code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
#
# Copyright 2016 The BigDL Authors.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | [
"langchain.llms.utils.enforce_stop_tokens"
] | [((5354, 5476), 'transformers.pipeline', 'hf_pipeline', ([], {'task': 'task', 'model': 'model', 'tokenizer': 'tokenizer', 'device': '"""cpu"""', 'model_kwargs': '_model_kwargs'}), "(task=task, model=model, tokenizer=tokenizer, device='cpu',\n model_kwargs=_model_kwargs, **_pipeline_kwargs)\n", (5365, 5476), True, 'f... |
#
# Copyright 2016 The BigDL Authors.
#
# 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
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in ... | [
"langchain.llms.utils.enforce_stop_tokens"
] | [((5354, 5476), 'transformers.pipeline', 'hf_pipeline', ([], {'task': 'task', 'model': 'model', 'tokenizer': 'tokenizer', 'device': '"""cpu"""', 'model_kwargs': '_model_kwargs'}), "(task=task, model=model, tokenizer=tokenizer, device='cpu',\n model_kwargs=_model_kwargs, **_pipeline_kwargs)\n", (5365, 5476), True, 'f... |
from typing import AsyncGenerator, Optional, Tuple
from langchain import ConversationChain
import logging
from typing import Optional, Tuple
from pydantic.v1 import SecretStr
from vocode.streaming.agent.base_agent import RespondAgent
from vocode.streaming.agent.utils import get_sentence_from_buffer
from langchain im... | [
"langchain_community.chat_models.ChatAnthropic",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"langchain.schema.HumanMessage",
"langchain.schema.AIMessage",
"langchain.ConversationChain"
] | [((2147, 2238), 'langchain_community.chat_models.ChatAnthropic', 'ChatAnthropic', ([], {'model_name': 'agent_config.model_name', 'anthropic_api_key': 'anthropic_api_key'}), '(model_name=agent_config.model_name, anthropic_api_key=\n anthropic_api_key)\n', (2160, 2238), False, 'from langchain_community.chat_models imp... |
import os
from dotenv import load_dotenv, find_dotenv
from langchain import HuggingFaceHub
from langchain import PromptTemplate, LLMChain, OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.document_loaders import YoutubeL... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.LLMChain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.OpenAI",
"langchain.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.HuggingFaceHub",
"langchain.PromptTemplate"
] | [((955, 1048), 'langchain.HuggingFaceHub', 'HuggingFaceHub', ([], {'repo_id': 'repo_id', 'model_kwargs': "{'temperature': 0.1, 'max_new_tokens': 500}"}), "(repo_id=repo_id, model_kwargs={'temperature': 0.1,\n 'max_new_tokens': 500})\n", (969, 1048), False, 'from langchain import HuggingFaceHub\n'), ((1305, 1368), 'l... |
import os
from dotenv import load_dotenv, find_dotenv
from langchain import HuggingFaceHub
from langchain import PromptTemplate, LLMChain, OpenAI
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
from langchain.document_loaders import YoutubeL... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.LLMChain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.OpenAI",
"langchain.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.HuggingFaceHub",
"langchain.PromptTemplate"
] | [((955, 1048), 'langchain.HuggingFaceHub', 'HuggingFaceHub', ([], {'repo_id': 'repo_id', 'model_kwargs': "{'temperature': 0.1, 'max_new_tokens': 500}"}), "(repo_id=repo_id, model_kwargs={'temperature': 0.1,\n 'max_new_tokens': 500})\n", (969, 1048), False, 'from langchain import HuggingFaceHub\n'), ((1305, 1368), 'l... |
#
# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
#
import json
import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional, Tuple
import dpath.util
from airbyte_cdk.destinations.vector_db_based.config import ProcessingConfigModel, SeparatorSplitterConfigModel, Text... | [
"langchain.document_loaders.base.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.Language",
"langchain.utils.stringify_dict"
] | [((4888, 4935), 'logging.getLogger', 'logging.getLogger', (['"""airbyte.document_processor"""'], {}), "('airbyte.document_processor')\n", (4905, 4935), False, 'import logging\n'), ((6600, 6631), 'langchain.utils.stringify_dict', 'stringify_dict', (['relevant_fields'], {}), '(relevant_fields)\n', (6614, 6631), False, 'f... |
#
# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
#
import json
import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional, Tuple
import dpath.util
from airbyte_cdk.destinations.vector_db_based.config import ProcessingConfigModel, SeparatorSplitterConfigModel, Text... | [
"langchain.document_loaders.base.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.Language",
"langchain.utils.stringify_dict"
] | [((4888, 4935), 'logging.getLogger', 'logging.getLogger', (['"""airbyte.document_processor"""'], {}), "('airbyte.document_processor')\n", (4905, 4935), False, 'import logging\n'), ((6600, 6631), 'langchain.utils.stringify_dict', 'stringify_dict', (['relevant_fields'], {}), '(relevant_fields)\n', (6614, 6631), False, 'f... |
#
# Copyright (c) 2023 Airbyte, Inc., all rights reserved.
#
import json
import logging
from dataclasses import dataclass
from typing import Any, Dict, List, Mapping, Optional, Tuple
import dpath.util
from airbyte_cdk.destinations.vector_db_based.config import ProcessingConfigModel, SeparatorSplitterConfigModel, Text... | [
"langchain.document_loaders.base.Document",
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.Language",
"langchain.utils.stringify_dict"
] | [((4888, 4935), 'logging.getLogger', 'logging.getLogger', (['"""airbyte.document_processor"""'], {}), "('airbyte.document_processor')\n", (4905, 4935), False, 'import logging\n'), ((6600, 6631), 'langchain.utils.stringify_dict', 'stringify_dict', (['relevant_fields'], {}), '(relevant_fields)\n', (6614, 6631), False, 'f... |
from waifu.llm.Brain import Brain
from waifu.llm.VectorDB import VectorDB
from waifu.llm.SentenceTransformer import STEmbedding
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from typing import Any, List, Mapping, Optional
from langchain.schema import BaseMessage
import o... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI"
] | [((576, 690), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'api_key', 'model_name': 'model', 'streaming': 'stream', 'callbacks': '[callback]', 'temperature': '(0.85)'}), '(openai_api_key=api_key, model_name=model, streaming=stream,\n callbacks=[callback], temperature=0.85)\n', (586, 690)... |
from waifu.llm.Brain import Brain
from waifu.llm.VectorDB import VectorDB
from waifu.llm.SentenceTransformer import STEmbedding
from langchain.chat_models import ChatOpenAI
from langchain.embeddings import OpenAIEmbeddings
from typing import Any, List, Mapping, Optional
from langchain.schema import BaseMessage
import o... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chat_models.ChatOpenAI"
] | [((576, 690), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'openai_api_key': 'api_key', 'model_name': 'model', 'streaming': 'stream', 'callbacks': '[callback]', 'temperature': '(0.85)'}), '(openai_api_key=api_key, model_name=model, streaming=stream,\n callbacks=[callback], temperature=0.85)\n', (586, 690)... |
from time import sleep
import copy
import redis
import json
import pickle
import traceback
from flask import Response, request, stream_with_context
from typing import Dict, Union
import os
from langchain.schema import HumanMessage, SystemMessage
from backend.api.language_model import get_llm
from backend.main import ... | [
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((11305, 11357), 'backend.main.app.route', 'app.route', (['"""/api/chat_xlang_webot"""'], {'methods': "['POST']"}), "('/api/chat_xlang_webot', methods=['POST'])\n", (11314, 11357), False, 'from backend.main import app, message_id_register, message_pool, logger\n'), ((2664, 2689), 'real_agents.web_agent.WebBrowsingExec... |
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from output_parsers import summary_parser, ice_breaker_parser, topics_of_interest_parser
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
llm_creative = ChatOpenAI(temperature=1, ... | [
"langchain.chains.LLMChain",
"langchain_openai.ChatOpenAI"
] | [((225, 278), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (235, 278), False, 'from langchain_openai import ChatOpenAI\n'), ((294, 347), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '... |
from langchain.chains import LLMChain
from langchain_core.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from output_parsers import summary_parser, ice_breaker_parser, topics_of_interest_parser
llm = ChatOpenAI(temperature=0, model_name="gpt-3.5-turbo")
llm_creative = ChatOpenAI(temperature=1, ... | [
"langchain.chains.LLMChain",
"langchain_openai.ChatOpenAI"
] | [((225, 278), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)', 'model_name': '"""gpt-3.5-turbo"""'}), "(temperature=0, model_name='gpt-3.5-turbo')\n", (235, 278), False, 'from langchain_openai import ChatOpenAI\n'), ((294, 347), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '... |
import asyncio
import uvicorn
from typing import AsyncIterable, Awaitable
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.sch... | [
"langchain.callbacks.AsyncIteratorCallbackHandler",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((345, 358), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (356, 358), False, 'from dotenv import load_dotenv\n'), ((959, 968), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (966, 968), False, 'from fastapi import FastAPI\n'), ((616, 646), 'langchain.callbacks.AsyncIteratorCallbackHandler', 'AsyncIteratorCa... |
import asyncio
import uvicorn
from typing import AsyncIterable, Awaitable
from dotenv import load_dotenv
from fastapi import FastAPI
from fastapi.responses import FileResponse, StreamingResponse
from langchain.callbacks import AsyncIteratorCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.sch... | [
"langchain.callbacks.AsyncIteratorCallbackHandler",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((345, 358), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (356, 358), False, 'from dotenv import load_dotenv\n'), ((959, 968), 'fastapi.FastAPI', 'FastAPI', ([], {}), '()\n', (966, 968), False, 'from fastapi import FastAPI\n'), ((616, 646), 'langchain.callbacks.AsyncIteratorCallbackHandler', 'AsyncIteratorCa... |
""" Adapted from https://github.com/QwenLM/Qwen-7B/blob/main/examples/react_demo.py """
import json
import os
from langchain.llms import OpenAI
llm = OpenAI(
model_name="qwen",
temperature=0,
openai_api_base="http://192.168.0.53:7891/v1",
openai_api_key="xxx",
)
# 将一个插件的关键信息拼接成一段文本的模版。
TOOL_DESC = ... | [
"langchain.SerpAPIWrapper",
"langchain.llms.OpenAI"
] | [((153, 267), 'langchain.llms.OpenAI', 'OpenAI', ([], {'model_name': '"""qwen"""', 'temperature': '(0)', 'openai_api_base': '"""http://192.168.0.53:7891/v1"""', 'openai_api_key': '"""xxx"""'}), "(model_name='qwen', temperature=0, openai_api_base=\n 'http://192.168.0.53:7891/v1', openai_api_key='xxx')\n", (159, 267),... |
"""Wrapper around Cohere APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
fr... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((531, 558), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (548, 558), False, 'import logging\n'), ((3018, 3034), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (3032, 3034), False, 'from pydantic import Extra, root_validator\n'), ((3195, 3259), 'langchain.utils.get_from... |
"""Wrapper around Cohere APIs."""
from __future__ import annotations
import logging
from typing import Any, Callable, Dict, List, Optional
from pydantic import Extra, root_validator
from tenacity import (
before_sleep_log,
retry,
retry_if_exception_type,
stop_after_attempt,
wait_exponential,
)
fr... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((531, 558), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (548, 558), False, 'import logging\n'), ((3018, 3034), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (3032, 3034), False, 'from pydantic import Extra, root_validator\n'), ((3195, 3259), 'langchain.utils.get_from... |
"""Wrapper around GooseAI 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 = loggi... | [
"langchain.utils.get_from_dict_or_env"
] | [((315, 342), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'import logging\n'), ((1675, 1702), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1680, 1702), False, 'from pydantic import Extra, Field, root_validator\n'), ((1836... |
"""Wrapper around GooseAI 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 = loggi... | [
"langchain.utils.get_from_dict_or_env"
] | [((315, 342), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'import logging\n'), ((1675, 1702), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1680, 1702), False, 'from pydantic import Extra, Field, root_validator\n'), ((1836... |
"""Wrapper around GooseAI 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 = loggi... | [
"langchain.utils.get_from_dict_or_env"
] | [((315, 342), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'import logging\n'), ((1675, 1702), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1680, 1702), False, 'from pydantic import Extra, Field, root_validator\n'), ((1836... |
"""Wrapper around GooseAI 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 = loggi... | [
"langchain.utils.get_from_dict_or_env"
] | [((315, 342), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (332, 342), False, 'import logging\n'), ((1675, 1702), 'pydantic.Field', 'Field', ([], {'default_factory': 'dict'}), '(default_factory=dict)\n', (1680, 1702), False, 'from pydantic import Extra, Field, root_validator\n'), ((1836... |
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils ... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1679, 1695), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1693, 1695), False, 'from pydantic import Extra, root_validator\n'), ((1862, 1938), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""anyscale_service_url"""', '"""ANYSCALE_SERVICE_URL"""'], {}), "(values, 'any... |
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils ... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1679, 1695), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1693, 1695), False, 'from pydantic import Extra, root_validator\n'), ((1862, 1938), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""anyscale_service_url"""', '"""ANYSCALE_SERVICE_URL"""'], {}), "(values, 'any... |
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils ... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1679, 1695), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1693, 1695), False, 'from pydantic import Extra, root_validator\n'), ((1862, 1938), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""anyscale_service_url"""', '"""ANYSCALE_SERVICE_URL"""'], {}), "(values, 'any... |
"""Wrapper around Anyscale"""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langchain.utils ... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1679, 1695), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1693, 1695), False, 'from pydantic import Extra, root_validator\n'), ((1862, 1938), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""anyscale_service_url"""', '"""ANYSCALE_SERVICE_URL"""'], {}), "(values, 'any... |
from langchain.prompts import PromptTemplate
_symptom_extract_template = """Consider the following conversation patient note:
Patient note: {note}
Choose on of the symptoms to be the chief complaint (it is usually the first symptom mentioned).
Provide your response strictly in the following format, replacing only th... | [
"langchain.prompts.PromptTemplate.from_template"
] | [((830, 885), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_template', (['_symptom_extract_template'], {}), '(_symptom_extract_template)\n', (858, 885), False, 'from langchain.prompts import PromptTemplate\n'), ((904, 957), 'langchain.prompts.PromptTemplate.from_template', 'PromptTemplate.from_... |
import requests
from typing import Any, Dict, Optional
from langchain.chains.api.prompt import API_RESPONSE_PROMPT, API_URL_PROMPT
from langchain.chains import APIChain
from langchain.prompts import BasePromptTemplate
from langchain.base_language import BaseLanguageModel
from langchain.chains.llm import LLMChain
from... | [
"langchain.chains.llm.LLMChain"
] | [((1139, 1179), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'api_url_prompt'}), '(llm=llm, prompt=api_url_prompt)\n', (1147, 1179), False, 'from langchain.chains.llm import LLMChain\n'), ((1207, 1252), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'api_respons... |
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_... | [
"langchain.chains.sql_database.base.SQLDatabaseChain",
"langchain.prompts.loading.load_prompt_from_config",
"langchain.chains.qa_with_sources.base.QAWithSourcesChain",
"langchain.chains.pal.base.PALChain",
"langchain.chains.combine_documents.refine.RefineDocumentsChain",
"langchain.chains.llm.LLMChain",
... | [((2165, 2207), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt, **config)\n', (2173, 2207), False, 'from langchain.chains.llm import LLMChain\n'), ((2853, 2945), 'langchain.chains.hyde.base.HypotheticalDocumentEmbedder', 'HypotheticalDocumentEmbedder', ([... |
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_... | [
"langchain.chains.sql_database.base.SQLDatabaseChain",
"langchain.prompts.loading.load_prompt_from_config",
"langchain.chains.qa_with_sources.base.QAWithSourcesChain",
"langchain.chains.pal.base.PALChain",
"langchain.chains.combine_documents.refine.RefineDocumentsChain",
"langchain.chains.llm.LLMChain",
... | [((2165, 2207), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt, **config)\n', (2173, 2207), False, 'from langchain.chains.llm import LLMChain\n'), ((2853, 2945), 'langchain.chains.hyde.base.HypotheticalDocumentEmbedder', 'HypotheticalDocumentEmbedder', ([... |
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_... | [
"langchain.chains.sql_database.base.SQLDatabaseChain",
"langchain.prompts.loading.load_prompt_from_config",
"langchain.chains.qa_with_sources.base.QAWithSourcesChain",
"langchain.chains.pal.base.PALChain",
"langchain.chains.combine_documents.refine.RefineDocumentsChain",
"langchain.chains.llm.LLMChain",
... | [((2165, 2207), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt, **config)\n', (2173, 2207), False, 'from langchain.chains.llm import LLMChain\n'), ((2853, 2945), 'langchain.chains.hyde.base.HypotheticalDocumentEmbedder', 'HypotheticalDocumentEmbedder', ([... |
"""Functionality for loading chains."""
import json
from pathlib import Path
from typing import Any, Union
import yaml
from langchain.chains.api.base import APIChain
from langchain.chains.base import Chain
from langchain.chains.combine_documents.map_reduce import MapReduceDocumentsChain
from langchain.chains.combine_... | [
"langchain.chains.sql_database.base.SQLDatabaseChain",
"langchain.prompts.loading.load_prompt_from_config",
"langchain.chains.qa_with_sources.base.QAWithSourcesChain",
"langchain.chains.pal.base.PALChain",
"langchain.chains.combine_documents.refine.RefineDocumentsChain",
"langchain.chains.llm.LLMChain",
... | [((2165, 2207), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt, **config)\n', (2173, 2207), False, 'from langchain.chains.llm import LLMChain\n'), ((2853, 2945), 'langchain.chains.hyde.base.HypotheticalDocumentEmbedder', 'HypotheticalDocumentEmbedder', ([... |
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langcha... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1661, 1677), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1675, 1677), False, 'from pydantic import Extra, root_validator\n'), ((1848, 1936), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""huggingfacehub_api_token"""', '"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "(valu... |
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langcha... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1661, 1677), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1675, 1677), False, 'from pydantic import Extra, root_validator\n'), ((1848, 1936), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""huggingfacehub_api_token"""', '"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "(valu... |
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langcha... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1661, 1677), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1675, 1677), False, 'from pydantic import Extra, root_validator\n'), ((1848, 1936), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""huggingfacehub_api_token"""', '"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "(valu... |
"""Wrapper around HuggingFace APIs."""
from typing import Any, Dict, List, Mapping, Optional
import requests
from pydantic import Extra, root_validator
from langchain.callbacks.manager import CallbackManagerForLLMRun
from langchain.llms.base import LLM
from langchain.llms.utils import enforce_stop_tokens
from langcha... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env"
] | [((1661, 1677), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (1675, 1677), False, 'from pydantic import Extra, root_validator\n'), ((1848, 1936), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""huggingfacehub_api_token"""', '"""HUGGINGFACEHUB_API_TOKEN"""'], {}), "(valu... |
import os
from langchain.llms.bedrock import Bedrock
from langchain import PromptTemplate
def get_llm():
model_kwargs = {
"maxTokenCount": 1024,
"stopSequences": [],
"temperature": 0,
"topP": 0.9
}
llm = Bedrock(
# credentials_profile_name=os.environ... | [
"langchain.PromptTemplate.from_template"
] | [((844, 882), 'langchain.PromptTemplate.from_template', 'PromptTemplate.from_template', (['template'], {}), '(template)\n', (872, 882), False, 'from langchain import PromptTemplate\n'), ((437, 470), 'os.environ.get', 'os.environ.get', (['"""BWB_REGION_NAME"""'], {}), "('BWB_REGION_NAME')\n", (451, 470), False, 'import ... |
from langchain import PromptTemplate, LLMChain
from langchain.document_loaders import TextLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.llms import GPT4All
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.base import CallbackManager
from langchain.c... | [
"langchain.llms.GPT4All",
"langchain.LLMChain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.document_loaders.TextLoader",
"langchain.vectorstores.faiss.FAISS.load_local",
"langchain.vectorstores.faiss.FAISS.f... | [((968, 1007), 'langchain.document_loaders.TextLoader', 'TextLoader', (['"""./docs/shortened_sotu.txt"""'], {}), "('./docs/shortened_sotu.txt')\n", (978, 1007), False, 'from langchain.document_loaders import TextLoader\n'), ((1021, 1062), 'langchain.embeddings.LlamaCppEmbeddings', 'LlamaCppEmbeddings', ([], {'model_pat... |
from langchain import PromptTemplate, LLMChain
from langchain.document_loaders import TextLoader
from langchain.embeddings import LlamaCppEmbeddings
from langchain.llms import GPT4All
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.callbacks.base import CallbackManager
from langchain.c... | [
"langchain.llms.GPT4All",
"langchain.LLMChain",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.document_loaders.TextLoader",
"langchain.vectorstores.faiss.FAISS.load_local",
"langchain.vectorstores.faiss.FAISS.f... | [((968, 1007), 'langchain.document_loaders.TextLoader', 'TextLoader', (['"""./docs/shortened_sotu.txt"""'], {}), "('./docs/shortened_sotu.txt')\n", (978, 1007), False, 'from langchain.document_loaders import TextLoader\n'), ((1021, 1062), 'langchain.embeddings.LlamaCppEmbeddings', 'LlamaCppEmbeddings', ([], {'model_pat... |
import os
import requests
from langchain.tools import tool
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from sec_api import QueryApi
from unstructured.partition.html import partition_html
class SECTools... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.tools.tool"
] | [((327, 351), 'langchain.tools.tool', 'tool', (['"""Search 10-Q form"""'], {}), "('Search 10-Q form')\n", (331, 351), False, 'from langchain.tools import tool\n'), ((1325, 1349), 'langchain.tools.tool', 'tool', (['"""Search 10-K form"""'], {}), "('Search 10-K form')\n", (1329, 1349), False, 'from langchain.tools import... |
import os
import requests
from langchain.tools import tool
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from sec_api import QueryApi
from unstructured.partition.html import partition_html
class SECTools... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.tools.tool"
] | [((327, 351), 'langchain.tools.tool', 'tool', (['"""Search 10-Q form"""'], {}), "('Search 10-Q form')\n", (331, 351), False, 'from langchain.tools import tool\n'), ((1325, 1349), 'langchain.tools.tool', 'tool', (['"""Search 10-K form"""'], {}), "('Search 10-K form')\n", (1329, 1349), False, 'from langchain.tools import... |
import os
import requests
from langchain.tools import tool
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from sec_api import QueryApi
from unstructured.partition.html import partition_html
class SECTools... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.tools.tool"
] | [((327, 351), 'langchain.tools.tool', 'tool', (['"""Search 10-Q form"""'], {}), "('Search 10-Q form')\n", (331, 351), False, 'from langchain.tools import tool\n'), ((1325, 1349), 'langchain.tools.tool', 'tool', (['"""Search 10-K form"""'], {}), "('Search 10-K form')\n", (1329, 1349), False, 'from langchain.tools import... |
# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to... | [
"langchain.prompts.prompt.PromptTemplate"
] | [((957, 1028), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '_PROMPT_TEMPLATE'}), "(input_variables=['question'], template=_PROMPT_TEMPLATE)\n", (971, 1028), False, 'from langchain.prompts.prompt import PromptTemplate\n')] |
# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to... | [
"langchain.prompts.prompt.PromptTemplate"
] | [((957, 1028), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '_PROMPT_TEMPLATE'}), "(input_variables=['question'], template=_PROMPT_TEMPLATE)\n", (971, 1028), False, 'from langchain.prompts.prompt import PromptTemplate\n')] |
# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to... | [
"langchain.prompts.prompt.PromptTemplate"
] | [((957, 1028), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '_PROMPT_TEMPLATE'}), "(input_variables=['question'], template=_PROMPT_TEMPLATE)\n", (971, 1028), False, 'from langchain.prompts.prompt import PromptTemplate\n')] |
# flake8: noqa
from langchain.prompts.prompt import PromptTemplate
_PROMPT_TEMPLATE = """You are GPT-3, and you can't do math.
You can do basic math, and your memorization abilities are impressive, but you can't do any complex calculations that a human could not do in their head. You also have an annoying tendency to... | [
"langchain.prompts.prompt.PromptTemplate"
] | [((957, 1028), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '_PROMPT_TEMPLATE'}), "(input_variables=['question'], template=_PROMPT_TEMPLATE)\n", (971, 1028), False, 'from langchain.prompts.prompt import PromptTemplate\n')] |
import streamlit as st
from langchain.prompts import PromptTemplate
chat_template = PromptTemplate(
input_variables=['transcript','summary','chat_history','user_message', 'sentiment_report'],
template='''
You are an AI chatbot intended to discuss about the user's audio transcription.
\nT... | [
"langchain.prompts.PromptTemplate"
] | [((88, 562), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['transcript', 'summary', 'chat_history', 'user_message', 'sentiment_report']", 'template': '"""\n You are an AI chatbot intended to discuss about the user\'s audio transcription.\n \nTRANSCRIPT: "{transcript}"\n ... |
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.chat_models import ChatOpenAI
from dotenv import load_dotenv
import os
from langchain.chains import SimpleSequentialChain
# Create a .env file in the root of your project and add your OpenAI API key to it
# Load env files... | [
"langchain.chains.LLMChain",
"langchain.chains.SimpleSequentialChain",
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((321, 334), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (332, 334), False, 'from dotenv import load_dotenv\n'), ((352, 384), 'os.environ.get', 'os.environ.get', (['"""openai_api_key"""'], {}), "('openai_api_key')\n", (366, 384), False, 'import os\n'), ((469, 524), 'langchain.chat_models.ChatOpenAI', 'ChatO... |
import os
import streamlit as st
from PyPDF2 import PdfReader, PdfWriter
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms i... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((481, 579), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'separator': '"""\n"""', 'chunk_size': '(1000)', 'chunk_overlap': '(200)', 'length_function': 'len'}), "(separator='\\n', chunk_size=1000, chunk_overlap=200,\n length_function=len)\n", (502, 579), False, 'from langchain.tex... |
import os
import streamlit as st
from PyPDF2 import PdfReader, PdfWriter
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import FAISS
from langchain.chains.question_answering import load_qa_chain
from langchain.llms i... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((481, 579), 'langchain.text_splitter.CharacterTextSplitter', 'CharacterTextSplitter', ([], {'separator': '"""\n"""', 'chunk_size': '(1000)', 'chunk_overlap': '(200)', 'length_function': 'len'}), "(separator='\\n', chunk_size=1000, chunk_overlap=200,\n length_function=len)\n", (502, 579), False, 'from langchain.tex... |
"""Toolkit for the Wolfram Alpha API."""
from typing import List
from langchain.tools.base import BaseTool, BaseToolkit
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
class WolframAlphaToolkit(BaseToolkit):
"""Tool that ad... | [
"langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper",
"langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun"
] | [((509, 577), 'langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper', 'WolframAlphaAPIWrapper', ([], {'wolfram_alpha_appid': 'self.wolfram_alpha_appid'}), '(wolfram_alpha_appid=self.wolfram_alpha_appid)\n', (531, 577), False, 'from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper\n'), ((607, 648), 'l... |
"""Toolkit for the Wolfram Alpha API."""
from typing import List
from langchain.tools.base import BaseTool, BaseToolkit
from langchain.tools.wolfram_alpha.tool import WolframAlphaQueryRun
from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper
class WolframAlphaToolkit(BaseToolkit):
"""Tool that ad... | [
"langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper",
"langchain.tools.wolfram_alpha.tool.WolframAlphaQueryRun"
] | [((509, 577), 'langchain.utilities.wolfram_alpha.WolframAlphaAPIWrapper', 'WolframAlphaAPIWrapper', ([], {'wolfram_alpha_appid': 'self.wolfram_alpha_appid'}), '(wolfram_alpha_appid=self.wolfram_alpha_appid)\n', (531, 577), False, 'from langchain.utilities.wolfram_alpha import WolframAlphaAPIWrapper\n'), ((607, 648), 'l... |
"""The function tools tht are actually implemented"""
import json
import subprocess
from langchain.agents.load_tools import load_tools
from langchain.tools import BaseTool
from langchain.utilities.bash import BashProcess
from toolemu.tools.tool_interface import (
ArgException,
ArgParameter,
ArgReturn,
... | [
"langchain.agents.load_tools.load_tools"
] | [((2863, 2930), 'json.dumps', 'json.dumps', (["{'output': tool_output[0], 'exit_code': tool_output[1]}"], {}), "({'output': tool_output[0], 'exit_code': tool_output[1]})\n", (2873, 2930), False, 'import json\n'), ((4269, 4296), 'langchain.agents.load_tools.load_tools', 'load_tools', (["['python_repl']"], {}), "(['pytho... |
from typing import List, Optional, Any, Dict
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
from pydantic import Extra, root_validator
from sam.gpt.quora import PoeClient, PoeResponse
# token = "KaEMfvDPEXoS115jzAFRRg%3D%3D"
# prompt = "write a java function that prints the nt... | [
"langchain.utils.get_from_dict_or_env"
] | [((573, 589), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (587, 589), False, 'from pydantic import Extra, root_validator\n'), ((663, 714), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""token"""', '"""POE_COOKIE"""'], {}), "(values, 'token', 'POE_COOKIE')\n", (683, 71... |
from typing import List, Optional, Any, Dict
from langchain.llms.base import LLM
from langchain.utils import get_from_dict_or_env
from pydantic import Extra, root_validator
from sam.gpt.quora import PoeClient, PoeResponse
# token = "KaEMfvDPEXoS115jzAFRRg%3D%3D"
# prompt = "write a java function that prints the nt... | [
"langchain.utils.get_from_dict_or_env"
] | [((573, 589), 'pydantic.root_validator', 'root_validator', ([], {}), '()\n', (587, 589), False, 'from pydantic import Extra, root_validator\n'), ((663, 714), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""token"""', '"""POE_COOKIE"""'], {}), "(values, 'token', 'POE_COOKIE')\n", (683, 71... |
from __future__ import annotations
from typing import List, Optional
from pydantic import ValidationError
from langchain.chains.llm import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.experimental.autonomous_agents.autogpt.output_parser import (
AutoGPTOutputParser,
BaseAutoGP... | [
"langchain.experimental.autonomous_agents.autogpt.output_parser.AutoGPTOutputParser",
"langchain.tools.human.tool.HumanInputRun",
"langchain.experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt",
"langchain.schema.HumanMessage",
"langchain.chains.llm.LLMChain",
"langchain.schema.AIMessage",
"lang... | [((1753, 1918), 'langchain.experimental.autonomous_agents.autogpt.prompt.AutoGPTPrompt', 'AutoGPTPrompt', ([], {'ai_name': 'ai_name', 'ai_role': 'ai_role', 'tools': 'tools', 'input_variables': "['memory', 'messages', 'goals', 'user_input']", 'token_counter': 'llm.get_num_tokens'}), "(ai_name=ai_name, ai_role=ai_role, t... |
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from la... | [
"langchain.chains.ReduceDocumentsChain",
"langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain",
"langchain.chains.combine_documents.stuff.StuffDocumentsChain",
"langchain.docstore.document.Document",
"langchain.chains.llm.LLMChain",
"langchain.callbacks.manager.CallbackManagerForChainRun... | [((1734, 1787), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt', 'callbacks': 'callbacks'}), '(llm=llm, prompt=prompt, callbacks=callbacks)\n', (1742, 1787), False, 'from langchain.chains.llm import LLMChain\n'), ((1810, 1930), 'langchain.chains.combine_documents.stuff.StuffDocuments... |
"""Map-reduce chain.
Splits up a document, sends the smaller parts to the LLM with one prompt,
then combines the results with another one.
"""
from __future__ import annotations
from typing import Any, Dict, List, Mapping, Optional
from langchain.callbacks.manager import CallbackManagerForChainRun, Callbacks
from la... | [
"langchain.chains.ReduceDocumentsChain",
"langchain.chains.combine_documents.map_reduce.MapReduceDocumentsChain",
"langchain.chains.combine_documents.stuff.StuffDocumentsChain",
"langchain.docstore.document.Document",
"langchain.chains.llm.LLMChain",
"langchain.callbacks.manager.CallbackManagerForChainRun... | [((1734, 1787), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt', 'callbacks': 'callbacks'}), '(llm=llm, prompt=prompt, callbacks=callbacks)\n', (1742, 1787), False, 'from langchain.chains.llm import LLMChain\n'), ((1810, 1930), 'langchain.chains.combine_documents.stuff.StuffDocuments... |
from typing import Any, Dict, List, Optional, Sequence
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.utils import get_from_dict_or_env
cla... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env",
"langchain.pydantic_v1.root_validator"
] | [((6229, 6245), 'langchain.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (6243, 6245), False, 'from langchain.pydantic_v1 import Extra, root_validator\n'), ((6411, 6485), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""aleph_alpha_api_key"""', '"""ALEPH_ALPHA_API_KEY""... |
from typing import Any, Dict, List, Optional, Sequence
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.utils import get_from_dict_or_env
cla... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env",
"langchain.pydantic_v1.root_validator"
] | [((6229, 6245), 'langchain.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (6243, 6245), False, 'from langchain.pydantic_v1 import Extra, root_validator\n'), ((6411, 6485), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""aleph_alpha_api_key"""', '"""ALEPH_ALPHA_API_KEY""... |
from typing import Any, Dict, List, Optional, Sequence
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.utils import get_from_dict_or_env
cla... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env",
"langchain.pydantic_v1.root_validator"
] | [((6229, 6245), 'langchain.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (6243, 6245), False, 'from langchain.pydantic_v1 import Extra, root_validator\n'), ((6411, 6485), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""aleph_alpha_api_key"""', '"""ALEPH_ALPHA_API_KEY""... |
from typing import Any, Dict, List, Optional, Sequence
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.utils import get_from_dict_or_env
cla... | [
"langchain.llms.utils.enforce_stop_tokens",
"langchain.utils.get_from_dict_or_env",
"langchain.pydantic_v1.root_validator"
] | [((6229, 6245), 'langchain.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (6243, 6245), False, 'from langchain.pydantic_v1 import Extra, root_validator\n'), ((6411, 6485), 'langchain.utils.get_from_dict_or_env', 'get_from_dict_or_env', (['values', '"""aleph_alpha_api_key"""', '"""ALEPH_ALPHA_API_KEY""... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.llms import OpenAI
from langchain.chains import VectorDBQA
from langchain.document_loaders import TextLoader
from typing import List
from langchai... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.TextLoader",
"langchain.llms.OpenAI",
"langchain.vectorstores.Chroma.from_documents",
"langchain.embeddings.OpenAIEmbeddings"
] | [((515, 541), 'langchain.document_loaders.TextLoader', 'TextLoader', (['self.file_path'], {}), '(self.file_path)\n', (525, 541), False, 'from langchain.document_loaders import TextLoader\n'), ((886, 950), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(1... |
import logging
from pathlib import Path
from typing import List, Optional, Tuple
from dotenv import load_dotenv
load_dotenv()
from queue import Empty, Queue
from threading import Thread
import gradio as gr
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chat_models imp... | [
"langchain.schema.AIMessage",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((114, 127), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (125, 127), False, 'from dotenv import load_dotenv\n'), ((604, 698), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""[%(asctime)s %(levelname)s]: %(message)s"""', 'level': 'logging.INFO'}), "(format='[%(asctime)s %(levelname)s]: %(me... |
import logging
from pathlib import Path
from typing import List, Optional, Tuple
from dotenv import load_dotenv
load_dotenv()
from queue import Empty, Queue
from threading import Thread
import gradio as gr
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chat_models imp... | [
"langchain.schema.AIMessage",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((114, 127), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (125, 127), False, 'from dotenv import load_dotenv\n'), ((604, 698), 'logging.basicConfig', 'logging.basicConfig', ([], {'format': '"""[%(asctime)s %(levelname)s]: %(message)s"""', 'level': 'logging.INFO'}), "(format='[%(asctime)s %(levelname)s]: %(me... |
"""
View stage example selector.
| Copyright 2017-2023, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
import os
import pickle
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
import numpy as np
import pandas as pd
from scipy.spatial.distance import cosine
# pylint: disable=relative-b... | [
"langchain.prompts.FewShotPromptTemplate",
"langchain.prompts.PromptTemplate"
] | [((489, 523), 'os.path.join', 'os.path.join', (['ROOT_DIR', '"""examples"""'], {}), "(ROOT_DIR, 'examples')\n", (501, 523), False, 'import os\n'), ((551, 605), 'os.path.join', 'os.path.join', (['EXAMPLES_DIR', '"""viewstage_embeddings.pkl"""'], {}), "(EXAMPLES_DIR, 'viewstage_embeddings.pkl')\n", (563, 605), False, 'im... |
"""
View stage example selector.
| Copyright 2017-2023, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
import os
import pickle
from langchain.prompts import FewShotPromptTemplate, PromptTemplate
import numpy as np
import pandas as pd
from scipy.spatial.distance import cosine
# pylint: disable=relative-b... | [
"langchain.prompts.FewShotPromptTemplate",
"langchain.prompts.PromptTemplate"
] | [((489, 523), 'os.path.join', 'os.path.join', (['ROOT_DIR', '"""examples"""'], {}), "(ROOT_DIR, 'examples')\n", (501, 523), False, 'import os\n'), ((551, 605), 'os.path.join', 'os.path.join', (['EXAMPLES_DIR', '"""viewstage_embeddings.pkl"""'], {}), "(EXAMPLES_DIR, 'viewstage_embeddings.pkl')\n", (563, 605), False, 'im... |
import base64
import email
from enum import Enum
from typing import Any, Dict, List, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.gmail.base import GmailBaseTool
from langchain.tools.gmail.utils import clean_ema... | [
"langchain.pydantic_v1.Field",
"langchain.tools.gmail.utils.clean_email_body"
] | [((606, 1054), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""The Gmail query. Example filters include from:sender, to:recipient, subject:subject, -filtered_term, in:folder, is:important|read|starred, after:year/mo/date, before:year/mo/date, label:label_name "exact phrase". Search newer/older tha... |
import base64
import email
from enum import Enum
from typing import Any, Dict, List, Optional, Type
from langchain.callbacks.manager import CallbackManagerForToolRun
from langchain.pydantic_v1 import BaseModel, Field
from langchain.tools.gmail.base import GmailBaseTool
from langchain.tools.gmail.utils import clean_ema... | [
"langchain.pydantic_v1.Field",
"langchain.tools.gmail.utils.clean_email_body"
] | [((606, 1054), 'langchain.pydantic_v1.Field', 'Field', (['...'], {'description': '"""The Gmail query. Example filters include from:sender, to:recipient, subject:subject, -filtered_term, in:folder, is:important|read|starred, after:year/mo/date, before:year/mo/date, label:label_name "exact phrase". Search newer/older tha... |
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.agents.initialize_agent",
"langchain.tools.python.tool.PythonREPLTool",
"langchain.agents.load_tools",
"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.ConversationSummaryMemory",
"langchain.memory.ConversationBufferMemory",
"langchain.memory.ChatMessageHistory"
] | [((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 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.ConversationSummaryMemory",
"langchain.memory.ConversationBufferMemory",
"langchain.memory.ChatMessageHistory"
] | [((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.document_loaders.PyPDFLoader",
"langchain.text_splitter.CharacterTextSplitter",
"langchain_openai.llms.OpenAI",
"langchain_community.document_loaders.csv_loader.CSVLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain_community.document_loaders.UnstructuredHTML... | [((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.utils.get_from_env",
"langchain.docstore.document.Document"
] | [((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.utils.get_from_env",
"langchain.docstore.document.Document"
] | [((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.utils.get_from_env",
"langchain.docstore.document.Document"
] | [((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.utils.get_from_env",
"langchain.docstore.document.Document"
] | [((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.agents.initialize_agent",
"langchain.llms.OpenAI"
] | [((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.prompts.prompt.PromptTemplate",
"langchain.LLMChain",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.llms.OpenAI"
] | [((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.agents.agent_toolkits.json.toolkit.JsonToolkit",
"langchain.agents.ZeroShotAgent.create_prompt",
"langchain.agents.ZeroShotAgent",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage",
"l... | [((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_community.document_transformers.Html2TextTransformer",
"langchain_community.document_loaders.AsyncHtmlLoader",
"langchain_community.embeddings.HuggingFaceEmbeddings",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((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.text_splitter.CharacterTextSplitter",
"langchain.document_loaders.DirectoryLoader",
"langchain_core.output_parsers.StrOutputParser",
"langchain.vectorstores.FAISS.from_documents",
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain_nvidia_ai_endpoints.NVIDIAEmbeddings",
"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.ConversationChain",
"langchain.prompts.prompt.PromptTemplate",
"langchain.vectorstores.base.VectorStoreRetriever",
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.prompt.Prompt... | [((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')] |
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