prompt stringlengths 43 25.9k | completion stringlengths 7 362 | api stringlengths 18 90 |
|---|---|---|
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI
template = """Answer the users question ... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
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 ... | RunnableLambda(split_image_text_types) | langchain_core.runnables.RunnableLambda |
import re
from IPython.display import Image, display
from steamship import Block, Steamship
from langchain.agents import AgentType, initialize_agent
from langchain.tools import SteamshipImageGenerationTool
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
tools = [SteamshipImageGenerationTool(mode... | SteamshipImageGenerationTool(model_name="stable-diffusion") | langchain.tools.SteamshipImageGenerationTool |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet google-search-results')
import os
from langchain_community.tools.google_finance import GoogleFinanceQueryRun
from langchain_community.utilities.google_finance import GoogleFinanceAPIWrapper
os.environ["SERPAPI_API_KEY"] = ""
tool = GoogleFinanceQueryRu... | OpenAI() | langchain_openai.OpenAI |
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()... | Tair.from_documents(docs, embeddings, tair_url=tair_url) | langchain_community.vectorstores.Tair.from_documents |
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.tools.you import YouSearchTool
from langchain_community.utilities.you import YouSearchAPIWrapper
api_wrapper = YouSearchAP... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain.prompts import PromptTemplate
prompt = (
PromptTemplate.from_template("Tell me a joke about {topic}")
+ ", make it funny"
+ "\n\nand in {language}"
)
prompt
prompt.format(topic="sports", language="spanish")
from langchain.chains import LLMChain
from langchain_openai import ChatOpenAI... | SystemMessage(content="You are a nice pirate") | langchain_core.messages.SystemMessage |
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") | langchain_community.chat_models.ChatAnthropic |
from langchain_community.document_loaders import CoNLLULoader
loader = | CoNLLULoader("example_data/conllu.conllu") | langchain_community.document_loaders.CoNLLULoader |
get_ipython().system('pip install databricks-sql-connector')
from langchain_community.utilities import SQLDatabase
db = SQLDatabase.from_databricks(catalog="samples", schema="nyctaxi")
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0, model_name="gpt-4")
from langchain_community.utiliti... | SQLDatabaseChain.from_llm(llm, db, verbose=True) | langchain_community.utilities.SQLDatabaseChain.from_llm |
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_... | LLMChain(llm=llm, prompt=CONDENSE_QUESTION_PROMPT) | langchain.chains.llm.LLMChain |
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import ForefrontAI
from getpass import getpass
FOREFRONTAI_API_KEY = getpass()
os.environ["FOREFRONTAI_API_KEY"] = FOREFRONTAI_API_KEY
llm = ForefrontAI(endpoint_url="YOUR ENDPOINT URL H... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from langchain.output_parsers import XMLOutputParser
from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatAnthropic
model = ChatAnthropic(model="claude-2", max_tokens_to_sample=512, temperature=0.1)
actor_query = "Generate the shortened filmography for Tom Hanks."
output = m... | XMLOutputParser() | langchain.output_parsers.XMLOutputParser |
from langchain.chains import LLMMathChain
from langchain_community.utilities import DuckDuckGoSearchAPIWrapper
from langchain_core.tools import Tool
from langchain_experimental.plan_and_execute import (
PlanAndExecute,
load_agent_executor,
load_chat_planner,
)
from langchain_openai import ChatOpenAI, OpenAI... | DuckDuckGoSearchAPIWrapper() | langchain_community.utilities.DuckDuckGoSearchAPIWrapper |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet unstructured')
from langchain_community.document_loaders import UnstructuredEmailLoader
loader = UnstructuredEmailLoader("example_data/fake-email.eml")
data = loader.load()
data
loader = | UnstructuredEmailLoader("example_data/fake-email.eml", mode="elements") | langchain_community.document_loaders.UnstructuredEmailLoader |
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(
... | TFIDFRetriever.load_local("testing.pkl") | langchain_community.retrievers.TFIDFRetriever.load_local |
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... | ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0) | langchain_openai.ChatOpenAI |
from langchain.prompts import ChatMessagePromptTemplate
prompt = "May the {subject} be with you"
chat_message_prompt = ChatMessagePromptTemplate.from_template(
role="Jedi", template=prompt
)
chat_message_prompt.format(subject="force")
from langchain.prompts import (
ChatPromptTemplate,
HumanMessageProm... | MessagesPlaceholder(variable_name="conversation") | langchain.prompts.MessagesPlaceholder |
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... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | ChatPromptTemplate.from_messages(messages) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet duckduckgo-search')
from langchain.tools import DuckDuckGoSearchRun
search = DuckDuckGoSearchRun()
search.run("Obama's first name?")
from langchain.tools import DuckDuckGoSearchResults
search = | DuckDuckGoSearchResults() | langchain.tools.DuckDuckGoSearchResults |
from getpass import getpass
WRITER_API_KEY = getpass()
import os
os.environ["WRITER_API_KEY"] = WRITER_API_KEY
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Writer
template = """Question: {question}
Answer: Let's think step by step."""
... | LLMChain(prompt=prompt, llm=llm) | langchain.chains.LLMChain |
from typing import Callable, List
from langchain.memory import ConversationBufferMemory
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
class DialogueAgent:
def __... | ChatOpenAI(temperature=1.0) | langchain_openai.ChatOpenAI |
from langchain.memory.motorhead_memory import MotorheadMemory
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
template = """You are a chatbot having a conversation with a human.
{chat_history}
Human: {human_input}
AI:"""
prompt = PromptTemplat... | OpenAI() | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3 langchain-openai tiktoken python-dotenv')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "amazon-textract-caller>=0.2.0"')
from langchain_community.document_loaders import AmazonTextractPDFLoader
loader = | AmazonTextractPDFLoader("example_data/alejandro_rosalez_sample-small.jpeg") | langchain_community.document_loaders.AmazonTextractPDFLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet arxiv')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent, load_tools
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0.0)
tools = load_tools(
["arxiv"],
)
prompt = hub.pull("hwchase17/reac... | create_react_agent(llm, tools, prompt) | langchain.agents.create_react_agent |
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = load... | Chroma.from_documents(documents=splits, embedding=embedding) | langchain_community.vectorstores.Chroma.from_documents |
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... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
from langchain_openai import OpenAI
llm = | OpenAI(temperature=1, max_tokens=512, model="gpt-3.5-turbo-instruct") | langchain_openai.OpenAI |
from langchain.callbacks import HumanApprovalCallbackHandler
from langchain.tools import ShellTool
tool = ShellTool()
print(tool.run("echo Hello World!"))
tool = ShellTool(callbacks=[HumanApprovalCallbackHandler()])
print(tool.run("ls /usr"))
print(tool.run("ls /private"))
from langchain.agents import Age... | HumanApprovalCallbackHandler(should_check=_should_check, approve=_approve) | langchain.callbacks.HumanApprovalCallbackHandler |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
from langchain_core.tools import tool
@tool
def complex_tool(int_arg: int, float_arg: float, dict_arg: dict) -> int:
"""Do something complex... | ChatOpenAI(model="gpt-4-1106-preview", temperature=0) | langchain_openai.ChatOpenAI |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferWindowMemory
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
def initialize_chain(instructions, memory=None):
if memory is None:
memory = ConversationBufferWindowMemory()
memory.ai... | ConversationBufferWindowMemory() | langchain.memory.ConversationBufferWindowMemory |
from langchain_community.document_loaders import WebBaseLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
data = load... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
from langchain_community.embeddings import FakeEmbeddings
from langchain_community.vectorstores import Vectara
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnableLambda, RunnablePassthrough
vectara = Vectara.fro... | MultiQueryRetriever.from_llm(retriever=retriever, llm=llm) | langchain.retrievers.multi_query.MultiQueryRetriever.from_llm |
import os
import pprint
os.environ["SERPER_API_KEY"] = ""
from langchain_community.utilities import GoogleSerperAPIWrapper
search = GoogleSerperAPIWrapper()
search.run("Obama's first name?")
os.environ["OPENAI_API_KEY"] = ""
from langchain.agents import AgentType, Tool, initialize_agent
from langchain_commu... | GoogleSerperAPIWrapper() | langchain_community.utilities.GoogleSerperAPIWrapper |
get_ipython().system(' pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub chromadb hnswlib --upgrade --quiet')
from pprint import pprint
from docugami import Docugami
from docugami.lib.upload import upload_to_named_docset, wait_for_dgml
DOCSET_NAME = "NTSB Aviation Incident Reports"
FIL... | ChatPromptTemplate.from_messages([system_prompt, human_prompt]) | langchain.prompts.ChatPromptTemplate.from_messages |
from langchain.agents import AgentExecutor, Tool, ZeroShotAgent
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory, ReadOnlySharedMemory
from langchain.prompts import PromptTemplate
from langchain_community.utilities import GoogleSearchAPIWrapper
from langchain_openai import Ope... | PromptTemplate(input_variables=["input", "chat_history"], template=template) | langchain.prompts.PromptTemplate |
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 |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet wikipedia')
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain_community.tools import WikipediaQueryRun
from langchain_community.utilities import WikipediaAPIWrapper
from langchain_openai import OpenAI... | WikipediaQueryRun(api_wrapper=api_wrapper) | langchain_community.tools.WikipediaQueryRun |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet usearch')
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 USearch
from langchain_openai import OpenAIE... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
import os
os.environ["OPENAI_API_KEY"] = "...input your openai api key here..."
from langchain_experimental.agents.agent_toolkits import create_spark_dataframe_agent
from langchain_openai import OpenAI
from pyspark.sql import SparkSession
spark = SparkSession.builder.getOrCreate()
csv_file_path = "titanic.csv"
df ... | OpenAI(temperature=0) | langchain_openai.OpenAI |
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 TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAI... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain_openai import ChatOpenAI
model = | ChatOpenAI(temperature=0, model="gpt-4-turbo-preview") | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet predictionguard langchain')
import os
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import PredictionGuard
os.environ["OPENAI_API_KEY"] = "<your OpenAI api key>"
os.environ["PREDICTI... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pyvespa')
from vespa.package import ApplicationPackage, Field, RankProfile
app_package = ApplicationPackage(name="testapp")
app_package.schema.add_fields(
Field(
name="text", type="string", indexing=["index", "summary"], index="enable-bm25"... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
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(description="question to set up a joke") | langchain_core.pydantic_v1.Field |
get_ipython().system('pip install --upgrade volcengine')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
from langchain.document_loaders import TextLoader
from langchain.vectorstores.vikingdb import VikingDB, VikingDBConfig
from langchain_openai import OpenAIEmbeddings
f... | TextLoader("./test.txt") | langchain.document_loaders.TextLoader |
get_ipython().run_line_magic('pip', 'install tika')
import os
from langchain_community.vectorstores import LLMRails
os.environ["LLM_RAILS_DATASTORE_ID"] = "Your datastore id "
os.environ["LLM_RAILS_API_KEY"] = "Your API Key"
llm_rails = | LLMRails.from_texts(["Your text here"]) | langchain_community.vectorstores.LLMRails.from_texts |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet rapidfuzz')
from langchain.evaluation import load_evaluator
evaluator = | load_evaluator("string_distance") | langchain.evaluation.load_evaluator |
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... | LLMChain(llm=chat, prompt=chat_prompt_template, callbacks=[callback]) | langchain.chains.LLMChain |
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 chain
from langchain_openai import ChatOpenAI
prompt1 = ChatPromptTemplate... | ChatOpenAI() | langchain_openai.ChatOpenAI |
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... | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
from langchain_community.document_loaders.blob_loaders.youtube_audio import (
YoutubeAudioLoader,
)
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import (
OpenAIWhisperParser,
OpenAIWhisperParserLocal,
)
get_ipython().run_line_mag... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
from langchain.chains import ConversationalRetrievalChain
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import FakeEmbeddings
from langc... | Vectara() | langchain_community.vectorstores.Vectara |
import re
from IPython.display import Image, display
from steamship import Block, Steamship
from langchain.agents import AgentType, initialize_agent
from langchain.tools import SteamshipImageGenerationTool
from langchain_openai import OpenAI
llm = | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet manifest-ml')
from langchain_community.llms.manifest import ManifestWrapper
from manifest import Manifest
manifest = Manifest(
client_name="huggingface", client_connection="http://127.0.0.1:5000"
)
print(manifest.client_pool.get_current_client().ge... | MapReduceChain.from_params(llm, prompt, text_splitter) | langchain.chains.mapreduce.MapReduceChain.from_params |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain pypdf pymongo langchain-openai tiktoken')
import getpass
MONGODB_ATLAS_CLUSTER_URI = getpass.getpass("MongoDB Atlas Cluster URI:")
from pymongo im... | OpenAIEmbeddings(disallowed_special=()) | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tigrisdb openapi-schema-pydantic langchain-openai tiktoken')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["TIGRIS_PROJECT"] = getpass.getpass("Tigris Project Name:")
os.environ["TIGRIS_CLIENT_ID"... | Tigris.from_documents(docs, embeddings, index_name="my_embeddings") | langchain_community.vectorstores.Tigris.from_documents |
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.sentence_transformer import (
SentenceTransformerEmbeddings,
)
from langchain_community.vectorstores import Chroma
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_t... | Chroma.from_documents(docs, embedding_function, persist_directory="./chroma_db") | langchain_community.vectorstores.Chroma.from_documents |
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
with open("../../state_of_the_union.txt") as f:
state_of_the_union = f.read()
text_splitter = | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass()
import dspy
colbertv2 = dspy.ColBERTv2(url="http://20.102.90.50:2017/wiki17_abstracts")
from langchain.cache import SQLiteCache
from langchain.globals import set_llm_cache
from langchain_openai import OpenAI
set_llm_cache(SQLiteCache(data... | RunnablePassthrough.assign(context=retrieve) | langchain_core.runnables.RunnablePassthrough.assign |
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 |
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... | AgentFinish(return_values={"output": output.content}, log=output.content) | langchain_core.agents.AgentFinish |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai tiktoken')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["SUPABASE_URL"] = getpass.... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.model_laboratory import ModelLaboratory
from langchain.prompts import PromptTemplate
from langchain_community.llms import Cohere, HuggingFaceHub
from langchain_openai import OpenAI
import getpass
import os
o... | OpenAI(temperature=0) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet openlm')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
import os
from getpass import getpass
if "OPENAI_API_KEY" not in os.environ:
print("Enter your OpenAI API key:")
os.environ["OPENAI_API_KEY"] = getpass()... | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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... | ZeroShotAgent(llm_chain=llm_chain, allowed_tools=tool_names) | langchain.agents.ZeroShotAgent |
from langchain.chains import GraphCypherQAChain
from langchain_community.graphs import Neo4jGraph
from langchain_openai import ChatOpenAI
graph = Neo4jGraph(
url="bolt://localhost:7687", username="neo4j", password="pleaseletmein"
)
graph.query(
"""
MERGE (m:Movie {name:"Top Gun"})
WITH m
UNWIND ["Tom Cruis... | ChatOpenAI(temperature=0, model="gpt-3.5-turbo") | langchain_openai.ChatOpenAI |
from langchain.chains import ConversationChain
from langchain.memory import ConversationBufferMemory
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
conversation = ConversationChain(
llm=llm, verbose=True, memory=ConversationBufferMemory()
)
conversation.predict(input="Hi there!")
conversati... | PromptTemplate(input_variables=["history", "input"], template=template) | langchain.prompts.prompt.PromptTemplate |
from langchain_community.document_loaders import HuggingFaceDatasetLoader
dataset_name = "imdb"
page_content_column = "text"
loader = | HuggingFaceDatasetLoader(dataset_name, page_content_column) | langchain_community.document_loaders.hugging_face_dataset.HuggingFaceDatasetLoader |
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 aleph-alpha-client')
from getpass import getpass
ALEPH_ALPHA_API_KEY = getpass()
from langchain.prompts import PromptTemplate
from langchain_community.llms import AlephAlpha
template = """Q: {question}
A:"""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
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_verbose(True) | langchain.globals.set_verbose |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet lark chromadb')
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
from langchain_openai import OpenAIEmbeddings
docs = [
Document(
page_content="A bunch of scientists bring back dinosaurs and m... | StructuredQueryOutputParser.from_components() | langchain.chains.query_constructor.base.StructuredQueryOutputParser.from_components |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain langchain-openai')
from langchain.prompts import PromptTemplate
from langchain_core.runnables import ConfigurableField
from langchain_openai import ChatOpenAI
model = ChatOpenAI(temperature=0).configurable_fields(
temperature=ConfigurableF... | PromptTemplate.from_template("Tell me a joke about {topic}") | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet ain-py')
import os
os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"] = ""
import os
from ain.account import Account
if os.environ.get("AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY", None):
account = Account(os.environ["AIN_BLOCKCHAIN_ACCOUNT_PRIVATE_KEY"... | AINetworkToolkit() | langchain_community.agent_toolkits.ainetwork.toolkit.AINetworkToolkit |
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... | SystemMessage(content="You are an AI assistant") | langchain_core.messages.SystemMessage |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence-transformers > /dev/null')
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_community.document_transformers import (
LongContextReorder,
)
from langchain_community.embeddi... | Chroma.from_texts(texts, embedding=embeddings) | langchain_community.vectorstores.Chroma.from_texts |
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... | FAISS.from_documents(texts, embedding) | langchain_community.vectorstores.FAISS.from_documents |
from langchain_community.embeddings import TensorflowHubEmbeddings
embeddings = | TensorflowHubEmbeddings() | langchain_community.embeddings.TensorflowHubEmbeddings |
from ragatouille import RAGPretrainedModel
RAG = RAGPretrainedModel.from_pretrained("colbert-ir/colbertv2.0")
import requests
def get_wikipedia_page(title: str):
"""
Retrieve the full text content of a Wikipedia page.
:param title: str - Title of the Wikipedia page.
:return: str - Full text conten... | create_retrieval_chain(retriever, document_chain) | langchain.chains.create_retrieval_chain |
import nest_asyncio
nest_asyncio.apply()
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import SurrealDBStore
from langchain_text_splitters import CharacterTextSplitter
documents = TextLoader("../../... | CharacterTextSplitter(chunk_size=1000, chunk_overlap=0) | langchain_text_splitters.CharacterTextSplitter |
from langchain_community.utils.openai_functions import (
convert_pydantic_to_openai_function,
)
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.pydantic_v1 import BaseModel, Field, validator
from langchain_openai import ChatOpenAI
class Joke(BaseModel):
"""Joke to tell user."""
... | convert_pydantic_to_openai_function(Joke) | langchain_community.utils.openai_functions.convert_pydantic_to_openai_function |
from langchain.memory import ConversationTokenBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = ConversationTokenBufferMemory(llm=llm, max_token_limit=10)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})
memor... | OpenAI() | langchain_openai.OpenAI |
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import TextLoader
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../state_of_the_union.txt", encoding... | ConversationBufferMemory(memory_key="chat_history", return_messages=True) | langchain.memory.ConversationBufferMemory |
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... | RunnablePassthrough.assign(query=sql_response) | langchain_core.runnables.RunnablePassthrough.assign |
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... | TextLoader("../../../state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
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... | ChatOpenAI(temperature=0) | langchain_openai.ChatOpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet cohere')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet faiss-cpu')
import getpass
import os
os.environ["COHERE_API_KEY"] = getpass.getpass("Cohere API Key:")
... | Cohere(temperature=0) | langchain_community.llms.Cohere |
from langchain.memory import ConversationSummaryBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = ConversationSummaryBufferMemory(llm=llm, max_token_limit=10)
memory.save_context({"input": "hi"}, {"output": "whats up"})
memory.save_context({"input": "not much you"}, {"output": "not much"})
m... | OpenAI() | langchain_openai.OpenAI |
from typing import Callable, List
from langchain.memory import ConversationBufferMemory
from langchain.schema import (
AIMessage,
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
from langchain.agents import AgentType, initialize_agent, load_tools
class DialogueAgent:
def __... | load_tools(tool_names, **tool_kwargs) | langchain.agents.load_tools |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-openai')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet psycopg2-binary')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet tiktoken')
... | OpenAIEmbeddings() | langchain_openai.OpenAIEmbeddings |
get_ipython().run_line_magic('reload_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from datetime import datetime
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.clickup.toolkit import ClickupToolkit
from langchain_community.utilities.clickup import... | ClickupAPIWrapper.get_access_code_url(oauth_client_id, redirect_uri) | langchain_community.utilities.clickup.ClickupAPIWrapper.get_access_code_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 ... | SystemMessage(content="You can make a task more specific.") | langchain.schema.SystemMessage |
get_ipython().system(' pip install langchain docugami==0.0.8 dgml-utils==0.3.0 pydantic langchainhub chromadb hnswlib --upgrade --quiet')
from pprint import pprint
from docugami import Docugami
from docugami.lib.upload import upload_to_named_docset, wait_for_dgml
DOCSET_NAME = "NTSB Aviation Incident Reports"
FIL... | Document(page_content=s, metadata={id_key: table_ids[i]}) | langchain_core.documents.Document |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "unstructured[all-docs]"')
from langchain_community.document_loaders import UnstructuredFileLoader
loader = UnstructuredFileLoader("./example_data/state_of_the_union.txt")
docs = loader.load()
docs[0].page_content[:400]
files = ["./example_d... | UnstructuredFileLoader(files) | langchain_community.document_loaders.UnstructuredFileLoader |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet sentence-transformers > /dev/null')
from langchain.chains import LLMChain, StuffDocumentsChain
from langchain.prompts import PromptTemplate
from langchain_community.document_transformers import (
LongContextReorder,
)
from langchain_community.embeddi... | LLMChain(llm=llm, prompt=prompt) | langchain.chains.LLMChain |
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