prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet meilisearch')
import getpass
import os
os.environ["MEILI_HTTP_ADDR"] = getpass.getpass("Meilisearch HTTP address and port:")
os.environ["MEILI_MASTER_KEY"] = getpass.getpass("Meilisearch API Key:")
os.environ["OPENAI_API_KEY"] = getpass.getpass("Op... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
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 ... | ChatNVIDIA.get_available_models() | langchain_nvidia_ai_endpoints.ChatNVIDIA.get_available_models |
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.from_documents(docs, index_name=index_name) | langchain_community.vectorstores.Marqo.from_documents |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sodapy')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet geopandas')
import ast
import geopandas as gpd
import pandas as pd
from langchain_community.document_loader... | GeoDataFrameLoader(data_frame=gdf, page_content_column="geometry") | langchain_community.document_loaders.GeoDataFrameLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-steam-api python-decouple')
import os
os.environ["STEAM_KEY"] = "xyz"
os.environ["STEAM_ID"] = "123"
os.environ["OPENAI_API_KEY"] = "abc"
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.steam.t... | OpenAI(temperature=0) | langchain_openai.OpenAI |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | Databricks(cluster_driver_port="7777") | langchain_community.llms.Databricks |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet elasticsearch == 7.11.0')
import getpass
import os
os.environ["QIANFAN_AK"] = getpass.getpass("Your Qianfan AK:")
os.environ["QIANFAN_SK"] = getpass.getpass("Your Qianfan SK:")
from langchain_community.document_loaders import TextLoader
from langcha... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai duckduckgo-search')
from langchain.tools import DuckDuckGoSearchRun
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
searc... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet airbyte-source-shopify')
from langchain_community.document_loaders.airbyte import AirbyteShopifyLoader
config = {
}
loader = AirbyteShopifyLoader(
config=config, stream_name="orders"
) # check the documentation linked above for a list of all str... | Document(page_content=record.data["title"], metadata=record.data) | langchain.docstore.document.Document |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.agent_toolkits import NLAToolkit
from langchain_community.tools.plugi... | AIPlugin.from_url(url) | langchain_community.tools.plugin.AIPlugin.from_url |
get_ipython().run_line_magic('pip', 'install -qU langchain langchain-openai langchain-anthropic langchain-community wikipedia')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
os.environ["ANTHROPIC_API_KEY"] = getpass.getpass()
from langchain_community.retrievers import WikipediaRetrieve... | JsonOutputKeyToolsParser(key_name="quoted_answer", return_single=True) | langchain.output_parsers.openai_tools.JsonOutputKeyToolsParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-pinecone langchain-openai langchain')
from langchain_community.document_loaders import TextLoader
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/stat... | PineconeVectorStore(index_name=index_name, embedding=embeddings) | langchain_pinecone.PineconeVectorStore |
STAGE_BUCKET = "<bucket-name>"
get_ipython().run_cell_magic('bash', ' -s "$STAGE_BUCKET"', '\nrm -rf data\nmkdir -p data\ncd data\necho getting org ontology and sample org instances\nwget http://www.w3.org/ns/org.ttl \nwget https://raw.githubusercontent.com/aws-samples/amazon-neptune-ontology-example-blog/main/data/e... | BedrockChat(model_id="anthropic.claude-v2", client=bedrock_client) | langchain_community.chat_models.BedrockChat |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | create_openai_tools_agent(llm, tools, prompt) | langchain.agents.create_openai_tools_agent |
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... | Document(page_content=s, metadata={id_key: img_ids[i]}) | langchain_core.documents.Document |
from IPython.display import SVG
from langchain_experimental.cpal.base import CPALChain
from langchain_experimental.pal_chain import PALChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0, max_tokens=512) | langchain_openai.OpenAI |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain.tools import AIPluginTool
from langchain_openai import ChatOpenAI
tool = | AIPluginTool.from_plugin_url("https://www.klarna.com/.well-known/ai-plugin.json") | langchain.tools.AIPluginTool.from_plugin_url |
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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=... | TokenTextSplitter(chunk_size=10, chunk_overlap=0) | langchain_text_splitters.TokenTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "docarray[hnswlib]"')
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import DocArrayHnswSearch
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitt... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import getpass
import os
os.environ["POLYGON_API_KEY"] = getpass.getpass()
from langchain_community.tools.polygon.financials import PolygonFinancials
from langchain_community.tools.polygon.last_quote import PolygonLastQuote
from langchain_community.tools.polygon.ticker_news import PolygonTickerNews
from langchain_co... | PolygonAPIWrapper() | langchain_community.utilities.polygon.PolygonAPIWrapper |
get_ipython().system('pip install --quiet langchain_experimental langchain_openai')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_experimental.text_splitter import SemanticChunker
from langchain_openai.embeddings import OpenAIEmbeddings
text_splitter = Semantic... | OpenAIEmbeddings() | langchain_openai.embeddings.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-community langchainhub langchain-openai faiss-cpu')
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
from langchain_community.vectors... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain label-studio label-studio-sdk langchain-openai')
import os
os.environ["LABEL_STUDIO_URL"] = "<YOUR-LABEL-STUDIO-URL>" # e.g. http://localhost:8080
os.environ["LABEL_STUDIO_API_KEY"] = "<YOUR-LABEL-STUDIO-API-KEY>"
os.environ["OPENAI_API_KEY"... | HumanMessage(content="Tell me a joke") | langchain_core.messages.HumanMessage |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | HumanMessage(content="hello") | langchain_core.messages.HumanMessage |
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... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sagemaker')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
os.environ["OPENAI_API_KEY"] = "<ADD-KEY-HERE>"
os.environ[... | PromptTemplate.from_template(template=PROMPT_TEMPLATE_2) | langchain.prompts.PromptTemplate.from_template |
from langchain_community.llms.human import HumanInputLLM
from langchain.agents import AgentType, initialize_agent, load_tools
get_ipython().run_line_magic('pip', 'install wikipedia')
tools = | load_tools(["wikipedia"]) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet vald-client-python')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Vald
from langchain_text_splitters import CharacterTextSplitte... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain.chains import HypotheticalDocumentEmbedder, LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI, OpenAIEmbeddings
base_embeddings = OpenAIEmbeddings()
llm = OpenAI()
embeddings = HypotheticalDocumentEmbedder.from_llm(llm, base_embeddings, "web_search")
result ... | Chroma.from_texts(texts, embeddings) | langchain_community.vectorstores.Chroma.from_texts |
import re
from typing import Union
from langchain.agents import (
AgentExecutor,
AgentOutputParser,
LLMSingleActionAgent,
)
from langchain.chains import LLMChain
from langchain.prompts import StringPromptTemplate
from langchain_community.agent_toolkits import NLAToolkit
from langchain_community.tools.plugi... | OpenAI(temperature=0) | langchain_openai.OpenAI |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python')
get_ipython().system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install llama-cpp-python')
get_ipython().system('CMAKE_ARGS="-DLLAMA_CUBLAS=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cach... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
from langchain_community.chat_message_histories import SQLChatMessageHistory
chat_message_history = SQLChatMessageHistory(
session_id="test_session", connection_string="sqlite:///sqlite.db"
)
chat_message_history.add_user_message("Hello")
chat_message_history.add_ai_message("Hi")
chat_message_history.messages
... | MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark clickhouse-connect')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["MYSCALE_HOST"] = getpass.getpass("MyScale URL:")
os.environ["MYSCALE_PORT"] = getpass.getpass("MyScale Port:")
os.environ["... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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.BasedOn(["Vegetarian", "regular dairy is ok"]) | langchain_experimental.rl_chain.BasedOn |
from typing import Callable, List
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
class DialogueAgent:
def __init__(
self,
name: str,
system_message: SystemMessage,
model: ChatOpenAI,
) -> None:
self.name =... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
from typing import Optional
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_experimental.autonomous_agents import BabyAGI
from langchain_openai import OpenAI, OpenAIEmbeddings
get_ipython().run_line_magic('pip', 'install faiss-cpu > /dev/null')
get_ipython().run_lin... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence_transformers')
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loade... | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
from hugegraph.connection import PyHugeGraph
client = PyHugeGraph("localhost", "8080", user="admin", pwd="admin", graph="hugegraph")
"""schema"""
schema = client.schema()
schema.propertyKey("name").asText().ifNotExist().create()
schema.propertyKey("birthDate").asText().ifNotExist().create()
schema.vertexLabel("Pers... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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="bar") | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-text-splitters tiktoken')
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
from langchain_text_splitters import CharacterTextSplitter
text_splitter = CharacterTextSplitter.from_tiktoken_encoder(
chunk_size=... | SpacyTextSplitter(chunk_size=1000) | langchain_text_splitters.SpacyTextSplitter |
get_ipython().run_line_magic('pip', 'install -qU langchain-community langchain-openai')
from langchain_community.tools import MoveFileTool
from langchain_core.messages import HumanMessage
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_openai import ChatOpenAI
model = Cha... | HumanMessage(content="move file foo to bar") | langchain_core.messages.HumanMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet atlassian-python-api')
import os
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.jira.toolkit import JiraToolkit
from langchain_community.utilities.jira import JiraAPIWrapper
from langchain_openai import ... | JiraAPIWrapper() | langchain_community.utilities.jira.JiraAPIWrapper |
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]" pillow pydantic lxml pillow matplotlib chromadb tiktoken')
from langchain_text_splitters import CharacterTextSplitter
fro... | ChatOpenAI(model="gpt-4-vision-preview", max_tokens=1024) | langchain_openai.ChatOpenAI |
from langchain_community.document_loaders.chatgpt import ChatGPTLoader
loader = | ChatGPTLoader(log_file="./example_data/fake_conversations.json", num_logs=1) | langchain_community.document_loaders.chatgpt.ChatGPTLoader |
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... | RetrievalQA.from_chain_type(llm=llm, retriever=compression_retriever) | langchain.chains.RetrievalQA.from_chain_type |
from langchain.vectorstores import NeuralDBVectorStore
vectorstore = | NeuralDBVectorStore.from_scratch(thirdai_key="your-thirdai-key") | langchain.vectorstores.NeuralDBVectorStore.from_scratch |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet timescale-vector')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import os
from dotenv import find_dotenv, load_dotenv
_ = load_dotenv(find... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain_community.chat_models import ChatDatabricks
from langchain_core.messages import HumanMessage
from mlflow.deployments import get_deploy_client
client = get_deploy_client("databricks")
secret = "secrets/<scope>/openai-api-key" # replace `<scope>` with your scope
name = "my-chat" # rename this if my-cha... | Databricks(endpoint_name="dolly") | langchain_community.llms.Databricks |
get_ipython().system(' pip install langchain unstructured[all-docs] pydantic lxml langchainhub')
get_ipython().system(' brew install tesseract')
get_ipython().system(' brew install poppler')
path = "/Users/rlm/Desktop/Papers/LLaMA2/"
from typing import Any
from pydantic import BaseModel
from unstructured.parti... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llmlingua accelerate')
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
from langchain_community.document_loaders import TextLo... | LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu") | langchain_community.retrievers.document_compressors.LLMLinguaCompressor |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-community')
import os
os.environ["YDC_API_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
from langchain_community.utilities.you import YouSearchAPIWrapper
utility = YouSearchAPIWrapper(num_web_results=1)
utility
import json
response... | YouRetriever(num_web_results=1) | langchain_community.retrievers.you.YouRetriever |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-api-python-client > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-oauthlib > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-auth-httplib2 > /dev/null')
get_ipython().run_l... | hub.pull("langchain-ai/openai-functions-template") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llmlingua accelerate')
def pretty_print_docs(docs):
print(
f"\n{'-' * 100}\n".join(
[f"Document {i+1}:\n\n" + d.page_content for i, d in enumerate(docs)]
)
)
from langchain_community.document_loaders import TextLo... | OpenAIEmbeddings(model="text-embedding-ada-002") | langchain_openai.OpenAIEmbeddings |
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/photos/"
fr... | OpenCLIPEmbeddings() | langchain_experimental.open_clip.OpenCLIPEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet chromadb')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain_community.vectorstores import Chroma
from langchain_core.doc... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', "install --upgrade --quiet langchain-openai 'deeplake[enterprise]' tiktoken")
from langchain_community.vectorstores import DeepLake
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
import getpass
import os
os.environ["OP... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lm-format-enforcer > /dev/null')
import logging
from langchain_experimental.pydantic_v1 import BaseModel
logging.basicConfig(level=logging.ERROR)
class PlayerInformation(BaseModel):
first_name: str
last_name: str
num_seasons_in_nba: int
... | HuggingFacePipeline(pipeline=hf_model) | langchain_community.llms.HuggingFacePipeline |
from langchain_community.llms.azureml_endpoint import AzureMLOnlineEndpoint
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<you... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Tair
from langchain_text_splitters import CharacterTextSplitter
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()... | FakeEmbeddings(size=128) | langchain_community.embeddings.fake.FakeEmbeddings |
get_ipython().system(' nomic login')
get_ipython().system(' nomic login token')
get_ipython().system(' pip install -U langchain-nomic langchain_community tiktoken langchain-openai chromadb langchain')
import os
os.environ["LANGCHAIN_TRACING_V2"] = "true"
os.environ["LANGCHAIN_ENDPOINT"] = "https://api.smith.lang... | ChatPromptTemplate.from_template(template) | langchain_core.prompts.ChatPromptTemplate.from_template |
import json
from pprint import pprint
from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM
set_debug(True)
llm_sys = NIBittensorLLM(
system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project"
)
sys_resp = llm_sys... | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
from langchain.retrievers import ParentDocumentRetriever
from langchain.storage import InMemoryStore
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterText... | InMemoryStore() | langchain.storage.InMemoryStore |
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(... | Chroma.from_documents(documents=chunks, embedding=embedding) | langchain_community.vectorstores.chroma.Chroma.from_documents |
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 ... | ChatOpenAI(temperature=0, model="gpt-4") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "xinference[all]"')
get_ipython().system('xinference launch -n vicuna-v1.3 -f ggmlv3 -q q4_0')
from langchain_community.llms import Xinference
llm = Xinference(
server_url="http://0.0.0.0:9997", model_uid="7167b2b0-2a04-11ee-83f0-d29396a3f064"
)... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
from typing import List
from langchain.output_parsers import PydanticOutputParser
from langchain.prompts import PromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(d... | PydanticOutputParser(pydantic_object=Joke) | langchain.output_parsers.PydanticOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet neo4j')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Ke... | Document(page_content="foo") | langchain.docstore.document.Document |
from typing import List
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(description... | JsonOutputParser() | langchain_core.output_parsers.JsonOutputParser |
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... | OpenAI(temperature=0) | langchain_openai.OpenAI |
import pprint
from langchain_community.utilities import SearxSearchWrapper
search = | SearxSearchWrapper(searx_host="http://127.0.0.1:8888") | langchain_community.utilities.SearxSearchWrapper |
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 |
from typing import List
from langchain.prompts import PromptTemplate
from langchain_core.output_parsers import JsonOutputParser
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0)
class Joke(BaseModel):
setup: str = Field(description... | JsonOutputParser(pydantic_object=Joke) | langchain_core.output_parsers.JsonOutputParser |
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.BasedOn("Tom") | langchain_experimental.rl_chain.BasedOn |
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
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... | ElasticsearchStore.SparseVectorRetrievalStrategy() | langchain_elasticsearch.ElasticsearchStore.SparseVectorRetrievalStrategy |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet polars')
import polars as pl
df = pl.read_csv("example_data/mlb_teams_2012.csv")
df.head()
from langchain_community.document_loaders import PolarsDataFrameLoader
loader = | PolarsDataFrameLoader(df, page_content_column="Team") | langchain_community.document_loaders.PolarsDataFrameLoader |
from langchain.agents import AgentType, initialize_agent, load_tools
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
tools = load_tools(["google-serper"], llm=llm)
agent = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
agent.run("What is the weathe... | load_tools(["searchapi"], llm=llm) | langchain.agents.load_tools |
from langchain_experimental.pal_chain import PALChain
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0, max_tokens=512) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai context-python')
import os
from langchain.callbacks import ContextCallbackHandler
token = os.environ["CONTEXT_API_TOKEN"]
context_callback = ContextCallbackHandler(token)
import os
from langchain.callbacks import Conte... | HumanMessage(content="I love programming.") | langchain.schema.HumanMessage |
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0, model="gpt-4-turbo-preview")
from langchain import hub
from langchain_core.prompts import PromptTemplate
select_prompt = hub.pull("hwchase17/self-discovery-select")
select_prompt.pretty_print()
adapt_prompt = hub.pull("hwchase17/self-di... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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... | FakeEmbeddings(size=128) | langchain_community.embeddings.fake.FakeEmbeddings |
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import ModernTreasuryLoader
modern_treasury_loader = | ModernTreasuryLoader("payment_orders") | langchain_community.document_loaders.ModernTreasuryLoader |
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 ... | ChatNVIDIA(model="llama2_13b") | langchain_nvidia_ai_endpoints.ChatNVIDIA |
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
from langchain_community.chat_models import JinaChat
from langchain_core.messages import HumanMessage, SystemMessage
chat = | JinaChat(temperature=0) | langchain_community.chat_models.JinaChat |
from langchain.chains import GraphSparqlQAChain
from langchain_community.graphs import RdfGraph
from langchain_openai import ChatOpenAI
graph = RdfGraph(
source_file="http://www.w3.org/People/Berners-Lee/card",
standard="rdf",
local_copy="test.ttl",
)
graph.load_schema()
graph.get_schema
chain = ... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
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.BasedOn("Anna") | langchain_experimental.rl_chain.BasedOn |
import os
from langchain.chains import ConversationalRetrievalChain
from langchain_community.vectorstores import Vectara
from langchain_openai import OpenAI
from langchain_community.document_loaders import TextLoader
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
vectara = Vectara.from_... | load_qa_chain(streaming_llm, chain_type="stuff", prompt=QA_PROMPT) | langchain.chains.question_answering.load_qa_chain |
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... | ChatPromptTemplate.from_template(
"what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)"
) | langchain_core.prompts.ChatPromptTemplate.from_template |
from langchain_community.llms import Ollama
llm = Ollama(model="llama2")
llm("The first man on the moon was ...")
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Ollama(
model="llama2", callback_manager=CallbackManage... | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
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... | OpenAIFunctionsAgent(llm=llm, prompt=prompt, tools=tools) | langchain.agents.OpenAIFunctionsAgent |
from langchain.chains import RetrievalQA
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
llm = OpenAI(temperature=0)
from pathlib import Path
relevant_parts = []
for p in Path(".").absolute().parts:
... | Tool(
name="Ruff QA System",
func=ruff.run,
description="useful for when you need to answer questions about ruff (a python linter) | langchain.agents.Tool |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
import json
from pprint import pprint
from langchain.globals import set_debug
from langchain_community.llms import NIBittensorLLM
set_debug(True)
llm_sys = NIBittensorLLM(
system_prompt="Your task is to determine response based on user prompt.Explain me like I am technical lead of a project"
)
sys_resp = llm_sys... | GoogleSearchAPIWrapper() | langchain_community.utilities.GoogleSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet docx2txt')
from langchain_community.document_loaders import Docx2txtLoader
loader = | Docx2txtLoader("example_data/fake.docx") | langchain_community.document_loaders.Docx2txtLoader |
from langchain.output_parsers import (
OutputFixingParser,
PydanticOutputParser,
)
from langchain.prompts import (
PromptTemplate,
)
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_openai import ChatOpenAI, OpenAI
template = """Based on the user question, provide an Action and Actio... | PydanticOutputParser(pydantic_object=Action) | langchain.output_parsers.PydanticOutputParser |
get_ipython().run_line_magic('pip', 'install -qU langchain-community langchain-openai')
from langchain_community.tools import MoveFileTool
from langchain_core.messages import HumanMessage
from langchain_core.utils.function_calling import convert_to_openai_function
from langchain_openai import ChatOpenAI
model = Cha... | HumanMessage(content="move file foo to bar") | langchain_core.messages.HumanMessage |
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