code stringlengths 141 79.4k | apis listlengths 1 23 | extract_api stringlengths 126 73.2k |
|---|---|---|
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import contextmanager
from contextvars import ContextVar
from typing import Any, Dict, Generator, List, Optional, Type, TypeVar, Union, cast
from uuid import UUID, uuid4
import langchain
from la... | [
"langchain.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainTracer",
"langchain.callbacks.tracers.langchain_v1... | [((1036, 1063), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1053, 1063), False, 'import logging\n'), ((1208, 1251), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1218, 1251), False, 'from contextvars i... |
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Field, root_validator, validator
import langchain
from langchai... | [
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((816, 849), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (821, 849), False, 'from pydantic import BaseModel, Field, root_validator, validator\n'), ((904, 937), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exc... |
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Field, root_validator, validator
import langchain
from langchai... | [
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((816, 849), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (821, 849), False, 'from pydantic import BaseModel, Field, root_validator, validator\n'), ((904, 937), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exc... |
"""Base interface that all chains should implement."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Field, root_validator, validator
import langchain
from langchai... | [
"langchain.schema.RunInfo",
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.callbacks.manager.CallbackManager.configure"
] | [((816, 849), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exclude=True)\n', (821, 849), False, 'from pydantic import BaseModel, Field, root_validator, validator\n'), ((904, 937), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, exc... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import inspect
import json
import warnings
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Sequence, Tuple, Union
import yaml
from pydantic import Extra, Field, root_validator, validator
impor... | [
"langchain.callbacks.manager.AsyncCallbackManager.configure",
"langchain.schema.Generation",
"langchain.schema.get_buffer_string",
"langchain.callbacks.manager.CallbackManager.configure",
"langchain.schema.RunInfo",
"langchain.schema.AIMessage",
"langchain.llm_cache.lookup",
"langchain.llm_cache.updat... | [((2315, 2352), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (2320, 2352), False, 'from pydantic import Extra, Field, root_validator, validator\n'), ((2426, 2459), 'pydantic.Field', 'Field', ([], {'default': 'None', 'exclude': '(True)'}), '(default=None, ... |
"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import ge... | [
"langchain.schema.Generation",
"langchain.llm_cache.update",
"langchain.llm_cache.lookup",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((1991, 2028), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1996, 2028), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((2119, 2162), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=... |
"""Base interface for large language models to expose."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Mapping, Optional, Tuple, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import ge... | [
"langchain.schema.Generation",
"langchain.llm_cache.update",
"langchain.llm_cache.lookup",
"langchain.schema.LLMResult",
"langchain.callbacks.get_callback_manager"
] | [((1991, 2028), 'pydantic.Field', 'Field', ([], {'default_factory': '_get_verbosity'}), '(default_factory=_get_verbosity)\n', (1996, 2028), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((2119, 2162), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager'}), '(default_factory=... |
import discord
from discord import app_commands
from discord.ext import commands
import langchain
from langchain.document_loaders import YoutubeLoader
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.summarize import load_summarize_chain
import torch
class YoutubeSummaryCog(c... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.document_loaders.YoutubeLoader.from_youtube_url",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((425, 528), 'discord.app_commands.command', 'app_commands.command', ([], {'name': '"""youtubesummary"""', 'description': '"""Summarize a YouTube video given its URL"""'}), "(name='youtubesummary', description=\n 'Summarize a YouTube video given its URL')\n", (445, 528), False, 'from discord import app_commands\n')... |
"""Base interface that all chains should implement."""
import json
from abc import ABC, abstractmethod
from pathlib import Path
from typing import Any, Dict, List, Optional, Union
import yaml
from pydantic import BaseModel, Extra, Field, validator
import langchain
from langchain.callbacks import get_callback_manager
... | [
"langchain.callbacks.get_callback_manager"
] | [((1401, 1458), 'pydantic.Field', 'Field', ([], {'default_factory': 'get_callback_manager', 'exclude': '(True)'}), '(default_factory=get_callback_manager, exclude=True)\n', (1406, 1458), False, 'from pydantic import BaseModel, Extra, Field, validator\n'), ((1493, 1530), 'pydantic.Field', 'Field', ([], {'default_factory... |
#!/Users/mark/dev/ml/langchain/read_github/langchain_github/env/bin/python
# change above to the location of your local Python venv installation
import sys, os, shutil
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import pathlib
from langchain.docstore.docume... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langc... | [((245, 272), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (260, 272), False, 'import sys, os, shutil\n'), ((667, 692), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (677, 692), False, 'from langchain.chat_models import ChatOpenAI\n... |
#!/Users/mark/dev/ml/langchain/read_github/langchain_github/env/bin/python
# change above to the location of your local Python venv installation
import sys, os, shutil
parent_dir = os.path.abspath(os.path.join(os.path.dirname(__file__), '..'))
sys.path.append(parent_dir)
import pathlib
from langchain.docstore.docume... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.docstore.document.Document",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.chat_models.ChatOpenAI",
"langchain.document_loaders.unstructured.UnstructuredFileLoader",
"langchain.text_splitter.PythonCodeTextSplitter",
"langc... | [((245, 272), 'sys.path.append', 'sys.path.append', (['parent_dir'], {}), '(parent_dir)\n', (260, 272), False, 'import sys, os, shutil\n'), ((667, 692), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'temperature': '(0)'}), '(temperature=0)\n', (677, 692), False, 'from langchain.chat_models import ChatOpenAI\n... |
import os
import json
from typing import List
from dotenv import load_dotenv
from pydantic import BaseModel, Field
from supabase.client import Client, create_client
from langchain.chat_models import ChatOpenAI
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.tools import StructuredTool
from langc... | [
"langchain.chains.openai_functions.create_structured_output_chain",
"langchain.tools.StructuredTool",
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.prompts.SystemMessagePromptTemplate.from_... | [((528, 541), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (539, 541), False, 'from dotenv import load_dotenv\n'), ((799, 824), 'os.getenv', 'os.getenv', (['"""SUPABASE_URL"""'], {}), "('SUPABASE_URL')\n", (808, 824), False, 'import os\n'), ((840, 865), 'os.getenv', 'os.getenv', (['"""SUPABASE_KEY"""'], {}), ... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
import streamlit as st
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
from langchain.prompts.prompt import PromptTemplate
from langchain.callbacks import get_openai_callback
#fix Error: module 'langchain' has no attribute 'verbose'
import langchain
langchain.verb... | [
"langchain.chains.ConversationalRetrievalChain.from_llm",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.get_openai_callback",
"langchain.chat_models.ChatOpenAI"
] | [((1142, 1219), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'qa_template', 'input_variables': "['context', 'question']"}), "(template=qa_template, input_variables=['context', 'question'])\n", (1156, 1219), False, 'from langchain.prompts.prompt import PromptTemplate\n'), ((1364, 1432),... |
"""
A simple CUI application to visualize and query a customer database using the `textual` package.
"""
from dataclasses import dataclass
import langchain
from langchain.cache import SQLiteCache
from langchain.llms import OpenAI
from textual.app import App, ComposeResult
from textual.containers import Horizontal
from... | [
"langchain.llms.OpenAI",
"langchain.cache.SQLiteCache"
] | [((447, 460), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {}), '()\n', (458, 460), False, 'from langchain.cache import SQLiteCache\n'), ((472, 495), 'langchain.llms.OpenAI', 'OpenAI', ([], {'max_tokens': '(1024)'}), '(max_tokens=1024)\n', (478, 495), False, 'from langchain.llms import OpenAI\n'), ((499, 521), 'l... |
import os
import cassio
import langchain
from langchain.cache import CassandraCache
from langchain_community.chat_models import ChatOpenAI
from langchain_core.messages import BaseMessage
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda
use_cassandra = int(os.en... | [
"langchain_core.prompts.ChatPromptTemplate.from_template",
"langchain_community.chat_models.ChatOpenAI",
"langchain_core.runnables.RunnableLambda",
"langchain.cache.CassandraCache"
] | [((788, 831), 'langchain.cache.CassandraCache', 'CassandraCache', ([], {'session': 'None', 'keyspace': 'None'}), '(session=None, keyspace=None)\n', (802, 831), False, 'from langchain.cache import CassandraCache\n'), ((838, 850), 'langchain_community.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (848, 850), F... |
import streamlit as st
import langchain_helper as lch
import textwrap
import sys
import os
st.set_page_config(page_icon="🌈",page_title="Youtube Assistant",layout="centered")
os.environ["OPENAI_API_KEY"] == st.secrets["OPENAI_API_KEY"],
st.header("Youtube Assistant 🔥")
with st.form(key='my_form'):
video_url = st... | [
"langchain_helper.create_db_from_youtube_video_url",
"langchain_helper.get_response_from_query"
] | [((93, 182), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_icon': '"""🌈"""', 'page_title': '"""Youtube Assistant"""', 'layout': '"""centered"""'}), "(page_icon='🌈', page_title='Youtube Assistant', layout=\n 'centered')\n", (111, 182), True, 'import streamlit as st\n'), ((238, 270), 'streamlit.head... |
import numpy as np
from langchain.prompts import PromptTemplate
from langchain.schema import StrOutputParser, BaseRetriever
from langchain.schema.runnable import RunnablePassthrough
from langchain_google_genai import ChatGoogleGenerativeAI
from trulens_eval.feedback.provider.langchain import Langchain
from trulens_eva... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain_google_genai.ChatGoogleGenerativeAI"
] | [((778, 801), 'src.embeddings.build_base_embeddings', 'build_base_embeddings', ([], {}), '()\n', (799, 801), False, 'from src.embeddings import build_base_embeddings\n'), ((813, 844), 'src.vectordb.load_chroma', 'load_chroma', (['embedding_function'], {}), '(embedding_function)\n', (824, 844), False, 'from src.vectordb... |
import langchain.vectorstores.opensearch_vector_search as ovs
from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers
from langchain.vectorstores import OpenSearchVectorSearch
def create_ovs_client(
collection_id,
index_name,
region,
boto3_session,
bedrock_embeddings... | [
"langchain.vectorstores.OpenSearchVectorSearch"
] | [((496, 541), 'opensearchpy.AWSV4SignerAuth', 'AWSV4SignerAuth', (['credentials', 'region', 'service'], {}), '(credentials, region, service)\n', (511, 541), False, 'from opensearchpy import OpenSearch, RequestsHttpConnection, AWSV4SignerAuth, helpers\n'), ((569, 750), 'opensearchpy.OpenSearch', 'OpenSearch', ([], {'hos... |
# import environment variables
from data.env_variables import AZURE_OPENAI_DEPLOYMENT_NAME, AZURE_OPENAI_MODEL_NAME, \
AZURE_OPENAI_API_ENDPOINT, OPENAI_API_VERSION, AZURE_OPENAI_API_KEY, \
HUGGINGFACE_API_TOKEN, LLAMA2_API_TOKEN, OPENAI_API_KEY, NVIDIANGC_API_KEY
from dotenv import load_dotenv
# import softwa... | [
"langchain_community.document_loaders.PyPDFLoader",
"langchain_community.document_loaders.Docx2txtLoader",
"langchain.llms.huggingface_pipeline.HuggingFacePipeline.from_model_id",
"langchain.vectorstores.chroma.Chroma",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.callbacks.streami... | [((1820, 1835), 'langchain.globals.set_debug', 'set_debug', (['(True)'], {}), '(True)\n', (1829, 1835), False, 'from langchain.globals import set_debug\n'), ((1853, 1910), 'logging.basicConfig', 'log.basicConfig', ([], {'filename': '"""logs/app.log"""', 'level': 'log.DEBUG'}), "(filename='logs/app.log', level=log.DEBUG... |
import langchain
import openai
import streamlit
import hubspot
# Retrieve customer preferences and previous interactions from Hubspot
customer_preferences = hubspot.get_customer_preferences()
previous_interactions = hubspot.get_previous_interactions()
# Generate personalized reminders using Langchain analysi... | [
"langchain.analyze"
] | [((164, 198), 'hubspot.get_customer_preferences', 'hubspot.get_customer_preferences', ([], {}), '()\n', (196, 198), False, 'import hubspot\n'), ((224, 259), 'hubspot.get_previous_interactions', 'hubspot.get_previous_interactions', ([], {}), '()\n', (257, 259), False, 'import hubspot\n'), ((400, 437), 'langchain.analyze... |
import logging
import re
from typing import Any, List, Optional
import langchain
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import ChatOpenAI
from init_openai import init_openai
logger = logging.getLogger("SoCloverAI")
init_openai()
model_name = "gpt-4-11... | [
"langchain.llm_cache.get_cache_stats_summary",
"langchain_openai.ChatOpenAI",
"langchain.llm_cache.inner_cache.set_trial",
"langchain.llm_cache.clear_cache_stats",
"langchain.chains.LLMChain"
] | [((252, 283), 'logging.getLogger', 'logging.getLogger', (['"""SoCloverAI"""'], {}), "('SoCloverAI')\n", (269, 283), False, 'import logging\n'), ((284, 297), 'init_openai.init_openai', 'init_openai', ([], {}), '()\n', (295, 297), False, 'from init_openai import init_openai\n'), ((2273, 2297), 're.compile', 're.compile',... |
from langchain.chains.openai_functions.openapi import get_openapi_chain
import langchain
langchain.verbose=True
chain = get_openapi_chain("https://api.speak.com/openapi.yaml", verbose=True)
import json
# Insertion
data = {"name": "John", "age": 30, "city": "New York"}
print(json.dumps(data, indent=4, ensure_ascii=F... | [
"langchain.chains.openai_functions.openapi.get_openapi_chain"
] | [((122, 191), 'langchain.chains.openai_functions.openapi.get_openapi_chain', 'get_openapi_chain', (['"""https://api.speak.com/openapi.yaml"""'], {'verbose': '(True)'}), "('https://api.speak.com/openapi.yaml', verbose=True)\n", (139, 191), False, 'from langchain.chains.openai_functions.openapi import get_openapi_chain\n... |
import asyncio
import os
import json
import tiktoken
from transcribe import file_to_json_path, get_recordings, get_all_recordings, print_json
import langchain
from langchain.llms import OpenAI
from langchain.cache import SQLiteCache
from langchain.chat_models import ChatOpenAI
from langchain import PromptTemplate
from ... | [
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache",
"langchain.prompts.chat.HumanMessagePromptTemplate.from_template",
"langchain.prompts.chat.ChatPromptTemplate.from_messages"
] | [((822, 848), 'langchain.cache.SQLiteCache', 'SQLiteCache', (['database_path'], {}), '(database_path)\n', (833, 848), False, 'from langchain.cache import SQLiteCache\n'), ((919, 973), 'transformers.AutoTokenizer.from_pretrained', 'AutoTokenizer.from_pretrained', (['training_tokenizer_name'], {}), '(training_tokenizer_n... |
import json
import streamlit as st
import streamlit_ext as ste
import os
import time
import gc
import pandas as pd
from dotenv import load_dotenv
from langchain.chains import LLMChain # import LangChain libraries
from langchain.llms import OpenAI # import OpenAI model
from langchain.chat_models import ChatOpenAI # i... | [
"langchain.llms.OpenAI",
"langchain.llms.HuggingFacePipeline",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate"
] | [((813, 832), 'dotenv.load_dotenv', 'load_dotenv', (['""".env"""'], {}), "('.env')\n", (824, 832), False, 'from dotenv import load_dotenv\n'), ((1156, 1212), 'streamlit.markdown', 'st.markdown', (['hide_default_format'], {'unsafe_allow_html': '(True)'}), '(hide_default_format, unsafe_allow_html=True)\n', (1167, 1212), ... |
import os
import re
import streamlit as st
import pandas as pd
import langchain
from langchain.agents import AgentExecutor
from langchain.callbacks import StreamlitCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.tools import PythonAstREPLTool
from langchain.schema import SystemMessage
fro... | [
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.tools.PythonAstREPLTool",
"langchain.schema.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((1411, 1439), 'os.getenv', 'os.getenv', (['"""LANGCHAIN_DEBUG"""'], {}), "('LANGCHAIN_DEBUG')\n", (1420, 1439), False, 'import os\n'), ((1486, 1543), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""DataVizQA"""', 'page_icon': '"""🤖"""'}), "(page_title='DataVizQA', page_icon='🤖')\n", (1504... |
import inspect
from pathlib import Path
from typing import List
from langchain.chains import LLMChain
from langchain.chat_models.base import BaseChatModel
from langchain.prompts import PromptTemplate
def get_documents(file_path: Path, llm: BaseChatModel):
file_extension = file_path.suffix
loader_class_name =... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate"
] | [((946, 1275), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['file_extension', 'loaders']", 'template': '"""\n Among the following loaders, which is the best to load a "{file_extension}" file? Only give me one the class name without any other special characters. If no relev... |
"""Streamlit app for the ChatGPT clone."""
import dotenv
import langchain
import streamlit as st
import streamlit_chat
dotenv.load_dotenv(dotenv.find_dotenv(), override=True)
st.set_page_config(
page_title='You Custom Assistant',
page_icon='🤖'
)
st.subheader('Your Custom ChatGPT 🤖')
chat = langchain.chat_... | [
"langchain.schema.AIMessage",
"langchain.schema.HumanMessage",
"langchain.schema.SystemMessage",
"langchain.chat_models.ChatOpenAI"
] | [((178, 246), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""You Custom Assistant"""', 'page_icon': '"""🤖"""'}), "(page_title='You Custom Assistant', page_icon='🤖')\n", (196, 246), True, 'import streamlit as st\n'), ((257, 294), 'streamlit.subheader', 'st.subheader', (['"""Your Custom Chat... |
import streamlit as st
import langchain
from langchain.chains import ConversationChain
from langchain.chains.conversation.memory import ConversationEntityMemory
from langchain.chains.conversation.prompt import ENTITY_MEMORY_CONVERSATION_TEMPLATE
from langchain.chat_models import ChatOpenAI
def initializer():
if ... | [
"langchain.chains.ConversationChain",
"langchain.chains.conversation.memory.ConversationEntityMemory",
"langchain.chat_models.ChatOpenAI"
] | [((603, 631), 'streamlit.title', 'st.title', (['"""💬 simple Chatbot"""'], {}), "('💬 simple Chatbot')\n", (611, 631), True, 'import streamlit as st\n'), ((1260, 1319), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chatbot"""', 'layout': '"""centered"""'}), "(page_title='Chatbot', layout='c... |
import streamlit as st
import langchain_helper
st.title("Ingredients for the Dish")
diet = st.sidebar.selectbox("Pick a Diet", ("Vegetarian", "Non-Vegetarian", "Vegan", "Eggitarian", "Carnivore"))
if diet:
response = langchain_helper.generate_dish_name_and_ingredients(diet)
st.header(response['dish'].strip()... | [
"langchain_helper.generate_dish_name_and_ingredients"
] | [((48, 84), 'streamlit.title', 'st.title', (['"""Ingredients for the Dish"""'], {}), "('Ingredients for the Dish')\n", (56, 84), True, 'import streamlit as st\n'), ((93, 202), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Pick a Diet"""', "('Vegetarian', 'Non-Vegetarian', 'Vegan', 'Eggitarian', 'Carnivor... |
from dotenv import load_dotenv
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.agents import initialize_agent, AgentType
from agent.tools.ontology import ontology_tool
from agent.tools.interview import PAInterview
import os
from langchain.prompts import MessagesPlaceholder
from langchain.me... | [
"langchain.agents.initialize_agent",
"langchain.memory.ConversationBufferMemory",
"langchain.prompts.MessagesPlaceholder",
"langchain.chat_models.ChatOpenAI"
] | [((462, 529), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMemory', ([], {'memory_key': '"""memory"""', 'return_messages': '(True)'}), "(memory_key='memory', return_messages=True)\n", (486, 529), False, 'from langchain.memory import ConversationBufferMemory\n'), ((555, 568), 'dotenv.load_dotenv', 'lo... |
import langchain_helper as lch
import streamlit as st
import time
import pypdf
from bs4 import BeautifulSoup
import base64
import utilities as utl
import streamlit.components.v1 as components
st.set_page_config(
page_icon='CG-Logo.png',
layout="wide",
page_title='Consent Guardian',
initial_sidebar_state="expande... | [
"langchain_helper.process_document",
"langchain_helper.process_chatbot_query"
] | [((194, 322), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_icon': '"""CG-Logo.png"""', 'layout': '"""wide"""', 'page_title': '"""Consent Guardian"""', 'initial_sidebar_state': '"""expanded"""'}), "(page_icon='CG-Logo.png', layout='wide', page_title=\n 'Consent Guardian', initial_sidebar_state='expa... |
from langchain.vectorstores import chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from redudant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
langchain.debug = True
load_d... | [
"langchain.embeddings.OpenAIEmbeddings",
"langchain.vectorstores.chroma.Chroma",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((314, 327), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (325, 327), False, 'from dotenv import load_dotenv\n'), ((336, 348), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (346, 348), False, 'from langchain.chat_models import ChatOpenAI\n'), ((363, 381), 'langchain.embeddings.OpenAIEmb... |
"""Chat agent with question answering
"""
from dotenv import load_dotenv
from langchain.cache import InMemoryCache
import langchain
import os
from dataclasses import dataclass
from langchain.chains import LLMChain, LLMRequestsChain
from langchain import Wikipedia, OpenAI
from langchain.agents.react.base import Docstor... | [
"langchain.agents.initialize_agent",
"langchain.agents.AgentExecutor.from_agent_and_tools",
"langchain.cache.InMemoryCache",
"langchain.Wikipedia",
"langchain.agents.conversational.base.ConversationalAgent",
"langchain.agents.conversational.base.ConversationalAgent.create_prompt",
"langchain.agents.Tool... | [((681, 694), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (692, 694), False, 'from dotenv import load_dotenv\n'), ((718, 733), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (731, 733), False, 'from langchain.cache import InMemoryCache\n'), ((749, 774), 'os.getenv', 'os.getenv', (['"""NE... |
"""Beta Feature: base interface for cache."""
from __future__ import annotations
import hashlib
import inspect
import json
import logging
import warnings
from abc import ABC, abstractmethod
from datetime import timedelta
from typing import (
TYPE_CHECKING,
Any,
Callable,
Dict,
Optional,
Sequenc... | [
"langchain.utils.get_from_env",
"langchain.schema.Generation",
"langchain.load.dump.dumps",
"langchain.vectorstores.redis.Redis.from_existing_index",
"langchain.vectorstores.redis.Redis",
"langchain.load.load.loads"
] | [((950, 977), 'logging.getLogger', 'logging.getLogger', (['__file__'], {}), '(__file__)\n', (967, 977), False, 'import logging\n'), ((3422, 3440), 'sqlalchemy.ext.declarative.declarative_base', 'declarative_base', ([], {}), '()\n', (3438, 3440), False, 'from sqlalchemy.ext.declarative import declarative_base\n'), ((359... |
import streamlit as st
import openai
import os
from PyPDF2 import PdfReader
import io
import langchain
langchain.debug = True
from langchain.chains import LLMChain
from langchain.callbacks.base import BaseCallbackHandler
from langchain.chat_models import ChatOpenAI
from langchain.prompts import PromptTemplate
from lang... | [
"langchain.schema.ChatMessage",
"langchain.agents.initialize_agent",
"langchain.vectorstores.FAISS.load_local",
"langchain.output_parsers.StructuredOutputParser.from_response_schemas",
"langchain.chat_models.ChatOpenAI",
"langchain.utilities.BingSearchAPIWrapper",
"langchain.schema.HumanMessage",
"lan... | [((1448, 1480), 'os.environ.get', 'os.environ.get', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1462, 1480), False, 'import os\n'), ((1509, 1552), 'os.environ.get', 'os.environ.get', (['"""AZURE_BLOB_CONNECTION_STR"""'], {}), "('AZURE_BLOB_CONNECTION_STR')\n", (1523, 1552), False, 'import os\n'), ((3241, 3... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import (
ConversationalRetrievalChain, RetrievalQA
)
# from langchain.chains.chat_vector_db.prompts import (
# CONDENSE_... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.memory.ConversationBufferWindowMemory",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI"
] | [((1070, 1094), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[]'], {}), '([])\n', (1090, 1094), False, 'from langchain.callbacks.manager import AsyncCallbackManager\n'), ((1118, 1158), 'langchain.callbacks.manager.AsyncCallbackManager', 'AsyncCallbackManager', (['[question_handler]'], ... |
# Databricks notebook source
# MAGIC %md-sandbox
# MAGIC # 2/ Advanced chatbot with message history and filter using Langchain
# MAGIC
# MAGIC <img src="https://github.com/databricks-demos/dbdemos-resources/blob/main/images/product/chatbot-rag/llm-rag-self-managed-flow-2.png?raw=true" style="float: right; margin-left: ... | [
"langchain.schema.output_parser.StrOutputParser",
"langchain.embeddings.DatabricksEmbeddings",
"langchain.schema.runnable.RunnablePassthrough",
"langchain.vectorstores.DatabricksVectorSearch",
"langchain.chat_models.ChatDatabricks",
"langchain.schema.runnable.RunnableLambda",
"langchain.prompts.PromptTe... | [((2610, 2742), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'input_variables': "['question']", 'template': '"""You are an assistant. Give a short answer to this question: {question}"""'}), "(input_variables=['question'], template=\n 'You are an assistant. Give a short answer to this question: {questi... |
"""A tracer that runs evaluators over completed runs."""
from __future__ import annotations
import logging
from concurrent.futures import Future, ThreadPoolExecutor
from typing import Any, Dict, List, Optional, Sequence, Set, Union
from uuid import UUID
import langsmith
from langsmith.evaluation.evaluator import Eval... | [
"langchain.callbacks.tracers.langchain.get_client",
"langchain.callbacks.manager.tracing_v2_enabled"
] | [((562, 589), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (579, 589), False, 'import logging\n'), ((2581, 2597), 'uuid.UUID', 'UUID', (['example_id'], {}), '(example_id)\n', (2585, 2597), False, 'from uuid import UUID\n'), ((2687, 2716), 'langchain.callbacks.tracers.langchain.get_clien... |
import streamlit as st
import dotenv
import langchain
import json
from cassandra.cluster import Session
from cassandra.query import PreparedStatement
from langchain.agents.agent_toolkits import create_retriever_tool, create_conversational_retrieval_agent
from langchain.chat_models import ChatOpenAI
from langchain.emb... | [
"langchain.chat_models.ChatOpenAI",
"langchain.schema.Document",
"langchain.agents.agent_toolkits.create_conversational_retrieval_agent",
"langchain.agents.agent_toolkits.create_retriever_tool",
"langchain.schema.SystemMessage",
"langchain.embeddings.OpenAIEmbeddings"
] | [((5375, 5408), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""wide"""'}), "(layout='wide')\n", (5393, 5408), True, 'import streamlit as st\n'), ((5847, 5887), 'streamlit.chat_input', 'st.chat_input', ([], {'placeholder': '"""Ask chatbot"""'}), "(placeholder='Ask chatbot')\n", (5860, 5887), True... |
import langchain_helper as lch
import streamlit as st
st.title('pets name generator')
user_animal_type = st.sidebar.selectbox('what is your pet', ('cat', 'dog', 'cow'))
if user_animal_type == 'cat':
user_pet_color = st.sidebar.text_area('what color is your cat', max_chars=15)
if user_animal_type == 'dog':
us... | [
"langchain_helper.generate_pet_name"
] | [((55, 86), 'streamlit.title', 'st.title', (['"""pets name generator"""'], {}), "('pets name generator')\n", (63, 86), True, 'import streamlit as st\n'), ((107, 170), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""what is your pet"""', "('cat', 'dog', 'cow')"], {}), "('what is your pet', ('cat', 'dog', 'c... |
# import modules
import telebot
from telebot import *
import logging
import sqlite3
import os
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langch... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((571, 622), 'sqlite3.connect', 'sqlite3.connect', (['"""main.db"""'], {'check_same_thread': '(False)'}), "('main.db', check_same_thread=False)\n", (586, 622), False, 'import sqlite3\n'), ((661, 738), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'filename': '"""../info.log"""', 'filemod... |
import logging
import os
import langchain
from langchain_community.llms import Ollama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import OpenAI
from crawler import crawl_a... | [
"langchain_core.runnables.RunnablePassthrough",
"langchain_core.output_parsers.StrOutputParser",
"langchain_community.llms.Ollama",
"langchain_core.prompts.ChatPromptTemplate.from_messages",
"langchain_openai.OpenAI"
] | [((430, 450), 'os.getenv', 'os.getenv', (['"""VERBOSE"""'], {}), "('VERBOSE')\n", (439, 450), False, 'import os\n'), ((1029, 1072), 'langchain_community.llms.Ollama', 'Ollama', ([], {'base_url': 'base_url', 'model': 'model_name'}), '(base_url=base_url, model=model_name)\n', (1035, 1072), False, 'from langchain_communit... |
# Standard Library Imports
import ast
import json
import os
import re
# Third-Party Imports
import textwrap
from typing import Any, Dict, List, Optional, Type
import langchain
import streamlit as st
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.tools import BaseTool... | [
"langchain.chains.LLMChain",
"langchain.prompts.PromptTemplate",
"langchain.chat_models.ChatOpenAI"
] | [((20314, 20720), 'pydantic.Field', 'Field', (['(True)'], {'description': '"""Set to \'True\' (default) to save the log files and trajectories of the simulation. If set to \'False\', the simulation is considered as being in a testing or preliminary scripting stage, utilizing default parameters and results are not saved... |
import langchain
from langchain_openai import AzureChatOpenAI
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts.chat import MessagesPlaceholder
from tech_agents.command import Command, check_command
from tech_agents.dispatcher import MainDispatcherAgent
from tech_agents... | [
"langchain.memory.ReadOnlySharedMemory"
] | [((1669, 1709), 'langchain.memory.ReadOnlySharedMemory', 'ReadOnlySharedMemory', ([], {'memory': 'self.memory'}), '(memory=self.memory)\n', (1689, 1709), False, 'from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory\n'), ((1926, 1953), 'tech_agents.command.check_command', 'check_command', (['user_... |
from typing import List, TypedDict
import tiktoken
from langchain.schema import AIMessage, BaseMessage, HumanMessage, SystemMessage
from langchain_openai import ChatOpenAI
from app.enums.langchain_enums import LangchainRole
from config import langchain_config, settings
class MessagesType(TypedDict):
role: str
... | [
"langchain.schema.AIMessage",
"langchain_openai.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((1294, 1318), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {}), '(**parameters)\n', (1304, 1318), False, 'from langchain_openai import ChatOpenAI\n'), ((1726, 1760), 'tiktoken.get_encoding', 'tiktoken.get_encoding', (['encode_name'], {}), '(encode_name)\n', (1747, 1760), False, 'import tiktoken\n'), ((2281, 2318... |
# import modules
import telebot
from telebot import *
import logging
import sqlite3
import os
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter, CharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.document_loaders import TextLoader
from langch... | [
"langchain.prompts.PromptTemplate.from_template",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((571, 622), 'sqlite3.connect', 'sqlite3.connect', (['"""main.db"""'], {'check_same_thread': '(False)'}), "('main.db', check_same_thread=False)\n", (586, 622), False, 'import sqlite3\n'), ((661, 738), 'logging.basicConfig', 'logging.basicConfig', ([], {'level': 'logging.INFO', 'filename': '"""../info.log"""', 'filemod... |
import langchain as lc
import openai as ai
import datasets as ds
import tiktoken as tk
import os
from langchain_openai import ChatOpenAI
from dotenv import load_dotenv
import os
# Load environment variables from .env file
load_dotenv()
# Get the OpenAI API key from the environment variable
openai_api_key = os.getenv... | [
"langchain.schema.AIMessage",
"langchain_openai.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.schema.HumanMessage"
] | [((224, 237), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (235, 237), False, 'from dotenv import load_dotenv\n'), ((311, 338), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (320, 338), False, 'import os\n'), ((501, 563), 'langchain_openai.ChatOpenAI', 'ChatOpenAI', ([], {'... |
"""Push and pull to the LangChain Hub."""
from __future__ import annotations
from typing import TYPE_CHECKING, Any, Optional
from langchain.load.dump import dumps
from langchain.load.load import loads
from langchain.utils import get_from_env
if TYPE_CHECKING:
from langchainhub import Client
def _get_client(api... | [
"langchain.load.load.loads",
"langchainhub.Client",
"langchain.load.dump.dumps",
"langchain.utils.get_from_env"
] | [((862, 894), 'langchainhub.Client', 'Client', (['api_url'], {'api_key': 'api_key'}), '(api_url, api_key=api_key)\n', (868, 894), False, 'from langchainhub import Client\n'), ((1234, 1247), 'langchain.load.dump.dumps', 'dumps', (['object'], {}), '(object)\n', (1239, 1247), False, 'from langchain.load.dump import dumps\... |
from datetime import timedelta
import os
import subprocess
import whisper
import tempfile
import argparse
import langchain
from langchain.chat_models import ChatOpenAI, ChatGooglePalm
from langchain.schema import HumanMessage, SystemMessage, AIMessage
from langchain.prompts import (
ChatPromptTemplate,
PromptTe... | [
"langchain.prompts.HumanMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.callbacks.get_openai_callback",
"langchain.chains.LLMChain",
"langchain.prompts.SystemMessagePromptTemplate.from_template"
] | [((696, 747), 'langchain.prompts.SystemMessagePromptTemplate.from_template', 'SystemMessagePromptTemplate.from_template', (['template'], {}), '(template)\n', (737, 747), False, 'from langchain.prompts import ChatPromptTemplate, PromptTemplate, SystemMessagePromptTemplate, AIMessagePromptTemplate, HumanMessagePromptTemp... |
import langchain_helper as lch
import streamlit as st
st.title('Pets name generator')
animal_type = st.sidebar.selectbox("What is your pet?", ("cat", "dog", "bird", "fish"))
if animal_type == 'cat':
pet_color = st.sidebar.text_area(label="What color is your cat?", max_chars=15)
if animal_type == 'dog':
pet_co... | [
"langchain_helper.generate_pet_nam"
] | [((55, 86), 'streamlit.title', 'st.title', (['"""Pets name generator"""'], {}), "('Pets name generator')\n", (63, 86), True, 'import streamlit as st\n'), ((102, 175), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""What is your pet?"""', "('cat', 'dog', 'bird', 'fish')"], {}), "('What is your pet?', ('cat'... |
from langchain import OpenAI, LLMChain
from langchain.callbacks import StdOutCallbackHandler
from langchain.chat_models import ChatOpenAI
from src.agents.chat_chain import ChatChain
from src.agents.graphdb_traversal_chain import GraphDBTraversalChain, mem_query_template, mem_system_message
from src.memory.triple_modal... | [
"langchain.callbacks.StdOutCallbackHandler",
"langchain.chat_models.ChatOpenAI",
"langchain.cache.SQLiteCache"
] | [((495, 537), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (506, 537), False, 'from langchain.cache import SQLiteCache\n'), ((563, 576), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (574, 576), False, 'from dotenv import loa... |
from __future__ import annotations
import logging
from functools import lru_cache
from typing import List, Optional
import langchain
from langchain.agents import AgentExecutor, Tool, initialize_agent
from langchain.agents.agent_types import AgentType
from langchain.callbacks import get_openai_callback
from langchain.... | [
"langchain.agents.initialize_agent",
"langchain_experimental.plan_and_execute.PlanAndExecute",
"langchain.chat_models.ChatOpenAI",
"langchain_experimental.plan_and_execute.load_chat_planner",
"langchain.callbacks.get_openai_callback",
"langchain_experimental.plan_and_execute.load_agent_executor"
] | [((946, 973), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (963, 973), False, 'import logging\n'), ((1004, 1041), 'shared.llm_manager_base.Cost', 'Cost', ([], {'prompt': '(0.0015)', 'completion': '(0.002)'}), '(prompt=0.0015, completion=0.002)\n', (1008, 1041), False, 'from shared.llm_m... |
import os
import utils
import traceback
from langchain.chains.qa_with_sources import load_qa_with_sources_chain
from langchain.chains import ConversationChain
from langchain.llms import OpenAI
import langchain
from langchain.cache import InMemoryCache
from langchain.llms import OpenAI
from langchain.chains.conversati... | [
"langchain.chains.conversation.memory.ConversationSummaryBufferMemory",
"langchain.llms.OpenAI",
"langchain.llms.AI21",
"langchain.llms.Cohere",
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.llms.NLPCloud",
"langchain.prompts.PromptTemplate"
] | [((5785, 5803), 'SmartCache.SmartCache', 'SmartCache', (['CONFIG'], {}), '(CONFIG)\n', (5795, 5803), False, 'from SmartCache import SmartCache\n'), ((6330, 6345), 'flask.Flask', 'Flask', (['__name__'], {}), '(__name__)\n', (6335, 6345), False, 'from flask import Flask, send_from_directory\n'), ((9830, 9890), 'waitress.... |
#!/usr/bin/env python
# coding: utf-8
# #### Document summarization application Falcon LLM using Sagemaker Jumpstart
### Author : Dipjyoti Das
### Last Edited : Jan 19, 2024
### This script provides an example for how to use Sagemaker Jumpstart -for text summarization use case. It used Falcon 7B open source model
#... | [
"langchain.chains.summarize.load_summarize_chain",
"langchain.SagemakerEndpoint",
"langchain.PromptTemplate",
"langchain.text_splitter.RecursiveCharacterTextSplitter"
] | [((1513, 1546), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (1536, 1546), False, 'import warnings\n'), ((5586, 5694), 'langchain.text_splitter.RecursiveCharacterTextSplitter', 'RecursiveCharacterTextSplitter', ([], {'chunk_size': '(500)', 'chunk_overlap': '(20)', 'separ... |
import os
import streamlit as st
from PyPDF2 import PdfReader
import langchain
langchain.verbose = False
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_cha... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.llms.OpenAI",
"langchain.callbacks.get_openai_callback",
"langchain.vectorstores.FAISS.from_texts",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((583, 600), 'requests.get', 'requests.get', (['url'], {}), '(url)\n', (595, 600), False, 'import requests\n'), ((853, 902), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Webscrap chatbot"""'}), "(page_title='Webscrap chatbot')\n", (871, 902), True, 'import streamlit as st\n'), ((907, 936)... |
# Wrapper for Hugging Face APIs for llmlib
from llmlib.base_model_wrapper import BaseModelWrapper
from llama_index import ListIndex, SimpleDirectoryReader
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from llama_index import LangchainEmbedding, ServiceContext
from llama_index import ListIndex, Pr... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((735, 830), 'transformers.pipeline', 'pipeline', (['"""text-generation"""'], {'model': 'model_name', 'model_kwargs': "{'torch_dtype': torch.bfloat16}"}), "('text-generation', model=model_name, model_kwargs={'torch_dtype':\n torch.bfloat16})\n", (743, 830), False, 'from transformers import pipeline\n'), ((1022, 103... |
import logging
import ConsoleInterface
import langchain.schema
from langchain.agents import initialize_agent, AgentType #create_pandas_dataframe_agent
logger = logging.getLogger('ConsoleInterface')
'''
def PandasDataframeAgent(llm, Dataframe):
"""
Create a PandasDataframeAgent object.
Parameters:
... | [
"langchain.agents.initialize_agent"
] | [((165, 202), 'logging.getLogger', 'logging.getLogger', (['"""ConsoleInterface"""'], {}), "('ConsoleInterface')\n", (182, 202), False, 'import logging\n'), ((946, 1067), 'langchain.agents.initialize_agent', 'initialize_agent', ([], {'agent': 'AgentType.CONVERSATIONAL_REACT_DESCRIPTION', 'llm': 'llm', 'tools': 'Tools', ... |
import csv
from ctypes import Array
from typing import Any, Coroutine, List, Tuple
import io
import time
import re
import os
from fastapi import UploadFile
import asyncio
import langchain
from langchain.chat_models import ChatOpenAI
from langchain.agents import create_csv_agent, load_tools, initialize_agent, AgentTyp... | [
"langchain.agents.initialize_agent",
"langchain.memory.ConversationSummaryBufferMemory",
"langchain.output_parsers.PydanticOutputParser",
"langchain.tools.PythonAstREPLTool",
"langchain.agents.create_pandas_dataframe_agent",
"langchain.chat_models.ChatOpenAI",
"langchain.callbacks.tracers.ConsoleCallbac... | [((963, 990), 'os.environ.get', 'os.environ.get', (['"""REDIS_URL"""'], {}), "('REDIS_URL')\n", (977, 990), False, 'import os\n'), ((1270, 1285), 'pandas.read_csv', 'pd.read_csv', (['df'], {}), '(df)\n', (1281, 1285), True, 'import pandas as pd\n'), ((1302, 1537), 'langchain.agents.create_pandas_dataframe_agent', 'crea... |
from typing import Dict, List, Optional
from langchain.agents.load_tools import (
_EXTRA_LLM_TOOLS,
_EXTRA_OPTIONAL_TOOLS,
_LLM_TOOLS,
)
from langflow.custom import customs
from langflow.interface.base import LangChainTypeCreator
from langflow.interface.tools.constants import (
ALL_TOOLS_NAMES,
CU... | [
"langchain.agents.load_tools._LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_LLM_TOOLS.keys",
"langchain.agents.load_tools._EXTRA_OPTIONAL_TOOLS.keys"
] | [((690, 792), 'langflow.template.field.base.TemplateField', 'TemplateField', ([], {'field_type': '"""str"""', 'required': '(True)', 'is_list': '(False)', 'show': '(True)', 'placeholder': '""""""', 'value': '""""""'}), "(field_type='str', required=True, is_list=False, show=True,\n placeholder='', value='')\n", (703, ... |
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.chains.question_answering import load_qa_chain
from langchain.embeddings.openai import OpenAIEmbeddings
from streamlit_option_menu import option_menu
from deep_translator import GoogleTranslator
from langchain.vectorstores import Pinecone... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.vectorstores.Pinecone.from_texts",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((560, 573), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (571, 573), False, 'from dotenv import load_dotenv\n'), ((656, 749), 'pinecone.init', 'pinecone.init', ([], {'api_key': '"""db6b2a8c-d59e-48e1-8d5c-4c2704622937"""', 'environment': '"""gcp-starter"""'}), "(api_key='db6b2a8c-d59e-48e1-8d5c-4c2704622937... |
import langchain_helper as lch
import streamlit as st
st.title("Generador de nombres para mascotas")
st.markdown("Este es un generador de nombres para mascotas. Escriba el tipo de animal que tiene y presione el botón 'Generar nombres'.")
animal_type = st.sidebar.selectbox("¿cual es tu mascota?",("gato","perro","cabra... | [
"langchain_helper.generate_pet_name"
] | [((55, 101), 'streamlit.title', 'st.title', (['"""Generador de nombres para mascotas"""'], {}), "('Generador de nombres para mascotas')\n", (63, 101), True, 'import streamlit as st\n'), ((102, 248), 'streamlit.markdown', 'st.markdown', (['"""Este es un generador de nombres para mascotas. Escriba el tipo de animal que t... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
# invoking custom retriever
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
... | [
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((344, 357), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (355, 357), False, 'from dotenv import load_dotenv\n'), ((392, 404), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (402, 404), False, 'from langchain.chat_models import ChatOpenAI\n'), ((418, 436), 'langchain.embeddings.OpenAIEmb... |
import os
import logging
import pickle
import ssl
import dill
import langchain
from langchain.prompts import PromptTemplate
from langchain.llms import OpenAI, GooglePalm
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain, AnalyzeDocumentChain
from langchain.chains.qa_with_sources import load_qa_with_so... | [
"langchain.vectorstores.FAISS.from_documents",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.llms.OpenAI"
] | [((670, 710), 'langchain.llms.OpenAI', 'OpenAI', ([], {'temperature': '(0.7)', 'max_tokens': '(1024)'}), '(temperature=0.7, max_tokens=1024)\n', (676, 710), False, 'from langchain.llms import OpenAI, GooglePalm\n'), ((728, 746), 'langchain.embeddings.OpenAIEmbeddings', 'OpenAIEmbeddings', ([], {}), '()\n', (744, 746), ... |
# inspired by: https://github.com/rushic24/langchain-remember-me-llm/
# MIT license
import torch
from json_database import JsonStorageXDG
from langchain.embeddings.huggingface import HuggingFaceEmbeddings
from langchain.llms.base import LLM
from llama_index import Document
from llama_index import LLMPredictor, ServiceC... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((541, 570), 'json_database.JsonStorageXDG', 'JsonStorageXDG', (['"""personalLLM"""'], {}), "('personalLLM')\n", (555, 570), False, 'from json_database import JsonStorageXDG\n'), ((1152, 1263), 'transformers.pipeline', 'pipeline', (['"""text2text-generation"""'], {'model': 'model_name', 'device': '(0)', 'model_kwargs'... |
# imports
import os, shutil, json, re
import pathlib
from langchain.document_loaders.unstructured import UnstructuredFileLoader
from langchain.document_loaders.unstructured import UnstructuredAPIFileLoader
from langchain.document_loaders import UnstructuredURLLoader
from langchain.docstore.document import Document
fro... | [
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.document_loaders.unstructured.UnstructuredAPIFileLoader",
"langchain.text_splitter.MarkdownTextSplitter",
"langchain.schema.Document",
"langchain.document_loaders.unstructured.Unstructu... | [((719, 732), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (730, 732), False, 'from dotenv import load_dotenv\n'), ((784, 892), 're.compile', 're.compile', (['"""http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.&+]|[!*\\\\\\\\(\\\\\\\\),]|(?:%[0-9a-fA-F][0-9a-fA-F]))+"""'], {}), "(\n 'http[s]?://(?:[a-zA-Z]|[0-9]|[$-_@.... |
from langchain.llms import LlamaCpp
from langchain.chat_models import ChatOpenAI
from langchain.chains.llm import LLMChain
from langchain.prompts import PromptTemplate
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.c... | [
"langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.llms.LlamaCpp",
"langchain.cache.SQLiteCache",
"langchain.prompts.PromptTemplate.from_template"
] | [((476, 489), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (487, 489), False, 'from dotenv import load_dotenv\n'), ((505, 529), 'os.getenv', 'os.getenv', (['"""OPEN_AI_KEY"""'], {}), "('OPEN_AI_KEY')\n", (514, 529), False, 'import os\n'), ((584, 632), 'utils.setup_logger', 'setup_logger', (['"""contr_detector... |
import streamlit as st
import torch
from transformers import (
AutoTokenizer, AutoModelForCausalLM,
BitsAndBytesConfig,
TextStreamer,
)
import whisper
import os
############ config ############
# general config
whisper_model_names=["tiny", "base", "small", "medium", "large"]
data_root_path = os.path.join('... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((306, 331), 'os.path.join', 'os.path.join', (['"""."""', '"""data"""'], {}), "('.', 'data')\n", (318, 331), False, 'import os\n'), ((772, 798), 'streamlit.title', 'st.title', (['"""LLAMA RAG Demo"""'], {}), "('LLAMA RAG Demo')\n", (780, 798), True, 'import streamlit as st\n'), ((799, 811), 'streamlit.divider', 'st.di... |
import streamlit as st
import langchain
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain import OpenAI, VectorDBQA
from langchain.chains import RetrievalQAWithSourcesChain
import PyPDF2
#... | [
"langchain.chains.RetrievalQAWithSourcesChain.from_chain_type",
"langchain.vectorstores.Chroma.from_texts",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((868, 932), 'streamlit.set_page_config', 'st.set_page_config', ([], {'layout': '"""centered"""', 'page_title': '"""Multidoc_QnA"""'}), "(layout='centered', page_title='Multidoc_QnA')\n", (886, 932), True, 'import streamlit as st\n'), ((933, 958), 'streamlit.header', 'st.header', (['"""Multidoc_QnA"""'], {}), "('Multi... |
from __future__ import annotations
import asyncio
import functools
import logging
import os
import warnings
from contextlib import asynccontextmanager, contextmanager
from contextvars import ContextVar
from typing import (
Any,
AsyncGenerator,
Dict,
Generator,
List,
Optional,
Type,
Type... | [
"langchain.schema.get_buffer_string",
"langchain.callbacks.stdout.StdOutCallbackHandler",
"langchain.callbacks.tracers.wandb.WandbTracer",
"langchain.callbacks.openai_info.OpenAICallbackHandler",
"langchain.callbacks.tracers.stdout.ConsoleCallbackHandler",
"langchain.callbacks.tracers.langchain.LangChainT... | [((1114, 1141), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (1131, 1141), False, 'import logging\n'), ((1286, 1329), 'contextvars.ContextVar', 'ContextVar', (['"""openai_callback"""'], {'default': 'None'}), "('openai_callback', default=None)\n", (1296, 1329), False, 'from contextvars i... |
from langchain.prompts import PromptTemplate
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain.vectorstores.faiss import FAISS
from langchain_community.llms import CTransformers
from langchain.chains import RetrievalQA
import chainlit as cl
from chainlit import LangchainCallbackHandl... | [
"langchain.vectorstores.faiss.FAISS.load_local",
"langchain_community.llms.CTransformers",
"langchain.prompts.PromptTemplate",
"langchain_community.embeddings.HuggingFaceEmbeddings"
] | [((827, 919), 'langchain.prompts.PromptTemplate', 'PromptTemplate', ([], {'template': 'custom_prompt_template', 'input_variables': "['context', 'question']"}), "(template=custom_prompt_template, input_variables=['context',\n 'question'])\n", (841, 919), False, 'from langchain.prompts import PromptTemplate\n'), ((970... |
from typing import Any, Dict, List, Optional, Union
import os
from langchain_experimental.agents.agent_toolkits import create_csv_agent
# from langchain.llms import OpenAI
from langchain.agents.agent_types import AgentType
from langchain_google_genai import ChatGoogleGenerativeAI
# from langchain.chat_models import C... | [
"langchain_experimental.agents.agent_toolkits.create_csv_agent",
"langchain_google_genai.ChatGoogleGenerativeAI"
] | [((439, 481), 'langchain_google_genai.ChatGoogleGenerativeAI', 'ChatGoogleGenerativeAI', ([], {'model': '"""gemini-pro"""'}), "(model='gemini-pro')\n", (461, 481), False, 'from langchain_google_genai import ChatGoogleGenerativeAI\n'), ((530, 579), 'langchain_experimental.agents.agent_toolkits.create_csv_agent', 'create... |
import langchain
from langchain.llms import VertexAI
from langchain.prompts import PromptTemplate, load_prompt
import wandb
from wandb.integration.langchain import WandbTracer
import streamlit as st
from google.oauth2 import service_account
# account_info = dict(st.secrets["GOOGLE_APPLICATION_CREDENTIALS"])
# credenti... | [
"langchain.prompts.load_prompt"
] | [((469, 513), 'wandb.login', 'wandb.login', ([], {'key': "st.secrets['WANDB_API_KEY']"}), "(key=st.secrets['WANDB_API_KEY'])\n", (480, 513), False, 'import wandb\n'), ((519, 666), 'wandb.init', 'wandb.init', ([], {'project': '"""generate_prd_v3_palm"""', 'config': "{'model': 'text-bison-001', 'temperature': 0.2}", 'ent... |
#!/usr/bin/env python
# coding: utf-8
# # LangChain: Agents
#
# ## Outline:
#
# * Using built in LangChain tools: DuckDuckGo search and Wikipedia
# * Defining your own tools
# In[ ]:
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv()) # read local .env file
import warnings
warni... | [
"langchain.agents.initialize_agent",
"langchain.tools.python.tool.PythonREPLTool",
"langchain.agents.load_tools",
"langchain.chat_models.ChatOpenAI"
] | [((315, 348), 'warnings.filterwarnings', 'warnings.filterwarnings', (['"""ignore"""'], {}), "('ignore')\n", (338, 348), False, 'import warnings\n'), ((735, 761), 'datetime.date', 'datetime.date', (['(2024)', '(6)', '(12)'], {}), '(2024, 6, 12)\n', (748, 761), False, 'import datetime\n'), ((1324, 1366), 'langchain.chat_... |
import sys
import pandas as pd
from llama_index import Document, set_global_service_context, StorageContext, load_index_from_storage, VectorStoreIndex
from llama_index.indices.base import BaseIndex
from llama_index.storage.docstore import SimpleDocumentStore
from llama_index.storage.index_store import SimpleIndexStore... | [
"langchain.embeddings.OpenAIEmbeddings"
] | [((1194, 1252), 'logging.basicConfig', 'logging.basicConfig', ([], {'stream': 'sys.stdout', 'level': 'logging.INFO'}), '(stream=sys.stdout, level=logging.INFO)\n', (1213, 1252), False, 'import logging\n'), ((1435, 1462), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (1444, 1462), Fal... |
import arxiv
import openai
import langchain
import pinecone
from langchain_community.document_loaders import ArxivLoader
from langchain.docstore.document import Document
from langchain.text_splitter import RecursiveCharacterTextSplitter
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.vectorstore... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.chains.summarize.load_summarize_chain",
"langchain.vectorstores.Pinecone.from_documents",
"langchain.chat_models.ChatOpenAI",
"langchain.OpenAI",
"langchain.embeddings.openai.OpenAIEmbeddings"
] | [((690, 703), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (701, 703), False, 'from dotenv import load_dotenv\n'), ((722, 749), 'os.getenv', 'os.getenv', (['"""OPENAI_API_KEY"""'], {}), "('OPENAI_API_KEY')\n", (731, 749), False, 'import os\n'), ((769, 798), 'os.getenv', 'os.getenv', (['"""PINECONE_API_KEY"""'... |
"""Create a ChatVectorDBChain for question/answering."""
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain
from langchain.chains.chat_vector_db.prompts import (CONDENSE_QUESTION_PROMPT,
... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.prompts.chat.SystemMessagePromptTemplate.from_template",
"langchain.chat_models.ChatOpenAI",
"langchain.chains.llm.LLMChain",
"langchain.prom... | [((2109, 2151), 'langchain.prompts.chat.ChatPromptTemplate.from_messages', 'ChatPromptTemplate.from_messages', (['messages'], {}), '(messages)\n', (2141, 2151), False, 'from langchain.prompts.chat import ChatPromptTemplate, SystemMessagePromptTemplate, HumanMessagePromptTemplate\n'), ((1986, 2037), 'langchain.prompts.c... |
import langchain_helper as lch # Import custom helper module for LangChain operations
import streamlit as st # Import Streamlit for web app development
# Set up the Streamlit web page title
st.title("Data Structures Problems Generator")
# Define the list of topics for data structure problems
topic_options = [
"... | [
"langchain_helper.generate_DS_solution",
"langchain_helper.generate_DS_problem"
] | [((193, 239), 'streamlit.title', 'st.title', (['"""Data Structures Problems Generator"""'], {}), "('Data Structures Problems Generator')\n", (201, 239), True, 'import streamlit as st\n'), ((570, 639), 'streamlit.sidebar.selectbox', 'st.sidebar.selectbox', (['"""Choose a Topic for the Problem"""', 'topic_options'], {}),... |
from llama_index.core import VectorStoreIndex,SimpleDirectoryReader,ServiceContext
print("VectorStoreIndex,SimpleDirectoryReader,ServiceContext imported")
from llama_index.llms.huggingface import HuggingFaceLLM
print("HuggingFaceLLM imported")
from llama_index.core.prompts.prompts import SimpleInputPrompt
print("Simple... | [
"langchain.embeddings.huggingface.HuggingFaceEmbeddings"
] | [((872, 885), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (883, 885), False, 'from dotenv import load_dotenv\n'), ((905, 931), 'os.environ.get', 'os.environ.get', (['"""HF_TOKEN"""'], {}), "('HF_TOKEN')\n", (919, 931), False, 'import os\n'), ((1262, 1315), 'llama_index.core.prompts.prompts.SimpleInputPrompt'... |
import itertools
from langchain.cache import InMemoryCache, SQLiteCache
import langchain
import pandas as pd
from certa.utils import merge_sources
import ellmer.models
import ellmer.metrics
from time import sleep, time
import traceback
from tqdm import tqdm
cache = "sqlite"
samples = 2
explanation_granularity = "attri... | [
"langchain.cache.InMemoryCache",
"langchain.cache.SQLiteCache"
] | [((399, 414), 'langchain.cache.InMemoryCache', 'InMemoryCache', ([], {}), '()\n', (412, 414), False, 'from langchain.cache import InMemoryCache, SQLiteCache\n'), ((465, 507), 'langchain.cache.SQLiteCache', 'SQLiteCache', ([], {'database_path': '""".langchain.db"""'}), "(database_path='.langchain.db')\n", (476, 507), Fa... |
import streamlit as st
# Import the LangChain library
import langchain
# Load the AI model
model = langchain.load_model("model.pkl")
# Create a function to get the feedback from the AI model
def get_feedback(statement):
# Get the predictions from the AI model
predictions = model.predict(statement)
# Cre... | [
"langchain.load_model"
] | [((101, 134), 'langchain.load_model', 'langchain.load_model', (['"""model.pkl"""'], {}), "('model.pkl')\n", (121, 134), False, 'import langchain\n'), ((667, 718), 'streamlit.write', 'st.write', (['"""Here is the feedback from the AI model:"""'], {}), "('Here is the feedback from the AI model:')\n", (675, 718), True, 'i... |
# TODO speed up by extracting resume in structure and job beore sending to gpt4
import re
from bs4 import BeautifulSoup
from pyppeteer import launch
import uuid
import time
from PIL import Image
import numpy as np
from fastapi import FastAPI, File, UploadFile, Form
from fastapi import Request
from langchain.prompts ... | [
"langchain.schema.HumanMessage",
"langchain.cache.SQLiteCache",
"langchain.prompts.ChatPromptTemplate.from_messages",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.SystemMessage",
"langchain.chains.LLMChain"
] | [((1278, 1291), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1289, 1291), False, 'from dotenv import load_dotenv\n'), ((1523, 1570), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-4-0613"""', 'temperature': '(0.1)'}), "(model='gpt-4-0613', temperature=0.1)\n", (1533, 1570), False, '... |
# Blackboard-PAGI - LLM Proto-AGI using the Blackboard Pattern
# Copyright (c) 2023. Andreas Kirsch
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as published
# by the Free Software Foundation, either version 3 of the License,... | [
"langchain.schema.AIMessage",
"langchain.llm_cache.update",
"langchain.schema.Generation",
"langchain.llm_cache.lookup"
] | [((1195, 1255), 'langchain.llm_cache.lookup', 'langchain.llm_cache.lookup', (['messages_prompt', 'self.model_name'], {}), '(messages_prompt, self.model_name)\n', (1221, 1255), False, 'import langchain\n'), ((1771, 1875), 'langchain.schema.Generation', 'Generation', ([], {'text': 'chat_result.generations[0].message.cont... |
import streamlit as st
import langchain
# from dotenv import load_dotenv
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings, HuggingFaceInstructEmbeddings
from langchain.vectorstores import FAISS
from langchain.chat_models import ChatOpenAI
from langchain.memory ... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Pinecone.from_texts",
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI",
"langchain.embeddings.OpenAIEmbeddings"
] | [((669, 747), 'streamlit.set_page_config', 'st.set_page_config', ([], {'page_title': '"""Chat with multiple files"""', 'page_icon': '""":books:"""'}), "(page_title='Chat with multiple files', page_icon=':books:')\n", (687, 747), True, 'import streamlit as st\n'), ((752, 789), 'streamlit.write', 'st.write', (['css'], {'... |
# import langchain_experimental as lc
from langchain_experimental.llms import FakeListLLM
from langchain.llms.fake import FakeListLLM
from langchain.agents import load_tools
from langchain.agents import initialize_agent
from langchain.agents import AgentType
tools = load_tools(["python_repl"])
responses = ["Action... | [
"langchain.agents.initialize_agent",
"langchain.llms.fake.FakeListLLM",
"langchain.agents.load_tools"
] | [((271, 298), 'langchain.agents.load_tools', 'load_tools', (["['python_repl']"], {}), "(['python_repl'])\n", (281, 298), False, 'from langchain.agents import load_tools\n'), ((389, 421), 'langchain.llms.fake.FakeListLLM', 'FakeListLLM', ([], {'responses': 'responses'}), '(responses=responses)\n', (400, 421), False, 'fr... |
from langchain.vectorstores import Chroma
from langchain.embeddings import OpenAIEmbeddings
from langchain.chains import RetrievalQA
from langchain.chat_models import ChatOpenAI
from redundant_filter_retriever import RedundantFilterRetriever
from dotenv import load_dotenv
import langchain
# langchain.debug = True
loa... | [
"langchain.vectorstores.Chroma",
"langchain.embeddings.OpenAIEmbeddings",
"langchain.chains.RetrievalQA.from_chain_type",
"langchain.chat_models.ChatOpenAI"
] | [((317, 330), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (328, 330), False, 'from dotenv import load_dotenv\n'), ((339, 351), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {}), '()\n', (349, 351), False, 'from langchain.chat_models import ChatOpenAI\n'), ((365, 383), 'langchain.embeddings.OpenAIEmb... |
####################### FUNCTION #############################
from langchainGPT import langchainProcessor
from rabbitMQ import consumer_channel
##################################################################
##################################################################
####################### VARIABLE... | [
"langchainGPT.langchainProcessor"
] | [((533, 605), 'rabbitMQ.consumer_channel.queue_declare', 'consumer_channel.queue_declare', ([], {'queue': 'amqp_langchain_queue', 'durable': '(True)'}), '(queue=amqp_langchain_queue, durable=True)\n', (563, 605), False, 'from rabbitMQ import consumer_channel\n'), ((713, 790), 'rabbitMQ.consumer_channel.basic_consume', ... |
import json
import logging
import pathlib
from typing import List, Tuple
from langchain.text_splitter import CharacterTextSplitter
import langchain
import wandb
from langchain.cache import SQLiteCache
from langchain.docstore.document import Document
from langchain.document_loaders import TextLoader
from langchain.text_... | [
"langchain.text_splitter.CharacterTextSplitter",
"langchain.vectorstores.Qdrant.from_documents",
"langchain.document_loaders.TextLoader",
"langchain.cache.SQLiteCache",
"langchain.embeddings.OpenAIEmbeddings"
] | [((491, 510), 'dotenv.load_dotenv', 'load_dotenv', (['""".env"""'], {}), "('.env')\n", (502, 510), False, 'from dotenv import load_dotenv\n'), ((523, 563), 'os.path.join', 'os.path.join', (['"""documents"""', '"""iteration_1"""'], {}), "('documents', 'iteration_1')\n", (535, 563), False, 'import os\n'), ((586, 614), 'o... |
import langchain
from langchain.chains import LLMChain, SimpleSequentialChain, ConversationChain
from langchain.chat_models import ChatOpenAI
from langchain.memory import ConversationBufferMemory
langchain.verbose = True
chat = ChatOpenAI(model="gpt-3.5-turbo", temperature=0)
conversation = ConversationChain(
ll... | [
"langchain.memory.ConversationBufferMemory",
"langchain.chat_models.ChatOpenAI"
] | [((231, 279), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model='gpt-3.5-turbo', temperature=0)\n", (241, 279), False, 'from langchain.chat_models import ChatOpenAI\n'), ((339, 365), 'langchain.memory.ConversationBufferMemory', 'ConversationBufferMem... |
import logging
import sys
import langchain
from extract_100knocks_qa import extract_questions
from langchain.chat_models import ChatOpenAI
from llama_index import (GPTSQLStructStoreIndex, LLMPredictor, ServiceContext,
SQLDatabase)
from ruamel.yaml import YAML
from sqlalchemy import create_engi... | [
"langchain.chat_models.ChatOpenAI"
] | [((829, 856), 'sqlalchemy.create_engine', 'create_engine', (['database_url'], {}), '(database_url)\n', (842, 856), False, 'from sqlalchemy import create_engine\n'), ((934, 987), 'langchain.chat_models.ChatOpenAI', 'ChatOpenAI', ([], {'model_name': '"""gpt-3.5-turbo"""', 'temperature': '(0)'}), "(model_name='gpt-3.5-tur... |
import os
import openai
from dotenv import load_dotenv
import logging
import re
import hashlib
from langchain.embeddings.openai import OpenAIEmbeddings
from langchain.llms import AzureOpenAI
from langchain.vectorstores.base import VectorStore
from langchain.chains import ChatVectorDBChain
from langchain.chains import ... | [
"langchain.agents.initialize_agent",
"langchain.chains.llm.LLMChain",
"langchain.chat_models.ChatOpenAI",
"langchain.schema.HumanMessage",
"langchain.text_splitter.TokenTextSplitter",
"langchain.chains.qa_with_sources.load_qa_with_sources_chain",
"langchain.prompts.PromptTemplate",
"langchain.embeddin... | [((2224, 2237), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (2235, 2237), False, 'from dotenv import load_dotenv\n'), ((2298, 2326), 'os.getenv', 'os.getenv', (['"""OPENAI_API_BASE"""'], {}), "('OPENAI_API_BASE')\n", (2307, 2326), False, 'import os\n'), ((2402, 2429), 'os.getenv', 'os.getenv', (['"""OPENAI_A... |
# -*- coding: utf-8 -*-
# Copyright © Spyder Project Contributors
# Licensed under the terms of the MIT License
"""Kite completion HTTP client."""
# Standard library imports
import logging
import functools
import os
import os.path as osp
# Qt imports
from qtpy.QtCore import Slot
from qtpy.QtWidgets import QMessageB... | [
"langchain_provider.widgets.LangchainStatusWidget",
"langchain_provider.client.LangchainClient"
] | [((795, 822), 'logging.getLogger', 'logging.getLogger', (['__name__'], {}), '(__name__)\n', (812, 822), False, 'import logging\n'), ((3539, 3548), 'qtpy.QtCore.Slot', 'Slot', (['str'], {}), '(str)\n', (3543, 3548), False, 'from qtpy.QtCore import Slot\n'), ((3554, 3564), 'qtpy.QtCore.Slot', 'Slot', (['dict'], {}), '(di... |
import os
import streamlit as st
import pickle
import time
import langchain
import faiss
from langchain.llms import OpenAI
from langchain.chains import RetrievalQAWithSourcesChain
from langchain.chains.qa_with_sources.loading import load_qa_with_sources_chain
from langchain.text_splitter import RecursiveCharacterTextSp... | [
"langchain.vectorstores.FAISS.load_local",
"langchain.text_splitter.RecursiveCharacterTextSplitter",
"langchain.document_loaders.UnstructuredURLLoader",
"langchain.llms.OpenAI",
"langchain.vectorstores.FAISS.from_documents",
"langchain.embeddings.OpenAIEmbeddings"
] | [((1876, 1889), 'dotenv.load_dotenv', 'load_dotenv', ([], {}), '()\n', (1887, 1889), False, 'from dotenv import load_dotenv\n'), ((1891, 1918), 'streamlit.title', 'st.title', (['"""Website Summary"""'], {}), "('Website Summary')\n", (1899, 1918), True, 'import streamlit as st\n'), ((1919, 1951), 'streamlit.sidebar.titl... |
from langchain.vectorstores import Milvus
from langchain.chains.retrieval_qa.base import RetrievalQA
from typing import Any
from langchain.memory import ConversationBufferMemory
from langchain import PromptTemplate, FAISS
from langchain.schema import Document
from langchain.embeddings import DashScopeEmbeddings
from ll... | [
"langchain.memory.ConversationBufferMemory",
"langchain.schema.Document",
"langchain.vectorstores.Milvus",
"langchain.embeddings.DashScopeEmbeddings",
"langchain.PromptTemplate"
] | [((1149, 1243), 'langchain.embeddings.DashScopeEmbeddings', 'DashScopeEmbeddings', ([], {'model': '"""text-embedding-v1"""', 'dashscope_api_key': 'config.llm_tyqw_api_key'}), "(model='text-embedding-v1', dashscope_api_key=config.\n llm_tyqw_api_key)\n", (1168, 1243), False, 'from langchain.embeddings import DashScop... |
import os
from langchain.callbacks.manager import AsyncCallbackManager
from langchain.callbacks.tracers import LangChainTracer
from langchain.chains import ChatVectorDBChain, ConversationalRetrievalChain
from langchain.chains.chat_vector_db.prompts import CONDENSE_QUESTION_PROMPT
from langchain.prompts.prompt import Pr... | [
"langchain.chains.question_answering.load_qa_chain",
"langchain.text_splitter.CharacterTextSplitter",
"langchain.prompts.prompt.PromptTemplate",
"langchain.callbacks.tracers.LangChainTracer",
"langchain.callbacks.manager.AsyncCallbackManager",
"langchain.retrievers.ContextualCompressionRetriever",
"lang... | [((1253, 1356), 'langchain.prompts.prompt.PromptTemplate', 'PromptTemplate', ([], {'template': 'doc_template', 'input_variables': "['page_content', 'authors', 'href', 'title']"}), "(template=doc_template, input_variables=['page_content',\n 'authors', 'href', 'title'])\n", (1267, 1356), False, 'from langchain.prompts... |
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