prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "cassio>=0.1.4"')
import os
from getpass import getpass
from datasets import (
load_dataset,
)
from langchain_community.document_loaders import PyPDFLoader
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOu... | Document(page_content=entry["quote"], metadata=metadata) | langchain_core.documents.Document |
import os
import re
OPENAI_API_KEY = "sk-xx"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from typing import Any, Callable, Dict, List, Union
from langchain.agents import AgentExecutor, LLMSingleActionAgent, Tool
from langchain.agents.agent import AgentOutputParser
from langchain.agents.conversational.prompt import... | CharacterTextSplitter(chunk_size=10, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet protobuf')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nucliadb-protos')
import os
os.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1
os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"
from langchain_community.tools.nuclia imp... | NucliaUnderstandingAPI(enable_ml=False) | langchain_community.tools.nuclia.NucliaUnderstandingAPI |
from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool
from langchain_community.utilities import SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = SerpAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
... | AgentAction(tool="Search", tool_input=kwargs["input"], log="") | langchain_core.agents.AgentAction |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymilvus')
import os
OPENAI_API_KEY = "Use your OpenAI key:)"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain_community.vectorstores import Milvus
from langchain_c... | Milvus.from_documents(
docs,
embedding=embeddings,
connection_args={"uri": "Use your uri:) | langchain_community.vectorstores.Milvus.from_documents |
import os
import chromadb
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain_community.document_transformers import (
EmbeddingsClusteringFi... | MergerRetriever(retrievers=[retriever_all, retriever_multi_qa]) | langchain.retrievers.merger_retriever.MergerRetriever |
get_ipython().system(' docker run -d -p 8123:8123 -p9000:9000 --name langchain-clickhouse-server --ulimit nofile=262144:262144 clickhouse/clickhouse-server:23.4.2.11')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
if not os.environ["OPENAI_API_KEY"]... | ClickhouseSettings(table="clickhouse_vector_search_example") | langchain_community.vectorstores.ClickhouseSettings |
get_ipython().run_line_magic('pip', 'install -U --quiet langchain langchain_community openai chromadb langchain-experimental')
get_ipython().run_line_magic('pip', 'install --quiet "unstructured[all-docs]" pypdf pillow pydantic lxml pillow matplotlib chromadb tiktoken')
import logging
import zipfile
import requests... | RunnableLambda(split_image_text_types) | langchain_core.runnables.RunnableLambda |
get_ipython().system(' pip install -U langchain openai chromadb langchain-experimental # (newest versions required for multi-modal)')
get_ipython().system(' pip install "unstructured[all-docs]==0.10.19" pillow pydantic lxml pillow matplotlib tiktoken open_clip_torch torch')
path = "/Users/rlm/Desktop/cpi/"
from ... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
from langchain_community.chat_message_histories import RedisChatMessageHistory
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_opena... | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate, FewShotChatMessagePromptTemplate
from langchain_core.runnables import RunnableLambda
from langchain_openai import ChatOpenAI
examples = [
{
"input": "Could the members of The Police perform law... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
from langchain_community.document_loaders import UnstructuredExcelLoader
loader = | UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements") | langchain_community.document_loaders.UnstructuredExcelLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark pgvector psycopg2-binary')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import PGVector
from langchain_core.documents import Document
from langchain_openai impor... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet openllm')
from langchain_community.llms import OpenLLM
server_url = "http://localhost:3000" # Replace with remote host if you are running on a remote server
llm = | OpenLLM(server_url=server_url) | langchain_community.llms.OpenLLM |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import ChatOpenAI, OpenAI
llm = ChatOpenAI(temperature=0.0)
math_llm = | OpenAI(temperature=0.0) | langchain_openai.OpenAI |
get_ipython().system('poetry run pip install dgml-utils==0.3.0 --upgrade --quiet')
import os
from langchain_community.document_loaders import DocugamiLoader
DOCUGAMI_API_KEY = os.environ.get("DOCUGAMI_API_KEY")
docset_id = "26xpy3aes7xp"
document_ids = ["d7jqdzcj50sj", "cgd1eacfkchw"]
loader = DocugamiLoader(... | DocugamiLoader(docset_id="zo954yqy53wp") | langchain_community.document_loaders.DocugamiLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet marqo')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Marqo
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = Text... | Marqo(client, index_name, page_content_builder=get_content) | langchain_community.vectorstores.Marqo |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opensearch-py')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import OpenSearchVectorSearch
from langchain_... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub gpt4all chromadb')
from langchain_community.document_loaders import WebBaseLoader
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install -qU chromadb langchain langchain-community langchain-openai')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharact... | MessagesPlaceholder(variable_name="agent_scratchpad") | langchain_core.prompts.MessagesPlaceholder |
get_ipython().run_line_magic('pip', "install --upgrade --quiet faiss-gpu # For CUDA 7.5+ Supported GPU's.")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu # For CPU Installation')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_... | TextLoader("../../../extras/modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.tools.connery import ConneryService
from langchain_openai import ChatOpenAI
os.environ["CONNERY_RUNNER_URL"] = ""
os.environ["CONNERY_RUNNER_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
recepient_email = "test@example.co... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pygithub')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.github.toolkit import GitHubToolkit
from langchain_community.utilities.github import GitHubAPIWrapper
from langchain_openai import Ch... | GitHubAPIWrapper() | langchain_community.utilities.github.GitHubAPIWrapper |
import asyncio
import os
import nest_asyncio
import pandas as pd
from langchain.docstore.document import Document
from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain_experimental.autonomous_agents import AutoGPT
from langchain_openai import ChatOpenAI
nest_asyncio.a... | create_pandas_dataframe_agent(llm, df, max_iterations=30, verbose=True) | langchain_community.agent_toolkits.pandas.base.create_pandas_dataframe_agent |
from langchain_community.document_transformers.openai_functions import (
create_metadata_tagger,
)
from langchain_core.documents import Document
from langchain_openai import ChatOpenAI
schema = {
"properties": {
"movie_title": {"type": "string"},
"critic": {"type": "string"},
"tone": {... | create_metadata_tagger(Properties, llm) | langchain_community.document_transformers.openai_functions.create_metadata_tagger |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai deepeval')
get_ipython().system('deepeval login')
from deepeval.metrics.answer_relevancy import AnswerRelevancy
answer_relevancy_metric = AnswerRelevancy(minimum_score=0.5)
from langchain.callbacks.confident_callback i... | OpenAIEmbeddings(openai_api_key=openai_api_key) | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet transformers --quiet')
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
from langchai... | HuggingFacePipeline(pipeline=pipe) | langchain_community.llms.huggingface_pipeline.HuggingFacePipeline |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DB_USER = "sqlserver" # @param {type:"string"}
DB_PASS = "password" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_li... | MSSQLDocumentSaver(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mssql.MSSQLDocumentSaver |
from langchain.chains import ConversationChain
from langchain.memory import (
CombinedMemory,
ConversationBufferMemory,
ConversationSummaryMemory,
)
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
conv_memory = ConversationBufferMemory(
memory_key="chat_history_lines", ... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-memorystore-redis')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from google.colab import auth
auth.authenticate_user()
import redis
from langchain_goo... | RedisVectorStore.init_index(client=redis_client, index_config=index_config) | langchain_google_memorystore_redis.RedisVectorStore.init_index |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet protobuf')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nucliadb-protos')
import os
os.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1
os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"
from langchain_community.tools.nuclia im... | NucliaTextTransformer(nua) | langchain_community.document_transformers.nuclia_text_transform.NucliaTextTransformer |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet opaqueprompts langchain')
import os
os.environ["OPAQUEPROMPTS_API_KEY"] = "<OPAQUEPROMPTS_API_KEY>"
os.environ["OPENAI_API_KEY"] = "<OPENAI_API_KEY>"
from langchain.callbacks.stdout import StdOutCallbackHandler
from langchain.chains import LLMChain... | set_debug(True) | langchain.globals.set_debug |
get_ipython().system('pip install --upgrade langchain langchain-google-vertexai')
project: str = "PUT_YOUR_PROJECT_ID_HERE" # @param {type:"string"}
endpoint_id: str = "PUT_YOUR_ENDPOINT_ID_HERE" # @param {type:"string"}
location: str = "PUT_YOUR_ENDPOINT_LOCAtION_HERE" # @param {type:"string"}
from langchain_... | HumanMessage(content="How much is 2+2?") | langchain_core.messages.HumanMessage |
import zipfile
import requests
def download_and_unzip(url: str, output_path: str = "file.zip") -> None:
file_id = url.split("/")[-2]
download_url = f"https://drive.google.com/uc?export=download&id={file_id}"
response = requests.get(download_url)
if response.status_code != 200:
print("Failed ... | merge_chat_runs(chat_sessions) | langchain_community.chat_loaders.utils.merge_chat_runs |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandoc')
from langchain_community.document_loaders import UnstructuredEPubLoader
loader = | UnstructuredEPubLoader("winter-sports.epub") | langchain_community.document_loaders.UnstructuredEPubLoader |
import functools
import random
from collections import OrderedDict
from typing import Callable, List
import tenacity
from langchain.output_parsers import RegexParser
from langchain.prompts import (
PromptTemplate,
)
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
from typing import Any, Dict, List, Union
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.callbacks.base import BaseCallbackHandler
from langchain_core.agents import AgentAction
from langchain_openai import OpenAI
class MyCustomHandlerOne(BaseCallbackHandler):
def on_llm_start... | initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION) | langchain.agents.initialize_agent |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
path = "/Users/rlm/Desktop/Papers/LLaVA/"
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_im... | InMemoryStore() | langchain.storage.InMemoryStore |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-firestore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | FirestoreSaver() | langchain_google_firestore.FirestoreSaver |
import getpass
import os
os.environ["OPENAI_API_KEY"] = os.environ.get("OPENAI_API_KEY") or getpass.getpass(
"OpenAI API Key:"
)
from langchain.sql_database import SQLDatabase
from langchain_openai import ChatOpenAI
CONNECTION_STRING = "postgresql+psycopg2://postgres:test@localhost:5432/vectordb" # Replace wit... | RunnablePassthrough.assign(query=sql_query_chain) | langchain_core.runnables.RunnablePassthrough.assign |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet weaviate-client')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
WEAVIATE_URL = getpass.getpass("WEAVIATE_URL:")
os.environ["WEAVIATE_API_KEY"] = getpass.getpass("WEAVIATE_API_KEY:")
WEAVIATE_API_KEY = os... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml')
path = "/Users/rlm/Desktop/Papers/LLaVA/"
from typing import Any
from pydantic import BaseModel
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "LLaVA.pdf",
extract_i... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
import os
os.environ["LANGCHAIN_PROJECT"] = "movie-qa"
import pandas as pd
df = pd.read_csv("data/imdb_top_1000.csv")
df["Released_Year"] = df["Released_Year"].astype(int, errors="ignore")
from langchain.schema import Document
from langchain_community.vectorstores import Chroma
from langchain_openai import Op... | ChatOpenAI() | langchain_openai.ChatOpenAI |
from langchain.agents import load_tools
requests_tools = load_tools(["requests_all"])
requests_tools
requests_tools[0].requests_wrapper
from langchain_community.utilities import TextRequestsWrapper
requests = | TextRequestsWrapper() | langchain_community.utilities.TextRequestsWrapper |
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import Recursiv... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet redis redisvl langchain-openai tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_openai import OpenAIEmbeddings
embeddings = OpenAIEmbeddings()
redis_url = "redis://localhost:637... | Redis.delete(keys, redis_url="redis://localhost:6379") | langchain_community.vectorstores.redis.Redis.delete |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
SOURCE = "test" # @param {type:"Query"|"CollectionGroup"|"DocumentReference"|"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-datastore')
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
from goo... | DatastoreSaver("Collection") | langchain_google_datastore.DatastoreSaver |
get_ipython().system('pip install --upgrade langchain langchain-google-vertexai')
project: str = "PUT_YOUR_PROJECT_ID_HERE" # @param {type:"string"}
endpoint_id: str = "PUT_YOUR_ENDPOINT_ID_HERE" # @param {type:"string"}
location: str = "PUT_YOUR_ENDPOINT_LOCAtION_HERE" # @param {type:"string"}
from langchain_... | GemmaLocalKaggle(model_name=model_name, keras_backend=keras_backend) | langchain_google_vertexai.GemmaLocalKaggle |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 nltk')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain_experimental')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pydantic')
import os
import boto3
comprehend_client = boto3.client("comp... | BaseModerationConfig(filters=[pii_config, toxicity_config]) | langchain_experimental.comprehend_moderation.BaseModerationConfig |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-community')
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.schema.messages import AIMessage
from langchain_community.llms.chatglm3 import ChatGLM3
template = """{question}"""
prompt = PromptTempl... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken langchain-openai python-dotenv datasets langchain deeplake beautifulsoup4 html2text ragas')
ORG_ID = "..."
import getpass
import os
from langchain.chains import RetrievalQA
from langchain.vectorstores.deeplake import DeepLake
from langchain_... | create_structured_output_chain(Questions, llm, prompt, verbose=False) | langchain.chains.openai_functions.create_structured_output_chain |
from langchain.callbacks import get_openai_callback
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(model_name="gpt-4")
with get_openai_callback() as cb:
result = llm.invoke("Tell me a joke")
print(cb)
with get_openai_callback() as cb:
result = llm.invoke("Tell me a joke")
result2 = llm.... | get_openai_callback() | langchain.callbacks.get_openai_callback |
from langchain.agents.agent_types import AgentType
from langchain_experimental.agents.agent_toolkits import create_pandas_dataframe_agent
from langchain_openai import ChatOpenAI
import pandas as pd
from langchain_openai import OpenAI
df = pd.read_csv("titanic.csv")
agent = create_pandas_dataframe_agent(OpenAI(tem... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().system('pip install boto3')
from langchain_experimental.recommenders import AmazonPersonalize
recommender_arn = "<insert_arn>"
client = AmazonPersonalize(
credentials_profile_name="default",
region_name="us-west-2",
recommender_arn=recommender_arn,
)
client.get_recommendations(user_id="1... | PromptTemplate(input_variables=["result"], template=RANDOM_PROMPT_QUERY) | langchain.prompts.prompt.PromptTemplate |
from langchain.agents import AgentExecutor, BaseMultiActionAgent, Tool
from langchain_community.utilities import SerpAPIWrapper
def random_word(query: str) -> str:
print("\nNow I'm doing this!")
return "foo"
search = | SerpAPIWrapper() | langchain_community.utilities.SerpAPIWrapper |
get_ipython().system(' pip install langchain langchain-experimental openai elasticsearch')
from elasticsearch import Elasticsearch
from langchain.chains.elasticsearch_database import ElasticsearchDatabaseChain
from langchain_openai import ChatOpenAI
ELASTIC_SEARCH_SERVER = "https://elastic:pass@localhost:9200"
db... | ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, query_prompt=PROMPT) | langchain.chains.elasticsearch_database.ElasticsearchDatabaseChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elevenlabs')
import os
os.environ["ELEVEN_API_KEY"] = ""
from langchain.tools import ElevenLabsText2SpeechTool
text_to_speak = "Hello world! I am the real slim shady"
tts = ElevenLabsText2SpeechTool()
tts.name
speech_file = tts.run(text_to_speak... | load_tools(["eleven_labs_text2speech"]) | langchain.agents.load_tools |
import os
import chromadb
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import DocumentCompressorPipeline
from langchain.retrievers.merger_retriever import MergerRetriever
from langchain_community.document_transformers import (
EmbeddingsClusteringFi... | DocumentCompressorPipeline(transformers=[filter]) | langchain.retrievers.document_compressors.DocumentCompressorPipeline |
from langchain_community.document_loaders import UnstructuredPowerPointLoader
loader = | UnstructuredPowerPointLoader("example_data/fake-power-point.pptx") | langchain_community.document_loaders.UnstructuredPowerPointLoader |
from langchain_community.graphs import NeptuneGraph
host = "<neptune-host>"
port = 8182
use_https = True
graph = | NeptuneGraph(host=host, port=port, use_https=use_https) | langchain_community.graphs.NeptuneGraph |
from langchain_community.document_loaders import DocusaurusLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 lxml')
import nest_asyncio
nest_asyncio.apply()
loader = | DocusaurusLoader("https://python.langchain.com") | langchain_community.document_loaders.DocusaurusLoader |
import asyncio
import os
import nest_asyncio
import pandas as pd
from langchain.docstore.document import Document
from langchain_community.agent_toolkits.pandas.base import create_pandas_dataframe_agent
from langchain_experimental.autonomous_agents import AutoGPT
from langchain_openai import ChatOpenAI
nest_asyncio.a... | WriteFileTool(root_dir="./data") | langchain_community.tools.file_management.write.WriteFileTool |
from langchain import hub
from langchain.agents import AgentExecutor, tool
from langchain.agents.output_parsers import XMLAgentOutputParser
from langchain_community.chat_models import ChatAnthropic
model = ChatAnthropic(model="claude-2")
@tool
def search(query: str) -> str:
"""Search things about current events... | XMLAgentOutputParser() | langchain.agents.output_parsers.XMLAgentOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-elasticsearch langchain-openai tiktoken langchain')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbed... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai faiss-cpu tiktoken')
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, Runna... | ChatOpenAI() | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["OPENAI_API_BASE"] = getpass.getpass("OpenAI Base:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale Host:")
os.environ["MY... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet comet_ml langchain langchain-openai google-search-results spacy textstat pandas')
get_ipython().system('{sys.executable} -m spacy download en_core_web_sm')
import comet_ml
comet_ml.init(project_name="comet-example-langchain")
import os
os.envir... | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
get_ipython().system(' pip install langchain replicate')
from langchain_community.chat_models import ChatOllama
llama2_chat = ChatOllama(model="llama2:13b-chat")
llama2_code = ChatOllama(model="codellama:7b-instruct")
from langchain_community.llms import Replicate
replicate_id = "meta/llama-2-13b-chat:f4e2de70d66... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
import os
os.environ["EXA_API_KEY"] = "..."
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-exa')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnablePa... | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
from langchain_community.chat_models.llama_edge import LlamaEdgeChatService
from langchain_core.messages import HumanMessage, SystemMessage
service_url = "https://b008-54-186-154-209.ngrok-free.app"
chat = LlamaEdgeChatService(service_url=service_url)
system_message = SystemMessage(content="You are an AI assistant... | HumanMessage(content="What is the capital of Norway?") | langchain_core.messages.HumanMessage |
from langchain_community.llms.symblai_nebula import Nebula
llm = | Nebula(nebula_api_key="<your_api_key>") | langchain_community.llms.symblai_nebula.Nebula |
get_ipython().system('pip install termcolor > /dev/null')
import logging
logging.basicConfig(level=logging.ERROR)
from datetime import datetime, timedelta
from typing import List
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_commun... | ChatOpenAI(max_tokens=1500) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai faiss-cpu tiktoken')
from langchain.prompts import ChatPromptTemplate
from langchain.vectorstores import FAISS
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda, Runna... | ChatPromptTemplate.from_template(template) | langchain.prompts.ChatPromptTemplate.from_template |
from langchain_experimental.llm_symbolic_math.base import LLMSymbolicMathChain
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
llm_symbolic_math = | LLMSymbolicMathChain.from_llm(llm) | langchain_experimental.llm_symbolic_math.base.LLMSymbolicMathChain.from_llm |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-core langchain langchain-openai')
from langchain.utils.math import cosine_similarity
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
from langchain_core.runnables import RunnableLambda... | cosine_similarity([query_embedding], prompt_embeddings) | langchain.utils.math.cosine_similarity |
get_ipython().system('pip3 install clickhouse-sqlalchemy InstructorEmbedding sentence_transformers openai langchain-experimental')
import getpass
from os import environ
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.utilities import SQLDatabase
from langch... | StdOutCallbackHandler() | langchain.callbacks.StdOutCallbackHandler |
from datetime import datetime, timedelta
import faiss
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embed... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-robocorp')
from langchain.agents import AgentExecutor, OpenAIFunctionsAgent
from langchain_core.messages import SystemMessage
from langchain_openai import ChatOpenAI
from langchain_robocorp import ActionServerToolkit
llm = ChatOpenAI(model="g... | ActionServerToolkit(url="http://localhost:8080") | langchain_robocorp.ActionServerToolkit |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet transformers --quiet')
from langchain_community.llms.huggingface_pipeline import HuggingFacePipeline
hf = HuggingFacePipeline.from_model_id(
model_id="gpt2",
task="text-generation",
pipeline_kwargs={"max_new_tokens": 10},
)
from langchai... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-nvidia-ai-endpoints')
import getpass
import os
if not os.environ.get("NVIDIA_API_KEY", "").startswith("nvapi-"):
nvapi_key = getpass.getpass("Enter your NVIDIA API key: ")
assert nvapi_key.startswith("nvapi-"), f"{nvapi_key[:5]}... is ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet infinopy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet matplotlib')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import datetime as dt
import json
import time
import matplotlib.dates as md
import matplot... | OpenAI(temperature=0.1) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install -qU langchain-anthropic defusedxml')
from langchain_anthropic.experimental import ChatAnthropicTools
from langchain_core.pydantic_v1 import BaseModel
class Person(BaseModel):
name: str
age: int
model = | ChatAnthropicTools(model="claude-3-opus-20240229") | langchain_anthropic.experimental.ChatAnthropicTools |
from langchain_community.utilities import SerpAPIWrapper
search = SerpAPIWrapper()
search.run("Obama's first name?")
params = {
"engine": "bing",
"gl": "us",
"hl": "en",
}
search = | SerpAPIWrapper(params=params) | langchain_community.utilities.SerpAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_community.chat_models import ChatAnthropic
from langchain_openai import ChatOpenAI
from unittest.mock import patch
import httpx
from openai import RateLimitError
request = httpx.Request("GET", "/")
respons... | ChatOpenAI(max_retries=0) | langchain_openai.ChatOpenAI |
from typing import Callable, List
import tenacity
from langchain.output_parsers import RegexParser
from langchain.prompts import PromptTemplate
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
n... | SystemMessage(content="You can make a task more specific.") | langchain.schema.SystemMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet protobuf')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nucliadb-protos')
import os
os.environ["NUCLIA_ZONE"] = "<YOUR_ZONE>" # e.g. europe-1
os.environ["NUCLIA_NUA_KEY"] = "<YOUR_API_KEY>"
from langchain_community.tools.nuclia im... | Document(page_content="<TEXT 1>", metadata={}) | langchain_core.documents.Document |
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompt_values import PromptValue
from langchain_openai import ChatOpenAI
short_context_model = ChatOpenAI(model="gpt-3.5-turbo")
long_context_model = | ChatOpenAI(model="gpt-3.5-turbo-16k") | langchain_openai.ChatOpenAI |
import getpass
import os
os.environ["TAVILY_API_KEY"] = getpass.getpass()
from langchain.retrievers.tavily_search_api import TavilySearchAPIRetriever
retriever = TavilySearchAPIRetriever(k=3)
retriever.invoke("what year was breath of the wild released?")
from langchain_core.output_parsers import StrOutputPa... | ChatOpenAI(model="gpt-4-1106-preview") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('', 'pip install --upgrade --quiet flashrank')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss')
get_ipython().run_line_magic('', 'pip install --upgrade --quiet faiss_cpu')
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from datetime import datetime, timedelta
import faiss
from langchain.docstore import InMemoryDocstore
from langchain.retrievers import TimeWeightedVectorStoreRetriever
from langchain_community.vectorstores import FAISS
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
embed... | Document(page_content="hello world", metadata={"last_accessed_at": yesterday}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
prompt = ChatP... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().system('pip install pettingzoo pygame rlcard')
import collections
import inspect
import tenacity
from langchain.output_parsers import RegexParser
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class GymnasiumAgent:
@classmethod
... | ChatOpenAI(temperature=0.2) | langchain_openai.ChatOpenAI |
import uuid
from pathlib import Path
import langchain
import torch
from bs4 import BeautifulSoup as Soup
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryByteStore, LocalFileStore
from langchain_community.document_loaders.recursive_url_loader import (
Recursi... | hub.pull("rlm/rag-prompt") | langchain.hub.pull |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DB_USER = "sqlserver" # @param {type:"string"}
DB_PASS = "password" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_li... | MSSQLLoader(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mssql.MSSQLLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scikit-learn')
from langchain_community.retrievers import TFIDFRetriever
retriever = TFIDFRetriever.from_texts(["foo", "bar", "world", "hello", "foo bar"])
from langchain_core.documents import Document
retriever = TFIDFRetriever.from_documents(
... | Document(page_content="world") | langchain_core.documents.Document |
meals = [
"Beef Enchiladas with Feta cheese. Mexican-Greek fusion",
"Chicken Flatbreads with red sauce. Italian-Mexican fusion",
"Veggie sweet potato quesadillas with vegan cheese",
"One-Pan Tortelonni bake with peppers and onions",
]
from langchain_openai import OpenAI
llm = OpenAI(model="gpt-3.5-t... | rl_chain.ToSelectFrom(meals) | langchain_experimental.rl_chain.ToSelectFrom |
from typing import Optional
from langchain_experimental.autonomous_agents import BabyAGI
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain.docstore import InMemoryDocstore
from langchain_community.vectorstores import FAISS
embeddings_model = OpenAIEmbeddings()
import faiss
embedding_size = 153... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet nlpcloud')
from getpass import getpass
NLPCLOUD_API_KEY = getpass()
import os
os.environ["NLPCLOUD_API_KEY"] = NLPCLOUD_API_KEY
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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