code stringlengths 141 97.3k | apis listlengths 1 24 | extract_api stringlengths 113 214k |
|---|---|---|
from langchain import PromptTemplate
from codedog.templates import grimoire_en
TRANSLATE_PROMPT = PromptTemplate(
template=grimoire_en.TRANSLATE_PR_REVIEW, input_variables=["language", "description", "content"]
)
| [
"langchain.PromptTemplate"
] | [((100, 217), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'grimoire_en.TRANSLATE_PR_REVIEW', 'input_variables': "['language', 'description', 'content']"}), "(template=grimoire_en.TRANSLATE_PR_REVIEW, input_variables=[\n 'language', 'description', 'content'])\n", (114, 217), False, 'from langchain... |
from typing import Any, Dict, List, Union
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string
class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory inside a limited size window."""
human_prefix... | [
"langchain.schema.messages.get_buffer_string"
] | [((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, g... |
from typing import Any, Dict, List, Union
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string
class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory inside a limited size window."""
human_prefix... | [
"langchain.schema.messages.get_buffer_string"
] | [((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, g... |
from typing import Any, Dict, List, Union
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string
class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory inside a limited size window."""
human_prefix... | [
"langchain.schema.messages.get_buffer_string"
] | [((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, g... |
from typing import Any, Dict, List, Union
from langchain.memory.chat_memory import BaseChatMemory
from langchain.schema.messages import BaseMessage, get_buffer_string
class ConversationBufferWindowMemory(BaseChatMemory):
"""Buffer for storing conversation memory inside a limited size window."""
human_prefix... | [
"langchain.schema.messages.get_buffer_string"
] | [((899, 989), 'langchain.schema.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefix': 'self.ai_prefix'}), '(messages, human_prefix=self.human_prefix, ai_prefix=self.\n ai_prefix)\n', (916, 989), False, 'from langchain.schema.messages import BaseMessage, g... |
from typing import Any, Dict, Optional, Type # type: ignore
import langchain
from langchain import LLMChain, PromptTemplate
from langchain.experimental.autonomous_agents import AutoGPT
from sam.core.utils import logger
class AutoGptAgent:
agent: AutoGPT
def __init__(
self, ai_name: str, ai_role: s... | [
"langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools"
] | [((434, 535), 'langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools', 'AutoGPT.from_llm_and_tools', ([], {'ai_name': 'ai_name', 'ai_role': 'ai_role', 'llm': 'llm', 'memory': 'memory', 'tools': 'tools'}), '(ai_name=ai_name, ai_role=ai_role, llm=llm,\n memory=memory, tools=tools)\n', (460, 535), False, ... |
from typing import Any, Dict, Optional, Type # type: ignore
import langchain
from langchain import LLMChain, PromptTemplate
from langchain.experimental.autonomous_agents import AutoGPT
from sam.core.utils import logger
class AutoGptAgent:
agent: AutoGPT
def __init__(
self, ai_name: str, ai_role: s... | [
"langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools"
] | [((434, 535), 'langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools', 'AutoGPT.from_llm_and_tools', ([], {'ai_name': 'ai_name', 'ai_role': 'ai_role', 'llm': 'llm', 'memory': 'memory', 'tools': 'tools'}), '(ai_name=ai_name, ai_role=ai_role, llm=llm,\n memory=memory, tools=tools)\n', (460, 535), False, ... |
from typing import Any, Dict, Optional, Type # type: ignore
import langchain
from langchain import LLMChain, PromptTemplate
from langchain.experimental.autonomous_agents import AutoGPT
from sam.core.utils import logger
class AutoGptAgent:
agent: AutoGPT
def __init__(
self, ai_name: str, ai_role: s... | [
"langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools"
] | [((434, 535), 'langchain.experimental.autonomous_agents.AutoGPT.from_llm_and_tools', 'AutoGPT.from_llm_and_tools', ([], {'ai_name': 'ai_name', 'ai_role': 'ai_role', 'llm': 'llm', 'memory': 'memory', 'tools': 'tools'}), '(ai_name=ai_name, ai_role=ai_role, llm=llm,\n memory=memory, tools=tools)\n', (460, 535), False, ... |
#import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
import warnings
warnings.filterwarnings("ignore")
from langchain.agents.agent_toolkits import create_python_agent
from langchain.agents import load_tools, initialize_agent
from langchain.agents import AgentT... | [
"langchain.tools.python.tool.PythonREPLTool",
"langchain.agents.load_tools",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI"
] | [((128, 161), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (151, 161), False, 'import warnings\n'), ((489, 514), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (499, 514), False, 'from langchain.chat_models import Cha... |
from typing import List, Optional, Type
from langchain.memory import (
ChatMessageHistory,
ConversationBufferMemory,
ConversationSummaryMemory,
RedisChatMessageHistory,
RedisEntityStore,
VectorStoreRetrieverMemory,
)
class Memory:
@staticmethod
def messageHistory(path: str):
h... | [
"langchain.memory.ConversationBufferMemory",
"langchain.memory.ChatMessageHistory",
"langchain.memory.ConversationSummaryMemory"
] | [((329, 349), 'langchain.memory.ChatMessageHistory', 'ChatMessageHistory', ([], {}), '()\n', (347, 349), False, 'from langchain.memory import ChatMessageHistory, ConversationBufferMemory, ConversationSummaryMemory, RedisChatMessageHistory, RedisEntityStore, VectorStoreRetrieverMemory\n'), ((442, 468), 'langchain.memory... |
from langchain_community.document_loaders import PyPDFLoader
from langchain_community.document_loaders.csv_loader import CSVLoader
from langchain_community.document_loaders import HNLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.text_splitter import RecursiveCharacterTextSplitter
... | [
"langchain_community.vectorstores.Chroma",
"langchain_community.vectorstores.Chroma.from_texts",
"langchain.text_splitter.CharacterTextSplitter",
"langchain_community.document_loaders.PyPDFLoader",
"langchain_openai.llms.OpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_commu... | [((741, 785), 'langchain_community.document_loaders.PyPDFLoader', 'PyPDFLoader', (['"""attention is all you need.pdf"""'], {}), "('attention is all you need.pdf')\n", (752, 785), False, 'from langchain_community.document_loaders import PyPDFLoader\n'), ((838, 878), 'langchain_community.document_loaders.csv_loader.CSVLo... |
from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain.o... | [
"langchain_core.exceptions.OutputParserException",
"langchain.chains.llm.LLMChain"
] | [((371, 383), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (378, 383), False, 'from typing import Any, TypeVar\n'), ((1545, 1577), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (1553, 1577), False, 'from langchain.chains.llm import LLM... |
from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain.o... | [
"langchain_core.exceptions.OutputParserException",
"langchain.chains.llm.LLMChain"
] | [((371, 383), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (378, 383), False, 'from typing import Any, TypeVar\n'), ((1545, 1577), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (1553, 1577), False, 'from langchain.chains.llm import LLM... |
from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain.o... | [
"langchain_core.exceptions.OutputParserException",
"langchain.chains.llm.LLMChain"
] | [((371, 383), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (378, 383), False, 'from typing import Any, TypeVar\n'), ((1545, 1577), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (1553, 1577), False, 'from langchain.chains.llm import LLM... |
from __future__ import annotations
from typing import Any, TypeVar
from langchain_core.exceptions import OutputParserException
from langchain_core.language_models import BaseLanguageModel
from langchain_core.output_parsers import BaseOutputParser
from langchain_core.prompts import BasePromptTemplate
from langchain.o... | [
"langchain_core.exceptions.OutputParserException",
"langchain.chains.llm.LLMChain"
] | [((371, 383), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (378, 383), False, 'from typing import Any, TypeVar\n'), ((1545, 1577), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (1553, 1577), False, 'from langchain.chains.llm import LLM... |
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorSto... | [
"langchain.docstore.document.Document",
"langchain.utils.get_from_env"
] | [((965, 1009), 'meilisearch.Client', 'meilisearch.Client', ([], {'url': 'url', 'api_key': 'api_key'}), '(url=url, api_key=api_key)\n', (983, 1009), False, 'import meilisearch\n'), ((776, 814), 'langchain.utils.get_from_env', 'get_from_env', (['"""url"""', '"""MEILI_HTTP_ADDR"""'], {}), "('url', 'MEILI_HTTP_ADDR')\n", (... |
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorSto... | [
"langchain.docstore.document.Document",
"langchain.utils.get_from_env"
] | [((965, 1009), 'meilisearch.Client', 'meilisearch.Client', ([], {'url': 'url', 'api_key': 'api_key'}), '(url=url, api_key=api_key)\n', (983, 1009), False, 'import meilisearch\n'), ((776, 814), 'langchain.utils.get_from_env', 'get_from_env', (['"""url"""', '"""MEILI_HTTP_ADDR"""'], {}), "('url', 'MEILI_HTTP_ADDR')\n", (... |
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorSto... | [
"langchain.docstore.document.Document",
"langchain.utils.get_from_env"
] | [((965, 1009), 'meilisearch.Client', 'meilisearch.Client', ([], {'url': 'url', 'api_key': 'api_key'}), '(url=url, api_key=api_key)\n', (983, 1009), False, 'import meilisearch\n'), ((776, 814), 'langchain.utils.get_from_env', 'get_from_env', (['"""url"""', '"""MEILI_HTTP_ADDR"""'], {}), "('url', 'MEILI_HTTP_ADDR')\n", (... |
from __future__ import annotations
import uuid
from typing import TYPE_CHECKING, Any, Dict, Iterable, List, Optional, Tuple, Type
from langchain.docstore.document import Document
from langchain.embeddings.base import Embeddings
from langchain.utils import get_from_env
from langchain.vectorstores.base import VectorSto... | [
"langchain.docstore.document.Document",
"langchain.utils.get_from_env"
] | [((965, 1009), 'meilisearch.Client', 'meilisearch.Client', ([], {'url': 'url', 'api_key': 'api_key'}), '(url=url, api_key=api_key)\n', (983, 1009), False, 'import meilisearch\n'), ((776, 814), 'langchain.utils.get_from_env', 'get_from_env', (['"""url"""', '"""MEILI_HTTP_ADDR"""'], {}), "('url', 'MEILI_HTTP_ADDR')\n", (... |
## This is a fork/based from https://gist.github.com/wiseman/4a706428eaabf4af1002a07a114f61d6
from io import StringIO
import sys
import os
from typing import Dict, Optional
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents.tools import Tool
from langchain.llms... | [
"langchain.llms.OpenAI",
"langchain.agents.initialize_agent"
] | [((348, 409), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_BASE"""', '"""http://localhost:8080/v1"""'], {}), "('OPENAI_API_BASE', 'http://localhost:8080/v1')\n", (362, 409), False, 'import os\n'), ((423, 468), 'os.environ.get', 'os.environ.get', (['"""MODEL_NAME"""', '"""gpt-3.5-turbo"""'], {}), "('MODEL_NAME',... |
import time
from typing import List
import pandas as pd
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain_community.vectorstores import VectorStore
from mindsdb.integrations.handlers.rag_handler.settings import (
PersistedVectorStoreSaver,
... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((539, 562), 'mindsdb.utilities.log.getLogger', 'log.getLogger', (['__name__'], {}), '(__name__)\n', (552, 562), False, 'from mindsdb.utilities import log\n'), ((1455, 1519), 'mindsdb.integrations.handlers.rag_handler.settings.VectorStoreFactory.get_vectorstore_class', 'VectorStoreFactory.get_vectorstore_class', (['ar... |
"""
Multilingual retrieval based conversation system backed by ChatGPT
"""
import argparse
import os
from colossalqa.data_loader.document_loader import DocumentLoader
from colossalqa.memory import ConversationBufferWithSummary
from colossalqa.retriever import CustomRetriever
from langchain import LLMChain
from langch... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.prompts.prompt.PromptTemplate",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.LLMChain"
] | [((599, 709), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {'description': '"""Multilingual retrieval based conversation system backed by ChatGPT"""'}), "(description=\n 'Multilingual retrieval based conversation system backed by ChatGPT')\n", (622, 709), False, 'import argparse\n'), ((1258, 1281), 'la... |
from templates.common.suffix import suffix
from templates.common.format_instructions import format_instructions
from templates.common.docs_system_instructions import docs_system_instructions
from langchain.schema import (
# AIMessage,
HumanMessage,
SystemMessage
)
from langchain.tools.json.tool import JsonS... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.chat_models.ChatOpenAI",
"langchain.chat_models.AzureChatOpenAI",
"langchain.agents.agent_toolkits.json.toolkit.JsonToolkit",
"langchain.schema.HumanMessage",
"langchain.tools.json.tool.JsonSpec",
"langchain.schema.SystemMessage",
"lang... | [((701, 714), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (712, 714), False, 'from dotenv import load_dotenv\n'), ((810, 830), 'langchain.tools.json.tool.JsonSpec', 'JsonSpec', ([], {'dict_': 'docs'}), '(dict_=docs)\n', (818, 830), False, 'from langchain.tools.json.tool import JsonSpec\n'), ((854, 881), 'lan... |
import os
import threading
from chainlit.config import config
from chainlit.logger import logger
def init_lc_cache():
use_cache = config.project.cache is True and config.run.no_cache is False
if use_cache:
try:
import langchain
except ImportError:
return
from ... | [
"langchain.cache.SQLiteCache"
] | [((767, 783), 'threading.Lock', 'threading.Lock', ([], {}), '()\n', (781, 783), False, 'import threading\n'), ((487, 542), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': 'config.project.lc_cache_path'}), '(database_path=config.project.lc_cache_path)\n', (498, 542), False, 'from langchain.cache imp... |
import json
from typing import Any, List, Tuple
import requests
from taskweaver.plugin import Plugin, register_plugin
# response entry format: (title, url, snippet)
ResponseEntry = Tuple[str, str, str]
def browse_page(
query: str,
urls: List[str],
top_k: int = 3,
chunk_size: int = 1000,
chunk_o... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_community.document_loaders.AsyncHtmlLoader",
"langchain_community.document_transformers.Html2TextTransformer",
"langchain_community.embeddings.HuggingFaceEmbeddings"
] | [((725, 755), 'langchain_community.document_loaders.AsyncHtmlLoader', 'AsyncHtmlLoader', ([], {'web_path': 'urls'}), '(web_path=urls)\n', (740, 755), False, 'from langchain_community.document_loaders import AsyncHtmlLoader\n'), ((798, 820), 'langchain_community.document_transformers.Html2TextTransformer', 'Html2TextTra... |
import os
from typing import Optional
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.schema import BaseMessage, HumanMessage
from rebyte_langchain.rebyte_langchain import RebyteEndpoint
from realtime_ai_character.llm.base import (
AsyncCallbackAudioHandler,
Asyn... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.schema.HumanMessage"
] | [((473, 493), 'realtime_ai_character.logger.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (483, 493), False, 'from realtime_ai_character.logger import get_logger\n'), ((572, 603), 'os.getenv', 'os.getenv', (['"""REBYTE_API_KEY"""', '""""""'], {}), "('REBYTE_API_KEY', '')\n", (581, 603), False, 'import ... |
from celery import shared_task
from langchain.text_splitter import RecursiveCharacterTextSplitter
from shared.models.opencopilot_db.pdf_data_sources import (
insert_pdf_data_source,
update_pdf_data_source_status,
)
from langchain.document_loaders import UnstructuredMarkdownLoader
from shared.utils.opencopilot_... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((1830, 1925), 'shared.models.opencopilot_db.pdf_data_sources.update_pdf_data_source_status', 'update_pdf_data_source_status', ([], {'chatbot_id': 'chatbot_id', 'file_name': 'file_name', 'status': '"""PENDING"""'}), "(chatbot_id=chatbot_id, file_name=file_name,\n status='PENDING')\n", (1859, 1925), False, 'from sha... |
# SPDX-FileCopyrightText: Copyright (c) 2023 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
# SPDX-License-Identifier: Apache-2.0
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# ht... | [
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain.text_splitter.CharacterTextSplitter",
"langchain_core.output_parsers.StrOutputParser",
"langchain.document_loaders.DirectoryLoader",
"langchain_nvidia_ai_endpoints.NVIDIAEmbeddings",
"langchain.vectorstores.FAISS.from_documents",
"lan... | [((1034, 1067), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (1052, 1067), True, 'import streamlit as st\n'), ((2031, 2063), 'langchain_nvidia_ai_endpoints.ChatNVIDIA', 'ChatNVIDIA', ([], {'model': '"""mixtral_8x7b"""'}), "(model='mixtral_8x7b')\n", (2041, 2063... |
from langchain.chains import RetrievalQA, ConversationalRetrievalChain, ConversationChain
from langchain.prompts.prompt import PromptTemplate
from langchain.vectorstores.base import VectorStoreRetriever
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
import pickle
impo... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.PromptTemplate.from_template",
"langchain.prompts.prompt.PromptTemplate",
"langchain.vectorstores.base.VectorStoreRetriever",
"langchain.chains.ConversationChain",
"langchain.memory.Co... | [((727, 766), 'langchain.prompts.prompt.PromptTemplate.from_template', 'PromptTemplate.from_template', (['_template'], {}), '(_template)\n', (755, 766), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1521, 1595), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'templat... |
# flake8: noqa
from langchain.prompts import PromptTemplate
## Use a shorter template to reduce the number of tokens in the prompt
template = """Create a final answer to the given questions using the provided document excerpts (given in no particular order) as sources. ALWAYS include a "SOURCES" section in your answer... | [
"langchain.prompts.PromptTemplate"
] | [((2121, 2197), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'template', 'input_variables': "['summaries', 'question']"}), "(template=template, input_variables=['summaries', 'question'])\n", (2135, 2197), False, 'from langchain.prompts import PromptTemplate\n')] |
from langchain.agents import load_tools
from langchain.tools import AIPluginTool
from parse import *
from langchain.chat_models.base import BaseChatModel
from langchain.chat_models import ChatOpenAI, AzureChatOpenAI
import utils
def create_plugins_static():
plugins = [
AIPluginTool.from_plugin_url(
... | [
"langchain.agents.load_tools",
"langchain.chat_models.AzureChatOpenAI",
"langchain.tools.AIPluginTool.from_plugin_url",
"langchain.chat_models.ChatOpenAI"
] | [((410, 438), 'langchain.agents.load_tools', 'load_tools', (["['requests_all']"], {}), "(['requests_all'])\n", (420, 438), False, 'from langchain.agents import load_tools\n'), ((285, 371), 'langchain.tools.AIPluginTool.from_plugin_url', 'AIPluginTool.from_plugin_url', (['"""https://www.klarna.com/.well-known/ai-plugin.... |
import re
import string
from collections import Counter
import numpy as np
import pandas as pd
import tqdm
from langchain.evaluation.qa import QAEvalChain
from langchain.llms import OpenAI
from algos.PWS import PWS_Base, PWS_Extra
from algos.notool import CoT, IO
from algos.react import ReactBase
def normalize_answ... | [
"langchain.llms.OpenAI"
] | [((373, 410), 're.sub', 're.sub', (['"""\\\\b(a|an|the)\\\\b"""', '""" """', 'text'], {}), "('\\\\b(a|an|the)\\\\b', ' ', text)\n", (379, 410), False, 'import re\n'), ((1278, 1304), 'collections.Counter', 'Counter', (['prediction_tokens'], {}), '(prediction_tokens)\n', (1285, 1304), False, 'from collections import Coun... |
from datetime import date, datetime
from decimal import Decimal
from langchain.chains import LLMChain
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
from sqlalchemy import text
from dataherald.model.chat_model import ChatModel
from dataherald.repositories.database_conne... | [
"langchain.chains.LLMChain",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.prompts.chat.ChatPromptTemplate.from_messages"
] | [((1101, 1123), 'dataherald.model.chat_model.ChatModel', 'ChatModel', (['self.system'], {}), '(self.system)\n', (1110, 1123), False, 'from dataherald.model.chat_model import ChatModel\n'), ((1272, 1302), 'dataherald.repositories.prompts.PromptRepository', 'PromptRepository', (['self.storage'], {}), '(self.storage)\n', ... |
import os
import re
import urllib
import urllib.parse
import urllib.request
from typing import Any, List, Tuple, Union
from urllib.parse import urlparse
import requests
from bs4 import BeautifulSoup
from langchain.chains import LLMChain
from langchain.prompts import Prompt
from langchain.tools import BaseTool
from lan... | [
"langchain.utilities.GoogleSerperAPIWrapper"
] | [((2438, 2451), 'sherpa_ai.config.task_config.AgentConfig', 'AgentConfig', ([], {}), '()\n', (2449, 2451), False, 'from sherpa_ai.config.task_config import AgentConfig\n'), ((894, 986), 'loguru.logger.warning', 'logger.warning', (['"""No SERPER_API_KEY found in environment variables, skipping SearchTool"""'], {}), "(\n... |
from dotenv import load_dotenv
from langchain_core.prompts import PromptTemplate
load_dotenv()
from langchain import hub
from langchain.agents import create_react_agent, AgentExecutor
from langchain_core.tools import Tool
from langchain_openai import ChatOpenAI
from tools.tools import get_profile_url
def lookup(nam... | [
"langchain_core.prompts.PromptTemplate",
"langchain.agents.AgentExecutor",
"langchain.hub.pull",
"langchain_openai.ChatOpenAI",
"langchain_core.tools.Tool",
"langchain.agents.create_react_agent"
] | [((82, 95), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (93, 95), False, 'from dotenv import load_dotenv\n'), ((346, 399), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (356, 399), False, 'from ... |
import logging, json, os
from Utilities.envVars import *
from Utilities.envVars import *
# Import required libraries
from Utilities.cogSearchVsRetriever import CognitiveSearchVsRetriever
from langchain.chains import RetrievalQA
from langchain import PromptTemplate
from Utilities.evaluator import indexDocs
import json
i... | [
"langchain.document_loaders.PDFMinerLoader",
"langchain.chat_models.ChatOpenAI",
"langchain.PromptTemplate",
"langchain.chat_models.AzureChatOpenAI",
"langchain.evaluation.qa.QAEvalChain.from_llm",
"langchain.chains.RetrievalQA.from_chain_type"
] | [((911, 1164), 'collections.namedtuple', 'namedtuple', (['"""RunDoc"""', "['evalatorQaData', 'totalQuestions', 'promptStyle', 'documentId',\n 'splitMethods', 'chunkSizes', 'overlaps', 'retrieverType', 'reEvaluate',\n 'topK', 'model', 'fileName', 'embeddingModelType', 'temperature',\n 'tokenLength']"], {}), "('... |
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.chat_models import ChatOpenAI
from virl.config import cfg
from virl.utils.common_utils import print_prompt, print_answer, parse_answer_to_json
from .gpt_chat import GPTChat
from .azure_gpt import AzureGPTChat
__all__... | [
"langchain.agents.load_tools",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI"
] | [((679, 701), 'virl.utils.common_utils.print_prompt', 'print_prompt', (['question'], {}), '(question)\n', (691, 701), False, 'from virl.utils.common_utils import print_prompt, print_answer, parse_answer_to_json\n'), ((830, 850), 'virl.utils.common_utils.print_answer', 'print_answer', (['answer'], {}), '(answer)\n', (84... |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from tqdm import tqdm
from lmchain.tools import tool_register
class GLMToolChain:
def __init__(self, llm):
self.llm = llm
self.tool_register = tool_register
self.tools = tool_register.get_tools()
def ... | [
"langchain.chains.LLMChain"
] | [((3292, 3320), 'lmchain.agents.llmMultiAgent.AgentZhipuAI', 'llmMultiAgent.AgentZhipuAI', ([], {}), '()\n', (3318, 3320), False, 'from lmchain.agents import llmMultiAgent\n'), ((3381, 3408), 'lmchain.chains.toolchain.GLMToolChain', 'toolchain.GLMToolChain', (['llm'], {}), '(llm)\n', (3403, 3408), False, 'from lmchain.... |
import json
import time
import hashlib
from typing import Dict, Any, List, Tuple
import re
from os import environ
import streamlit as st
from langchain.schema import BaseRetriever
from langchain.tools import Tool
from langchain.pydantic_v1 import BaseModel, Field
from sqlalchemy import Column, Text, create_engine, Me... | [
"langchain.embeddings.SentenceTransformerEmbeddings",
"langchain.chains.query_constructor.base.VirtualColumnName",
"langchain.schema.messages.AIMessage",
"langchain.schema.messages.ToolMessage",
"langchain.pydantic_v1.Field",
"langchain.prompts.ChatPromptTemplate.from_strings",
"langchain.OpenAI",
"la... | [((3163, 3322), 'langchain.prompts.ChatPromptTemplate.from_strings', 'ChatPromptTemplate.from_strings', ([], {'string_messages': "[(SystemMessagePromptTemplate, combine_prompt_template), (\n HumanMessagePromptTemplate, '{question}')]"}), "(string_messages=[(\n SystemMessagePromptTemplate, combine_prompt_template)... |
"""Wrapper around Replicate API."""
import logging
from typing import Any, Dict, List, Mapping, Optional
from pydantic import Extra, Field, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
logger = log... | [
"langchain.utils.get_from_dict_or_env"
] | [((317, 344), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (334, 344), False, 'import logging\n'), ((1212, 1239), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1217, 1239), False, 'from pydantic import Extra, Field, root_validator\n'), ((1275... |
import datetime
import difflib
import logging
import os
from functools import wraps
from queue import Queue
from threading import Thread
from typing import Any, Callable, Dict, List
import numpy as np
import openai
import pandas as pd
import sqlalchemy
from google.api_core.exceptions import GoogleAPIError
from langcha... | [
"langchain.chains.llm.LLMChain",
"langchain.agents.agent.AgentExecutor.from_agent_and_tools",
"langchain.agents.mrkl.base.ZeroShotAgent",
"langchain_community.callbacks.get_openai_callback",
"langchain.agents.mrkl.base.ZeroShotAgent.create_prompt"
] | [((2000, 2027), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (2017, 2027), False, 'import logging\n'), ((2038, 2074), 'dataherald.sql_generator.SQLGenerator.get_upper_bound_limit', 'SQLGenerator.get_upper_bound_limit', ([], {}), '()\n', (2072, 2074), False, 'from dataherald.sql_generato... |
from marqo import Client
import pandas as pd
import numpy as np
from langchain_openai import OpenAI
from langchain.docstore.document import Document
from langchain.chains import LLMChain
from dotenv import load_dotenv
from utilities import (
load_data,
extract_text_from_highlights,
qna_prompt,
predic... | [
"langchain.docstore.document.Document",
"langchain_openai.OpenAI"
] | [((349, 362), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (360, 362), False, 'from dotenv import load_dotenv\n'), ((984, 992), 'marqo.Client', 'Client', ([], {}), '()\n', (990, 992), False, 'from marqo import Client\n'), ((1812, 1823), 'utilities.load_data', 'load_data', ([], {}), '()\n', (1821, 1823), False... |
from fastapi import FastAPI, Form, Request, Response, File, Depends, HTTPException, status
from fastapi.responses import RedirectResponse
from fastapi.staticfiles import StaticFiles
from fastapi.templating import Jinja2Templates
from fastapi.encoders import jsonable_encoder
from langchain.llms import CTransformers... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.prompts.PromptTemplate",
"langchain.vectorstores.FAISS.from_documents",
"langchain.document_loaders.PyPDFLoader",
"langchain.llms.CTransformers",
"langchain.docstore.document.Document",
"langchain.chains.summarize.load_summarize_chain"... | [((911, 920), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (918, 920), False, 'from fastapi import FastAPI, Form, Request, Response, File, Depends, HTTPException, status\n'), ((1008, 1046), 'fastapi.templating.Jinja2Templates', 'Jinja2Templates', ([], {'directory': '"""templates"""'}), "(directory='templates')\n", (... |
#!/usr/bin/env python
"""Example LangChain server exposes a retriever."""
from fastapi import FastAPI
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langserve import add_routes
vectorstore = FAISS.from_texts(
["cats like fish", "dogs like sticks"], embedding=OpenAI... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((381, 515), 'fastapi.FastAPI', 'FastAPI', ([], {'title': '"""LangChain Server"""', 'version': '"""1.0"""', 'description': '"""Spin up a simple api server using Langchain\'s Runnable interfaces"""'}), '(title=\'LangChain Server\', version=\'1.0\', description=\n "Spin up a simple api server using Langchain\'s Runna... |
## Conversational Q&A Chatbot
import streamlit as st
from langchain.schema import HumanMessage,SystemMessage,AIMessage
from langchain.chat_models import ChatOpenAI
## Streamlit UI
st.set_page_config(page_title="Conversational Q&A Chatbot")
st.header("Hey, Let's Chat")
from dotenv import load_dotenv
load_d... | [
"langchain.schema.SystemMessage",
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((189, 248), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Conversational Q&A Chatbot"""'}), "(page_title='Conversational Q&A Chatbot')\n", (207, 248), True, 'import streamlit as st\n'), ((250, 278), 'streamlit.header', 'st.header', (['"""Hey, Let\'s Chat"""'], {}), '("Hey, Let\'s Chat")\n... |
"""Wrapper around Google's PaLM Chat API."""
from __future__ import annotations
import logging
from typing import TYPE_CHECKING, Any, Callable, Dict, List, Mapping, Optional
from pydantic import BaseModel, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_aft... | [
"langchain.schema.AIMessage",
"langchain.utils.get_from_dict_or_env",
"langchain.schema.ChatMessage",
"langchain.schema.HumanMessage",
"langchain.schema.ChatResult"
] | [((792, 819), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (809, 819), False, 'import logging\n'), ((2563, 2598), 'langchain.schema.ChatResult', 'ChatResult', ([], {'generations': 'generations'}), '(generations=generations)\n', (2573, 2598), False, 'from langchain.schema import AIMessag... |
#!/usr/bin/env python
# -*- encoding: utf-8 -*-
'''
@File : create_db.py
@Time : 2023/12/14 10:56:31
@Author : Logan Zou
@Version : 1.0
@Contact : loganzou0421@163.com
@License : (C)Copyright 2017-2018, Liugroup-NLPR-CASIA
@Desc : 知识库搭建
'''
# 首先导入所需第三方库
from langchain.document_loaders import U... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain.vectorstores.Chroma.from_documents",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredFileLoader",
"langchain.document_loaders.UnstructuredMarkdownLoader"
] | [((2018, 2083), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(150)'}), '(chunk_size=500, chunk_overlap=150)\n', (2048, 2083), False, 'from langchain.text_splitter import RecursiveCharacterTextSplitter\n'), ((2164, 2237), 'lang... |
from flask import Flask, request
from flask_restful import Resource, Api, reqparse, abort
from werkzeug.utils import secure_filename
########################################################################
import tempfile
import os
from langchain.document_loaders import DirectoryLoader, PyMuPDFLoader
from langchain.te... | [
"langchain.vectorstores.Pinecone.from_existing_index",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.chat_models.ChatOpenAI",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.DirectoryLoader",
"... | [((718, 731), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (729, 731), False, 'from dotenv import load_dotenv\n'), ((753, 785), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (767, 785), False, 'import os\n'), ((809, 843), 'os.environ.get', 'os.environ.get', (['"""... |
from langchain.llms import LlamaCpp
from langchain.embeddings import HuggingFaceEmbeddings
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
def hf_embeddings():
return HuggingFaceEmbeddings(
model_name = "sentence-transf... | [
"langchain.llms.LlamaCpp",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler"
] | [((260, 335), 'langchain.embeddings.HuggingFaceEmbeddings', 'HuggingFaceEmbeddings', ([], {'model_name': '"""sentence-transformers/all-mpnet-base-v2"""'}), "(model_name='sentence-transformers/all-mpnet-base-v2')\n", (281, 335), False, 'from langchain.embeddings import HuggingFaceEmbeddings\n'), ((456, 642), 'langchain.... |
import os
import yaml
from types import SimpleNamespace
import openai
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity
from langchain.vectorstores import FAISS
from langchain.embeddings import HuggingFaceEmbeddings
with open("config.yml") as f:
config = yaml.safe_load(f)
config = SimpleN... | [
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.vectorstores.FAISS.load_local"
] | [((313, 338), 'types.SimpleNamespace', 'SimpleNamespace', ([], {}), '(**config)\n', (328, 338), False, 'from types import SimpleNamespace\n'), ((286, 303), 'yaml.safe_load', 'yaml.safe_load', (['f'], {}), '(f)\n', (300, 303), False, 'import yaml\n'), ((602, 627), 'numpy.argsort', 'np.argsort', (['(-similarities)'], {})... |
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
from langchain_app.models.vicuna_request_llm import VicunaLLM
# First, let's load the language model we're going to use to control the agent.
llm = VicunaLLM()
# Next, let's load some tools to... | [
"langchain.agents.load_tools",
"langchain_app.models.vicuna_request_llm.VicunaLLM",
"langchain.agents.initialize_agent"
] | [((275, 286), 'langchain_app.models.vicuna_request_llm.VicunaLLM', 'VicunaLLM', ([], {}), '()\n', (284, 286), False, 'from langchain_app.models.vicuna_request_llm import VicunaLLM\n'), ((405, 441), 'langchain.agents.load_tools', 'load_tools', (["['python_repl']"], {'llm': 'llm'}), "(['python_repl'], llm=llm)\n", (415, ... |
import logging
import sys
from typing import Callable
from langchain.prompts import MessagesPlaceholder
from langchain.agents import AgentType, AgentExecutor
from langchain.agents import initialize_agent as initialize_agent_base
from langchain.agents.agent_toolkits.base import BaseToolkit
from langchain.chains.base i... | [
"langchain.agents.initialize_agent",
"langchain.prompts.MessagesPlaceholder"
] | [((343, 370), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (360, 370), False, 'import logging\n'), ((2107, 2151), 'langchain.agents.initialize_agent', 'initialize_agent_base', ([], {'agent': 'agent'}), '(agent=agent, **kwargs)\n', (2128, 2151), True, 'from langchain.agents import initia... |
import os
os.environ["LANGCHAIN_TRACING"] = "true"
from langchain import OpenAI
from langchain.agents import initialize_agent, AgentType
from langchain.llms import OpenAI
from langchain.agents import initialize_agent, Tool
from langchain.agents import AgentType
def multiplier(a, b):
return a / b
def parsing_mu... | [
"langchain.llms.OpenAI",
"langchain.agents.initialize_agent",
"langchain.agents.Tool"
] | [((412, 433), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (418, 433), False, 'from langchain.llms import OpenAI\n'), ((826, 917), 'langchain.agents.initialize_agent', 'initialize_agent', (['tools', 'llm'], {'agent': 'AgentType.ZERO_SHOT_REACT_DESCRIPTION', 'verbose': '(True)'})... |
# Copyright 2023 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, ... | [
"langchain.memory.ConversationBufferMemory",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.llms.vertexai.VertexAI"
] | [((957, 985), 'sys.path.append', 'sys.path.append', (['current_dir'], {}), '(current_dir)\n', (972, 985), False, 'import sys\n'), ((1302, 1333), 'os.getenv', 'os.getenv', (['"""GOOGLE_CLOUD_REGIN"""'], {}), "('GOOGLE_CLOUD_REGIN')\n", (1311, 1333), False, 'import os\n'), ((1347, 1380), 'os.getenv', 'os.getenv', (['"""G... |
import boto3
from botocore.exceptions import ClientError
import json
import langchain
from importlib import reload
from langchain.agents.structured_chat import output_parser
from typing import List
import logging
import os
import sqlalchemy
from sqlalchemy import create_engine
from langchain.docstore.document import Do... | [
"langchain.tools.python.tool.PythonREPLTool",
"langchain.llms.bedrock.Bedrock",
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.PromptTemplate",
"langchain... | [((1364, 1397), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (1382, 1397), True, 'import streamlit as st\n'), ((1532, 1553), 'utility.custom_logga.Logger', 'custom_logga.Logger', ([], {}), '()\n', (1551, 1553), False, 'from utility import get_cfn_details, custo... |
from langchain.agents.agent_toolkits import create_python_agent
from langchain.tools.python.tool import PythonREPLTool
from langchain.python import PythonREPL
from langchain.llms.openai import OpenAI
from langchain.agents.agent_types import AgentType
from langchain.chat_models import ChatOpenAI
import os
agent_execut... | [
"langchain.tools.python.tool.PythonREPLTool",
"langchain.llms.openai.OpenAI"
] | [((354, 394), 'langchain.llms.openai.OpenAI', 'OpenAI', ([], {'temperature': '(0.5)', 'max_tokens': '(2000)'}), '(temperature=0.5, max_tokens=2000)\n', (360, 394), False, 'from langchain.llms.openai import OpenAI\n'), ((405, 421), 'langchain.tools.python.tool.PythonREPLTool', 'PythonREPLTool', ([], {}), '()\n', (419, 4... |
"""Loaders for Prefect."""
import asyncio
import httpx
import os
import shutil
import tempfile
from pathlib import Path
from typing import List
from langchain.docstore.document import Document
from langchain.document_loaders.base import BaseLoader
from langchain_prefect.types import GitHubComment, GitHubIssue
from pre... | [
"langchain_prefect.types.GitHubIssue",
"langchain.docstore.document.Document",
"langchain_prefect.types.GitHubComment"
] | [((4368, 4386), 'tempfile.mkdtemp', 'tempfile.mkdtemp', ([], {}), '()\n', (4384, 4386), False, 'import tempfile\n'), ((944, 974), 'os.environ.get', 'os.environ.get', (['"""GITHUB_TOKEN"""'], {}), "('GITHUB_TOKEN')\n", (958, 974), False, 'import os\n'), ((1493, 1590), 'httpx.get', 'httpx.get', ([], {'url': 'url', 'heade... |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.llms import OpenAI
from benchllm import SemanticEvaluator, Test, Tester
tools = load_tools(["serpapi", "llm-math"], llm=OpenAI(temperature=0))
agent = initialize_agent(tools, OpenAI(temperature=0), agent=AgentType.ZERO_SHOT_REACT_DESC... | [
"langchain.llms.OpenAI"
] | [((569, 588), 'benchllm.SemanticEvaluator', 'SemanticEvaluator', ([], {}), '()\n', (586, 588), False, 'from benchllm import SemanticEvaluator, Test, Tester\n'), ((261, 282), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (267, 282), False, 'from langchain.llms import OpenAI\n'), (... |
"""Wrapper around HuggingFace Pipeline APIs."""
import importlib.util
import logging
from typing import Any, List, Mapping, Optional
from pydantic import BaseModel, Extra
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
DEFAULT_MODEL_ID = "gpt2"
DEFAULT_TASK = "text-generation... | [
"langchain.llms.utils.enforce_stop_tokens"
] | [((390, 409), 'logging.getLogger', 'logging.getLogger', ([], {}), '()\n', (407, 409), False, 'import logging\n'), ((2546, 2602), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['model_id'], {}), '(model_id, **_model_kwargs)\n', (2575, 2602), False, 'from transformers import AutoModelFor... |
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain.chains.query_constructor.base import AttributeInfo
from datetime import datetime
current_time_iso = datetime.utcnow().isoformat() + "Z"
# example metadat
"""
{
"type": "file_load_gcs",
"attrs": "namespace:edmonbrain",
"source": ... | [
"langchain.chains.query_constructor.base.AttributeInfo",
"langchain.retrievers.self_query.base.SelfQueryRetriever.from_llm"
] | [((1179, 1311), 'langchain.chains.query_constructor.base.AttributeInfo', 'AttributeInfo', ([], {'name': '"""source"""', 'description': '"""The document source url or path to where the document is located"""', 'type': '"""string"""'}), "(name='source', description=\n 'The document source url or path to where the docu... |
import os
import re
from typing import List, Optional, Any
from langchain.schema import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from loguru import logger
from tqdm import tqdm
from src.config import local_embedding, retrieve_proxy, chunk_overlap, chunk_size, hf_emb_model_name
from ... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain_community.vectorstores.FAISS.load_local",
"langchain_community.document_loaders.TextLoader",
"langchain_community.vectorstores.FAISS.from_documents",
"langchain.document_loaders.UnstructuredWordDocumentLoader",
"langchain_community.embed... | [((440, 465), 'os.path.dirname', 'os.path.dirname', (['__file__'], {}), '(__file__)\n', (455, 465), False, 'import os\n'), ((3874, 3910), 'loguru.logger.debug', 'logger.debug', (['"""Loading documents..."""'], {}), "('Loading documents...')\n", (3886, 3910), False, 'from loguru import logger\n'), ((3915, 3956), 'loguru... |
from fastapi import FastAPI
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import ElasticVectorSearch
from config import openai_api_key
embedding = OpenAIEmbeddings(openai_api_key=openai_api_key... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.vectorstores.ElasticVectorSearch",
"langchain.chat_models.ChatOpenAI"
] | [((274, 321), 'langchain.embeddings.openai.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'openai_api_key': 'openai_api_key'}), '(openai_api_key=openai_api_key)\n', (290, 321), False, 'from langchain.embeddings.openai import OpenAIEmbeddings\n'), ((328, 444), 'langchain.vectorstores.ElasticVectorSearch', 'ElasticVectorSe... |
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Dict, List
import pandas as pd
import streamlit as st
from langchain.chains import LLMChain
from langchain.prompts.few_shot import FewShotPromptTemplate
from doccano_mini.components import (
display_download_button,
openai_model_f... | [
"langchain.chains.LLMChain"
] | [((763, 785), 'pandas.read_json', 'pd.read_json', (['filepath'], {}), '(filepath)\n', (775, 785), True, 'import pandas as pd\n'), ((921, 984), 'streamlit.experimental_data_editor', 'st.experimental_data_editor', (['df'], {'num_rows': '"""dynamic"""', 'width': '(1000)'}), "(df, num_rows='dynamic', width=1000)\n", (948, ... |
"""This module contains functions for loading and managing vector stores in the Wandbot ingestion system.
The module includes the following functions:
- `load`: Loads the vector store from the specified source artifact path and returns the name of the resulting artifact.
Typical usage example:
project = "wandbot... | [
"langchain.schema.Document"
] | [((944, 964), 'wandbot.utils.get_logger', 'get_logger', (['__name__'], {}), '(__name__)\n', (954, 964), False, 'from wandbot.utils import get_logger, load_index, load_service_context, load_storage_context\n'), ((1677, 1696), 'wandbot.ingestion.config.VectorStoreConfig', 'VectorStoreConfig', ([], {}), '()\n', (1694, 169... |
from textwrap import dedent
from langchain import OpenAI
from langchain.schema import BaseModel
from utils import format_prompt_components_without_tools
def extract_first_message(message: str) -> str:
"""The LLM can continue the conversation from the recipient. So extract just the first line."""
return mes... | [
"langchain.OpenAI"
] | [((627, 656), 'textwrap.dedent', 'dedent', (['inspirational_thought'], {}), '(inspirational_thought)\n', (633, 656), False, 'from textwrap import dedent\n'), ((912, 981), 'utils.format_prompt_components_without_tools', 'format_prompt_components_without_tools', (['ai_settings', 'contact_settings'], {}), '(ai_settings, c... |
"""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
import sqlalchemy
from pgvector.sqlalchemy import Vector
from sqlalchemy.dialects.postgresql import JSON, UUID
fr... | [
"langchain.docstore.document.Document",
"langchain.utils.get_from_dict_or_env"
] | [((593, 611), 'sqlalchemy.orm.declarative_base', 'declarative_base', ([], {}), '()\n', (609, 611), False, 'from sqlalchemy.orm import Session, declarative_base, relationship\n'), ((929, 965), 'sqlalchemy.Column', 'sqlalchemy.Column', (['sqlalchemy.String'], {}), '(sqlalchemy.String)\n', (946, 965), False, 'import sqlal... |
import tempfile
import time
import os
from utils import compute_sha1_from_file
from langchain.schema import Document
import streamlit as st
from langchain.text_splitter import RecursiveCharacterTextSplitter
from typing import List
from sqlite3 import Connection
from verse.sqlite_helper import *
def update_metadata(co... | [
"langchain.schema.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder"
] | [((1152, 1175), 'time.strftime', 'time.strftime', (['"""%Y%m%d"""'], {}), "('%Y%m%d')\n", (1165, 1175), False, 'import time\n'), ((1468, 1492), 'os.remove', 'os.remove', (['tmp_file.name'], {}), '(tmp_file.name)\n', (1477, 1492), False, 'import os\n'), ((1679, 1812), 'langchain.text_splitter.RecursiveCharacterTextSplit... |
import json
import logging
from typing import Any, Dict, Iterator, List, Optional
import requests
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.pydantic_v1 import Field
from langchain.schema.output import GenerationChunk
logger = logging.getLogger... | [
"langchain.pydantic_v1.Field",
"langchain.schema.output.GenerationChunk"
] | [((303, 330), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (320, 330), False, 'import logging\n'), ((1278, 1308), 'langchain.pydantic_v1.Field', 'Field', (['(True)'], {'alias': '"""do_sample"""'}), "(True, alias='do_sample')\n", (1283, 1308), False, 'from langchain.pydantic_v1 import Fi... |
# imports
from loguru import logger
# LLM modules
from langchain_community.llms.huggingface_hub import HuggingFaceHub
from langchain_community.llms.ollama import Ollama
from langchain_openai import ChatOpenAI, AzureChatOpenAI
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_std... | [
"langchain_openai.AzureChatOpenAI",
"langchain_community.llms.huggingface_hub.HuggingFaceHub",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain_openai.ChatOpenAI"
] | [((1610, 1675), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'client': 'None', 'model': 'self.llm_model_type', 'temperature': '(0)'}), '(client=None, model=self.llm_model_type, temperature=0)\n', (1620, 1675), False, 'from langchain_openai import ChatOpenAI, AzureChatOpenAI\n'), ((2163, 2272), 'langchain_communit... |
from typing import List
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain_core.documents import Document
from dotenv import load_dotenv
from themind.llm.func_instraction import instruct
from pydantic import BaseModel
import csv
from themind.vectorstores.chunking.... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((657, 675), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (673, 675), False, 'from langchain.embeddings import OpenAIEmbeddings\n')] |
import re
import time
import copy
import random
import numpy as np
import multiprocessing
import matplotlib.pyplot as plt
import modules.prompts as prompts
from langchain import PromptTemplate
from shapely.ops import substring
from shapely.geometry import Polygon, box, Point, LineString
class WallObjectGenerator():
... | [
"langchain.PromptTemplate"
] | [((704, 850), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['room_type', 'wall_height', 'floor_objects', 'wall_objects']", 'template': 'prompts.wall_object_constraints_prompt'}), "(input_variables=['room_type', 'wall_height', 'floor_objects',\n 'wall_objects'], template=prompts.wall_object... |
from langchain_openai import ChatOpenAI
from langchain_core.output_parsers import StrOutputParser
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import ChatPromptTemplate
from config.config import OPENAI_API_KEY
from game.poker import PokerGameManager
from db.db_utils import DatabaseManager
im... | [
"langchain_core.output_parsers.StrOutputParser",
"langchain.prompts.chat.ChatPromptTemplate.from_messages",
"langchain_openai.ChatOpenAI"
] | [((456, 489), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'model_name'}), '(model_name=model_name)\n', (466, 489), False, 'from langchain_openai import ChatOpenAI\n'), ((514, 531), 'langchain_core.output_parsers.StrOutputParser', 'StrOutputParser', ([], {}), '()\n', (529, 531), False, 'from langcha... |
import logging
from typing import Any, Dict, List, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import Extra, root_validator
from langchain.schema import Generation, LLMResul... | [
"langchain.schema.Generation",
"langchain.utils.get_from_dict_or_env",
"langchain.pydantic_v1.root_validator",
"langchain.schema.LLMResult",
"langchain.llms.utils.enforce_stop_tokens"
] | [((381, 408), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (398, 408), False, 'import logging\n'), ((1472, 1488), 'langchain.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (1486, 1488), False, 'from langchain.pydantic_v1 import Extra, root_validator\n'), ((1702, 1753),... |
"""This example shows how to use the ChatGPT API
with LangChain to answer questions about Prefect."""
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.text_splitter import CharacterTextSplitter
from langchain.chains import ChatVectorDBChain
from langchai... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain_prefect.loaders.GitHubRepoLoader",
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain... | [((680, 735), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'chunk_size': '(1000)', 'chunk_overlap': '(0)'}), '(chunk_size=1000, chunk_overlap=0)\n', (701, 735), False, 'from langchain.text_splitter import CharacterTextSplitter\n'), ((803, 821), 'langchain.embeddings.openai.OpenAIEmbed... |
from dotenv import load_dotenv
load_dotenv()
import os
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from langchain.prompts import (
PromptTemplate,
)
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain.agents import A... | [
"langchain.memory.ConversationBufferMemory",
"langchain.agents.ConversationalChatAgent.from_llm_and_tools",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.chat_models.ChatOpenAI"
] | [((31, 44), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (42, 44), False, 'from dotenv import load_dotenv\n'), ((574, 601), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (583, 601), False, 'import os\n'), ((1087, 1183), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([],... |
from __future__ import annotations
from abc import abstractmethod
from typing import TYPE_CHECKING, Any, Dict, List, Sequence
from langchain.load.serializable import Serializable
from langchain.pydantic_v1 import Field
if TYPE_CHECKING:
from langchain.prompts.chat import ChatPromptTemplate
def get_buffer_strin... | [
"langchain.pydantic_v1.Field",
"langchain.prompts.chat.ChatPromptTemplate"
] | [((2151, 2178), 'langchain.pydantic_v1.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (2156, 2178), False, 'from langchain.pydantic_v1 import Field\n'), ((2610, 2645), 'langchain.prompts.chat.ChatPromptTemplate', 'ChatPromptTemplate', ([], {'messages': '[self]'}), '(messages=[self])\n',... |
import logging
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.pydantic_v1 import BaseModel, Extra, Field, root_validator
from langchain.utils i... | [
"langchain.pydantic_v1.Field",
"langchain.llms.utils.enforce_stop_tokens",
"langchain.pydantic_v1.root_validator",
"langchain.utils.get_from_dict_or_env"
] | [((357, 384), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (374, 384), False, 'import logging\n'), ((1004, 1031), 'langchain.pydantic_v1.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1009, 1031), False, 'from langchain.pydantic_v1 import BaseModel, Ext... |
from typing import Optional, Type
import streamlit as st
import tldextract
import whois
import whoisit
from langchain.agents import AgentType, Tool, initialize_agent
from langchain.chat_models import ChatOpenAI
from langchain.tools import BaseTool
from langchain.tools.ddg_search import DuckDuckGoSearchRun
from pydanti... | [
"langchain.tools.ddg_search.DuckDuckGoSearchRun",
"langchain.agents.initialize_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.agents.Tool"
] | [((363, 390), 'streamlit.title', 'st.title', (['"""TakedownGPT ⬇️🤖"""'], {}), "('TakedownGPT ⬇️🤖')\n", (371, 390), True, 'import streamlit as st\n'), ((434, 467), 'streamlit.sidebar.header', 'st.sidebar.header', (['"""How to Use 📝"""'], {}), "('How to Use 📝')\n", (451, 467), True, 'import streamlit as st\n'), ((468... |
import sqlite3
import pandas as pd
import os
import json
import warnings
from langchain import SQLDatabase
from langchain.docstore.document import Document
from langchain.vectorstores import Chroma
from langchain.embeddings import HuggingFaceEmbeddings
from sqlalchemy import exc
from sqlalchemy.exc import SAWarning
... | [
"langchain.embeddings.HuggingFaceEmbeddings"
] | [((320, 373), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {'category': 'SAWarning'}), "('ignore', category=SAWarning)\n", (343, 373), False, 'import warnings\n'), ((973, 1033), 'src.data.setup.db_setup_functions.build_schema_info', 'build_schema_info', ([], {'filepath': 'data_directory', 'f... |
from langchain.llms import OpenAI
from callback import MyCallbackHandler
from langchain.callbacks.base import BaseCallbackManager
class QaLlm():
def __init__(self) -> None:
manager = BaseCallbackManager([MyCallbackHandler()])
self.llm = OpenAI(temperature=0, callback_manager=manager, model_name="g... | [
"langchain.llms.OpenAI"
] | [((259, 334), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0)', 'callback_manager': 'manager', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, callback_manager=manager, model_name='gpt-3.5-turbo')\n", (265, 334), False, 'from langchain.llms import OpenAI\n'), ((218, 237), 'callback.MyCallbackHandle... |
from langchain.llms import OpenAI
from langchain.chat_models import ChatOpenAI
from apikey import (
apikey,
google_search,
google_cse,
serp,
aws_access_key,
aws_secret_key,
aws_region,
)
import os
from typing import Dict
from langchain.prompts import PromptTemplate
from langchain.chains impo... | [
"langchain.chains.LLMChain",
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.memory.ConversationBufferMemory",
"langchain.utilities.GoogleSearchAPIWrapper"
] | [((765, 835), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.3)', 'max_tokens': '(100)', 'model_name': '"""text-davinci-003"""'}), "(temperature=0.3, max_tokens=100, model_name='text-davinci-003')\n", (771, 835), False, 'from langchain.llms import OpenAI\n'), ((860, 886), 'langchain.memory.ConversationBuff... |
from langchain.retrievers import AmazonKendraRetriever
from langchain.chains import ConversationalRetrievalChain
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
from langchain.prompts import PromptTemplate
import sys
import json
import os
class bcolors:
HEAD... | [
"langchain.retrievers.AmazonKendraRetriever",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.PromptTemplate.from_template",
"langchain.prompts.PromptTemplate",
"langchain.SagemakerEndpoint"
] | [((1327, 1604), 'langchain.SagemakerEndpoint', 'SagemakerEndpoint', ([], {'endpoint_name': 'endpoint_name', 'region_name': 'region', 'model_kwargs': "{'temperature': 0.8, 'max_new_tokens': 512, 'do_sample': True, 'top_p': 0.9,\n 'repetition_penalty': 1.03, 'stop': ['\\nUser:', '<|endoftext|>', '</s>']}", 'content_ha... |
#Make sure to install the following packages: dlt, langchain, duckdb, python-dotenv, openai, weaviate-client
import dlt
from langchain import PromptTemplate, LLMChain
from langchain.chains.openai_functions import create_structured_output_chain
from langchain.chat_models import ChatOpenAI
from langchain.document_loader... | [
"langchain.PromptTemplate",
"langchain.LLMMathChain.from_llm",
"langchain.prompts.ChatPromptTemplate",
"langchain.schema.HumanMessage",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.document_loaders.PyPDFLoader",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.ope... | [((741, 754), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (752, 754), False, 'from dotenv import load_dotenv\n'), ((848, 866), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (864, 866), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1129, 1146), 'langchain.do... |
import logging
from time import sleep
from langchain.llms import OpenAI
from scrapy import Request, Spider
from selenium import webdriver
from selenium.webdriver.common.keys import Keys
from conf import (
CONNECTION_REQUEST_LLM_PROMPT,
DEFAULT_CONNECTION_MESSAGE,
MAX_PROFILES_TO_CONNECT,
MAX_PROFILES_... | [
"langchain.PromptTemplate.from_template",
"langchain.llms.OpenAI"
] | [((689, 716), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (706, 716), False, 'import logging\n'), ((1186, 1271), 'linkedin.integrations.selenium.get_by_xpath_or_none', 'get_by_xpath_or_none', (['driver', '"""//button[@aria-label="Got it"]"""'], {'wait_timeout': '(0.5)'}), '(driver, \'/... |
import streamlit as st
import os
from azure.storage.blob import BlobServiceClient, BlobClient, ContainerClient
from PyPDF2 import PdfReader
# Import
#import textwrap
import openai
from langchain.llms import AzureOpenAI, OpenAI
from langchain.embeddings import OpenAIEmbeddings
from llama_index.vector_stores import Redis... | [
"langchain.llms.OpenAI",
"langchain.llms.AzureOpenAI",
"langchain.embeddings.OpenAIEmbeddings"
] | [((558, 616), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (577, 616), False, 'import logging\n'), ((744, 780), 'os.getenv', 'os.getenv', (['"""REDIS_HOST"""', '"""localhost"""'], {}), "('REDIS_HOST', 'localhost')\n",... |
from langchain.chat_models import ChatOpenAI
from langchain.schema import HumanMessage, SystemMessage
from whenx.models.team import Team
from whenx.models.scout import Scout
from whenx.models.sentinel import Sentinel
from whenx.models.soldier import Soldier
import re
from whenx.database import db
class Captain:
... | [
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((575, 587), 'whenx.database.db.add', 'db.add', (['team'], {}), '(team)\n', (581, 587), False, 'from whenx.database import db\n'), ((596, 607), 'whenx.database.db.commit', 'db.commit', ([], {}), '()\n', (605, 607), False, 'from whenx.database import db\n'), ((624, 675), 'whenx.models.scout.Scout', 'Scout', ([], {'inst... |
import json
import re
from langchain.chains import RetrievalQA
from utils.functions import find_nth, remove_extra_heading, add_json_characters, Timeout
from langchain import LLMChain
from langchain.chat_models import ChatOpenAI
def section_schemas(heading, keyword, format_instructions, retriever, prompt):
ch... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.LLMChain",
"langchain.chat_models.ChatOpenAI"
] | [((325, 387), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo-16k-0613"""'}), "(temperature=0, model_name='gpt-3.5-turbo-16k-0613')\n", (335, 387), False, 'from langchain.chat_models import ChatOpenAI\n'), ((433, 466), 'langchain.LLMChain', 'LLMChain', ([], ... |
"""Experiment with different models."""
from __future__ import annotations
from typing import List, Optional, Sequence
from langchain_core.language_models.llms import BaseLLM
from langchain_core.prompts.prompt import PromptTemplate
from langchain_core.utils.input import get_color_mapping, print_text
from langchain.... | [
"langchain.chains.llm.LLMChain",
"langchain_core.utils.input.get_color_mapping",
"langchain_core.prompts.prompt.PromptTemplate",
"langchain_core.utils.input.print_text"
] | [((1752, 1782), 'langchain_core.utils.input.get_color_mapping', 'get_color_mapping', (['chain_range'], {}), '(chain_range)\n', (1769, 1782), False, 'from langchain_core.utils.input import get_color_mapping, print_text\n'), ((2307, 2370), 'langchain_core.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_var... |
# Copyright Amazon.com, Inc. or its affiliates. All Rights Reserved. 2023
# SPDX-License-Identifier: Apache-2.0
from typing import Any, Dict, List, Optional
from langchain.agents import tool
from langchain.chains.base import Chain
from langchain.chains import LLMChain
from langchain import PromptTemplate
from langcha... | [
"langchain.chains.LLMChain",
"langchain.callbacks.manager.CallbackManagerForChainRun.get_noop_manager"
] | [((1178, 1216), 'chainlit.context.context.session.emit', 'context.session.emit', (['"""view"""', 'entityId'], {}), "('view', entityId)\n", (1198, 1216), False, 'from chainlit.context import context\n'), ((2370, 2412), 'langchain.chains.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=p... |
from langchain.retrievers import AmazonKendraRetriever
from langchain.chains import RetrievalQA
from langchain import OpenAI
from langchain.prompts import PromptTemplate
from langchain import SagemakerEndpoint
from langchain.llms.sagemaker_endpoint import LLMContentHandler
import json
import os
def build_chain():
... | [
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.prompts.PromptTemplate",
"langchain.retrievers.AmazonKendraRetriever",
"langchain.SagemakerEndpoint"
] | [((1839, 1906), 'langchain.retrievers.AmazonKendraRetriever', 'AmazonKendraRetriever', ([], {'index_id': 'kendra_index_id', 'region_name': 'region'}), '(index_id=kendra_index_id, region_name=region)\n', (1860, 1906), False, 'from langchain.retrievers import AmazonKendraRetriever\n'), ((2373, 2458), 'langchain.prompts.P... |
'''
This script takes the True/False style questions from the csv file and save the result as another csv file.
This script makes use of Llama model.
Before running this script, make sure to configure the filepaths in config.yaml file.
'''
from langchain import PromptTemplate, LLMChain
from kg_rag.utility import *
im... | [
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((1786, 1860), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'template', 'input_variables': "['context', 'question']"}), "(template=template, input_variables=['context', 'question'])\n", (1800, 1860), False, 'from langchain import PromptTemplate, LLMChain\n'), ((1877, 1909), 'langchain.LLMChain', 'LL... |
import os
from typing import Any, Callable
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
from langchain.chains import LLMChain
import registry
from .base import BaseChat, ChatHistory, Response
TEMPLATE = '''
You are a web3 assistant. You help users use web3 apps, such as Uniswap, AA... | [
"langchain.llms.OpenAI",
"langchain.prompts.PromptTemplate",
"langchain.chains.LLMChain"
] | [((2418, 2494), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['task_info', 'question']", 'template': 'TEMPLATE'}), "(input_variables=['task_info', 'question'], template=TEMPLATE)\n", (2432, 2494), False, 'from langchain.prompts import PromptTemplate\n'), ((2549, 2587), 'langchain.llms... |
from typing import List
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
import langchain.docstore.document as docstore
from loguru import logger
from settings import COLLECTION_NAME, PERSIST_DIRECTORY
from .vortex_pdf_parser import VortexPdfParser
from .vortext_content_iter... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.Chroma.from_documents"
] | [((985, 1014), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {'client': 'None'}), '(client=None)\n', (1001, 1014), False, 'from langchain.embeddings import OpenAIEmbeddings\n'), ((1023, 1055), 'loguru.logger.info', 'logger.info', (['"""Loaded embeddings"""'], {}), "('Loaded embeddings')\n", (1034, 1... |
# -*- coding: utf-8 -*-
import os
import re
import sys
sys.path.append('.')
sys.path.append('..')
from langchain.agents import Tool, AgentExecutor, LLMSingleActionAgent, AgentOutputParser
from langchain.prompts import StringPromptTemplate
from langchain import OpenAI, GoogleSearchAPIWrapper, LLMChain
from typing import... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.GoogleSearchAPIWrapper",
"langchain.schema.Document",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.agents.LLMSingleActionAgent",
"langchain.LLMChain",
"langchain.OpenAI",
"langchain.agents.Tool"
] | [((55, 75), 'sys.path.append', 'sys.path.append', (['"""."""'], {}), "('.')\n", (70, 75), False, 'import sys\n'), ((76, 97), 'sys.path.append', 'sys.path.append', (['""".."""'], {}), "('..')\n", (91, 97), False, 'import sys\n'), ((4014, 4038), 'langchain.GoogleSearchAPIWrapper', 'GoogleSearchAPIWrapper', ([], {}), '()\... |
import base64
from email.message import EmailMessage
from typing import List, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.gmail.base import GmailBaseTool
class CreateDraftSchema(BaseModel):
"""Input for C... | [
"langchain.pydantic_v1.Field"
] | [((359, 421), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""The message to include in the draft."""'}), "(..., description='The message to include in the draft.')\n", (364, 421), False, 'from langchain.pydantic_v1 import BaseModel, Field\n'), ((465, 514), 'langchain.pydantic_v1.Field', 'Field', ... |
from langchain import PromptTemplate
from langchain.chains.summarize import load_summarize_chain
from langchain.chains.question_answering import load_qa_chain
from langchain.llms import OpenAI
from langchain.docstore.document import Document
base_prompt = """A profound and powerful writer, you have been given a contex... | [
"langchain.PromptTemplate",
"langchain.llms.OpenAI",
"langchain.chains.question_answering.load_qa_chain",
"langchain.docstore.document.Document",
"langchain.chains.summarize.load_summarize_chain"
] | [((1309, 1372), 'langchain.PromptTemplate', 'PromptTemplate', ([], {'template': 'final_prompt', 'input_variables': "['text']"}), "(template=final_prompt, input_variables=['text'])\n", (1323, 1372), False, 'from langchain import PromptTemplate\n'), ((1399, 1561), 'langchain.chains.summarize.load_summarize_chain', 'load_... |
import streamlit as st
from langchain.llms import OpenAI
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chains import RetrievalQA
def generate_response(uploaded_file, openai_api_key, query_text):
#... | [
"langchain.llms.OpenAI",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.Chroma.from_documents"
] | [((1040, 1091), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""🦜🔗 Ask the Doc App"""'}), "(page_title='🦜🔗 Ask the Doc App')\n", (1058, 1091), True, 'import streamlit as st\n'), ((1092, 1122), 'streamlit.title', 'st.title', (['"""🦜🔗 Ask the Doc App"""'], {}), "('🦜🔗 Ask the Doc App')\n... |
import os
import os.path as osp
from typing import List
from tqdm import tqdm
from langchain.docstore.document import Document
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.text_splitter import NLTKTextSplitter
from langchain.vectorstores.faiss import FAISS
import pandas as pd
import nltk
nltk... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.docstore.document.Document",
"langchain.text_splitter.NLTKTextSplitter"
] | [((316, 338), 'nltk.download', 'nltk.download', (['"""punkt"""'], {}), "('punkt')\n", (329, 338), False, 'import nltk\n'), ((810, 843), 'langchain.text_splitter.NLTKTextSplitter', 'NLTKTextSplitter', ([], {'chunk_size': '(1024)'}), '(chunk_size=1024)\n', (826, 843), False, 'from langchain.text_splitter import NLTKTextS... |
"""
相关资料：
llama-cpp-python文档：https://llama-cpp-python.readthedocs.io/en/latest/
前提：
1.安装C++环境
https://developer.microsoft.com/en-us/windows/downloads/windows-sdk/
勾选“使用C++桌面开发”
2.安装模块
pip install llama-cpp-python
pip install llama-cpp-python[server]
3.运行服务
python... | [
"langchain.llms.llamacpp.LlamaCpp",
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.prompts.PromptTemplate.from_template",
"langchain.vectorstores.Chroma.from_documents",
"langchain.document_loaders.DirectoryLoader",
"langchain.chains... | [((2537, 2663), 'langchain.llms.llamacpp.LlamaCpp', 'LlamaCpp', ([], {'model_path': '"""G:\\\\models\\\\llama2\\\\llama-2-7b-chat-q4\\\\llama-2-7b-chat.Q4_0.gguf"""', 'n_ctx': '(2048)', 'stop': "['Human:']"}), "(model_path=\n 'G:\\\\models\\\\llama2\\\\llama-2-7b-chat-q4\\\\llama-2-7b-chat.Q4_0.gguf',\n n_ctx=204... |
from langchain.tools import tool
from graph_chain import get_results
@tool("graph-tool")
def graph_tool(query:str) -> str:
"""Tool for returning aggregations of Manager or Company or Industry data or if answer is dependent on relationships between a Company and other objects. Use this tool second and to verify res... | [
"langchain.tools.tool"
] | [((71, 89), 'langchain.tools.tool', 'tool', (['"""graph-tool"""'], {}), "('graph-tool')\n", (75, 89), False, 'from langchain.tools import tool\n'), ((366, 384), 'graph_chain.get_results', 'get_results', (['query'], {}), '(query)\n', (377, 384), False, 'from graph_chain import get_results\n')] |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.