code stringlengths 141 78.9k | apis listlengths 1 23 | extract_api stringlengths 142 73.2k |
|---|---|---|
"""
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... |
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 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'... |
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... |
import json
import os.path
import logging
import time
from langchain.vectorstores import FAISS
from langchain import PromptTemplate
from utils.references import References
from utils.knowledge import Knowledge
from utils.file_operations import make_archive, copy_templates
from utils.tex_processing import create_copies... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.PromptTemplate"
] | [((1271, 1292), 'logging.info', 'logging.info', (['message'], {}), '(message)\n', (1283, 1292), False, 'import logging\n'), ((1552, 1587), 'utils.gpt_interaction.GPTModel', 'GPTModel', ([], {'model': '"""gpt-3.5-turbo-16k"""'}), "(model='gpt-3.5-turbo-16k')\n", (1560, 1587), False, 'from utils.gpt_interaction import GP... |
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__))... | [
"langchain.llms.openai.OpenAI",
"langchain.agents.tools.Tool",
"langchain.chains.conversation.memory.ConversationBufferMemory",
"langchain.agents.initialize.initialize_agent"
] | [((3966, 3992), 'scipy.io.wavfile.read', 'wavfile.read', (['audio_path_1'], {}), '(audio_path_1)\n', (3978, 3992), True, 'import scipy.io.wavfile as wavfile\n'), ((4014, 4040), 'scipy.io.wavfile.read', 'wavfile.read', (['audio_path_2'], {}), '(audio_path_2)\n', (4026, 4040), True, 'import scipy.io.wavfile as wavfile\n'... |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import FAISS
loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv")
docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)
inde... | [
"langchain_community.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((174, 229), 'langchain_community.document_loaders.CSVLoader', 'CSVLoader', (['"""/Users/harrisonchase/Downloads/titanic.csv"""'], {}), "('/Users/harrisonchase/Downloads/titanic.csv')\n", (183, 229), False, 'from langchain_community.document_loaders import CSVLoader\n'), ((268, 314), 'langchain.indexes.VectorstoreInde... |
# ruff: noqa: E402
"""Main entrypoint into package."""
import warnings
from importlib import metadata
from typing import Any, Optional
from langchain_core._api.deprecation import surface_langchain_deprecation_warnings
try:
__version__ = metadata.version(__package__)
except metadata.PackageNotFoundError:
# Cas... | [
"langchain.utils.interactive_env.is_interactive_env",
"langchain_core._api.deprecation.surface_langchain_deprecation_warnings"
] | [((1348, 1388), 'langchain_core._api.deprecation.surface_langchain_deprecation_warnings', 'surface_langchain_deprecation_warnings', ([], {}), '()\n', (1386, 1388), False, 'from langchain_core._api.deprecation import surface_langchain_deprecation_warnings\n'), ((243, 272), 'importlib.metadata.version', 'metadata.version... |
from typing import Any, Dict, List, Type, Union
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import... | [
"langchain_community.graphs.networkx_graph.get_entities",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string",
"langchain_community.graphs.networkx_graph.parse_triples"
] | [((1062, 1104), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'NetworkxEntityGraph'}), '(default_factory=NetworkxEntityGraph)\n', (1067, 1104), False, 'from langchain_core.pydantic_v1 import Field\n'), ((3163, 3223), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.llm', 'prompt':... |
"""
**LLM** classes provide
access to the large language model (**LLM**) APIs and services.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
**Main helpers:**
.. code-block::
LLMResult, PromptValue,
CallbackManagerForLLMRun... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((11338, 11358), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (11356, 11358), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((11368, 11729), 'warnings.warn', 'warnings.warn', (['f"""Importing LLMs from langchain is deprecated. Importing fro... |
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from langchain_community.utilities.redis import get_client
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_stri... | [
"langchain_community.utilities.redis.get_client",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string"
] | [((701, 728), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (718, 728), False, 'import logging\n'), ((10994, 11036), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'InMemoryEntityStore'}), '(default_factory=InMemoryEntityStore)\n', (10999, 11036), False, 'from lang... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((2151, 2171), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (2169, 2171), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((2185, 2548), 'warnings.warn', 'warnings.warn', (['f"""Importing tools from langchain is deprecated. Importing from lan... |
from functools import partial
from typing import Optional
from langchain_core.callbacks.manager import (
Callbacks,
)
from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
f... | [
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.format_document",
"langchain.tools.Tool",
"langchain_core.prompts.PromptTemplate.from_template"
] | [((439, 489), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'description': '"""query to look up in retriever"""'}), "(description='query to look up in retriever')\n", (444, 489), False, 'from langchain_core.pydantic_v1 import BaseModel, Field\n'), ((1996, 2126), 'functools.partial', 'partial', (['_get_relevant_doc... |
from typing import Any, List, Sequence, Tuple, Union
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTempla... | [
"langchain_core.prompts.chat.AIMessagePromptTemplate.from_template",
"langchain_core.prompts.chat.ChatPromptTemplate.from_template",
"langchain.agents.output_parsers.XMLAgentOutputParser",
"langchain.agents.format_scratchpad.format_xml",
"langchain_core._api.deprecated"
] | [((875, 943), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_xml_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_xml_agent', removal='0.2.0')\n", (885, 943), False, 'from langchain_core._api import deprecated\n'), ((1644, 1696), 'langchain_core.prompts... |
"""**Graphs** provide a natural language interface to graph databases."""
import warnings
from typing import Any
from langchain_core._api import LangChainDeprecationWarning
from langchain.utils.interactive_env import is_interactive_env
def __getattr__(name: str) -> Any:
from langchain_community import graphs
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((378, 398), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (396, 398), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((408, 773), 'warnings.warn', 'warnings.warn', (['f"""Importing graphs from langchain is deprecated. Importing from langchai... |
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.parse import urlparse
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_core.callbacks im... | [
"langchain_core.callbacks.AsyncCallbackManagerForChainRun.get_noop_manager",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain",
"langchain_community.utilities.requests.TextRequestsWrapper",
"langchain_core.pydantic_v1.Field",
"langchain_core.pydantic_v... | [((979, 992), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (987, 992), False, 'from urllib.parse import urlparse\n'), ((2555, 2574), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'exclude': '(True)'}), '(exclude=True)\n', (2560, 2574), False, 'from langchain_core.pydantic_v1 import Field, root_va... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_mo... | [
"langchain.chains.hyde.prompts.PROMPT_MAP.keys",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain"
] | [((3148, 3180), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (3156, 3180), False, 'from langchain.chains.llm import LLMChain\n'), ((2258, 2303), 'langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager', 'CallbackManagerForChainRun.get... |
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.langua... | [
"langchain.agents.mrkl.output_parser.MRKLOutputParser",
"langchain.agents.utils.validate_tools_single_input",
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.PromptTemplate",
"langchain_core._api.deprecated",
"langchain.chains.LLMChain",
"langchain.agents.tools.Tool",
"langchain_core.promp... | [((1278, 1348), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_react_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_react_agent', removal='0.2.0')\n", (1288, 1348), False, 'from langchain_core._api import deprecated\n'), ((5068, 5104), 'langchain_core... |
import base64
import io
import os
import uuid
from io import BytesIO
from pathlib import Path
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import LocalFileStore
from langchain_community.chat_models import ChatOllama
from langchain_community.embeddings import OllamaEmbedding... | [
"langchain_core.documents.Document",
"langchain_community.embeddings.OllamaEmbeddings",
"langchain_community.chat_models.ChatOllama",
"langchain_core.messages.HumanMessage",
"langchain.retrievers.multi_vector.MultiVectorRetriever"
] | [((731, 774), 'langchain_community.chat_models.ChatOllama', 'ChatOllama', ([], {'model': '"""bakllava"""', 'temperature': '(0)'}), "(model='bakllava', temperature=0)\n", (741, 774), False, 'from langchain_community.chat_models import ChatOllama\n'), ((2494, 2525), 'base64.b64decode', 'base64.b64decode', (['base64_strin... |
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", ... |
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", ... |
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", ... |
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')] |
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
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 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 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... |
import os
import re
import uuid
import cv2
import torch
import requests
import io, base64
import numpy as np
import gradio as gr
from PIL import Image
from omegaconf import OmegaConf
from transformers import pipeline, BlipProcessor, BlipForConditionalGeneration, BlipForQuestionAnswering
from transformers import AutoMod... | [
"langchain.llms.openai.OpenAI",
"langchain.agents.tools.Tool",
"langchain.chains.conversation.memory.ConversationBufferMemory",
"langchain.agents.initialize.initialize_agent"
] | [((3812, 3837), 'torch.cuda.is_available', 'torch.cuda.is_available', ([], {}), '()\n', (3835, 3837), False, 'import torch\n'), ((3891, 3907), 'cv2.imread', 'cv2.imread', (['path'], {}), '(path)\n', (3901, 3907), False, 'import cv2\n'), ((3929, 3954), 'cv2.imencode', 'cv2.imencode', (['""".jpg"""', 'img'], {}), "('.jpg... |
from typing import Any, Callable, Dict, TypeVar
from langchain import BasePromptTemplate, LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.schema import BaseOutputParser, OutputParserException
from openai.error import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
... | [
"langchain.LLMChain"
] | [((469, 481), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (476, 481), False, 'from typing import Any, Callable, Dict, TypeVar\n'), ((2486, 2520), 'langchain.LLMChain', 'LLMChain', ([], {'llm': 'model', 'prompt': 'prompt'}), '(llm=model, prompt=prompt)\n', (2494, 2520), False, 'from langchain import BaseP... |
from typing import Any, Callable, Dict, TypeVar
from langchain import BasePromptTemplate, LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.schema import BaseOutputParser, OutputParserException
from openai.error import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
... | [
"langchain.LLMChain"
] | [((469, 481), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (476, 481), False, 'from typing import Any, Callable, Dict, TypeVar\n'), ((2486, 2520), 'langchain.LLMChain', 'LLMChain', ([], {'llm': 'model', 'prompt': 'prompt'}), '(llm=model, prompt=prompt)\n', (2494, 2520), False, 'from langchain import BaseP... |
from typing import Any, Callable, Dict, TypeVar
from langchain import BasePromptTemplate, LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.schema import BaseOutputParser, OutputParserException
from openai.error import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
... | [
"langchain.LLMChain"
] | [((469, 481), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (476, 481), False, 'from typing import Any, Callable, Dict, TypeVar\n'), ((2486, 2520), 'langchain.LLMChain', 'LLMChain', ([], {'llm': 'model', 'prompt': 'prompt'}), '(llm=model, prompt=prompt)\n', (2494, 2520), False, 'from langchain import BaseP... |
from typing import Any, Callable, Dict, TypeVar
from langchain import BasePromptTemplate, LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.schema import BaseOutputParser, OutputParserException
from openai.error import (
AuthenticationError,
InvalidRequestError,
RateLimitError,
... | [
"langchain.LLMChain"
] | [((469, 481), 'typing.TypeVar', 'TypeVar', (['"""T"""'], {}), "('T')\n", (476, 481), False, 'from typing import Any, Callable, Dict, TypeVar\n'), ((2486, 2520), 'langchain.LLMChain', 'LLMChain', ([], {'llm': 'model', 'prompt': 'prompt'}), '(llm=model, prompt=prompt)\n', (2494, 2520), False, 'from langchain import BaseP... |
import json
import os.path
import logging
import time
from langchain.vectorstores import FAISS
from langchain import PromptTemplate
from utils.references import References
from utils.knowledge import Knowledge
from utils.file_operations import make_archive, copy_templates
from utils.tex_processing import create_copies... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.PromptTemplate"
] | [((1271, 1292), 'logging.info', 'logging.info', (['message'], {}), '(message)\n', (1283, 1292), False, 'import logging\n'), ((1552, 1587), 'utils.gpt_interaction.GPTModel', 'GPTModel', ([], {'model': '"""gpt-3.5-turbo-16k"""'}), "(model='gpt-3.5-turbo-16k')\n", (1560, 1587), False, 'from utils.gpt_interaction import GP... |
import json
import os.path
import logging
import time
from langchain.vectorstores import FAISS
from langchain import PromptTemplate
from utils.references import References
from utils.knowledge import Knowledge
from utils.file_operations import make_archive, copy_templates
from utils.tex_processing import create_copies... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.PromptTemplate"
] | [((1271, 1292), 'logging.info', 'logging.info', (['message'], {}), '(message)\n', (1283, 1292), False, 'import logging\n'), ((1552, 1587), 'utils.gpt_interaction.GPTModel', 'GPTModel', ([], {'model': '"""gpt-3.5-turbo-16k"""'}), "(model='gpt-3.5-turbo-16k')\n", (1560, 1587), False, 'from utils.gpt_interaction import GP... |
import json
import os.path
import logging
import time
from langchain.vectorstores import FAISS
from langchain import PromptTemplate
from utils.references import References
from utils.knowledge import Knowledge
from utils.file_operations import make_archive, copy_templates
from utils.tex_processing import create_copies... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.PromptTemplate"
] | [((1271, 1292), 'logging.info', 'logging.info', (['message'], {}), '(message)\n', (1283, 1292), False, 'import logging\n'), ((1552, 1587), 'utils.gpt_interaction.GPTModel', 'GPTModel', ([], {'model': '"""gpt-3.5-turbo-16k"""'}), "(model='gpt-3.5-turbo-16k')\n", (1560, 1587), False, 'from utils.gpt_interaction import GP... |
import sys
import os
sys.path.append(os.path.dirname(os.path.realpath(__file__)))
sys.path.append(os.path.dirname(os.path.dirname(os.path.realpath(__file__))))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__)), 'NeuralSeq'))
sys.path.append(os.path.join(os.path.dirname(os.path.realpath(__file__))... | [
"langchain.llms.openai.OpenAI",
"langchain.agents.tools.Tool",
"langchain.chains.conversation.memory.ConversationBufferMemory",
"langchain.agents.initialize.initialize_agent"
] | [((3966, 3992), 'scipy.io.wavfile.read', 'wavfile.read', (['audio_path_1'], {}), '(audio_path_1)\n', (3978, 3992), True, 'import scipy.io.wavfile as wavfile\n'), ((4014, 4040), 'scipy.io.wavfile.read', 'wavfile.read', (['audio_path_2'], {}), '(audio_path_2)\n', (4026, 4040), True, 'import scipy.io.wavfile as wavfile\n'... |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import FAISS
loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv")
docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)
inde... | [
"langchain_community.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((174, 229), 'langchain_community.document_loaders.CSVLoader', 'CSVLoader', (['"""/Users/harrisonchase/Downloads/titanic.csv"""'], {}), "('/Users/harrisonchase/Downloads/titanic.csv')\n", (183, 229), False, 'from langchain_community.document_loaders import CSVLoader\n'), ((268, 314), 'langchain.indexes.VectorstoreInde... |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import FAISS
loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv")
docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)
inde... | [
"langchain_community.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((174, 229), 'langchain_community.document_loaders.CSVLoader', 'CSVLoader', (['"""/Users/harrisonchase/Downloads/titanic.csv"""'], {}), "('/Users/harrisonchase/Downloads/titanic.csv')\n", (183, 229), False, 'from langchain_community.document_loaders import CSVLoader\n'), ((268, 314), 'langchain.indexes.VectorstoreInde... |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import FAISS
loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv")
docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)
inde... | [
"langchain_community.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((174, 229), 'langchain_community.document_loaders.CSVLoader', 'CSVLoader', (['"""/Users/harrisonchase/Downloads/titanic.csv"""'], {}), "('/Users/harrisonchase/Downloads/titanic.csv')\n", (183, 229), False, 'from langchain_community.document_loaders import CSVLoader\n'), ((268, 314), 'langchain.indexes.VectorstoreInde... |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import CSVLoader
from langchain_community.vectorstores import FAISS
loader = CSVLoader("/Users/harrisonchase/Downloads/titanic.csv")
docs = loader.load()
index_creator = VectorstoreIndexCreator(vectorstore_cls=FAISS)
inde... | [
"langchain_community.document_loaders.CSVLoader",
"langchain.indexes.VectorstoreIndexCreator"
] | [((174, 229), 'langchain_community.document_loaders.CSVLoader', 'CSVLoader', (['"""/Users/harrisonchase/Downloads/titanic.csv"""'], {}), "('/Users/harrisonchase/Downloads/titanic.csv')\n", (183, 229), False, 'from langchain_community.document_loaders import CSVLoader\n'), ((268, 314), 'langchain.indexes.VectorstoreInde... |
from typing import Any, Dict, List, Type, Union
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import... | [
"langchain_community.graphs.networkx_graph.get_entities",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string",
"langchain_community.graphs.networkx_graph.parse_triples"
] | [((1062, 1104), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'NetworkxEntityGraph'}), '(default_factory=NetworkxEntityGraph)\n', (1067, 1104), False, 'from langchain_core.pydantic_v1 import Field\n'), ((3163, 3223), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.llm', 'prompt':... |
from typing import Any, Dict, List, Type, Union
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import... | [
"langchain_community.graphs.networkx_graph.get_entities",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string",
"langchain_community.graphs.networkx_graph.parse_triples"
] | [((1062, 1104), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'NetworkxEntityGraph'}), '(default_factory=NetworkxEntityGraph)\n', (1067, 1104), False, 'from langchain_core.pydantic_v1 import Field\n'), ((3163, 3223), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.llm', 'prompt':... |
from typing import Any, Dict, List, Type, Union
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import... | [
"langchain_community.graphs.networkx_graph.get_entities",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string",
"langchain_community.graphs.networkx_graph.parse_triples"
] | [((1062, 1104), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'NetworkxEntityGraph'}), '(default_factory=NetworkxEntityGraph)\n', (1067, 1104), False, 'from langchain_core.pydantic_v1 import Field\n'), ((3163, 3223), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.llm', 'prompt':... |
from typing import Any, Dict, List, Type, Union
from langchain_community.graphs import NetworkxEntityGraph
from langchain_community.graphs.networkx_graph import (
KnowledgeTriple,
get_entities,
parse_triples,
)
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import... | [
"langchain_community.graphs.networkx_graph.get_entities",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string",
"langchain_community.graphs.networkx_graph.parse_triples"
] | [((1062, 1104), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'NetworkxEntityGraph'}), '(default_factory=NetworkxEntityGraph)\n', (1067, 1104), False, 'from langchain_core.pydantic_v1 import Field\n'), ((3163, 3223), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'self.llm', 'prompt':... |
"""
**LLM** classes provide
access to the large language model (**LLM**) APIs and services.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
**Main helpers:**
.. code-block::
LLMResult, PromptValue,
CallbackManagerForLLMRun... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((11338, 11358), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (11356, 11358), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((11368, 11729), 'warnings.warn', 'warnings.warn', (['f"""Importing LLMs from langchain is deprecated. Importing fro... |
"""
**LLM** classes provide
access to the large language model (**LLM**) APIs and services.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
**Main helpers:**
.. code-block::
LLMResult, PromptValue,
CallbackManagerForLLMRun... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((11338, 11358), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (11356, 11358), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((11368, 11729), 'warnings.warn', 'warnings.warn', (['f"""Importing LLMs from langchain is deprecated. Importing fro... |
"""
**LLM** classes provide
access to the large language model (**LLM**) APIs and services.
**Class hierarchy:**
.. code-block::
BaseLanguageModel --> BaseLLM --> LLM --> <name> # Examples: AI21, HuggingFaceHub, OpenAI
**Main helpers:**
.. code-block::
LLMResult, PromptValue,
CallbackManagerForLLMRun... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((11338, 11358), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (11356, 11358), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((11368, 11729), 'warnings.warn', 'warnings.warn', (['f"""Importing LLMs from langchain is deprecated. Importing fro... |
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from langchain_community.utilities.redis import get_client
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_stri... | [
"langchain_community.utilities.redis.get_client",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string"
] | [((701, 728), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (718, 728), False, 'import logging\n'), ((10994, 11036), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'InMemoryEntityStore'}), '(default_factory=InMemoryEntityStore)\n', (10999, 11036), False, 'from lang... |
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from langchain_community.utilities.redis import get_client
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_stri... | [
"langchain_community.utilities.redis.get_client",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string"
] | [((701, 728), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (718, 728), False, 'import logging\n'), ((10994, 11036), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'InMemoryEntityStore'}), '(default_factory=InMemoryEntityStore)\n', (10999, 11036), False, 'from lang... |
import logging
from abc import ABC, abstractmethod
from itertools import islice
from typing import Any, Dict, Iterable, List, Optional
from langchain_community.utilities.redis import get_client
from langchain_core.language_models import BaseLanguageModel
from langchain_core.messages import BaseMessage, get_buffer_stri... | [
"langchain_community.utilities.redis.get_client",
"langchain.chains.llm.LLMChain",
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.pydantic_v1.Field",
"langchain_core.messages.get_buffer_string"
] | [((701, 728), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (718, 728), False, 'import logging\n'), ((10994, 11036), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'default_factory': 'InMemoryEntityStore'}), '(default_factory=InMemoryEntityStore)\n', (10999, 11036), False, 'from lang... |
from typing import Any, Dict, List, Optional
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
class ConversationBufferMe... | [
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.messages.get_buffer_string",
"langchain_core.pydantic_v1.root_validator"
] | [((2888, 2904), 'langchain_core.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (2902, 2904), False, 'from langchain_core.pydantic_v1 import root_validator\n'), ((983, 1073), 'langchain_core.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefi... |
from typing import Any, Dict, List, Optional
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
class ConversationBufferMe... | [
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.messages.get_buffer_string",
"langchain_core.pydantic_v1.root_validator"
] | [((2888, 2904), 'langchain_core.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (2902, 2904), False, 'from langchain_core.pydantic_v1 import root_validator\n'), ((983, 1073), 'langchain_core.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefi... |
from typing import Any, Dict, List, Optional
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
class ConversationBufferMe... | [
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.messages.get_buffer_string",
"langchain_core.pydantic_v1.root_validator"
] | [((2888, 2904), 'langchain_core.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (2902, 2904), False, 'from langchain_core.pydantic_v1 import root_validator\n'), ((983, 1073), 'langchain_core.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefi... |
from typing import Any, Dict, List, Optional
from langchain_core.messages import BaseMessage, get_buffer_string
from langchain_core.pydantic_v1 import root_validator
from langchain.memory.chat_memory import BaseChatMemory, BaseMemory
from langchain.memory.utils import get_prompt_input_key
class ConversationBufferMe... | [
"langchain.memory.utils.get_prompt_input_key",
"langchain_core.messages.get_buffer_string",
"langchain_core.pydantic_v1.root_validator"
] | [((2888, 2904), 'langchain_core.pydantic_v1.root_validator', 'root_validator', ([], {}), '()\n', (2902, 2904), False, 'from langchain_core.pydantic_v1 import root_validator\n'), ((983, 1073), 'langchain_core.messages.get_buffer_string', 'get_buffer_string', (['messages'], {'human_prefix': 'self.human_prefix', 'ai_prefi... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((2151, 2171), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (2169, 2171), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((2185, 2548), 'warnings.warn', 'warnings.warn', (['f"""Importing tools from langchain is deprecated. Importing from lan... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((2151, 2171), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (2169, 2171), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((2185, 2548), 'warnings.warn', 'warnings.warn', (['f"""Importing tools from langchain is deprecated. Importing from lan... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((2151, 2171), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (2169, 2171), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((2185, 2548), 'warnings.warn', 'warnings.warn', (['f"""Importing tools from langchain is deprecated. Importing from lan... |
"""**Tools** are classes that an Agent uses to interact with the world.
Each tool has a **description**. Agent uses the description to choose the right
tool for the job.
**Class hierarchy:**
.. code-block::
ToolMetaclass --> BaseTool --> <name>Tool # Examples: AIPluginTool, BaseGraphQLTool
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((2151, 2171), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (2169, 2171), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((2185, 2548), 'warnings.warn', 'warnings.warn', (['f"""Importing tools from langchain is deprecated. Importing from lan... |
from functools import partial
from typing import Optional
from langchain_core.callbacks.manager import (
Callbacks,
)
from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
f... | [
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.format_document",
"langchain.tools.Tool",
"langchain_core.prompts.PromptTemplate.from_template"
] | [((439, 489), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'description': '"""query to look up in retriever"""'}), "(description='query to look up in retriever')\n", (444, 489), False, 'from langchain_core.pydantic_v1 import BaseModel, Field\n'), ((1996, 2126), 'functools.partial', 'partial', (['_get_relevant_doc... |
from functools import partial
from typing import Optional
from langchain_core.callbacks.manager import (
Callbacks,
)
from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
f... | [
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.format_document",
"langchain.tools.Tool",
"langchain_core.prompts.PromptTemplate.from_template"
] | [((439, 489), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'description': '"""query to look up in retriever"""'}), "(description='query to look up in retriever')\n", (444, 489), False, 'from langchain_core.pydantic_v1 import BaseModel, Field\n'), ((1996, 2126), 'functools.partial', 'partial', (['_get_relevant_doc... |
from functools import partial
from typing import Optional
from langchain_core.callbacks.manager import (
Callbacks,
)
from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
f... | [
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.format_document",
"langchain.tools.Tool",
"langchain_core.prompts.PromptTemplate.from_template"
] | [((439, 489), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'description': '"""query to look up in retriever"""'}), "(description='query to look up in retriever')\n", (444, 489), False, 'from langchain_core.pydantic_v1 import BaseModel, Field\n'), ((1996, 2126), 'functools.partial', 'partial', (['_get_relevant_doc... |
from functools import partial
from typing import Optional
from langchain_core.callbacks.manager import (
Callbacks,
)
from langchain_core.prompts import BasePromptTemplate, PromptTemplate, format_document
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.retrievers import BaseRetriever
f... | [
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.format_document",
"langchain.tools.Tool",
"langchain_core.prompts.PromptTemplate.from_template"
] | [((439, 489), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'description': '"""query to look up in retriever"""'}), "(description='query to look up in retriever')\n", (444, 489), False, 'from langchain_core.pydantic_v1 import BaseModel, Field\n'), ((1996, 2126), 'functools.partial', 'partial', (['_get_relevant_doc... |
from typing import Any, List, Sequence, Tuple, Union
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTempla... | [
"langchain_core.prompts.chat.AIMessagePromptTemplate.from_template",
"langchain_core.prompts.chat.ChatPromptTemplate.from_template",
"langchain.agents.output_parsers.XMLAgentOutputParser",
"langchain.agents.format_scratchpad.format_xml",
"langchain_core._api.deprecated"
] | [((875, 943), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_xml_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_xml_agent', removal='0.2.0')\n", (885, 943), False, 'from langchain_core._api import deprecated\n'), ((1644, 1696), 'langchain_core.prompts... |
from typing import Any, List, Sequence, Tuple, Union
from langchain_core._api import deprecated
from langchain_core.agents import AgentAction, AgentFinish
from langchain_core.callbacks import Callbacks
from langchain_core.language_models import BaseLanguageModel
from langchain_core.prompts.base import BasePromptTempla... | [
"langchain_core.prompts.chat.AIMessagePromptTemplate.from_template",
"langchain_core.prompts.chat.ChatPromptTemplate.from_template",
"langchain.agents.output_parsers.XMLAgentOutputParser",
"langchain.agents.format_scratchpad.format_xml",
"langchain_core._api.deprecated"
] | [((875, 943), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_xml_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_xml_agent', removal='0.2.0')\n", (885, 943), False, 'from langchain_core._api import deprecated\n'), ((1644, 1696), 'langchain_core.prompts... |
"""**Graphs** provide a natural language interface to graph databases."""
import warnings
from typing import Any
from langchain_core._api import LangChainDeprecationWarning
from langchain.utils.interactive_env import is_interactive_env
def __getattr__(name: str) -> Any:
from langchain_community import graphs
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((378, 398), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (396, 398), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((408, 773), 'warnings.warn', 'warnings.warn', (['f"""Importing graphs from langchain is deprecated. Importing from langchai... |
"""**Graphs** provide a natural language interface to graph databases."""
import warnings
from typing import Any
from langchain_core._api import LangChainDeprecationWarning
from langchain.utils.interactive_env import is_interactive_env
def __getattr__(name: str) -> Any:
from langchain_community import graphs
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((378, 398), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (396, 398), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((408, 773), 'warnings.warn', 'warnings.warn', (['f"""Importing graphs from langchain is deprecated. Importing from langchai... |
"""**Graphs** provide a natural language interface to graph databases."""
import warnings
from typing import Any
from langchain_core._api import LangChainDeprecationWarning
from langchain.utils.interactive_env import is_interactive_env
def __getattr__(name: str) -> Any:
from langchain_community import graphs
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((378, 398), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (396, 398), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((408, 773), 'warnings.warn', 'warnings.warn', (['f"""Importing graphs from langchain is deprecated. Importing from langchai... |
"""**Graphs** provide a natural language interface to graph databases."""
import warnings
from typing import Any
from langchain_core._api import LangChainDeprecationWarning
from langchain.utils.interactive_env import is_interactive_env
def __getattr__(name: str) -> Any:
from langchain_community import graphs
... | [
"langchain.utils.interactive_env.is_interactive_env"
] | [((378, 398), 'langchain.utils.interactive_env.is_interactive_env', 'is_interactive_env', ([], {}), '()\n', (396, 398), False, 'from langchain.utils.interactive_env import is_interactive_env\n'), ((408, 773), 'warnings.warn', 'warnings.warn', (['f"""Importing graphs from langchain is deprecated. Importing from langchai... |
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.parse import urlparse
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_core.callbacks im... | [
"langchain_core.callbacks.AsyncCallbackManagerForChainRun.get_noop_manager",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain",
"langchain_community.utilities.requests.TextRequestsWrapper",
"langchain_core.pydantic_v1.Field",
"langchain_core.pydantic_v... | [((979, 992), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (987, 992), False, 'from urllib.parse import urlparse\n'), ((2555, 2574), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'exclude': '(True)'}), '(exclude=True)\n', (2560, 2574), False, 'from langchain_core.pydantic_v1 import Field, root_va... |
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.parse import urlparse
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_core.callbacks im... | [
"langchain_core.callbacks.AsyncCallbackManagerForChainRun.get_noop_manager",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain",
"langchain_community.utilities.requests.TextRequestsWrapper",
"langchain_core.pydantic_v1.Field",
"langchain_core.pydantic_v... | [((979, 992), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (987, 992), False, 'from urllib.parse import urlparse\n'), ((2555, 2574), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'exclude': '(True)'}), '(exclude=True)\n', (2560, 2574), False, 'from langchain_core.pydantic_v1 import Field, root_va... |
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.parse import urlparse
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_core.callbacks im... | [
"langchain_core.callbacks.AsyncCallbackManagerForChainRun.get_noop_manager",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain",
"langchain_community.utilities.requests.TextRequestsWrapper",
"langchain_core.pydantic_v1.Field",
"langchain_core.pydantic_v... | [((979, 992), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (987, 992), False, 'from urllib.parse import urlparse\n'), ((2555, 2574), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'exclude': '(True)'}), '(exclude=True)\n', (2560, 2574), False, 'from langchain_core.pydantic_v1 import Field, root_va... |
"""Chain that makes API calls and summarizes the responses to answer a question."""
from __future__ import annotations
from typing import Any, Dict, List, Optional, Sequence, Tuple
from urllib.parse import urlparse
from langchain_community.utilities.requests import TextRequestsWrapper
from langchain_core.callbacks im... | [
"langchain_core.callbacks.AsyncCallbackManagerForChainRun.get_noop_manager",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain",
"langchain_community.utilities.requests.TextRequestsWrapper",
"langchain_core.pydantic_v1.Field",
"langchain_core.pydantic_v... | [((979, 992), 'urllib.parse.urlparse', 'urlparse', (['url'], {}), '(url)\n', (987, 992), False, 'from urllib.parse import urlparse\n'), ((2555, 2574), 'langchain_core.pydantic_v1.Field', 'Field', ([], {'exclude': '(True)'}), '(exclude=True)\n', (2560, 2574), False, 'from langchain_core.pydantic_v1 import Field, root_va... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_mo... | [
"langchain.chains.hyde.prompts.PROMPT_MAP.keys",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain"
] | [((3148, 3180), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (3156, 3180), False, 'from langchain.chains.llm import LLMChain\n'), ((2258, 2303), 'langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager', 'CallbackManagerForChainRun.get... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_mo... | [
"langchain.chains.hyde.prompts.PROMPT_MAP.keys",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain"
] | [((3148, 3180), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (3156, 3180), False, 'from langchain.chains.llm import LLMChain\n'), ((2258, 2303), 'langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager', 'CallbackManagerForChainRun.get... |
"""Hypothetical Document Embeddings.
https://arxiv.org/abs/2212.10496
"""
from __future__ import annotations
from typing import Any, Dict, List, Optional
import numpy as np
from langchain_core.callbacks import CallbackManagerForChainRun
from langchain_core.embeddings import Embeddings
from langchain_core.language_mo... | [
"langchain.chains.hyde.prompts.PROMPT_MAP.keys",
"langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager",
"langchain.chains.llm.LLMChain"
] | [((3148, 3180), 'langchain.chains.llm.LLMChain', 'LLMChain', ([], {'llm': 'llm', 'prompt': 'prompt'}), '(llm=llm, prompt=prompt)\n', (3156, 3180), False, 'from langchain.chains.llm import LLMChain\n'), ((2258, 2303), 'langchain_core.callbacks.CallbackManagerForChainRun.get_noop_manager', 'CallbackManagerForChainRun.get... |
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.langua... | [
"langchain.agents.mrkl.output_parser.MRKLOutputParser",
"langchain.agents.utils.validate_tools_single_input",
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.PromptTemplate",
"langchain_core._api.deprecated",
"langchain.chains.LLMChain",
"langchain.agents.tools.Tool",
"langchain_core.promp... | [((1278, 1348), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_react_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_react_agent', removal='0.2.0')\n", (1288, 1348), False, 'from langchain_core._api import deprecated\n'), ((5068, 5104), 'langchain_core... |
"""Attempt to implement MRKL systems as described in arxiv.org/pdf/2205.00445.pdf."""
from __future__ import annotations
from typing import Any, Callable, List, NamedTuple, Optional, Sequence
from langchain_core._api import deprecated
from langchain_core.callbacks import BaseCallbackManager
from langchain_core.langua... | [
"langchain.agents.mrkl.output_parser.MRKLOutputParser",
"langchain.agents.utils.validate_tools_single_input",
"langchain_core.pydantic_v1.Field",
"langchain_core.prompts.PromptTemplate",
"langchain_core._api.deprecated",
"langchain.chains.LLMChain",
"langchain.agents.tools.Tool",
"langchain_core.promp... | [((1278, 1348), 'langchain_core._api.deprecated', 'deprecated', (['"""0.1.0"""'], {'alternative': '"""create_react_agent"""', 'removal': '"""0.2.0"""'}), "('0.1.0', alternative='create_react_agent', removal='0.2.0')\n", (1288, 1348), False, 'from langchain_core._api import deprecated\n'), ((5068, 5104), 'langchain_core... |
import streamlit as st
import datetime
import os
import psycopg2
from dotenv import load_dotenv
from langchain.prompts import PromptTemplate
from langchain.docstore.document import Document
def log(message):
current_time = datetime.datetime.now()
milliseconds = current_time.microsecond // 1000
timestamp ... | [
"langchain.docstore.document.Document",
"langchain.prompts.PromptTemplate"
] | [((2668, 2806), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['input_question', 'table_info', 'columns_info', 'top_k', 'no_answer_text']", 'template': '_postgres_prompt'}), "(input_variables=['input_question', 'table_info',\n 'columns_info', 'top_k', 'no_answer_text'], template=_po... |
import os
import pandas as pd
from langchain.chains import LLMChain
from langchain.llms import OpenAI
from langchain.prompts import PromptTemplate
import mlflow
assert (
"OPENAI_API_KEY" in os.environ
), "Please set the OPENAI_API_KEY environment variable to run this example."
def build_and_evalute_model_with_... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.llms.OpenAI"
] | [((1832, 1932), 'mlflow.load_table', 'mlflow.load_table', (['"""eval_results_table.json"""'], {'extra_columns': "['run_id', 'params.prompt_template']"}), "('eval_results_table.json', extra_columns=['run_id',\n 'params.prompt_template'])\n", (1849, 1932), False, 'import mlflow\n'), ((349, 367), 'mlflow.start_run', 'm... |
import os
import voyager.utils as U
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.schema import HumanMessage, SystemMessage
from langchain.vectorstores import Chroma
from voyager.prompts import load_prompt
from voyager.control_primitives import lo... | [
"langchain.embeddings.openai.OpenAIEmbeddings",
"langchain.schema.HumanMessage",
"langchain.chat_models.ChatOpenAI"
] | [((583, 678), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': 'model_name', 'temperature': 'temperature', 'request_timeout': 'request_timout'}), '(model_name=model_name, temperature=temperature, request_timeout=\n request_timout)\n', (593, 678), False, 'from langchain.chat_models import ChatOpe... |
from langflow import CustomComponent
from langchain.agents import AgentExecutor, create_json_agent
from langflow.field_typing import (
BaseLanguageModel,
)
from langchain_community.agent_toolkits.json.toolkit import JsonToolkit
class JsonAgentComponent(CustomComponent):
display_name = "JsonAgent"
descript... | [
"langchain.agents.create_json_agent"
] | [((657, 700), 'langchain.agents.create_json_agent', 'create_json_agent', ([], {'llm': 'llm', 'toolkit': 'toolkit'}), '(llm=llm, toolkit=toolkit)\n', (674, 700), False, 'from langchain.agents import AgentExecutor, create_json_agent\n')] |
import os
from fedml.serving import FedMLPredictor
from fedml.serving import FedMLInferenceRunner
from langchain import PromptTemplate, LLMChain
from langchain.llms import HuggingFacePipeline
import torch
from transformers import (
AutoConfig,
AutoModelForCausalLM,
AutoTokenizer,
TextGenerationPipeline,... | [
"langchain.LLMChain",
"langchain.PromptTemplate"
] | [((2184, 2209), 'argparse.ArgumentParser', 'argparse.ArgumentParser', ([], {}), '()\n', (2207, 2209), False, 'import argparse\n'), ((2559, 2588), 'fedml.serving.FedMLInferenceRunner', 'FedMLInferenceRunner', (['chatbot'], {}), '(chatbot)\n', (2579, 2588), False, 'from fedml.serving import FedMLInferenceRunner\n'), ((70... |
from langchain.utilities import BashProcess
from langchain.agents import load_tools
def get_built_in_tools(tools: list[str]):
bash = BashProcess()
load_tools(["human"])
return [bash]
| [
"langchain.utilities.BashProcess",
"langchain.agents.load_tools"
] | [((139, 152), 'langchain.utilities.BashProcess', 'BashProcess', ([], {}), '()\n', (150, 152), False, 'from langchain.utilities import BashProcess\n'), ((158, 179), 'langchain.agents.load_tools', 'load_tools', (["['human']"], {}), "(['human'])\n", (168, 179), False, 'from langchain.agents import load_tools\n')] |
#
# 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... |
"""
This module provides an implementation for generating question data from documents.
Supported types of document sources include:
- plain text
- unstructured files: Text, PDF, PowerPoint, HTML, Images,
Excel spreadsheets, Word documents, Markdown, etc.
- documents from Google Drive (provide file... | [
"langchain.document_loaders.GoogleDriveLoader",
"langchain.schema.Document",
"langchain.document_loaders.UnstructuredFileLoader",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((7802, 7926), 'yival.data_generators.base_data_generator.BaseDataGenerator.register_data_generator', 'BaseDataGenerator.register_data_generator', (['"""document_data_generator"""', 'DocumentDataGenerator', 'DocumentDataGeneratorConfig'], {}), "('document_data_generator',\n DocumentDataGenerator, DocumentDataGenera... |
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... |
from typing import Any, Dict
from langchain.base_language import BaseLanguageModel
from langchain.prompts import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
MessagesPlaceholder,
SystemMessagePromptTemplate,
)
from langchain.chains import ConversationChain
from real_agents.adapters.exe... | [
"langchain.chains.ConversationChain",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.prompts.SystemMessagePromptTemplate.from_template",
"langchain.prompts.MessagesPlaceholder"
] | [((894, 940), 'real_agents.adapters.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'return_messages': '(True)'}), '(return_messages=True)\n', (918, 940), False, 'from real_agents.adapters.memory import ConversationBufferMemory\n'), ((1746, 1824), 'langchain.chains.ConversationChain', 'ConversationC... |
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... |
from dotenv import load_dotenv
from langchain import OpenAI
from langchain.document_loaders.csv_loader import CSVLoader
load_dotenv()
filepath = "academy/academy.csv"
loader = CSVLoader(filepath)
data = loader.load()
print(data)
llm = OpenAI(temperature=0)
from langchain.agents import create_csv_agent
agent = crea... | [
"langchain.document_loaders.csv_loader.CSVLoader",
"langchain.agents.create_csv_agent",
"langchain.OpenAI"
] | [((122, 135), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (133, 135), False, 'from dotenv import load_dotenv\n'), ((179, 198), 'langchain.document_loaders.csv_loader.CSVLoader', 'CSVLoader', (['filepath'], {}), '(filepath)\n', (188, 198), False, 'from langchain.document_loaders.csv_loader import CSVLoader\n'... |
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)... |
import re
from typing import Union
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from src.agents.agent import AgentOutputParser
class ReActOutputParser(AgentOutputParser):
"""Output parser for the ReAct agent."""
def parse(self, text: str) -> Union[AgentAction, AgentFinish]:
... | [
"langchain.schema.AgentFinish",
"langchain.schema.AgentAction",
"langchain.schema.OutputParserException"
] | [((685, 726), 're.search', 're.search', (['"""(.*?)\\\\[(.*?)\\\\]"""', 'action_str'], {}), "('(.*?)\\\\[(.*?)\\\\]', action_str)\n", (694, 726), False, 'import re\n'), ((444, 504), 'langchain.schema.OutputParserException', 'OutputParserException', (['f"""Could not parse LLM Output: {text}"""'], {}), "(f'Could not pars... |
import re
from langchain.agents import AgentOutputParser
from langchain.schema import AgentAction, AgentFinish, OutputParserException
from typing import Union
from cat.mad_hatter.mad_hatter import MadHatter
from cat.log import log
class ChooseProcedureOutputParser(AgentOutputParser):
def parse(self, llm_output:... | [
"langchain.schema.AgentFinish",
"langchain.schema.OutputParserException"
] | [((936, 975), 're.search', 're.search', (['regex', 'llm_output', 're.DOTALL'], {}), '(regex, llm_output, re.DOTALL)\n', (945, 975), False, 'import re\n'), ((1551, 1562), 'cat.mad_hatter.mad_hatter.MadHatter', 'MadHatter', ([], {}), '()\n', (1560, 1562), False, 'from cat.mad_hatter.mad_hatter import MadHatter\n'), ((101... |
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