a31d8953102bb0fd7d1b6f8a2111a3d"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
name = "chromadb"
|
||||
version = "0.3.21"
|
||||
description = "Chroma."
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "chromadb-0.3.21-py3-none-any.whl", hash = "sha256:b497516ef403d357944742b2363eb729019d68ec0d1a7062a6abe8e127ccf28f"},
|
||||
{file = "chromadb-0.3.21.tar.gz", hash = "sha256:7b3417892666dc90df10eafae719ee189037c448c1c96e6c7964daa870483c3a"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
clickhouse-connect = ">=0.5.7"
|
||||
duckdb = ">=0.7.1"
|
||||
fastapi = ">=0.85.1"
|
||||
hnswlib = ">=0.7"
|
||||
numpy = ">=1.21.6"
|
||||
pandas = ">=1.3"
|
||||
posthog = ">=2.4.0"
|
||||
pydantic = ">=1.9"
|
||||
requests = ">=2.28"
|
||||
sentence-transformers = ">=2.2.2"
|
||||
uvicorn = {version = ">=0.18.3", extras = ["standard"]}
|
||||
|
||||
[[package]]
|
||||
name = "click"
|
||||
version = "8.1.3"
|
||||
@@ -976,126 +951,6 @@ files = [
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[[package]]
|
||||
name = "clickhouse-connect"
|
||||
version = "0.5.20"
|
||||
description = "ClickHouse core driver, SqlAlchemy, and Superset libraries"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "~=3.7"
|
||||
files = [
|
||||
{file = "clickhouse-connect-0.5.20.tar.gz", hash = "sha256:5fc9a84849f3c3b6f6928b45a0df17fa63ebcf4e518b3a48ec70720957e18683"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c29cf8b2c90eed6b83366c13ab5ad471ff6ef2e334f35818729330854b9747ac"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:5c03ded1b006fa2cf8f7d823f0ff9c6d294e442a123c96ca2a9ebc4b293bfb7f"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:9eb0024160412d9c6079fa6982cb29abda4db8412b4f63918de7a1bde1dcb7aa"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:170bd258d21bc828557f8a55f23affe22cc4e671c93f645a6316ef874e359f8e"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:dc70fee875fdba42c0a6f519fa376659a08253fd36d188b8b304f4ccda572177"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:18837e06846797db475b6aee13f03928fb169f64d0efb268e2bb04e015990b5b"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:76f7a7d2d41377e6f382a7ada825be594c2d316481f3194bfffd025727633258"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:3bac453f1199af29ec7292d2fd2a8cb0cc0e6692bec9c9da50ce5aec10ff0339"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-win32.whl", hash = "sha256:14983562d2687b18d03a35f27b4e7f28cf013c280ff4fee726501e03bae7528d"},
|
||||
{file = "clickhouse_connect-0.5.20-cp310-cp310-win_amd64.whl", hash = "sha256:3d618a9c15ee4d2facc7a79e59a646262da64e6ec39d2a1ac6a68167d52266bf"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:6bdfb74ba2bf5157230f576e16c7d708f20ffa7e4b19c54288d7db2b55ebcd17"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:fce7e54ad14b732479c5630948324f7088c3092a74a2442bf015a7cab4bc0a41"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:1e6a2b6d123f5de362d49f079c509a0a43cfbaecae0130c860706ef738af12b7"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:7a9391128387013755de8e420bb7e17c6c809f77ca3233fdc966a1df023fa85d"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1df976816913675b46134e8dd9dee2cf315cc4bf42e258211f8036099b8fc280"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:f1ddeb651bc75b87ec5fa1fbe17fe3a589d00f42cad76d6e64918067f5025798"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:caf60b4bfb7214d80455137eee45ca0943a370885d65f4298fafde0d431e837a"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:5c0bdcb72607244dc920f543ee6363a6094e836770aaac07f20556936af85813"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-win32.whl", hash = "sha256:cc3f77df2b1cab2aa99b59f529aead2cc96beac1639ed18f7fd8dba392957623"},
|
||||
{file = "clickhouse_connect-0.5.20-cp311-cp311-win_amd64.whl", hash = "sha256:e44c3b7e40402ce0650f69cbc31f2f503073e2bec9f2b31befbd823150f2431d"},
|
||||
{file = "clickhouse_connect-0.5.20-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:ba78e7d270d78f9559e4a836c6c4f55ab54d9f2b6505c0d05db6260e8e2a4f6a"},
|
||||
{file = "clickhouse_connect-0.5.20-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:3e8924824cd19b739cc920d867bf291a31a5da406637e0c575f6eb961cfb0557"},
|
||||
{file = "clickhouse_connect-0.5.20-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:672c260c471fd18a87a4f5130e6d72590cd4f57289669c58feff5be934810d28"},
|
||||
{file = "clickhouse_connect-0.5.20-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:69887898f8f5ea6e70c30aa51c756f8a752ef0eb1df747d4aec7b7d10de5e103"},
|
||||
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[package.extras]
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flake8 = ["flake8"]
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||||
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||||
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||||
[[package]]
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name = "manifest-ml"
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||||
version = "0.0.1"
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@@ -3690,18 +3470,6 @@ files = [
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{file = "mistune-2.0.5.tar.gz", hash = "sha256:0246113cb2492db875c6be56974a7c893333bf26cd92891c85f63151cee09d34"},
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[[package]]
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||||
name = "monotonic"
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||||
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||||
description = "An implementation of time.monotonic() for Python 2 & < 3.3"
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||||
category = "dev"
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||||
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||||
python-versions = "*"
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files = [
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{file = "monotonic-1.6-py2.py3-none-any.whl", hash = "sha256:68687e19a14f11f26d140dd5c86f3dba4bf5df58003000ed467e0e2a69bca96c"},
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[[package]]
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||||
name = "more-itertools"
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||||
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@@ -3714,24 +3482,6 @@ files = [
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||||
[[package]]
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||||
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||||
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||||
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||||
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[package.extras]
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||||
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||||
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||||
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||||
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||||
[[package]]
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||||
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||||
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@@ -4156,7 +3906,7 @@ name = "networkx"
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||||
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||||
description = "Python package for creating and manipulating graphs and networks"
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||||
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||||
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||||
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@@ -4190,7 +3940,7 @@ name = "nltk"
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||||
description = "Natural Language Toolkit"
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||||
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||||
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@@ -4368,7 +4118,7 @@ name = "nvidia-cublas-cu11"
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||||
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|
||||
description = "CUBLAS native runtime libraries"
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||||
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||||
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||||
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||||
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||||
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@@ -4379,28 +4129,12 @@ files = [
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||||
setuptools = "*"
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||||
wheel = "*"
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||||
|
||||
[[package]]
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||||
name = "nvidia-cuda-cupti-cu11"
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||||
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||||
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||||
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||||
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||||
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||||
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[package.dependencies]
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||||
setuptools = "*"
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||||
wheel = "*"
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||||
|
||||
[[package]]
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||||
name = "nvidia-cuda-nvrtc-cu11"
|
||||
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|
||||
description = "NVRTC native runtime libraries"
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||||
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||||
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|
||||
optional = true
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||||
python-versions = ">=3"
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||||
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||||
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||||
@@ -4417,7 +4151,7 @@ name = "nvidia-cuda-runtime-cu11"
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||||
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|
||||
description = "CUDA Runtime native Libraries"
|
||||
category = "main"
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||||
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||||
optional = true
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||||
python-versions = ">=3"
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||||
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||||
@@ -4433,7 +4167,7 @@ name = "nvidia-cudnn-cu11"
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||||
version = "8.5.0.96"
|
||||
description = "cuDNN runtime libraries"
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||||
category = "main"
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||||
optional = false
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||||
optional = true
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||||
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||||
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@@ -4444,94 +4178,6 @@ files = [
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||||
setuptools = "*"
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||||
wheel = "*"
|
||||
|
||||
[[package]]
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||||
name = "nvidia-cufft-cu11"
|
||||
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|
||||
description = "CUFFT native runtime libraries"
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||||
category = "main"
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||||
optional = false
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||||
python-versions = ">=3"
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||||
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||||
[[package]]
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||||
name = "nvidia-curand-cu11"
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
[package.dependencies]
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||||
setuptools = "*"
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||||
wheel = "*"
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||||
|
||||
[[package]]
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||||
name = "nvidia-cusolver-cu11"
|
||||
version = "11.4.0.1"
|
||||
description = "CUDA solver native runtime libraries"
|
||||
category = "main"
|
||||
optional = false
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||||
python-versions = ">=3"
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||||
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||||
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||||
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||||
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||||
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||||
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||||
|
||||
[[package]]
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||||
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|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
|
||||
[[package]]
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||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
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||||
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||||
|
||||
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||||
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|
||||
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|
||||
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|
||||
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|
||||
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||||
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||||
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||||
{file = "nvidia_nvtx_cu11-11.7.91-py3-none-win_amd64.whl", hash = "sha256:dfd7fcb2a91742513027d63a26b757f38dd8b07fecac282c4d132a9d373ff064"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
setuptools = "*"
|
||||
wheel = "*"
|
||||
|
||||
[[package]]
|
||||
name = "oauthlib"
|
||||
version = "3.2.2"
|
||||
@@ -5267,30 +4913,6 @@ files = [
|
||||
dev = ["pre-commit", "tox"]
|
||||
testing = ["pytest", "pytest-benchmark"]
|
||||
|
||||
[[package]]
|
||||
name = "posthog"
|
||||
version = "2.4.2"
|
||||
description = "Integrate PostHog into any python application."
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = "*"
|
||||
files = [
|
||||
{file = "posthog-2.4.2-py2.py3-none-any.whl", hash = "sha256:8c7c37de997d955aea61bf0aa0069970e71f0d9d79c9e6b3a134e6593d5aa3d6"},
|
||||
{file = "posthog-2.4.2.tar.gz", hash = "sha256:652a628623aab26597e8421a7ddf9caaf19dd93cc1288901a6b23db9693d34e5"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
backoff = ">=1.10.0"
|
||||
monotonic = ">=1.5"
|
||||
python-dateutil = ">2.1"
|
||||
requests = ">=2.7,<3.0"
|
||||
six = ">=1.5"
|
||||
|
||||
[package.extras]
|
||||
dev = ["black", "flake8", "flake8-print", "isort", "pre-commit"]
|
||||
sentry = ["django", "sentry-sdk"]
|
||||
test = ["coverage", "flake8", "freezegun (==0.3.15)", "mock (>=2.0.0)", "pylint", "pytest"]
|
||||
|
||||
[[package]]
|
||||
name = "pox"
|
||||
version = "0.3.2"
|
||||
@@ -5930,17 +5552,18 @@ files = [
|
||||
|
||||
[[package]]
|
||||
name = "pytest"
|
||||
version = "7.3.0"
|
||||
version = "7.2.2"
|
||||
description = "pytest: simple powerful testing with Python"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
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{file = "pytest-7.3.0.tar.gz", hash = "sha256:58ecc27ebf0ea643ebfdf7fb1249335da761a00c9f955bcd922349bcb68ee57d"},
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||||
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||||
{file = "pytest-7.2.2.tar.gz", hash = "sha256:c99ab0c73aceb050f68929bc93af19ab6db0558791c6a0715723abe9d0ade9d4"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
attrs = ">=19.2.0"
|
||||
colorama = {version = "*", markers = "sys_platform == \"win32\""}
|
||||
exceptiongroup = {version = ">=1.0.0rc8", markers = "python_version < \"3.11\""}
|
||||
iniconfig = "*"
|
||||
@@ -5949,7 +5572,7 @@ pluggy = ">=0.12,<2.0"
|
||||
tomli = {version = ">=1.0.0", markers = "python_version < \"3.11\""}
|
||||
|
||||
[package.extras]
|
||||
testing = ["argcomplete", "attrs (>=19.2.0)", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
|
||||
testing = ["argcomplete", "hypothesis (>=3.56)", "mock", "nose", "pygments (>=2.7.2)", "requests", "xmlschema"]
|
||||
|
||||
[[package]]
|
||||
name = "pytest-asyncio"
|
||||
@@ -6309,14 +5932,14 @@ cffi = {version = "*", markers = "implementation_name == \"pypy\""}
|
||||
|
||||
[[package]]
|
||||
name = "qdrant-client"
|
||||
version = "1.1.3"
|
||||
version = "1.1.2"
|
||||
description = "Client library for the Qdrant vector search engine"
|
||||
category = "main"
|
||||
optional = true
|
||||
python-versions = ">=3.7,<3.12"
|
||||
files = [
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||||
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{file = "qdrant_client-1.1.2.tar.gz", hash = "sha256:708b7a6291dfeeeaa8c5ac2e61a0f73b61fa66b45e31a568e3f406ab08393100"},
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||||
]
|
||||
|
||||
[package.dependencies]
|
||||
@@ -6655,7 +6278,7 @@ name = "scikit-learn"
|
||||
version = "1.2.2"
|
||||
description = "A set of python modules for machine learning and data mining"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "scikit-learn-1.2.2.tar.gz", hash = "sha256:8429aea30ec24e7a8c7ed8a3fa6213adf3814a6efbea09e16e0a0c71e1a1a3d7"},
|
||||
@@ -6698,7 +6321,7 @@ name = "scipy"
|
||||
version = "1.9.3"
|
||||
description = "Fundamental algorithms for scientific computing in Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
{file = "scipy-1.9.3-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:1884b66a54887e21addf9c16fb588720a8309a57b2e258ae1c7986d4444d3bc0"},
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||||
@@ -6754,7 +6377,7 @@ name = "sentence-transformers"
|
||||
version = "2.2.2"
|
||||
description = "Multilingual text embeddings"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6.0"
|
||||
files = [
|
||||
{file = "sentence-transformers-2.2.2.tar.gz", hash = "sha256:dbc60163b27de21076c9a30d24b5b7b6fa05141d68cf2553fa9a77bf79a29136"},
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||||
@@ -6777,7 +6400,7 @@ name = "sentencepiece"
|
||||
version = "0.1.97"
|
||||
description = "SentencePiece python wrapper"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = "*"
|
||||
files = [
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||||
{file = "sentencepiece-0.1.97-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:6f249c8f1852893be86eae66b19d522c5fb30bbad4fe2d1b07f06fdc86e1907e"},
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||||
@@ -7433,7 +7056,7 @@ name = "starlette"
|
||||
version = "0.26.1"
|
||||
description = "The little ASGI library that shines."
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
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||||
{file = "starlette-0.26.1-py3-none-any.whl", hash = "sha256:e87fce5d7cbdde34b76f0ac69013fd9d190d581d80681493016666e6f96c6d5e"},
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||||
@@ -7447,21 +7070,6 @@ typing-extensions = {version = ">=3.10.0", markers = "python_version < \"3.10\""
|
||||
[package.extras]
|
||||
full = ["httpx (>=0.22.0)", "itsdangerous", "jinja2", "python-multipart", "pyyaml"]
|
||||
|
||||
[[package]]
|
||||
name = "sympy"
|
||||
version = "1.11.1"
|
||||
description = "Computer algebra system (CAS) in Python"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
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||||
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||||
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||||
|
||||
[package.dependencies]
|
||||
mpmath = ">=0.19"
|
||||
|
||||
[[package]]
|
||||
name = "tabulate"
|
||||
version = "0.9.0"
|
||||
@@ -7830,7 +7438,7 @@ name = "threadpoolctl"
|
||||
version = "3.1.0"
|
||||
description = "threadpoolctl"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.6"
|
||||
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||||
@@ -7842,7 +7450,7 @@ name = "tiktoken"
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||||
version = "0.3.3"
|
||||
description = "tiktoken is a fast BPE tokeniser for use with OpenAI's models"
|
||||
category = "main"
|
||||
optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.8"
|
||||
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||||
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||||
@@ -7983,55 +7591,40 @@ files = [
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||||
|
||||
[[package]]
|
||||
name = "torch"
|
||||
version = "2.0.0"
|
||||
version = "1.13.1"
|
||||
description = "Tensors and Dynamic neural networks in Python with strong GPU acceleration"
|
||||
category = "main"
|
||||
optional = false
|
||||
python-versions = ">=3.8.0"
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||||
optional = true
|
||||
python-versions = ">=3.7.0"
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||||
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[package.extras]
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[package.dependencies]
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numpy = "*"
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pillow = ">=5.3.0,<8.3.0 || >=8.4.0"
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requests = "*"
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torch = "2.0.0"
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torch = "1.13.1"
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typing-extensions = "*"
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[package.extras]
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scipy = ["scipy"]
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@@ -8202,44 +7795,6 @@ torchhub = ["filelock", "huggingface-hub (>=0.11.0,<1.0)", "importlib-metadata",
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video = ["av (==9.2.0)", "decord (==0.6.0)"]
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[[package]]
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lit = "*"
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torch = "*"
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[package.extras]
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[[package]]
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@@ -8432,7 +7987,7 @@ name = "uvicorn"
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@@ -8458,7 +8013,7 @@ name = "uvloop"
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@@ -8703,7 +8258,7 @@ name = "websockets"
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@@ -8801,7 +8356,7 @@ name = "wheel"
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optional = false
|
||||
optional = true
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "wheel-0.40.0-py3-none-any.whl", hash = "sha256:d236b20e7cb522daf2390fa84c55eea81c5c30190f90f29ae2ca1ad8355bf247"},
|
||||
@@ -9090,73 +8645,6 @@ files = [
|
||||
docs = ["furo", "jaraco.packaging (>=9)", "jaraco.tidelift (>=1.4)", "rst.linker (>=1.9)", "sphinx (>=3.5)", "sphinx-lint"]
|
||||
testing = ["big-O", "flake8 (<5)", "jaraco.functools", "jaraco.itertools", "more-itertools", "pytest (>=6)", "pytest-black (>=0.3.7)", "pytest-checkdocs (>=2.4)", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-flake8", "pytest-mypy (>=0.9.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "zstandard"
|
||||
version = "0.20.0"
|
||||
description = "Zstandard bindings for Python"
|
||||
category = "dev"
|
||||
optional = false
|
||||
python-versions = ">=3.6"
|
||||
files = [
|
||||
{file = "zstandard-0.20.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c4efa051799703dc37c072e22af1f0e4c77069a78fb37caf70e26414c738ca1d"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:f847701d77371d90783c0ce6cfdb7ebde4053882c2aaba7255c70ae3c3eb7af0"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0aa4d178560d7ee32092ddfd415c2cdc6ab5ddce9554985c75f1a019a0ff4c55"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0488f2a238b4560828b3a595f3337daac4d3725c2a1637ffe2a0d187c091da59"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:cd0aa9a043c38901925ae1bba49e1e638f2d9c3cdf1b8000868993c642deb7f2"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cdd769da7add8498658d881ce0eeb4c35ea1baac62e24c5a030c50f859f29724"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:9aea3c7bab4276212e5ac63d28e6bd72a79ff058d57e06926dfe30a52451d943"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-win32.whl", hash = "sha256:0d213353d58ad37fb5070314b156fb983b4d680ed5f3fce76ab013484cf3cf12"},
|
||||
{file = "zstandard-0.20.0-cp310-cp310-win_amd64.whl", hash = "sha256:d08459f7f7748398a6cc65eb7f88aa7ef5731097be2ddfba544be4b558acd900"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:c1929afea64da48ec59eca9055d7ec7e5955801489ac40ac2a19dde19e7edad9"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:b6d718f1b7cd30adb02c2a46dde0f25a84a9de8865126e0fff7d0162332d6b92"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5499d65d4a1978dccf0a9c2c0d12415e16d4995ffad7a0bc4f72cc66691cf9f2"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:302a31400de0280f17c4ce67a73444a7a069f228db64048e4ce555cd0c02fbc4"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:39ae788dcdc404c07ef7aac9b11925185ea0831b985db0bbc43f95acdbd1c2ce"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-win32.whl", hash = "sha256:e3f6887d2bdfb5752d5544860bd6b778e53ebfaf4ab6c3f9d7fd388445429d41"},
|
||||
{file = "zstandard-0.20.0-cp311-cp311-win_amd64.whl", hash = "sha256:4abf9a9e0841b844736d1ae8ead2b583d2cd212815eab15391b702bde17477a7"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-macosx_10_9_x86_64.whl", hash = "sha256:dc47cec184e66953f635254e5381df8a22012a2308168c069230b1a95079ccd0"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:84c1dae0c0a21eea245b5691286fe6470dc797d5e86e0c26b57a3afd1e750b48"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:059316f07e39b7214cd9eed565d26ab239035d2c76835deeff381995f7a27ba8"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:9aca916724d0802d3e70dc68adeff893efece01dffe7252ee3ae0053f1f1990f"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b07f391fd85e3d07514c05fb40c5573b398d0063ab2bada6eb09949ec6004772"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:2adf65cfce73ce94ef4c482f6cc01f08ddf5e1ca0c1ec95f2b63840f9e4c226c"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-win32.whl", hash = "sha256:ee2a1510e06dfc7706ea9afad363efe222818a1eafa59abc32d9bbcd8465fba7"},
|
||||
{file = "zstandard-0.20.0-cp36-cp36m-win_amd64.whl", hash = "sha256:29699746fae2760d3963a4ffb603968e77da55150ee0a3326c0569f4e35f319f"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:78fb35d07423f25efd0fc90d0d4710ae83cfc86443a32192b0c6cb8475ec79a5"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:40466adfa071f58bfa448d90f9623d6aff67c6d86de6fc60be47a26388f6c74d"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ba86f931bf925e9561ccd6cb978acb163e38c425990927feb38be10c894fa937"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:b671b75ae88139b1dd022fa4aa66ba419abd66f98869af55a342cb9257a1831e"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:cc98c8bcaa07150d3f5d7c4bd264eaa4fdd4a4dfb8fd3f9d62565ae5c4aba227"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:0b815dec62e2d5a1bf7a373388f2616f21a27047b9b999de328bca7462033708"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-win32.whl", hash = "sha256:5a3578b182c21b8af3c49619eb4cd0b9127fa60791e621b34217d65209722002"},
|
||||
{file = "zstandard-0.20.0-cp37-cp37m-win_amd64.whl", hash = "sha256:f1ba6bbd28ad926d130f0af8016f3a2930baa013c2128cfff46ca76432f50669"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:b0f556c74c6f0f481b61d917e48c341cdfbb80cc3391511345aed4ce6fb52fdc"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:862ad0a5c94670f2bd6f64fff671bd2045af5f4ed428a3f2f69fa5e52483f86a"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a56036c08645aa6041d435a50103428f0682effdc67f5038de47cea5e4221d6f"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:4af5d1891eebef430038ea4981957d31b1eb70aca14b906660c3ac1c3e7a8612"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:489959e2d52f7f1fe8ea275fecde6911d454df465265bf3ec51b3e755e769a5e"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:7041efe3a93d0975d2ad16451720932e8a3d164be8521bfd0873b27ac917b77a"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:c28c7441638c472bfb794f424bd560a22c7afce764cd99196e8d70fbc4d14e85"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-win32.whl", hash = "sha256:ba4bb4c5a0cac802ff485fa1e57f7763df5efa0ad4ee10c2693ecc5a018d2c1a"},
|
||||
{file = "zstandard-0.20.0-cp38-cp38-win_amd64.whl", hash = "sha256:a5efe366bf0545a1a5a917787659b445ba16442ae4093f102204f42a9da1ecbc"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:79c3058ccbe1fa37356a73c9d3c0475ec935ab528f5b76d56fc002a5a23407c7"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:39cbaf8fe3fa3515d35fb790465db4dc1ff45e58e1e00cbaf8b714e85437f039"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f199d58f3fd7dfa0d447bc255ff22571f2e4e5e5748bfd1c41370454723cb053"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0f32a8f3a697ef87e67c0d0c0673b245babee6682b2c95e46eb30208ffb720bd"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_12_i686.manylinux2010_i686.whl", hash = "sha256:4a3c36284c219a4d2694e52b2582fe5d5f0ecaf94a22cf0ea959b527dbd8a2a6"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:2eeb9e1ecd48ac1d352608bfe0dc1ed78a397698035a1796cf72f0c9d905d219"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl", hash = "sha256:6179808ebd1ebc42b1e2f221a23c28a22d3bc8f79209ae4a3cc114693c380bff"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-win32.whl", hash = "sha256:afbcd2ed0c1145e24dd3df8440a429688a1614b83424bc871371b176bed429f9"},
|
||||
{file = "zstandard-0.20.0-cp39-cp39-win_amd64.whl", hash = "sha256:e6b4de1ba2f3028fafa0d82222d1e91b729334c8d65fbf04290c65c09d7457e1"},
|
||||
{file = "zstandard-0.20.0.tar.gz", hash = "sha256:613daadd72c71b1488742cafb2c3b381c39d0c9bb8c6cc157aa2d5ea45cc2efc"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
cffi = {version = ">=1.11", markers = "platform_python_implementation == \"PyPy\""}
|
||||
|
||||
[package.extras]
|
||||
cffi = ["cffi (>=1.11)"]
|
||||
|
||||
[extras]
|
||||
all = ["aleph-alpha-client", "anthropic", "beautifulsoup4", "cohere", "deeplake", "elasticsearch", "faiss-cpu", "google-api-python-client", "google-search-results", "huggingface_hub", "jina", "jinja2", "manifest-ml", "networkx", "nlpcloud", "nltk", "nomic", "openai", "opensearch-py", "pgvector", "pinecone-client", "psycopg2-binary", "pyowm", "pypdf", "qdrant-client", "redis", "sentence-transformers", "spacy", "tensorflow-text", "tiktoken", "torch", "transformers", "weaviate-client", "wikipedia", "wolframalpha"]
|
||||
cohere = ["cohere"]
|
||||
@@ -9167,4 +8655,4 @@ qdrant = ["qdrant-client"]
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = ">=3.8.1,<4.0"
|
||||
content-hash = "a8fde2558f92b4c5ec1dce45f830adc6158dd2cf8c425a34a06523ee8e74487d"
|
||||
content-hash = "56e8666167102cc23b605c8d91b26a62c0858216637cc281b866315da766ad71"
|
||||
|
||||
@@ -1,6 +1,6 @@
|
||||
[tool.poetry]
|
||||
name = "langchain"
|
||||
version = "0.0.137"
|
||||
version = "0.0.136"
|
||||
description = "Building applications with LLMs through composability"
|
||||
authors = []
|
||||
license = "MIT"
|
||||
@@ -28,7 +28,7 @@ spacy = {version = "^3", optional = true}
|
||||
nltk = {version = "^3", optional = true}
|
||||
transformers = {version = "^4", optional = true}
|
||||
beautifulsoup4 = {version = "^4", optional = true}
|
||||
torch = {version = "^2", optional = true}
|
||||
torch = {version = "^1", optional = true}
|
||||
jinja2 = {version = "^3", optional = true}
|
||||
tiktoken = {version = "^0.3.2", optional = true, python="^3.9"}
|
||||
pinecone-client = {version = "^2", optional = true}
|
||||
@@ -75,7 +75,7 @@ linkchecker = "^10.2.1"
|
||||
sphinx-copybutton = "^0.5.1"
|
||||
|
||||
[tool.poetry.group.test.dependencies]
|
||||
pytest = "^7.3.0"
|
||||
pytest = "^7.2.0"
|
||||
pytest-cov = "^4.0.0"
|
||||
pytest-dotenv = "^0.5.2"
|
||||
duckdb-engine = "^0.7.0"
|
||||
@@ -88,19 +88,16 @@ pytest-asyncio = "^0.20.3"
|
||||
optional = true
|
||||
|
||||
[tool.poetry.group.test_integration.dependencies]
|
||||
pytest-vcr = "^1.0.2"
|
||||
wrapt = "^1.15.0"
|
||||
openai = "^0.27.4"
|
||||
elasticsearch = {extras = ["async"], version = "^8.6.2"}
|
||||
pytest-vcr = "^1.0.2"
|
||||
wrapt = "^1.15.0"
|
||||
redis = "^4.5.4"
|
||||
pinecone-client = "^2.2.1"
|
||||
pgvector = "^0.1.6"
|
||||
transformers = "^4.27.4"
|
||||
pandas = "^2.0.0"
|
||||
deeplake = "^3.2.21"
|
||||
torch = "^2.0.0"
|
||||
chromadb = "^0.3.21"
|
||||
tiktoken = "^0.3.3"
|
||||
|
||||
[tool.poetry.group.lint.dependencies]
|
||||
ruff = "^0.0.249"
|
||||
|
||||
@@ -27,7 +27,7 @@ Any new dependencies should be added by running:
|
||||
|
||||
```bash
|
||||
# add package and install it after adding:
|
||||
poetry add tiktoken@latest --group "test_integration" && poetry install --with test_integration
|
||||
poetry add deeplake --group "test_integration" && poetry install --with test_integration
|
||||
```
|
||||
|
||||
Before running any tests, you should start a specific Docker container that has all the
|
||||
@@ -55,12 +55,4 @@ new cassettes. Here's an example:
|
||||
|
||||
```bash
|
||||
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --vcr-record=none
|
||||
```
|
||||
|
||||
### Run some tests with coverage:
|
||||
|
||||
```bash
|
||||
pytest tests/integration_tests/vectorstores/test_elasticsearch.py --cov=langchain --cov-report=html
|
||||
start "" htmlcov/index.html || open htmlcov/index.html
|
||||
|
||||
```
|
||||
@@ -1,20 +0,0 @@
|
||||
from langchain.document_loaders.bilibili import BiliBiliLoader
|
||||
|
||||
|
||||
def test_bilibili_loader() -> None:
|
||||
"""Test Bilibili Loader."""
|
||||
loader = BiliBiliLoader(
|
||||
[
|
||||
"https://www.bilibili.com/video/BV1xt411o7Xu/",
|
||||
"https://www.bilibili.com/video/av330407025/",
|
||||
]
|
||||
)
|
||||
docs = loader.load()
|
||||
|
||||
assert len(docs) == 2
|
||||
|
||||
assert len(docs[0].page_content) > 0
|
||||
assert docs[1].metadata["owner"]["mid"] == 398095160
|
||||
|
||||
assert docs[1].page_content == ""
|
||||
assert docs[1].metadata["owner"]["mid"] == 398095160
|
||||
@@ -1,40 +1,31 @@
|
||||
interactions:
|
||||
- request:
|
||||
body: '{"input": [[2059, 7341, 527, 264, 1912, 315, 658, 10753, 677, 81, 3581,
|
||||
7795, 32971, 555, 264, 7558, 321, 351, 61798, 30535, 11, 4330, 311, 8254, 342,
|
||||
484, 1776, 1220, 389, 279, 11314, 315, 279, 2010, 11, 323, 281, 1279, 278, 66079,
|
||||
430, 527, 539, 75754, 311, 279, 2010, 13, 18766, 61535, 527, 21771, 2949, 279,
|
||||
1206, 1037, 24082, 613, 318, 269, 4055, 320, 269, 24082, 613, 3893, 8, 323,
|
||||
527, 279, 13219, 1912, 311, 279, 426, 4428, 42877, 320, 66243, 323, 24890, 570,
|
||||
4427, 8336, 13334, 279, 4751, 330, 939, 847, 1, 439, 459, 42887, 5699, 2737,
|
||||
69918, 3697, 315, 921, 2159, 14172, 339, 9891, 320, 11707, 321, 351, 61798,
|
||||
7795, 8, 449, 264, 44892, 12970, 79612, 11, 1778, 439, 6409, 65, 86815, 82,
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||||
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H9bhIAAA
|
||||
headers:
|
||||
CF-Cache-Status:
|
||||
- DYNAMIC
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CF-RAY:
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- 7b50c215fcf91678-DME
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||||
- 7b3ba1a41cc19235-FRA
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Connection:
|
||||
- keep-alive
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Content-Encoding:
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@@ -560,7 +551,7 @@ interactions:
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||||
Content-Type:
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||||
- application/json
|
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Date:
|
||||
- Sun, 09 Apr 2023 06:40:21 GMT
|
||||
- Thu, 06 Apr 2023 17:08:12 GMT
|
||||
Server:
|
||||
- cloudflare
|
||||
Transfer-Encoding:
|
||||
@@ -572,7 +563,7 @@ interactions:
|
||||
openai-organization:
|
||||
- own-45h3iv
|
||||
openai-processing-ms:
|
||||
- '14'
|
||||
- '737'
|
||||
openai-version:
|
||||
- '2020-10-01'
|
||||
strict-transport-security:
|
||||
@@ -584,7 +575,7 @@ interactions:
|
||||
x-ratelimit-reset-requests:
|
||||
- 20ms
|
||||
x-request-id:
|
||||
- dfb9f22839291be2e6a38403a0861937
|
||||
- f2f02f88b1097ce728e4b79198958fde
|
||||
status:
|
||||
code: 200
|
||||
message: OK
|
||||
|
||||
File diff suppressed because it is too large
Load Diff
File diff suppressed because it is too large
Load Diff
@@ -2,22 +2,26 @@ version: "3"
|
||||
|
||||
services:
|
||||
elasticsearch:
|
||||
image: docker.elastic.co/elasticsearch/elasticsearch:8.7.0 # https://www.docker.elastic.co/r/elasticsearch/elasticsearch
|
||||
image: docker.elastic.co/elasticsearch/elasticsearch:8.7.0
|
||||
environment:
|
||||
- discovery.type=single-node
|
||||
- xpack.security.enabled=false # security has been disabled, so no login or password is required.
|
||||
- xpack.security.enabled=false
|
||||
- xpack.security.http.ssl.enabled=false
|
||||
- ELASTIC_PASSWORD=password
|
||||
ports:
|
||||
- "9200:9200"
|
||||
healthcheck:
|
||||
test: [ "CMD-SHELL", "curl --silent --fail http://localhost:9200/_cluster/health || exit 1" ]
|
||||
interval: 10s
|
||||
retries: 60
|
||||
interval: 1s
|
||||
retries: 360
|
||||
|
||||
kibana:
|
||||
image: docker.elastic.co/kibana/kibana:8.7.0
|
||||
environment:
|
||||
- ELASTICSEARCH_URL=http://elasticsearch:9200
|
||||
- ELASTICSEARCH_USERNAME=kibana_system
|
||||
- ELASTICSEARCH_PASSWORD=password
|
||||
- KIBANA_PASSWORD=password
|
||||
ports:
|
||||
- "5601:5601"
|
||||
healthcheck:
|
||||
|
||||
@@ -1,6 +1,4 @@
|
||||
"""Test Chroma functionality."""
|
||||
import pytest
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.vectorstores import Chroma
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
@@ -16,17 +14,6 @@ def test_chroma() -> None:
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
@pytest.mark.asyncio
|
||||
async def test_chroma_async() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
docsearch = Chroma.from_texts(
|
||||
collection_name="test_collection", texts=texts, embedding=FakeEmbeddings()
|
||||
)
|
||||
output = await docsearch.asimilarity_search("foo", k=1)
|
||||
assert output == [Document(page_content="foo")]
|
||||
|
||||
|
||||
def test_chroma_with_metadatas() -> None:
|
||||
"""Test end to end construction and search."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
|
||||
@@ -8,7 +8,9 @@ import pytest
|
||||
from elasticsearch import Elasticsearch
|
||||
|
||||
from langchain.docstore.document import Document
|
||||
from langchain.document_loaders import TextLoader
|
||||
from langchain.embeddings import OpenAIEmbeddings
|
||||
from langchain.text_splitter import CharacterTextSplitter
|
||||
from langchain.vectorstores.elastic_vector_search import ElasticVectorSearch
|
||||
from tests.integration_tests.vectorstores.fake_embeddings import FakeEmbeddings
|
||||
|
||||
@@ -43,6 +45,16 @@ class TestElasticsearch:
|
||||
|
||||
yield openai_api_key
|
||||
|
||||
@pytest.fixture(scope="class")
|
||||
def documents(self) -> Generator[List[Document], None, None]:
|
||||
"""Return a generator that yields a list of documents."""
|
||||
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
|
||||
|
||||
documents = TextLoader(
|
||||
os.path.join(os.path.dirname(__file__), "fixtures", "sharks.txt")
|
||||
).load()
|
||||
yield text_splitter.split_documents(documents)
|
||||
|
||||
def test_similarity_search_without_metadata(self, elasticsearch_url: str) -> None:
|
||||
"""Test end to end construction and search without metadata."""
|
||||
texts = ["foo", "bar", "baz"]
|
||||
|
||||
@@ -1,10 +1,7 @@
|
||||
"""Test functionality of Python REPL."""
|
||||
import sys
|
||||
|
||||
import pytest
|
||||
|
||||
from langchain.python import PythonREPL
|
||||
from langchain.tools.python.tool import PythonAstREPLTool, PythonREPLTool
|
||||
from langchain.tools.python.tool import PythonREPLTool
|
||||
|
||||
_SAMPLE_CODE = """
|
||||
```
|
||||
@@ -14,14 +11,6 @@ multiply()
|
||||
```
|
||||
"""
|
||||
|
||||
_AST_SAMPLE_CODE = """
|
||||
```
|
||||
def multiply():
|
||||
return(5*6)
|
||||
multiply()
|
||||
```
|
||||
"""
|
||||
|
||||
|
||||
def test_python_repl() -> None:
|
||||
"""Test functionality when globals/locals are not provided."""
|
||||
@@ -71,15 +60,6 @@ def test_functionality_multiline() -> None:
|
||||
assert output == "30\n"
|
||||
|
||||
|
||||
def test_python_ast_repl_multiline() -> None:
|
||||
"""Test correct functionality for ChatGPT multiline commands."""
|
||||
if sys.version_info < (3, 9):
|
||||
pytest.skip("Python 3.9+ is required for this test")
|
||||
tool = PythonAstREPLTool()
|
||||
output = tool.run(_AST_SAMPLE_CODE)
|
||||
assert output == 30
|
||||
|
||||
|
||||
def test_function() -> None:
|
||||
"""Test correct functionality."""
|
||||
chain = PythonREPL()
|
||||
|
||||
Reference in New Issue
Block a user