code stringlengths 141 79.4k | apis sequencelengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
from langchain_community.chat_models import ChatOpenAI
from langchain_community.embeddings import OpenAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import Ba... | [
"langchain_community.chat_models.ChatOpenAI",
"langchain_core.prompts.ChatPromptTemplate.from_template",
"langchain_core.output_parsers.StrOutputParser",
"langchain_community.embeddings.OpenAIEmbeddings",
"langchain_core.runnables.RunnableParallel"
] | [((1516, 1558), 'langchain_core.prompts.ChatPromptTemplate.from_template', 'ChatPromptTemplate.from_template', (['template'], {}), '(template)\n', (1548, 1558), False, 'from langchain_core.prompts import ChatPromptTemplate\n'), ((1574, 1586), 'langchain_community.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n',... |
"""
AI Module
This module provides an AI class that interfaces with language models to perform various tasks such as
starting a conversation, advancing the conversation, and handling message serialization. It also includes
backoff strategies for handling rate limit errors from the OpenAI API.
Classes:
AI: A class... | [
"langchain.schema.messages_to_dict",
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.schema.messages_from_dict",
"langchain.schema.HumanMessage",
"langchain.schema.AIMessage",
"langchain.schema.SystemMessage"
] | [((1266, 1293), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1283, 1293), False, 'import logging\n'), ((7101, 7188), 'backoff.on_exception', 'backoff.on_exception', (['backoff.expo', 'openai.RateLimitError'], {'max_tries': '(7)', 'max_time': '(45)'}), '(backoff.expo, openai.RateLimitEr... |
import os
import csv
from datetime import datetime
from constants import EMBEDDING_MODEL_NAME
from langchain.embeddings import HuggingFaceInstructEmbeddings
from langchain.embeddings import HuggingFaceBgeEmbeddings
from langchain.embeddings import HuggingFaceEmbeddings
def log_to_csv(question, answer):
log_dir, ... | [
"langchain.embeddings.HuggingFaceInstructEmbeddings",
"langchain.embeddings.HuggingFaceEmbeddings",
"langchain.embeddings.HuggingFaceBgeEmbeddings"
] | [((531, 562), 'os.path.join', 'os.path.join', (['log_dir', 'log_file'], {}), '(log_dir, log_file)\n', (543, 562), False, 'import os\n'), ((426, 449), 'os.path.exists', 'os.path.exists', (['log_dir'], {}), '(log_dir)\n', (440, 449), False, 'import os\n'), ((459, 479), 'os.makedirs', 'os.makedirs', (['log_dir'], {}), '(l... |
from fastapi import Body
from sse_starlette.sse import EventSourceResponse
from configs import LLM_MODELS, TEMPERATURE
from server.utils import wrap_done, get_OpenAI
from langchain.chains import LLMChain
from langchain.callbacks import AsyncIteratorCallbackHandler
from typing import AsyncIterable, Optional
import async... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate.from_template",
"langchain.callbacks.AsyncIteratorCallbackHandler"
] | [((450, 498), 'fastapi.Body', 'Body', (['...'], {'description': '"""用户输入"""', 'examples': "['恼羞成怒']"}), "(..., description='用户输入', examples=['恼羞成怒'])\n", (454, 498), False, 'from fastapi import Body\n'), ((536, 567), 'fastapi.Body', 'Body', (['(False)'], {'description': '"""流式输出"""'}), "(False, description='流式输出')\n", ... |
# — coding: utf-8 –
import openai
import json
import logging
import sys
import argparse
from langchain.chat_models import ChatOpenAI
from langchain.prompts import (
ChatPromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
HumanMessagePromptTemplate
)
from langchain import LLMCh... | [
"langchain.LLMChain",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_template"
] | [((717, 746), 'os.path.exists', 'os.path.exists', (['progress_file'], {}), '(progress_file)\n', (731, 746), False, 'import os\n'), ((1210, 1243), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'model_name'}), '(model_name=model_name)\n', (1220, 1243), False, 'from langchain.chat_models import Cha... |
from langchain.llms import Ollama
input = input("What is your question?")
llm = Ollama(model="llama2")
res = llm.predict(input)
print (res)
| [
"langchain.llms.Ollama"
] | [((81, 103), 'langchain.llms.Ollama', 'Ollama', ([], {'model': '"""llama2"""'}), "(model='llama2')\n", (87, 103), False, 'from langchain.llms import Ollama\n')] |
import os
from pathlib import Path
from typing import Union
import cloudpickle
import yaml
from mlflow.exceptions import MlflowException
from mlflow.langchain.utils import (
_BASE_LOAD_KEY,
_CONFIG_LOAD_KEY,
_MODEL_DATA_FOLDER_NAME,
_MODEL_DATA_KEY,
_MODEL_DATA_PKL_FILE_NAME,
_MODEL_DATA_YAML_... | [
"langchain.chains.loading.load_chain",
"langchain.prompts.loading.load_prompt",
"langchain.schema.runnable.RunnableSequence",
"langchain.schema.runnable.RunnableParallel",
"langchain.schema.runnable.passthrough.RunnableAssign",
"langchain.schema.runnable.RunnableBranch",
"langchain.llms.get_type_to_cls_... | [((2386, 2443), 'mlflow.exceptions.MlflowException', 'MlflowException', (['f"""Unsupported type {_type} for loading."""'], {}), "(f'Unsupported type {_type} for loading.')\n", (2401, 2443), False, 'from mlflow.exceptions import MlflowException\n'), ((2853, 2915), 'mlflow.exceptions.MlflowException', 'MlflowException', ... |
import os
import tempfile
from typing import List, Union
import streamlit as st
import tiktoken
from langchain.text_splitter import (
CharacterTextSplitter,
RecursiveCharacterTextSplitter,
)
from langchain.text_splitter import (
TextSplitter as LCSplitter,
)
from langchain.text_splitter import TokenTextSpl... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.CharacterTextSplitter.from_tiktoken_encoder",
"langchain.text_splitter.TokenTextSplitter"
] | [((718, 772), 'streamlit.sidebar.text_area', 'st.sidebar.text_area', (['"""Enter text"""'], {'value': 'DEFAULT_TEXT'}), "('Enter text', value=DEFAULT_TEXT)\n", (738, 772), True, 'import streamlit as st\n'), ((790, 857), 'streamlit.sidebar.file_uploader', 'st.sidebar.file_uploader', (['"""Upload file"""'], {'accept_mult... |
import json
from langchain.schema import OutputParserException
def parse_json_markdown(json_string: str) -> dict:
# Remove the triple backticks if present
json_string = json_string.strip()
start_index = json_string.find("```json")
end_index = json_string.find("```", start_index + len("```json"))
... | [
"langchain.schema.OutputParserException"
] | [((526, 555), 'json.loads', 'json.loads', (['extracted_content'], {}), '(extracted_content)\n', (536, 555), False, 'import json\n'), ((871, 900), 'json.loads', 'json.loads', (['extracted_content'], {}), '(extracted_content)\n', (881, 900), False, 'import json\n'), ((1322, 1383), 'langchain.schema.OutputParserException'... |
# From project chatglm-langchain
from langchain.document_loaders import UnstructuredFileLoader
from langchain.text_splitter import CharacterTextSplitter
import re
from typing import List
class ChineseTextSplitter(CharacterTextSplitter):
def __init__(self, pdf: bool = False, sentence_size: int = None, **kwargs):
... | [
"langchain.document_loaders.UnstructuredFileLoader"
] | [((3017, 3066), 'langchain.document_loaders.UnstructuredFileLoader', 'UnstructuredFileLoader', (['filepath'], {'mode': '"""elements"""'}), "(filepath, mode='elements')\n", (3039, 3066), False, 'from langchain.document_loaders import UnstructuredFileLoader\n'), ((657, 714), 're.compile', 're.compile', (['"""([﹒﹔﹖﹗．。！？][... |
import os
import uuid
from typing import Any, Dict, List, Optional, Tuple
from langchain.agents.agent import RunnableAgent
from langchain.agents.tools import tool as LangChainTool
from langchain.memory import ConversationSummaryMemory
from langchain.tools.render import render_text_description
from langchain_core.agent... | [
"langchain.memory.ConversationSummaryMemory",
"langchain.agents.agent.RunnableAgent",
"langchain.tools.render.render_text_description"
] | [((2392, 2405), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {}), '()\n', (2403, 2405), False, 'from pydantic import UUID4, BaseModel, ConfigDict, Field, InstanceOf, PrivateAttr, field_validator, model_validator\n'), ((2443, 2468), 'pydantic.PrivateAttr', 'PrivateAttr', ([], {'default': 'None'}), '(default=None)\n', (24... |
import os
import logging
import hashlib
import PyPDF2
from tqdm import tqdm
from modules.presets import *
from modules.utils import *
from modules.config import local_embedding
def get_documents(file_src):
from langchain.schema import Document
from langchain.text_splitter import TokenTextSplitter
text_s... | [
"langchain.document_loaders.UnstructuredWordDocumentLoader",
"langchain.embeddings.huggingface.HuggingFaceEmbeddings",
"langchain.vectorstores.FAISS.load_local",
"langchain.document_loaders.TextLoader",
"langchain.document_loaders.UnstructuredPowerPointLoader",
"langchain.document_loaders.UnstructuredEPub... | [((330, 381), 'langchain.text_splitter.TokenTextSplitter', 'TokenTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(30)'}), '(chunk_size=500, chunk_overlap=30)\n', (347, 381), False, 'from langchain.text_splitter import TokenTextSplitter\n'), ((406, 443), 'logging.debug', 'logging.debug', (['"""Loading docu... |
import re
from typing import Union
from langchain.agents.mrkl.output_parser import MRKLOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
FORMAT_INSTRUCTIONS0 = """Use the following format and be sure to use new lines after each task.
Question: the input question you must answe... | [
"langchain.schema.AgentAction",
"langchain.schema.OutputParserException"
] | [((3055, 3088), 're.search', 're.search', (['regex', 'text', 're.DOTALL'], {}), '(regex, text, re.DOTALL)\n', (3064, 3088), False, 'import re\n'), ((3689, 3749), 're.search', 're.search', (['"""Action\\\\s*\\\\d*\\\\s*:[\\\\s]*(.*?)"""', 'text', 're.DOTALL'], {}), "('Action\\\\s*\\\\d*\\\\s*:[\\\\s]*(.*?)', text, re.DO... |
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