prompt
stringlengths 51
10k
| completion
stringlengths 8
362
| api
stringlengths 18
90
|
|---|---|---|
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:")
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.embeddings import CohereEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
documents = TextLoader("../../modules/state_of_the_union.txt").load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, CohereEmbeddings()).as_retriever(
search_kwargs={"k": 20}
)
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain_community.llms import Cohere
llm = | Cohere(temperature=0) | langchain_community.llms.Cohere |
from langchain.prompts import (
ChatPromptTemplate,
FewShotChatMessagePromptTemplate,
)
examples = [
{"input": "2+2", "output": "4"},
{"input": "2+3", "output": "5"},
]
example_prompt = ChatPromptTemplate.from_messages(
[
("human", "{input}"),
("ai", "{output}"),
]
)
few_shot_prompt = FewShotChatMessagePromptTemplate(
example_prompt=example_prompt,
examples=examples,
)
print(few_shot_prompt.format())
final_prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a wondrous wizard of math."),
few_shot_prompt,
("human", "{input}"),
]
)
from langchain_community.chat_models import ChatAnthropic
chain = final_prompt | | ChatAnthropic(temperature=0.0) | langchain_community.chat_models.ChatAnthropic |
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"] = "<YOUR-OPENAI-API-KEY>"
from langchain.callbacks import LabelStudioCallbackHandler
from langchain_openai import OpenAI
llm = OpenAI(
temperature=0, callbacks=[ | LabelStudioCallbackHandler(project_name="My Project") | langchain.callbacks.LabelStudioCallbackHandler |
from langchain_community.llms.fake import FakeListLLM
from langchain.agents import AgentType, initialize_agent, load_tools
tools = load_tools(["python_repl"])
responses = ["Action: Python REPL\nAction Input: print(2 + 2)", "Final Answer: 4"]
llm = | FakeListLLM(responses=responses) | langchain_community.llms.fake.FakeListLLM |
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 (
EmbeddingsClusteringFilter,
EmbeddingsRedundantFilter,
)
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
all_mini = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2")
multi_qa_mini = HuggingFaceEmbeddings(model_name="multi-qa-MiniLM-L6-dot-v1")
filter_embeddings = OpenAIEmbeddings()
ABS_PATH = os.path.dirname(os.path.abspath(__file__))
DB_DIR = os.path.join(ABS_PATH, "db")
client_settings = chromadb.config.Settings(
is_persistent=True,
persist_directory=DB_DIR,
anonymized_telemetry=False,
)
db_all = Chroma(
collection_name="project_store_all",
persist_directory=DB_DIR,
client_settings=client_settings,
embedding_function=all_mini,
)
db_multi_qa = Chroma(
collection_name="project_store_multi",
persist_directory=DB_DIR,
client_settings=client_settings,
embedding_function=multi_qa_mini,
)
retriever_all = db_all.as_retriever(
search_type="similarity", search_kwargs={"k": 5, "include_metadata": True}
)
retriever_multi_qa = db_multi_qa.as_retriever(
search_type="mmr", search_kwargs={"k": 5, "include_metadata": True}
)
lotr = MergerRetriever(retrievers=[retriever_all, retriever_multi_qa])
filter = EmbeddingsRedundantFilter(embeddings=filter_embeddings)
pipeline = DocumentCompressorPipeline(transformers=[filter])
compression_retriever = ContextualCompressionRetriever(
base_compressor=pipeline, base_retriever=lotr
)
filter_ordered_cluster = EmbeddingsClusteringFilter(
embeddings=filter_embeddings,
num_clusters=10,
num_closest=1,
)
filter_ordered_by_retriever = EmbeddingsClusteringFilter(
embeddings=filter_embeddings,
num_clusters=10,
num_closest=1,
sorted=True,
)
pipeline = | DocumentCompressorPipeline(transformers=[filter_ordered_by_retriever]) | langchain.retrievers.document_compressors.DocumentCompressorPipeline |
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")
punchline: str = Field(description="answer to resolve the joke")
@validator("setup")
def question_ends_with_question_mark(cls, field):
if field[-1] != "?":
raise ValueError("Badly formed question!")
return field
joke_query = "Tell me a joke."
parser = PydanticOutputParser(pydantic_object=Joke)
prompt = PromptTemplate(
template="Answer the user query.\n{format_instructions}\n{query}\n",
input_variables=["query"],
partial_variables={"format_instructions": parser.get_format_instructions()},
)
chain = prompt | model | parser
chain.invoke({"query": joke_query})
class Actor(BaseModel):
name: str = Field(description="name of an actor")
film_names: List[str] = Field(description="list of names of films they starred in")
actor_query = "Generate the filmography for a random actor."
parser = | PydanticOutputParser(pydantic_object=Actor) | langchain.output_parsers.PydanticOutputParser |
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().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
api_wrapper = WikipediaAPIWrapper(top_k_results=1, doc_content_chars_max=100)
tool = WikipediaQueryRun(api_wrapper=api_wrapper)
tools = [tool]
prompt = hub.pull("hwchase17/react")
llm = OpenAI(temperature=0)
agent = | create_react_agent(llm, tools, prompt) | langchain.agents.create_react_agent |
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>.<your_region>.inference.ml.azure.com/score",
endpoint_api_type=AzureMLEndpointApiType.realtime,
endpoint_api_key="my-api-key",
content_formatter=LlamaContentFormatter(),
model_kwargs={"temperature": 0.8, "max_new_tokens": 400},
)
response = llm.invoke("Write me a song about sparkling water:")
response
response = llm.invoke("Write me a song about sparkling water:", temperature=0.5)
response
from langchain_community.llms.azureml_endpoint import (
AzureMLEndpointApiType,
LlamaContentFormatter,
)
from langchain_core.messages import HumanMessage
llm = AzureMLOnlineEndpoint(
endpoint_url="https://<your-endpoint>.<your_region>.inference.ml.azure.com/v1/completions",
endpoint_api_type=AzureMLEndpointApiType.serverless,
endpoint_api_key="my-api-key",
content_formatter= | LlamaContentFormatter() | langchain_community.llms.azureml_endpoint.LlamaContentFormatter |
import os
import yaml
get_ipython().system('wget https://raw.githubusercontent.com/openai/openai-openapi/master/openapi.yaml -O openai_openapi.yaml')
get_ipython().system('wget https://www.klarna.com/us/shopping/public/openai/v0/api-docs -O klarna_openapi.yaml')
get_ipython().system('wget https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/spotify.com/1.0.0/openapi.yaml -O spotify_openapi.yaml')
from langchain_community.agent_toolkits.openapi.spec import reduce_openapi_spec
with open("openai_openapi.yaml") as f:
raw_openai_api_spec = yaml.load(f, Loader=yaml.Loader)
openai_api_spec = reduce_openapi_spec(raw_openai_api_spec)
with open("klarna_openapi.yaml") as f:
raw_klarna_api_spec = yaml.load(f, Loader=yaml.Loader)
klarna_api_spec = reduce_openapi_spec(raw_klarna_api_spec)
with open("spotify_openapi.yaml") as f:
raw_spotify_api_spec = yaml.load(f, Loader=yaml.Loader)
spotify_api_spec = | reduce_openapi_spec(raw_spotify_api_spec) | langchain_community.agent_toolkits.openapi.spec.reduce_openapi_spec |
import asyncio
from langchain.callbacks import get_openai_callback
from langchain_openai import OpenAI
llm = OpenAI(temperature=0)
with get_openai_callback() as cb:
llm("What is the square root of 4?")
total_tokens = cb.total_tokens
assert total_tokens > 0
with | get_openai_callback() | langchain.callbacks.get_openai_callback |
get_ipython().run_cell_magic('writefile', 'discord_chats.txt', "talkingtower — 08/15/2023 11:10 AM\nLove music! Do you like jazz?\nreporterbob — 08/15/2023 9:27 PM\nYes! Jazz is fantastic. Ever heard this one?\nWebsite\nListen to classic jazz track...\n\ntalkingtower — Yesterday at 5:03 AM\nIndeed! Great choice. 🎷\nreporterbob — Yesterday at 5:23 AM\nThanks! How about some virtual sightseeing?\nWebsite\nVirtual tour of famous landmarks...\n\ntalkingtower — Today at 2:38 PM\nSounds fun! Let's explore.\nreporterbob — Today at 2:56 PM\nEnjoy the tour! See you around.\ntalkingtower — Today at 3:00 PM\nThank you! Goodbye! 👋\nreporterbob — Today at 3:02 PM\nFarewell! Happy exploring.\n")
import logging
import re
from typing import Iterator, List
from langchain_community.chat_loaders import base as chat_loaders
from langchain_core.messages import BaseMessage, HumanMessage
logger = logging.getLogger()
class DiscordChatLoader(chat_loaders.BaseChatLoader):
def __init__(self, path: str):
"""
Initialize the Discord chat loader.
Args:
path: Path to the exported Discord chat text file.
"""
self.path = path
self._message_line_regex = re.compile(
r"(.+?) — (\w{3,9} \d{1,2}(?:st|nd|rd|th)?(?:, \d{4})? \d{1,2}:\d{2} (?:AM|PM)|Today at \d{1,2}:\d{2} (?:AM|PM)|Yesterday at \d{1,2}:\d{2} (?:AM|PM))", # noqa
flags=re.DOTALL,
)
def _load_single_chat_session_from_txt(
self, file_path: str
) -> chat_loaders.ChatSession:
"""
Load a single chat session from a text file.
Args:
file_path: Path to the text file containing the chat messages.
Returns:
A `ChatSession` object containing the loaded chat messages.
"""
with open(file_path, "r", encoding="utf-8") as file:
lines = file.readlines()
results: List[BaseMessage] = []
current_sender = None
current_timestamp = None
current_content = []
for line in lines:
if re.match(
r".+? — (\d{2}/\d{2}/\d{4} \d{1,2}:\d{2} (?:AM|PM)|Today at \d{1,2}:\d{2} (?:AM|PM)|Yesterday at \d{1,2}:\d{2} (?:AM|PM))", # noqa
line,
):
if current_sender and current_content:
results.append(
HumanMessage(
content="".join(current_content).strip(),
additional_kwargs={
"sender": current_sender,
"events": [{"message_time": current_timestamp}],
},
)
)
current_sender, current_timestamp = line.split(" — ")[:2]
current_content = [
line[len(current_sender) + len(current_timestamp) + 4 :].strip()
]
elif re.match(r"\[\d{1,2}:\d{2} (?:AM|PM)\]", line.strip()):
results.append(
HumanMessage(
content="".join(current_content).strip(),
additional_kwargs={
"sender": current_sender,
"events": [{"message_time": current_timestamp}],
},
)
)
current_timestamp = line.strip()[1:-1]
current_content = []
else:
current_content.append("\n" + line.strip())
if current_sender and current_content:
results.append(
HumanMessage(
content="".join(current_content).strip(),
additional_kwargs={
"sender": current_sender,
"events": [{"message_time": current_timestamp}],
},
)
)
return chat_loaders.ChatSession(messages=results)
def lazy_load(self) -> Iterator[chat_loaders.ChatSession]:
"""
Lazy load the messages from the chat file and yield them in the required format.
Yields:
A `ChatSession` object containing the loaded chat messages.
"""
yield self._load_single_chat_session_from_txt(self.path)
loader = DiscordChatLoader(
path="./discord_chats.txt",
)
from typing import List
from langchain_community.chat_loaders.base import ChatSession
from langchain_community.chat_loaders.utils import (
map_ai_messages,
merge_chat_runs,
)
raw_messages = loader.lazy_load()
merged_messages = merge_chat_runs(raw_messages)
messages: List[ChatSession] = list(
| map_ai_messages(merged_messages, sender="talkingtower") | langchain_community.chat_loaders.utils.map_ai_messages |
from langchain_community.document_loaders import UnstructuredExcelLoader
loader = | UnstructuredExcelLoader("example_data/stanley-cups.xlsx", mode="elements") | langchain_community.document_loaders.UnstructuredExcelLoader |
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 langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader(path + "cpi.pdf")
pdf_pages = loader.load()
from langchain_text_splitters import RecursiveCharacterTextSplitter
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits_pypdf = text_splitter.split_documents(pdf_pages)
all_splits_pypdf_texts = [d.page_content for d in all_splits_pypdf]
from unstructured.partition.pdf import partition_pdf
raw_pdf_elements = partition_pdf(
filename=path + "cpi.pdf",
extract_images_in_pdf=True,
infer_table_structure=True,
chunking_strategy="by_title",
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path,
)
tables = []
texts = []
for element in raw_pdf_elements:
if "unstructured.documents.elements.Table" in str(type(element)):
tables.append(str(element))
elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
texts.append(str(element))
from langchain_community.vectorstores import Chroma
from langchain_openai import OpenAIEmbeddings
baseline = Chroma.from_texts(
texts=all_splits_pypdf_texts,
collection_name="baseline",
embedding=OpenAIEmbeddings(),
)
retriever_baseline = baseline.as_retriever()
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \
These summaries will be embedded and used to retrieve the raw text or table elements. \
Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """
prompt = ChatPromptTemplate.from_template(prompt_text)
model = ChatOpenAI(temperature=0, model="gpt-4")
summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser()
text_summaries = summarize_chain.batch(texts, {"max_concurrency": 5})
table_summaries = summarize_chain.batch(tables, {"max_concurrency": 5})
import base64
import io
import os
from io import BytesIO
from langchain_core.messages import HumanMessage
from PIL import Image
def encode_image(image_path):
"""Getting the base64 string"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def image_summarize(img_base64, prompt):
"""Image summary"""
chat = ChatOpenAI(model="gpt-4-vision-preview", max_tokens=1024)
msg = chat.invoke(
[
HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{img_base64}"},
},
]
)
]
)
return msg.content
img_base64_list = []
image_summaries = []
prompt = """You are an assistant tasked with summarizing images for retrieval. \
These summaries will be embedded and used to retrieve the raw image. \
Give a concise summary of the image that is well optimized for retrieval."""
for img_file in sorted(os.listdir(path)):
if img_file.endswith(".jpg"):
img_path = os.path.join(path, img_file)
base64_image = encode_image(img_path)
img_base64_list.append(base64_image)
image_summaries.append(image_summarize(base64_image, prompt))
import uuid
from base64 import b64decode
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryStore
from langchain_core.documents import Document
def create_multi_vector_retriever(
vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images
):
store = InMemoryStore()
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore,
docstore=store,
id_key=id_key,
)
def add_documents(retriever, doc_summaries, doc_contents):
doc_ids = [str(uuid.uuid4()) for _ in doc_contents]
summary_docs = [
Document(page_content=s, metadata={id_key: doc_ids[i]})
for i, s in enumerate(doc_summaries)
]
retriever.vectorstore.add_documents(summary_docs)
retriever.docstore.mset(list(zip(doc_ids, doc_contents)))
if text_summaries:
add_documents(retriever, text_summaries, texts)
if table_summaries:
add_documents(retriever, table_summaries, tables)
if image_summaries:
add_documents(retriever, image_summaries, images)
return retriever
multi_vector_img = Chroma(
collection_name="multi_vector_img", embedding_function=OpenAIEmbeddings()
)
retriever_multi_vector_img = create_multi_vector_retriever(
multi_vector_img,
text_summaries,
texts,
table_summaries,
tables,
image_summaries,
img_base64_list,
)
query = "What percentage of CPI is dedicated to Housing, and how does it compare to the combined percentage of Medical Care, Apparel, and Other Goods and Services?"
suffix_for_images = " Include any pie charts, graphs, or tables."
docs = retriever_multi_vector_img.get_relevant_documents(query + suffix_for_images)
from IPython.display import HTML, display
def plt_img_base64(img_base64):
image_html = f'<img src="data:image/jpeg;base64,{img_base64}" />'
display(HTML(image_html))
plt_img_base64(docs[1])
multi_vector_text = Chroma(
collection_name="multi_vector_text", embedding_function=OpenAIEmbeddings()
)
retriever_multi_vector_img_summary = create_multi_vector_retriever(
multi_vector_text,
text_summaries,
texts,
table_summaries,
tables,
image_summaries,
image_summaries,
)
from langchain_experimental.open_clip import OpenCLIPEmbeddings
multimodal_embd = Chroma(
collection_name="multimodal_embd", embedding_function= | OpenCLIPEmbeddings() | langchain_experimental.open_clip.OpenCLIPEmbeddings |
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 = embeddings.embed_query("Where is the Taj Mahal?")
multi_llm = | OpenAI(n=4, best_of=4) | langchain_openai.OpenAI |
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 content of the page as raw string.
"""
URL = "https://en.wikipedia.org/w/api.php"
params = {
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
}
headers = {"User-Agent": "RAGatouille_tutorial/0.0.1 (ben@clavie.eu)"}
response = requests.get(URL, params=params, headers=headers)
data = response.json()
page = next(iter(data["query"]["pages"].values()))
return page["extract"] if "extract" in page else None
full_document = get_wikipedia_page("Hayao_Miyazaki")
RAG.index(
collection=[full_document],
index_name="Miyazaki-123",
max_document_length=180,
split_documents=True,
)
results = RAG.search(query="What animation studio did Miyazaki found?", k=3)
results
retriever = RAG.as_langchain_retriever(k=3)
retriever.invoke("What animation studio did Miyazaki found?")
from langchain.chains import create_retrieval_chain
from langchain.chains.combine_documents import create_stuff_documents_chain
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_template(
"""Answer the following question based only on the provided context:
<context>
{context}
</context>
Question: {input}"""
)
llm = ChatOpenAI()
document_chain = create_stuff_documents_chain(llm, prompt)
retrieval_chain = | create_retrieval_chain(retriever, document_chain) | langchain.chains.create_retrieval_chain |
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("comprehend", region_name="us-east-1")
from langchain_experimental.comprehend_moderation import AmazonComprehendModerationChain
comprehend_moderation = AmazonComprehendModerationChain(
client=comprehend_client,
verbose=True, # optional
)
from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
from langchain_experimental.comprehend_moderation.base_moderation_exceptions import (
ModerationPiiError,
)
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)
chain = (
prompt
| comprehend_moderation
| {"input": (lambda x: x["output"]) | llm}
| comprehend_moderation
)
try:
response = chain.invoke(
{
"question": "A sample SSN number looks like this 123-22-3345. Can you give me some more samples?"
}
)
except ModerationPiiError as e:
print(str(e))
else:
print(response["output"])
from langchain_experimental.comprehend_moderation import (
BaseModerationConfig,
ModerationPiiConfig,
ModerationPromptSafetyConfig,
ModerationToxicityConfig,
)
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")
toxicity_config = ModerationToxicityConfig(threshold=0.5)
prompt_safety_config = ModerationPromptSafetyConfig(threshold=0.5)
moderation_config = BaseModerationConfig(
filters=[pii_config, toxicity_config, prompt_safety_config]
)
comp_moderation_with_config = AmazonComprehendModerationChain(
moderation_config=moderation_config, # specify the configuration
client=comprehend_client, # optionally pass the Boto3 Client
verbose=True,
)
from langchain.prompts import PromptTemplate
from langchain_community.llms.fake import FakeListLLM
template = """Question: {question}
Answer:"""
prompt = PromptTemplate.from_template(template)
responses = [
"Final Answer: A credit card number looks like 1289-2321-1123-2387. A fake SSN number looks like 323-22-9980. John Doe's phone number is (999)253-9876.",
"Final Answer: This is a really <expletive> way of constructing a birdhouse. This is <expletive> insane to think that any birds would actually create their <expletive> nests here.",
]
llm = FakeListLLM(responses=responses)
chain = (
prompt
| comp_moderation_with_config
| {"input": (lambda x: x["output"]) | llm}
| comp_moderation_with_config
)
try:
response = chain.invoke(
{
"question": "A sample SSN number looks like this 123-45-7890. Can you give me some more samples?"
}
)
except Exception as e:
print(str(e))
else:
print(response["output"])
from langchain_experimental.comprehend_moderation import BaseModerationCallbackHandler
class MyModCallback(BaseModerationCallbackHandler):
async def on_after_pii(self, output_beacon, unique_id):
import json
moderation_type = output_beacon["moderation_type"]
chain_id = output_beacon["moderation_chain_id"]
with open(f"output-{moderation_type}-{chain_id}.json", "w") as file:
data = {"beacon_data": output_beacon, "unique_id": unique_id}
json.dump(data, file)
"""
async def on_after_toxicity(self, output_beacon, unique_id):
pass
async def on_after_prompt_safety(self, output_beacon, unique_id):
pass
"""
my_callback = MyModCallback()
pii_config = ModerationPiiConfig(labels=["SSN"], redact=True, mask_character="X")
toxicity_config = | ModerationToxicityConfig(threshold=0.5) | langchain_experimental.comprehend_moderation.ModerationToxicityConfig |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet amadeus > /dev/null')
import os
os.environ["AMADEUS_CLIENT_ID"] = "CLIENT_ID"
os.environ["AMADEUS_CLIENT_SECRET"] = "CLIENT_SECRET"
os.environ["OPENAI_API_KEY"] = "YOUR_OPENAI_API_KEY"
from langchain_community.agent_toolkits.amadeus.toolkit import AmadeusToolkit
toolkit = AmadeusToolkit()
tools = toolkit.get_tools()
from langchain_community.llms import HuggingFaceHub
os.environ["HUGGINGFACEHUB_API_TOKEN"] = "YOUR_HF_API_TOKEN"
llm = HuggingFaceHub(
repo_id="tiiuae/falcon-7b-instruct",
model_kwargs={"temperature": 0.5, "max_length": 64},
)
toolkit_hf = AmadeusToolkit(llm=llm)
from langchain import hub
from langchain.agents import AgentExecutor, create_react_agent
from langchain.agents.output_parsers import ReActJsonSingleInputOutputParser
from langchain.tools.render import render_text_description_and_args
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)
prompt = | hub.pull("hwchase17/react-json") | langchain.hub.pull |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet clearml')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pandas')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet textstat')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet spacy')
get_ipython().system('python -m spacy download en_core_web_sm')
import os
os.environ["CLEARML_API_ACCESS_KEY"] = ""
os.environ["CLEARML_API_SECRET_KEY"] = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["SERPAPI_API_KEY"] = ""
from langchain.callbacks import ClearMLCallbackHandler
from langchain.callbacks import StdOutCallbackHandler
from langchain_openai import OpenAI
clearml_callback = ClearMLCallbackHandler(
task_type="inference",
project_name="langchain_callback_demo",
task_name="llm",
tags=["test"],
visualize=True,
complexity_metrics=True,
stream_logs=True,
)
callbacks = [StdOutCallbackHandler(), clearml_callback]
llm = | OpenAI(temperature=0, callbacks=callbacks) | langchain_openai.OpenAI |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet pymysql')
get_ipython().system('pip install sqlalchemy')
get_ipython().system('pip install langchain')
from langchain.chains import RetrievalQA
from langchain_community.document_loaders import (
DirectoryLoader,
UnstructuredMarkdownLoader,
)
from langchain_community.vectorstores.apache_doris import (
ApacheDoris,
ApacheDorisSettings,
)
from langchain_openai import OpenAI, OpenAIEmbeddings
from langchain_text_splitters import TokenTextSplitter
update_vectordb = False
loader = DirectoryLoader(
"./docs", glob="**/*.md", loader_cls=UnstructuredMarkdownLoader
)
documents = loader.load()
text_splitter = TokenTextSplitter(chunk_size=400, chunk_overlap=50)
split_docs = text_splitter.split_documents(documents)
update_vectordb = True
def gen_apache_doris(update_vectordb, embeddings, settings):
if update_vectordb:
docsearch = ApacheDoris.from_documents(split_docs, embeddings, config=settings)
else:
docsearch = ApacheDoris(embeddings, settings)
return docsearch
import os
from getpass import getpass
os.environ["OPENAI_API_KEY"] = getpass()
update_vectordb = True
embeddings = OpenAIEmbeddings()
settings = | ApacheDorisSettings() | langchain_community.vectorstores.apache_doris.ApacheDorisSettings |
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 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')
YBUSER = "[SANDBOX USER]"
YBPASSWORD = "[SANDBOX PASSWORD]"
YBDATABASE = "[SANDBOX_DATABASE]"
YBHOST = "trialsandbox.sandbox.aws.yellowbrickcloud.com"
OPENAI_API_KEY = "[OPENAI API KEY]"
import os
import pathlib
import re
import sys
import urllib.parse as urlparse
from getpass import getpass
import psycopg2
from IPython.display import Markdown, display
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain
from langchain.docstore.document import Document
from langchain_community.vectorstores import Yellowbrick
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
yellowbrick_connection_string = (
f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YBDATABASE}"
)
YB_DOC_DATABASE = "sample_data"
YB_DOC_TABLE = "yellowbrick_documentation"
embedding_table = "my_embeddings"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
system_template = """If you don't know the answer, Make up your best guess."""
messages = [
| SystemMessagePromptTemplate.from_template(system_template) | langchain.prompts.chat.SystemMessagePromptTemplate.from_template |
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.from_files(["state_of_the_union.txt"]) | langchain_community.vectorstores.Vectara.from_files |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet GitPython')
from git import Repo
repo = Repo.clone_from(
"https://github.com/langchain-ai/langchain", to_path="./example_data/test_repo1"
)
branch = repo.head.reference
from langchain_community.document_loaders import GitLoader
loader = | GitLoader(repo_path="./example_data/test_repo1/", branch=branch) | langchain_community.document_loaders.GitLoader |
from langchain_core.messages import (
AIMessage,
BaseMessage,
FunctionMessage,
HumanMessage,
SystemMessage,
ToolMessage,
)
from langchain_core.messages import (
AIMessageChunk,
FunctionMessageChunk,
HumanMessageChunk,
SystemMessageChunk,
ToolMessageChunk,
)
AIMessageChunk(content="Hello") + AIMessageChunk(content=" World!")
from typing import Any, AsyncIterator, Dict, Iterator, List, Optional
from langchain_core.callbacks import (
AsyncCallbackManagerForLLMRun,
CallbackManagerForLLMRun,
)
from langchain_core.language_models import BaseChatModel, SimpleChatModel
from langchain_core.messages import AIMessageChunk, BaseMessage, HumanMessage
from langchain_core.outputs import ChatGeneration, ChatGenerationChunk, ChatResult
from langchain_core.runnables import run_in_executor
class CustomChatModelAdvanced(BaseChatModel):
"""A custom chat model that echoes the first `n` characters of the input.
When contributing an implementation to LangChain, carefully document
the model including the initialization parameters, include
an example of how to initialize the model and include any relevant
links to the underlying models documentation or API.
Example:
.. code-block:: python
model = CustomChatModel(n=2)
result = model.invoke([HumanMessage(content="hello")])
result = model.batch([[HumanMessage(content="hello")],
[HumanMessage(content="world")]])
"""
n: int
"""The number of characters from the last message of the prompt to be echoed."""
def _generate(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> ChatResult:
"""Override the _generate method to implement the chat model logic.
This can be a call to an API, a call to a local model, or any other
implementation that generates a response to the input prompt.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
last_message = messages[-1]
tokens = last_message.content[: self.n]
message = AIMessage(content=tokens)
generation = ChatGeneration(message=message)
return ChatResult(generations=[generation])
def _stream(
self,
messages: List[BaseMessage],
stop: Optional[List[str]] = None,
run_manager: Optional[CallbackManagerForLLMRun] = None,
**kwargs: Any,
) -> Iterator[ChatGenerationChunk]:
"""Stream the output of the model.
This method should be implemented if the model can generate output
in a streaming fashion. If the model does not support streaming,
do not implement it. In that case streaming requests will be automatically
handled by the _generate method.
Args:
messages: the prompt composed of a list of messages.
stop: a list of strings on which the model should stop generating.
If generation stops due to a stop token, the stop token itself
SHOULD BE INCLUDED as part of the output. This is not enforced
across models right now, but it's a good practice to follow since
it makes it much easier to parse the output of the model
downstream and understand why generation stopped.
run_manager: A run manager with callbacks for the LLM.
"""
last_message = messages[-1]
tokens = last_message.content[: self.n]
for token in tokens:
chunk = ChatGenerationChunk(message= | AIMessageChunk(content=token) | langchain_core.messages.AIMessageChunk |
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 OpenAI
template = """This is a conversation between a human and a bot:
{chat_history}
Write a summary of the conversation for {input}:
"""
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history")
readonlymemory = ReadOnlySharedMemory(memory=memory)
summary_chain = LLMChain(
llm=OpenAI(),
prompt=prompt,
verbose=True,
memory=readonlymemory, # use the read-only memory to prevent the tool from modifying the memory
)
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
),
Tool(
name="Summary",
func=summary_chain.run,
description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.",
),
]
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True)
agent_chain = AgentExecutor.from_agent_and_tools(
agent=agent, tools=tools, verbose=True, memory=memory
)
agent_chain.run(input="What is ChatGPT?")
agent_chain.run(input="Who developed it?")
agent_chain.run(
input="Thanks. Summarize the conversation, for my daughter 5 years old."
)
print(agent_chain.memory.buffer)
template = """This is a conversation between a human and a bot:
{chat_history}
Write a summary of the conversation for {input}:
"""
prompt = PromptTemplate(input_variables=["input", "chat_history"], template=template)
memory = ConversationBufferMemory(memory_key="chat_history")
summary_chain = LLMChain(
llm=OpenAI(),
prompt=prompt,
verbose=True,
memory=memory, # <--- this is the only change
)
search = GoogleSearchAPIWrapper()
tools = [
Tool(
name="Search",
func=search.run,
description="useful for when you need to answer questions about current events",
),
Tool(
name="Summary",
func=summary_chain.run,
description="useful for when you summarize a conversation. The input to this tool should be a string, representing who will read this summary.",
),
]
prefix = """Have a conversation with a human, answering the following questions as best you can. You have access to the following tools:"""
suffix = """Begin!"
{chat_history}
Question: {input}
{agent_scratchpad}"""
prompt = ZeroShotAgent.create_prompt(
tools,
prefix=prefix,
suffix=suffix,
input_variables=["input", "chat_history", "agent_scratchpad"],
)
llm_chain = LLMChain(llm=OpenAI(temperature=0), prompt=prompt)
agent = | ZeroShotAgent(llm_chain=llm_chain, tools=tools, verbose=True) | langchain.agents.ZeroShotAgent |
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 mayhem breaks loose",
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
Document(
page_content="Leo DiCaprio gets lost in a dream within a dream within a dream within a ...",
metadata={"year": 2010, "director": "Christopher Nolan", "rating": 8.2},
),
Document(
page_content="A psychologist / detective gets lost in a series of dreams within dreams within dreams and Inception reused the idea",
metadata={"year": 2006, "director": "Satoshi Kon", "rating": 8.6},
),
Document(
page_content="A bunch of normal-sized women are supremely wholesome and some men pine after them",
metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
),
Document(
page_content="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated"},
),
Document(
page_content="Three men walk into the Zone, three men walk out of the Zone",
metadata={
"year": 1979,
"director": "Andrei Tarkovsky",
"genre": "thriller",
"rating": 9.9,
},
),
]
vectorstore = Chroma.from_documents(docs, OpenAIEmbeddings())
from langchain.chains.query_constructor.base import AttributeInfo
from langchain.retrievers.self_query.base import SelfQueryRetriever
from langchain_openai import ChatOpenAI
metadata_field_info = [
AttributeInfo(
name="genre",
description="The genre of the movie. One of ['science fiction', 'comedy', 'drama', 'thriller', 'romance', 'action', 'animated']",
type="string",
),
AttributeInfo(
name="year",
description="The year the movie was released",
type="integer",
),
AttributeInfo(
name="director",
description="The name of the movie director",
type="string",
),
AttributeInfo(
name="rating", description="A 1-10 rating for the movie", type="float"
),
]
document_content_description = "Brief summary of a movie"
llm = ChatOpenAI(temperature=0)
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
)
retriever.invoke("I want to watch a movie rated higher than 8.5")
retriever.invoke("Has Greta Gerwig directed any movies about women")
retriever.invoke("What's a highly rated (above 8.5) science fiction film?")
retriever.invoke(
"What's a movie after 1990 but before 2005 that's all about toys, and preferably is animated"
)
retriever = SelfQueryRetriever.from_llm(
llm,
vectorstore,
document_content_description,
metadata_field_info,
enable_limit=True,
)
retriever.invoke("What are two movies about dinosaurs")
from langchain.chains.query_constructor.base import (
StructuredQueryOutputParser,
get_query_constructor_prompt,
)
prompt = get_query_constructor_prompt(
document_content_description,
metadata_field_info,
)
output_parser = StructuredQueryOutputParser.from_components()
query_constructor = prompt | llm | output_parser
print(prompt.format(query="dummy question"))
query_constructor.invoke(
{
"query": "What are some sci-fi movies from the 90's directed by Luc Besson about taxi drivers"
}
)
from langchain.retrievers.self_query.chroma import ChromaTranslator
retriever = SelfQueryRetriever(
query_constructor=query_constructor,
vectorstore=vectorstore,
structured_query_translator= | ChromaTranslator() | langchain.retrievers.self_query.chroma.ChromaTranslator |
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/2023-06-23-agent/")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_community.vectorstores import Chroma
vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
question = "What are the approaches to Task Decomposition?"
docs = vectorstore.similarity_search(question)
len(docs)
docs[0]
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python')
get_ipython().system(' CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dir')
from langchain_community.llms import LlamaCpp
n_gpu_layers = 1 # Metal set to 1 is enough.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
llm = LlamaCpp(
model_path="/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
n_ctx=2048,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
verbose=True,
)
llm.invoke("Simulate a rap battle between Stephen Colbert and John Oliver")
from langchain_community.llms import GPT4All
gpt4all = GPT4All(
model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",
max_tokens=2048,
)
from langchain_community.llms.llamafile import Llamafile
llamafile = Llamafile()
llamafile.invoke("Here is my grandmother's beloved recipe for spaghetti and meatballs:")
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
prompt = PromptTemplate.from_template(
"Summarize the main themes in these retrieved docs: {docs}"
)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
chain = {"docs": format_docs} | prompt | llm | StrOutputParser()
question = "What are the approaches to Task Decomposition?"
docs = vectorstore.similarity_search(question)
chain.invoke(docs)
from langchain import hub
rag_prompt = hub.pull("rlm/rag-prompt")
rag_prompt.messages
from langchain_core.runnables import RunnablePassthrough, RunnablePick
chain = (
RunnablePassthrough.assign(context=RunnablePick("context") | format_docs)
| rag_prompt
| llm
| StrOutputParser()
)
chain.invoke({"context": docs, "question": question})
rag_prompt_llama = hub.pull("rlm/rag-prompt-llama")
rag_prompt_llama.messages
chain = (
RunnablePassthrough.assign(context= | RunnablePick("context") | langchain_core.runnables.RunnablePick |
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/2023-06-23-agent/")
data = loader.load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=0)
all_splits = text_splitter.split_documents(data)
from langchain_community.embeddings import GPT4AllEmbeddings
from langchain_community.vectorstores import Chroma
vectorstore = Chroma.from_documents(documents=all_splits, embedding=GPT4AllEmbeddings())
question = "What are the approaches to Task Decomposition?"
docs = vectorstore.similarity_search(question)
len(docs)
docs[0]
get_ipython().run_line_magic('pip', 'install --upgrade --quiet llama-cpp-python')
get_ipython().system(' CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 /Users/rlm/miniforge3/envs/llama/bin/pip install -U llama-cpp-python --no-cache-dir')
from langchain_community.llms import LlamaCpp
n_gpu_layers = 1 # Metal set to 1 is enough.
n_batch = 512 # Should be between 1 and n_ctx, consider the amount of RAM of your Apple Silicon Chip.
llm = LlamaCpp(
model_path="/Users/rlm/Desktop/Code/llama.cpp/models/llama-2-13b-chat.ggufv3.q4_0.bin",
n_gpu_layers=n_gpu_layers,
n_batch=n_batch,
n_ctx=2048,
f16_kv=True, # MUST set to True, otherwise you will run into problem after a couple of calls
verbose=True,
)
llm.invoke("Simulate a rap battle between Stephen Colbert and John Oliver")
from langchain_community.llms import GPT4All
gpt4all = GPT4All(
model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",
max_tokens=2048,
)
from langchain_community.llms.llamafile import Llamafile
llamafile = Llamafile()
llamafile.invoke("Here is my grandmother's beloved recipe for spaghetti and meatballs:")
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import PromptTemplate
prompt = PromptTemplate.from_template(
"Summarize the main themes in these retrieved docs: {docs}"
)
def format_docs(docs):
return "\n\n".join(doc.page_content for doc in docs)
chain = {"docs": format_docs} | prompt | llm | StrOutputParser()
question = "What are the approaches to Task Decomposition?"
docs = vectorstore.similarity_search(question)
chain.invoke(docs)
from langchain import hub
rag_prompt = hub.pull("rlm/rag-prompt")
rag_prompt.messages
from langchain_core.runnables import RunnablePassthrough, RunnablePick
chain = (
RunnablePassthrough.assign(context=RunnablePick("context") | format_docs)
| rag_prompt
| llm
| StrOutputParser()
)
chain.invoke({"context": docs, "question": question})
rag_prompt_llama = hub.pull("rlm/rag-prompt-llama")
rag_prompt_llama.messages
chain = (
RunnablePassthrough.assign(context=RunnablePick("context") | format_docs)
| rag_prompt_llama
| llm
| StrOutputParser()
)
chain.invoke({"context": docs, "question": question})
retriever = vectorstore.as_retriever()
qa_chain = (
{"context": retriever | format_docs, "question": | RunnablePassthrough() | langchain_core.runnables.RunnablePassthrough |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2')
import os
from langchain_community.llms import HuggingFaceTextGenInference
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
llm = HuggingFaceTextGenInference(
inference_server_url=ENDPOINT_URL,
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
server_kwargs={
"headers": {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json",
}
},
)
from langchain_community.llms import HuggingFaceEndpoint
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
llm = HuggingFaceEndpoint(
endpoint_url=ENDPOINT_URL,
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 50,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain_community.llms import HuggingFaceHub
llm = HuggingFaceHub(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_community.chat_models.huggingface import ChatHuggingFace
messages = [
SystemMessage(content="You're a helpful assistant"),
HumanMessage(
content="What happens when an unstoppable force meets an immovable object?"
),
]
chat_model = ChatHuggingFace(llm=llm)
chat_model.model_id
chat_model._to_chat_prompt(messages)
res = chat_model.invoke(messages)
print(res.content)
from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
from langchain_community.utilities import SerpAPIWrapper
tools = load_tools(["serpapi", "llm-math"], llm=llm)
prompt = | hub.pull("hwchase17/react-json") | langchain.hub.pull |
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."""
setup: str = | Field(description="question to set up a joke") | langchain_core.pydantic_v1.Field |
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 langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_openai import OpenAI
from sqlalchemy import MetaData, create_engine
MYSCALE_HOST = "msc-4a9e710a.us-east-1.aws.staging.myscale.cloud"
MYSCALE_PORT = 443
MYSCALE_USER = "chatdata"
MYSCALE_PASSWORD = "myscale_rocks"
OPENAI_API_KEY = getpass.getpass("OpenAI API Key:")
engine = create_engine(
f"clickhouse://{MYSCALE_USER}:{MYSCALE_PASSWORD}@{MYSCALE_HOST}:{MYSCALE_PORT}/default?protocol=https"
)
metadata = MetaData(bind=engine)
environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain_community.embeddings import HuggingFaceInstructEmbeddings
from langchain_experimental.sql.vector_sql import VectorSQLOutputParser
output_parser = VectorSQLOutputParser.from_embeddings(
model=HuggingFaceInstructEmbeddings(
model_name="hkunlp/instructor-xl", model_kwargs={"device": "cpu"}
)
)
from langchain.callbacks import StdOutCallbackHandler
from langchain_community.utilities.sql_database import SQLDatabase
from langchain_experimental.sql.prompt import MYSCALE_PROMPT
from langchain_experimental.sql.vector_sql import VectorSQLDatabaseChain
from langchain_openai import OpenAI
chain = VectorSQLDatabaseChain(
llm_chain=LLMChain(
llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
prompt=MYSCALE_PROMPT,
),
top_k=10,
return_direct=True,
sql_cmd_parser=output_parser,
database=SQLDatabase(engine, None, metadata),
)
import pandas as pd
pd.DataFrame(
chain.run(
"Please give me 10 papers to ask what is PageRank?",
callbacks=[StdOutCallbackHandler()],
)
)
from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain
from langchain_experimental.retrievers.vector_sql_database import (
VectorSQLDatabaseChainRetriever,
)
from langchain_experimental.sql.prompt import MYSCALE_PROMPT
from langchain_experimental.sql.vector_sql import (
VectorSQLDatabaseChain,
VectorSQLRetrieveAllOutputParser,
)
from langchain_openai import ChatOpenAI
output_parser_retrieve_all = VectorSQLRetrieveAllOutputParser.from_embeddings(
output_parser.model
)
chain = VectorSQLDatabaseChain.from_llm(
llm=OpenAI(openai_api_key=OPENAI_API_KEY, temperature=0),
prompt=MYSCALE_PROMPT,
top_k=10,
return_direct=True,
db= | SQLDatabase(engine, None, metadata) | langchain_community.utilities.sql_database.SQLDatabase |
from langchain_community.vectorstores import Bagel
texts = ["hello bagel", "hello langchain", "I love salad", "my car", "a dog"]
cluster = Bagel.from_texts(cluster_name="testing", texts=texts)
cluster.similarity_search("bagel", k=3)
cluster.similarity_search_with_score("bagel", k=3)
cluster.delete_cluster()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)[:10]
cluster = Bagel.from_documents(cluster_name="testing_with_docs", documents=docs)
query = "What did the president say about Ketanji Brown Jackson"
docs = cluster.similarity_search(query)
print(docs[0].page_content[:102])
texts = ["hello bagel", "this is langchain"]
cluster = Bagel.from_texts(cluster_name="testing", texts=texts)
cluster_data = cluster.get()
cluster_data.keys()
cluster_data
cluster.delete_cluster()
texts = ["hello bagel", "this is langchain"]
metadatas = [{"source": "notion"}, {"source": "google"}]
cluster = | Bagel.from_texts(cluster_name="testing", texts=texts, metadatas=metadatas) | langchain_community.vectorstores.Bagel.from_texts |
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 google.colab import auth
auth.authenticate_user()
get_ipython().system('gcloud services enable firestore.googleapis.com')
from langchain_core.documents.base import Document
from langchain_google_firestore import FirestoreSaver
saver = FirestoreSaver()
data = [Document(page_content="Hello, World!")]
saver.upsert_documents(data)
saver = FirestoreSaver("Collection")
saver.upsert_documents(data)
doc_ids = ["AnotherCollection/doc_id", "foo/bar"]
saver = FirestoreSaver()
saver.upsert_documents(documents=data, document_ids=doc_ids)
from langchain_google_firestore import FirestoreLoader
loader_collection = FirestoreLoader("Collection")
loader_subcollection = | FirestoreLoader("Collection/doc/SubCollection") | langchain_google_firestore.FirestoreLoader |
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:")
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.embeddings import CohereEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_text_splitters import RecursiveCharacterTextSplitter
documents = TextLoader("../../modules/state_of_the_union.txt").load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
retriever = FAISS.from_documents(texts, CohereEmbeddings()).as_retriever(
search_kwargs={"k": 20}
)
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import CohereRerank
from langchain_community.llms import Cohere
llm = Cohere(temperature=0)
compressor = CohereRerank()
compression_retriever = ContextualCompressionRetriever(
base_compressor=compressor, base_retriever=retriever
)
compressed_docs = compression_retriever.get_relevant_documents(
"What did the president say about Ketanji Jackson Brown"
)
pretty_print_docs(compressed_docs)
from langchain.chains import RetrievalQA
chain = RetrievalQA.from_chain_type(
llm= | Cohere(temperature=0) | langchain_community.llms.Cohere |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain sentence_transformers')
from langchain_community.embeddings import HuggingFaceEmbeddings
embeddings = HuggingFaceEmbeddings()
text = "This is a test document."
query_result = embeddings.embed_query(text)
query_result[:3]
doc_result = embeddings.embed_documents([text])
import getpass
inference_api_key = getpass.getpass("Enter your HF Inference API Key:\n\n")
from langchain_community.embeddings import HuggingFaceInferenceAPIEmbeddings
embeddings = HuggingFaceInferenceAPIEmbeddings(
api_key=inference_api_key, model_name="sentence-transformers/all-MiniLM-l6-v2"
)
query_result = embeddings.embed_query(text)
query_result[:3]
get_ipython().system('pip install huggingface_hub')
from langchain_community.embeddings import HuggingFaceHubEmbeddings
embeddings = | HuggingFaceHubEmbeddings() | langchain_community.embeddings.HuggingFaceHubEmbeddings |
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."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
openai_functions = [convert_pydantic_to_openai_function(Joke)]
model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate.from_messages(
[("system", "You are helpful assistant"), ("user", "{input}")]
)
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
parser = JsonOutputFunctionsParser()
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me a joke"})
for s in chain.stream({"input": "tell me a joke"}):
print(s)
from typing import List
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
class Jokes(BaseModel):
"""Jokes to tell user."""
joke: List[Joke]
funniness_level: int
parser = | JsonKeyOutputFunctionsParser(key_name="joke") | langchain.output_parsers.openai_functions.JsonKeyOutputFunctionsParser |
from langchain.agents import AgentType, initialize_agent
from langchain_community.agent_toolkits.nasa.toolkit import NasaToolkit
from langchain_community.utilities.nasa import NasaAPIWrapper
from langchain_openai import OpenAI
llm = OpenAI(temperature=0, openai_api_key="")
nasa = | NasaAPIWrapper() | langchain_community.utilities.nasa.NasaAPIWrapper |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
from langchain_text_splitters import HTMLHeaderTextSplitter
html_string = """
<!DOCTYPE html>
<html>
<body>
<div>
<h1>Foo</h1>
<p>Some intro text about Foo.</p>
<div>
<h2>Bar main section</h2>
<p>Some intro text about Bar.</p>
<h3>Bar subsection 1</h3>
<p>Some text about the first subtopic of Bar.</p>
<h3>Bar subsection 2</h3>
<p>Some text about the second subtopic of Bar.</p>
</div>
<div>
<h2>Baz</h2>
<p>Some text about Baz</p>
</div>
<br>
<p>Some concluding text about Foo</p>
</div>
</body>
</html>
"""
headers_to_split_on = [
("h1", "Header 1"),
("h2", "Header 2"),
("h3", "Header 3"),
]
html_splitter = HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on)
html_header_splits = html_splitter.split_text(html_string)
html_header_splits
from langchain_text_splitters import RecursiveCharacterTextSplitter
url = "https://plato.stanford.edu/entries/goedel/"
headers_to_split_on = [
("h1", "Header 1"),
("h2", "Header 2"),
("h3", "Header 3"),
("h4", "Header 4"),
]
html_splitter = | HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on) | langchain_text_splitters.HTMLHeaderTextSplitter |
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["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
activeloop_token = getpass.getpass("activeloop token:")
embeddings = OpenAIEmbeddings()
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = DeepLake(dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)
db.add_documents(docs)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
db = DeepLake(dataset_path="./my_deeplake/", embedding=embeddings, read_only=True)
docs = db.similarity_search(query)
from langchain.chains import RetrievalQA
from langchain_openai import OpenAIChat
qa = RetrievalQA.from_chain_type(
llm=OpenAIChat(model="gpt-3.5-turbo"),
chain_type="stuff",
retriever=db.as_retriever(),
)
query = "What did the president say about Ketanji Brown Jackson"
qa.run(query)
import random
for d in docs:
d.metadata["year"] = random.randint(2012, 2014)
db = DeepLake.from_documents(
docs, embeddings, dataset_path="./my_deeplake/", overwrite=True
)
db.similarity_search(
"What did the president say about Ketanji Brown Jackson",
filter={"metadata": {"year": 2013}},
)
db.similarity_search(
"What did the president say about Ketanji Brown Jackson?", distance_metric="cos"
)
db.max_marginal_relevance_search(
"What did the president say about Ketanji Brown Jackson?"
)
db.delete_dataset()
DeepLake.force_delete_by_path("./my_deeplake")
os.environ["ACTIVELOOP_TOKEN"] = activeloop_token
username = "<USERNAME_OR_ORG>" # your username on app.activeloop.ai
dataset_path = f"hub://{username}/langchain_testing_python" # could be also ./local/path (much faster locally), s3://bucket/path/to/dataset, gcs://path/to/dataset, etc.
docs = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings()
db = | DeepLake(dataset_path=dataset_path, embedding=embeddings, overwrite=True) | langchain_community.vectorstores.DeepLake |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet supabase')
import getpass
import os
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
os.environ["SUPABASE_URL"] = getpass.getpass("Supabase URL:")
os.environ["SUPABASE_SERVICE_KEY"] = getpass.getpass("Supabase Service Key:")
from dotenv import load_dotenv
load_dotenv()
import os
from langchain_community.vectorstores import SupabaseVectorStore
from langchain_openai import OpenAIEmbeddings
from supabase.client import Client, create_client
supabase_url = os.environ.get("SUPABASE_URL")
supabase_key = os.environ.get("SUPABASE_SERVICE_KEY")
supabase: Client = create_client(supabase_url, supabase_key)
embeddings = OpenAIEmbeddings()
from langchain_community.document_loaders import TextLoader
from langchain_text_splitters import CharacterTextSplitter
loader = | TextLoader("../../modules/state_of_the_union.txt") | langchain_community.document_loaders.TextLoader |
import os
os.environ["BING_SUBSCRIPTION_KEY"] = "<key>"
os.environ["BING_SEARCH_URL"] = "https://api.bing.microsoft.com/v7.0/search"
from langchain_community.utilities import BingSearchAPIWrapper
search = BingSearchAPIWrapper()
search.run("python")
search = BingSearchAPIWrapper(k=1)
search.run("python")
search = | BingSearchAPIWrapper() | langchain_community.utilities.BingSearchAPIWrapper |
import boto3
dynamodb = boto3.resource("dynamodb")
table = dynamodb.create_table(
TableName="SessionTable",
KeySchema=[{"AttributeName": "SessionId", "KeyType": "HASH"}],
AttributeDefinitions=[{"AttributeName": "SessionId", "AttributeType": "S"}],
BillingMode="PAY_PER_REQUEST",
)
table.meta.client.get_waiter("table_exists").wait(TableName="SessionTable")
print(table.item_count)
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
history = DynamoDBChatMessageHistory(table_name="SessionTable", session_id="0")
history.add_user_message("hi!")
history.add_ai_message("whats up?")
history.messages
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
history = DynamoDBChatMessageHistory(
table_name="SessionTable",
session_id="0",
endpoint_url="http://localhost.localstack.cloud:4566",
)
from langchain_community.chat_message_histories import DynamoDBChatMessageHistory
composite_table = dynamodb.create_table(
TableName="CompositeTable",
KeySchema=[
{"AttributeName": "PK", "KeyType": "HASH"},
{"AttributeName": "SK", "KeyType": "RANGE"},
],
AttributeDefinitions=[
{"AttributeName": "PK", "AttributeType": "S"},
{"AttributeName": "SK", "AttributeType": "S"},
],
BillingMode="PAY_PER_REQUEST",
)
composite_table.meta.client.get_waiter("table_exists").wait(TableName="CompositeTable")
print(composite_table.item_count)
my_key = {
"PK": "session_id::0",
"SK": "langchain_history",
}
composite_key_history = DynamoDBChatMessageHistory(
table_name="CompositeTable",
session_id="0",
endpoint_url="http://localhost.localstack.cloud:4566",
key=my_key,
)
composite_key_history.add_user_message("hello, composite dynamodb table!")
composite_key_history.messages
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_core.runnables.history import RunnableWithMessageHistory
from langchain_openai import ChatOpenAI
prompt = ChatPromptTemplate.from_messages(
[
("system", "You are a helpful assistant."),
| MessagesPlaceholder(variable_name="history") | langchain_core.prompts.MessagesPlaceholder |
from langchain_community.tools.edenai import (
EdenAiExplicitImageTool,
EdenAiObjectDetectionTool,
EdenAiParsingIDTool,
EdenAiParsingInvoiceTool,
EdenAiSpeechToTextTool,
EdenAiTextModerationTool,
EdenAiTextToSpeechTool,
)
from langchain.agents import AgentType, initialize_agent
from langchain_community.llms import EdenAI
llm = EdenAI(
feature="text", provider="openai", params={"temperature": 0.2, "max_tokens": 250}
)
tools = [
EdenAiTextModerationTool(providers=["openai"], language="en"),
| EdenAiObjectDetectionTool(providers=["google", "api4ai"]) | langchain_community.tools.edenai.EdenAiObjectDetectionTool |
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 Action Input for what step should be taken.
{format_instructions}
Question: {query}
Response:"""
class Action(BaseModel):
action: str = Field(description="action to take")
action_input: str = Field(description="input to the action")
parser = | PydanticOutputParser(pydantic_object=Action) | langchain.output_parsers.PydanticOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet slack_sdk > /dev/null')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet beautifulsoup4 > /dev/null # This is optional but is useful for parsing HTML messages')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet python-dotenv > /dev/null # This is for loading environmental variables from a .env file')
import dotenv
dotenv.load_dotenv()
from langchain_community.agent_toolkits import SlackToolkit
toolkit = | SlackToolkit() | langchain_community.agent_toolkits.SlackToolkit |
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_images_in_pdf=True,
infer_table_structure=True,
chunking_strategy="by_title",
max_characters=4000,
new_after_n_chars=3800,
combine_text_under_n_chars=2000,
image_output_dir_path=path,
)
category_counts = {}
for element in raw_pdf_elements:
category = str(type(element))
if category in category_counts:
category_counts[category] += 1
else:
category_counts[category] = 1
unique_categories = set(category_counts.keys())
category_counts
class Element(BaseModel):
type: str
text: Any
categorized_elements = []
for element in raw_pdf_elements:
if "unstructured.documents.elements.Table" in str(type(element)):
categorized_elements.append(Element(type="table", text=str(element)))
elif "unstructured.documents.elements.CompositeElement" in str(type(element)):
categorized_elements.append(Element(type="text", text=str(element)))
table_elements = [e for e in categorized_elements if e.type == "table"]
print(len(table_elements))
text_elements = [e for e in categorized_elements if e.type == "text"]
print(len(text_elements))
from langchain_community.chat_models import ChatOllama
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
prompt_text = """You are an assistant tasked with summarizing tables and text. \
Give a concise summary of the table or text. Table or text chunk: {element} """
prompt = ChatPromptTemplate.from_template(prompt_text)
model = | ChatOllama(model="llama2:13b-chat") | langchain_community.chat_models.ChatOllama |
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."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
openai_functions = [convert_pydantic_to_openai_function(Joke)]
model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate.from_messages(
[("system", "You are helpful assistant"), ("user", "{input}")]
)
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
parser = JsonOutputFunctionsParser()
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me a joke"})
for s in chain.stream({"input": "tell me a joke"}):
print(s)
from typing import List
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
class Jokes(BaseModel):
"""Jokes to tell user."""
joke: List[Joke]
funniness_level: int
parser = JsonKeyOutputFunctionsParser(key_name="joke")
openai_functions = [convert_pydantic_to_openai_function(Jokes)]
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me two jokes"})
for s in chain.stream({"input": "tell me two jokes"}):
print(s)
from langchain.output_parsers.openai_functions import PydanticOutputFunctionsParser
class Joke(BaseModel):
"""Joke to tell user."""
setup: str = | Field(description="question to set up a joke") | langchain_core.pydantic_v1.Field |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet apify-client')
from langchain_community.document_loaders import ApifyDatasetLoader
from langchain_community.document_loaders.base import Document
loader = ApifyDatasetLoader(
dataset_id="your-dataset-id",
dataset_mapping_function=lambda dataset_item: Document(
page_content=dataset_item["text"], metadata={"source": dataset_item["url"]}
),
)
data = loader.load()
from langchain.docstore.document import Document
from langchain.indexes import VectorstoreIndexCreator
from langchain_community.document_loaders import ApifyDatasetLoader
loader = ApifyDatasetLoader(
dataset_id="your-dataset-id",
dataset_mapping_function=lambda item: Document(
page_content=item["text"] or "", metadata={"source": item["url"]}
),
)
index = | VectorstoreIndexCreator() | langchain.indexes.VectorstoreIndexCreator |
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()
if "HF_API_TOKEN" not in os.environ:
print("Enter your HuggingFace Hub API key:")
os.environ["HF_API_TOKEN"] = getpass()
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import OpenLM
question = "What is the capital of France?"
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
for model in ["text-davinci-003", "huggingface.co/gpt2"]:
llm = | OpenLM(model=model) | langchain_community.llms.OpenLM |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-cloud-sql-pg langchain-google-vertexai')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services enable sqladmin.googleapis.com')
REGION = "us-central1" # @param {type: "string"}
INSTANCE = "my-pg-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vector_store" # @param {type: "string"}
from langchain_google_cloud_sql_pg import PostgreSQLEngine
engine = await PostgreSQLEngine.afrom_instance(
project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE
)
from langchain_google_cloud_sql_pg import PostgreSQLEngine
await engine.ainit_vectorstore_table(
table_name=TABLE_NAME,
vector_size=768, # Vector size for VertexAI model(textembedding-gecko@latest)
)
get_ipython().system('gcloud services enable aiplatform.googleapis.com')
from langchain_google_vertexai import VertexAIEmbeddings
embedding = VertexAIEmbeddings(
model_name="textembedding-gecko@latest", project=PROJECT_ID
)
from langchain_google_cloud_sql_pg import PostgresVectorStore
store = await | PostgresVectorStore.create( # Use .create() | langchain_google_cloud_sql_pg.PostgresVectorStore.create |
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["PREDICTIONGUARD_TOKEN"] = "<your Prediction Guard access token>"
pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
pgllm("Tell me a joke")
template = """Respond to the following query based on the context.
Context: EVERY comment, DM + email suggestion has led us to this EXCITING announcement! 🎉 We have officially added TWO new candle subscription box options! 📦
Exclusive Candle Box - $80
Monthly Candle Box - $45 (NEW!)
Scent of The Month Box - $28 (NEW!)
Head to stories to get ALLL the deets on each box! 👆 BONUS: Save 50% on your first box with code 50OFF! 🎉
Query: {query}
Result: """
prompt = PromptTemplate.from_template(template)
pgllm(prompt.format(query="What kind of post is this?"))
pgllm = PredictionGuard(
model="OpenAI-text-davinci-003",
output={
"type": "categorical",
"categories": ["product announcement", "apology", "relational"],
},
)
pgllm(prompt.format(query="What kind of post is this?"))
pgllm = PredictionGuard(model="OpenAI-text-davinci-003")
template = """Question: {question}
Answer: Let's think step by step."""
prompt = PromptTemplate.from_template(template)
llm_chain = LLMChain(prompt=prompt, llm=pgllm, verbose=True)
question = "What NFL team won the Super Bowl in the year Justin Beiber was born?"
llm_chain.predict(question=question)
template = """Write a {adjective} poem about {subject}."""
prompt = | PromptTemplate.from_template(template) | langchain.prompts.PromptTemplate.from_template |
get_ipython().run_cell_magic('writefile', 'whatsapp_chat.txt', "[8/15/23, 9:12:33 AM] Dr. Feather: \u200eMessages and calls are end-to-end encrypted. No one outside of this chat, not even WhatsApp, can read or listen to them.\n[8/15/23, 9:12:43 AM] Dr. Feather: I spotted a rare Hyacinth Macaw yesterday in the Amazon Rainforest. Such a magnificent creature!\n\u200e[8/15/23, 9:12:48 AM] Dr. Feather: \u200eimage omitted\n[8/15/23, 9:13:15 AM] Jungle Jane: That's stunning! Were you able to observe its behavior?\n\u200e[8/15/23, 9:13:23 AM] Dr. Feather: \u200eimage omitted\n[8/15/23, 9:14:02 AM] Dr. Feather: Yes, it seemed quite social with other macaws. They're known for their playful nature.\n[8/15/23, 9:14:15 AM] Jungle Jane: How's the research going on parrot communication?\n\u200e[8/15/23, 9:14:30 AM] Dr. Feather: \u200eimage omitted\n[8/15/23, 9:14:50 AM] Dr. Feather: It's progressing well. We're learning so much about how they use sound and color to communicate.\n[8/15/23, 9:15:10 AM] Jungle Jane: That's fascinating! Can't wait to read your paper on it.\n[8/15/23, 9:15:20 AM] Dr. Feather: Thank you! I'll send you a draft soon.\n[8/15/23, 9:25:16 PM] Jungle Jane: Looking forward to it! Keep up the great work.\n")
from langchain_community.chat_loaders.whatsapp import WhatsAppChatLoader
loader = WhatsAppChatLoader(
path="./whatsapp_chat.txt",
)
from typing import List
from langchain_community.chat_loaders.base import ChatSession
from langchain_community.chat_loaders.utils import (
map_ai_messages,
merge_chat_runs,
)
raw_messages = loader.lazy_load()
merged_messages = | merge_chat_runs(raw_messages) | langchain_community.chat_loaders.utils.merge_chat_runs |
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')
YBUSER = "[SANDBOX USER]"
YBPASSWORD = "[SANDBOX PASSWORD]"
YBDATABASE = "[SANDBOX_DATABASE]"
YBHOST = "trialsandbox.sandbox.aws.yellowbrickcloud.com"
OPENAI_API_KEY = "[OPENAI API KEY]"
import os
import pathlib
import re
import sys
import urllib.parse as urlparse
from getpass import getpass
import psycopg2
from IPython.display import Markdown, display
from langchain.chains import LLMChain, RetrievalQAWithSourcesChain
from langchain.docstore.document import Document
from langchain_community.vectorstores import Yellowbrick
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
yellowbrick_connection_string = (
f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YBDATABASE}"
)
YB_DOC_DATABASE = "sample_data"
YB_DOC_TABLE = "yellowbrick_documentation"
embedding_table = "my_embeddings"
os.environ["OPENAI_API_KEY"] = OPENAI_API_KEY
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
SystemMessagePromptTemplate,
)
system_template = """If you don't know the answer, Make up your best guess."""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = ChatPromptTemplate.from_messages(messages)
chain_type_kwargs = {"prompt": prompt}
llm = ChatOpenAI(
model_name="gpt-3.5-turbo", # Modify model_name if you have access to GPT-4
temperature=0,
max_tokens=256,
)
chain = LLMChain(
llm=llm,
prompt=prompt,
verbose=False,
)
def print_result_simple(query):
result = chain(query)
output_text = f"""### Question:
{query}
{result['text']}
"""
display(Markdown(output_text))
print_result_simple("How many databases can be in a Yellowbrick Instance?")
print_result_simple("What's an easy way to add users in bulk to Yellowbrick?")
try:
conn = psycopg2.connect(yellowbrick_connection_string)
except psycopg2.Error as e:
print(f"Error connecting to the database: {e}")
exit(1)
cursor = conn.cursor()
create_table_query = f"""
CREATE TABLE if not exists {embedding_table} (
id uuid,
embedding_id integer,
text character varying(60000),
metadata character varying(1024),
embedding double precision
)
DISTRIBUTE ON (id);
truncate table {embedding_table};
"""
try:
cursor.execute(create_table_query)
print(f"Table '{embedding_table}' created successfully!")
except psycopg2.Error as e:
print(f"Error creating table: {e}")
conn.rollback()
conn.commit()
cursor.close()
conn.close()
yellowbrick_doc_connection_string = (
f"postgres://{urlparse.quote(YBUSER)}:{YBPASSWORD}@{YBHOST}:5432/{YB_DOC_DATABASE}"
)
conn = psycopg2.connect(yellowbrick_doc_connection_string)
cursor = conn.cursor()
query = f"SELECT path, document FROM {YB_DOC_TABLE}"
cursor.execute(query)
yellowbrick_documents = cursor.fetchall()
print(f"Extracted {len(yellowbrick_documents)} documents successfully!")
cursor.close()
conn.close()
DOCUMENT_BASE_URL = "https://docs.yellowbrick.com/6.7.1/" # Actual URL
separator = "\n## " # This separator assumes Markdown docs from the repo uses ### as logical main header most of the time
chunk_size_limit = 2000
max_chunk_overlap = 200
documents = [
Document(
page_content=document[1],
metadata={"source": DOCUMENT_BASE_URL + document[0].replace(".md", ".html")},
)
for document in yellowbrick_documents
]
text_splitter = RecursiveCharacterTextSplitter(
chunk_size=chunk_size_limit,
chunk_overlap=max_chunk_overlap,
separators=[separator, "\nn", "\n", ",", " ", ""],
)
split_docs = text_splitter.split_documents(documents)
docs_text = [doc.page_content for doc in split_docs]
embeddings = OpenAIEmbeddings()
vector_store = Yellowbrick.from_documents(
documents=split_docs,
embedding=embeddings,
connection_string=yellowbrick_connection_string,
table=embedding_table,
)
print(f"Created vector store with {len(documents)} documents")
system_template = """Use the following pieces of context to answer the users question.
Take note of the sources and include them in the answer in the format: "SOURCES: source1 source2", use "SOURCES" in capital letters regardless of the number of sources.
If you don't know the answer, just say that "I don't know", don't try to make up an answer.
----------------
{summaries}"""
messages = [
SystemMessagePromptTemplate.from_template(system_template),
HumanMessagePromptTemplate.from_template("{question}"),
]
prompt = | ChatPromptTemplate.from_messages(messages) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
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 = embeddings.embed_query("Where is the Taj Mahal?")
multi_llm = OpenAI(n=4, best_of=4)
embeddings = HypotheticalDocumentEmbedder.from_llm(
multi_llm, base_embeddings, "web_search"
)
result = embeddings.embed_query("Where is the Taj Mahal?")
prompt_template = """Please answer the user's question about the most recent state of the union address
Question: {question}
Answer:"""
prompt = PromptTemplate(input_variables=["question"], template=prompt_template)
llm_chain = LLMChain(llm=llm, prompt=prompt)
embeddings = HypotheticalDocumentEmbedder(
llm_chain=llm_chain, base_embeddings=base_embeddings
)
result = embeddings.embed_query(
"What did the president say about Ketanji Brown Jackson"
)
from langchain_community.vectorstores import Chroma
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 |
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-cache-dir')
get_ipython().system('CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install llama-cpp-python')
get_ipython().system('CMAKE_ARGS="-DLLAMA_METAL=on" FORCE_CMAKE=1 pip install --upgrade --force-reinstall llama-cpp-python --no-cache-dir')
get_ipython().system('python -m pip install -e . --force-reinstall --no-cache-dir')
from langchain.callbacks.manager import CallbackManager
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import LlamaCpp
template = """Question: {question}
Answer: Let's work this out in a step by step way to be sure we have the right answer."""
prompt = PromptTemplate.from_template(template)
callback_manager = CallbackManager([ | StreamingStdOutCallbackHandler() | langchain.callbacks.streaming_stdout.StreamingStdOutCallbackHandler |
from langchain_core.prompts import ChatPromptTemplate, MessagesPlaceholder
from langchain_openai.chat_models import ChatOpenAI
model = ChatOpenAI()
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're an assistant who's good at {ability}. Respond in 20 words or fewer",
),
MessagesPlaceholder(variable_name="history"),
("human", "{input}"),
]
)
runnable = prompt | model
from langchain_community.chat_message_histories import ChatMessageHistory
from langchain_core.chat_history import BaseChatMessageHistory
from langchain_core.runnables.history import RunnableWithMessageHistory
store = {}
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
)
with_message_history.invoke(
{"ability": "math", "input": "What does cosine mean?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "abc123"}},
)
with_message_history.invoke(
{"ability": "math", "input": "What?"},
config={"configurable": {"session_id": "def234"}},
)
from langchain_core.runnables import ConfigurableFieldSpec
store = {}
def get_session_history(user_id: str, conversation_id: str) -> BaseChatMessageHistory:
if (user_id, conversation_id) not in store:
store[(user_id, conversation_id)] = ChatMessageHistory()
return store[(user_id, conversation_id)]
with_message_history = RunnableWithMessageHistory(
runnable,
get_session_history,
input_messages_key="input",
history_messages_key="history",
history_factory_config=[
ConfigurableFieldSpec(
id="user_id",
annotation=str,
name="User ID",
description="Unique identifier for the user.",
default="",
is_shared=True,
),
ConfigurableFieldSpec(
id="conversation_id",
annotation=str,
name="Conversation ID",
description="Unique identifier for the conversation.",
default="",
is_shared=True,
),
],
)
with_message_history.invoke(
{"ability": "math", "input": "Hello"},
config={"configurable": {"user_id": "123", "conversation_id": "1"}},
)
from langchain_core.messages import HumanMessage
from langchain_core.runnables import RunnableParallel
chain = RunnableParallel({"output_message": ChatOpenAI()})
def get_session_history(session_id: str) -> BaseChatMessageHistory:
if session_id not in store:
store[session_id] = ChatMessageHistory()
return store[session_id]
with_message_history = RunnableWithMessageHistory(
chain,
get_session_history,
output_messages_key="output_message",
)
with_message_history.invoke(
[HumanMessage(content="What did Simone de Beauvoir believe about free will")],
config={"configurable": {"session_id": "baz"}},
)
with_message_history.invoke(
[HumanMessage(content="How did this compare to Sartre")],
config={"configurable": {"session_id": "baz"}},
)
RunnableWithMessageHistory(
ChatOpenAI(),
get_session_history,
)
from operator import itemgetter
RunnableWithMessageHistory(
itemgetter("input_messages") | | ChatOpenAI() | langchain_openai.chat_models.ChatOpenAI |
from langchain_community.llms import Ollama
llm = | Ollama(model="llama2") | langchain_community.llms.Ollama |
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="gpt-4", temperature=0)
toolkit = ActionServerToolkit(url="http://localhost:8080", report_trace=True)
tools = toolkit.get_tools()
system_message = SystemMessage(content="You are a helpful assistant")
prompt = OpenAIFunctionsAgent.create_prompt(system_message)
agent = OpenAIFunctionsAgent(llm=llm, prompt=prompt, tools=tools)
executor = AgentExecutor(agent=agent, tools=tools, verbose=True)
executor.invoke("What is the current weather today in San Francisco in fahrenheit?")
toolkit = | ActionServerToolkit(url="http://localhost:8080") | langchain_robocorp.ActionServerToolkit |
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", "/")
response = httpx.Response(200, request=request)
error = RateLimitError("rate limit", response=response, body="")
openai_llm = ChatOpenAI(max_retries=0)
anthropic_llm = ChatAnthropic()
llm = openai_llm.with_fallbacks([anthropic_llm])
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(openai_llm.invoke("Why did the chicken cross the road?"))
except RateLimitError:
print("Hit error")
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(llm.invoke("Why did the chicken cross the road?"))
except RateLimitError:
print("Hit error")
from langchain_core.prompts import ChatPromptTemplate
prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a nice assistant who always includes a compliment in your response",
),
("human", "Why did the {animal} cross the road"),
]
)
chain = prompt | llm
with patch("openai.resources.chat.completions.Completions.create", side_effect=error):
try:
print(chain.invoke({"animal": "kangaroo"}))
except RateLimitError:
print("Hit error")
from langchain_core.output_parsers import StrOutputParser
chat_prompt = ChatPromptTemplate.from_messages(
[
(
"system",
"You're a nice assistant who always includes a compliment in your response",
),
("human", "Why did the {animal} cross the road"),
]
)
chat_model = ChatOpenAI(model_name="gpt-fake")
bad_chain = chat_prompt | chat_model | StrOutputParser()
from langchain.prompts import PromptTemplate
from langchain_openai import OpenAI
prompt_template = """Instructions: You should always include a compliment in your response.
Question: Why did the {animal} cross the road?"""
prompt = PromptTemplate.from_template(prompt_template)
llm = OpenAI()
good_chain = prompt | llm
chain = bad_chain.with_fallbacks([good_chain])
chain.invoke({"animal": "turtle"})
short_llm = ChatOpenAI()
long_llm = ChatOpenAI(model="gpt-3.5-turbo-16k")
llm = short_llm.with_fallbacks([long_llm])
inputs = "What is the next number: " + ", ".join(["one", "two"] * 3000)
try:
print(short_llm.invoke(inputs))
except Exception as e:
print(e)
try:
print(llm.invoke(inputs))
except Exception as e:
print(e)
from langchain.output_parsers import DatetimeOutputParser
prompt = ChatPromptTemplate.from_template(
"what time was {event} (in %Y-%m-%dT%H:%M:%S.%fZ format - only return this value)"
)
openai_35 = ChatOpenAI() | DatetimeOutputParser()
openai_4 = ChatOpenAI(model="gpt-4") | | DatetimeOutputParser() | langchain.output_parsers.DatetimeOutputParser |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet langchain-google-spanner')
from google.colab import auth
auth.authenticate_user()
PROJECT_ID = "my-project-id" # @param {type:"string"}
get_ipython().system('gcloud config set project {PROJECT_ID}')
get_ipython().system('gcloud services enable spanner.googleapis.com')
INSTANCE = "my-instance" # @param {type: "string"}
DATABASE = "my-database" # @param {type: "string"}
TABLE_NAME = "vectors_search_data" # @param {type: "string"}
from langchain_google_spanner import SecondaryIndex, SpannerVectorStore, TableColumn
SpannerVectorStore.init_vector_store_table(
instance_id=INSTANCE,
database_id=DATABASE,
table_name=TABLE_NAME,
id_column="row_id",
metadata_columns=[
| TableColumn(name="metadata", type="JSON", is_null=True) | langchain_google_spanner.TableColumn |
import os
embaas_api_key = "YOUR_API_KEY"
os.environ["EMBAAS_API_KEY"] = "YOUR_API_KEY"
from langchain_community.embeddings import EmbaasEmbeddings
embeddings = | EmbaasEmbeddings() | langchain_community.embeddings.EmbaasEmbeddings |
from langchain.chains import LLMChain
from langchain.memory import ConversationBufferMemory
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}
Chatbot:"""
prompt = PromptTemplate(
input_variables=["chat_history", "human_input"], template=template
)
memory = | ConversationBufferMemory(memory_key="chat_history") | langchain.memory.ConversationBufferMemory |
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="gpt-4", temperature=0)
toolkit = ActionServerToolkit(url="http://localhost:8080", report_trace=True)
tools = toolkit.get_tools()
system_message = SystemMessage(content="You are a helpful assistant")
prompt = OpenAIFunctionsAgent.create_prompt(system_message)
agent = OpenAIFunctionsAgent(llm=llm, prompt=prompt, tools=tools)
executor = | AgentExecutor(agent=agent, tools=tools, verbose=True) | langchain.agents.AgentExecutor |
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 based only on the following context:
<context>
{context}
</context>
Question: {question}
"""
prompt = ChatPromptTemplate.from_template(template)
model = ChatOpenAI(temperature=0)
search = DuckDuckGoSearchAPIWrapper()
def retriever(query):
return search.run(query)
chain = (
{"context": retriever, "question": RunnablePassthrough()}
| prompt
| model
| StrOutputParser()
)
simple_query = "what is langchain?"
chain.invoke(simple_query)
distracted_query = "man that sam bankman fried trial was crazy! what is langchain?"
chain.invoke(distracted_query)
retriever(distracted_query)
template = """Provide a better search query for \
web search engine to answer the given question, end \
the queries with ’**’. Question: \
{x} Answer:"""
rewrite_prompt = ChatPromptTemplate.from_template(template)
from langchain import hub
rewrite_prompt = hub.pull("langchain-ai/rewrite")
print(rewrite_prompt.template)
def _parse(text):
return text.strip("**")
rewriter = rewrite_prompt | ChatOpenAI(temperature=0) | | StrOutputParser() | langchain_core.output_parsers.StrOutputParser |
import os
os.environ["SEARCHAPI_API_KEY"] = ""
from langchain_community.utilities import SearchApiAPIWrapper
search = | SearchApiAPIWrapper() | langchain_community.utilities.SearchApiAPIWrapper |
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 = Elasticsearch(ELASTIC_SEARCH_SERVER)
llm = ChatOpenAI(model_name="gpt-4", temperature=0)
chain = ElasticsearchDatabaseChain.from_llm(llm=llm, database=db, verbose=True)
question = "What are the first names of all the customers?"
chain.run(question)
from langchain.prompts.prompt import PromptTemplate
PROMPT_TEMPLATE = """Given an input question, create a syntactically correct Elasticsearch query to run. Unless the user specifies in their question a specific number of examples they wish to obtain, always limit your query to at most {top_k} results. You can order the results by a relevant column to return the most interesting examples in the database.
Unless told to do not query for all the columns from a specific index, only ask for a the few relevant columns given the question.
Pay attention to use only the column names that you can see in the mapping description. Be careful to not query for columns that do not exist. Also, pay attention to which column is in which index. Return the query as valid json.
Use the following format:
Question: Question here
ESQuery: Elasticsearch Query formatted as json
"""
PROMPT = PromptTemplate.from_template(
PROMPT_TEMPLATE,
)
chain = | 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 text-generation transformers google-search-results numexpr langchainhub sentencepiece jinja2')
import os
from langchain_community.llms import HuggingFaceTextGenInference
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
HF_TOKEN = os.getenv("HUGGINGFACEHUB_API_TOKEN")
llm = HuggingFaceTextGenInference(
inference_server_url=ENDPOINT_URL,
max_new_tokens=512,
top_k=50,
temperature=0.1,
repetition_penalty=1.03,
server_kwargs={
"headers": {
"Authorization": f"Bearer {HF_TOKEN}",
"Content-Type": "application/json",
}
},
)
from langchain_community.llms import HuggingFaceEndpoint
ENDPOINT_URL = "<YOUR_ENDPOINT_URL_HERE>"
llm = HuggingFaceEndpoint(
endpoint_url=ENDPOINT_URL,
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 50,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain_community.llms import HuggingFaceHub
llm = HuggingFaceHub(
repo_id="HuggingFaceH4/zephyr-7b-beta",
task="text-generation",
model_kwargs={
"max_new_tokens": 512,
"top_k": 30,
"temperature": 0.1,
"repetition_penalty": 1.03,
},
)
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_community.chat_models.huggingface import ChatHuggingFace
messages = [
SystemMessage(content="You're a helpful assistant"),
HumanMessage(
content="What happens when an unstoppable force meets an immovable object?"
),
]
chat_model = ChatHuggingFace(llm=llm)
chat_model.model_id
chat_model._to_chat_prompt(messages)
res = chat_model.invoke(messages)
print(res.content)
from langchain import hub
from langchain.agents import AgentExecutor, load_tools
from langchain.agents.format_scratchpad import format_log_to_str
from langchain.agents.output_parsers import (
ReActJsonSingleInputOutputParser,
)
from langchain.tools.render import render_text_description
from langchain_community.utilities import SerpAPIWrapper
tools = | load_tools(["serpapi", "llm-math"], llm=llm) | langchain.agents.load_tools |
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 |
get_ipython().run_line_magic('pip', 'install -qU esprima esprima tree_sitter tree_sitter_languages')
import warnings
warnings.filterwarnings("ignore")
from pprint import pprint
from langchain_community.document_loaders.generic import GenericLoader
from langchain_community.document_loaders.parsers import LanguageParser
from langchain_text_splitters import Language
loader = GenericLoader.from_filesystem(
"./example_data/source_code",
glob="*",
suffixes=[".py", ".js"],
parser= | LanguageParser() | langchain_community.document_loaders.parsers.LanguageParser |
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 TextLoader
from langchain_community.vectorstores import FAISS
from langchain_openai import OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
documents = TextLoader(
"../../modules/state_of_the_union.txt",
).load()
text_splitter = RecursiveCharacterTextSplitter(chunk_size=500, chunk_overlap=100)
texts = text_splitter.split_documents(documents)
embedding = OpenAIEmbeddings(model="text-embedding-ada-002")
retriever = FAISS.from_documents(texts, embedding).as_retriever(search_kwargs={"k": 20})
query = "What did the president say about Ketanji Brown Jackson"
docs = retriever.get_relevant_documents(query)
pretty_print_docs(docs)
from langchain.retrievers import ContextualCompressionRetriever
from langchain_community.retrievers.document_compressors import LLMLinguaCompressor
from langchain_openai import ChatOpenAI
llm = ChatOpenAI(temperature=0)
compressor = | LLMLinguaCompressor(model_name="openai-community/gpt2", device_map="cpu") | langchain_community.retrievers.document_compressors.LLMLinguaCompressor |
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(model_name="dall-e")]
mrkl = initialize_agent(
tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True
)
output = mrkl.run("How would you visualize a parot playing soccer?")
def show_output(output):
"""Display the multi-modal output from the agent."""
UUID_PATTERN = re.compile(
r"([0-9A-Za-z]{8}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{4}-[0-9A-Za-z]{12})"
)
outputs = UUID_PATTERN.split(output)
outputs = [
re.sub(r"^\W+", "", el) for el in outputs
] # Clean trailing and leading non-word characters
for output in outputs:
maybe_block_id = UUID_PATTERN.search(output)
if maybe_block_id:
display(Image(Block.get(Steamship(), _id=maybe_block_id.group()).raw()))
else:
print(output, end="\n\n")
tools = [ | SteamshipImageGenerationTool(model_name="stable-diffusion") | langchain.tools.SteamshipImageGenerationTool |
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 not a valid key"
os.environ["NVIDIA_API_KEY"] = nvapi_key
from langchain_nvidia_ai_endpoints import ChatNVIDIA
llm = ChatNVIDIA(model="mixtral_8x7b")
result = llm.invoke("Write a ballad about LangChain.")
print(result.content)
print(llm.batch(["What's 2*3?", "What's 2*6?"]))
for chunk in llm.stream("How far can a seagull fly in one day?"):
print(chunk.content, end="|")
async for chunk in llm.astream(
"How long does it take for monarch butterflies to migrate?"
):
print(chunk.content, end="|")
ChatNVIDIA.get_available_models()
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_nvidia_ai_endpoints import ChatNVIDIA
prompt = | ChatPromptTemplate.from_messages(
[("system", "You are a helpful AI assistant named Fred."), ("user", "{input}") | langchain_core.prompts.ChatPromptTemplate.from_messages |
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-turbo-instruct")
from langchain.prompts import PromptTemplate
PROMPT_TEMPLATE = """Here is the description of a meal: "{meal}".
Embed the meal into the given text: "{text_to_personalize}".
Prepend a personalized message including the user's name "{user}"
and their preference "{preference}".
Make it sound good.
"""
PROMPT = PromptTemplate(
input_variables=["meal", "text_to_personalize", "user", "preference"],
template=PROMPT_TEMPLATE,
)
import langchain_experimental.rl_chain as rl_chain
chain = rl_chain.PickBest.from_llm(llm=llm, prompt=PROMPT)
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs \
believe you will love it!",
)
print(response["response"])
for _ in range(5):
try:
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference=rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]),
text_to_personalize="This is the weeks specialty dish, our master chefs believe you will love it!",
)
except Exception as e:
print(e)
print(response["response"])
print()
scoring_criteria_template = (
"Given {preference} rank how good or bad this selection is {meal}"
)
chain = rl_chain.PickBest.from_llm(
llm=llm,
prompt=PROMPT,
selection_scorer=rl_chain.AutoSelectionScorer(
llm=llm, scoring_criteria_template_str=scoring_criteria_template
),
)
response = chain.run(
meal=rl_chain.ToSelectFrom(meals),
user=rl_chain.BasedOn("Tom"),
preference= | rl_chain.BasedOn(["Vegetarian", "regular dairy is ok"]) | langchain_experimental.rl_chain.BasedOn |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http://localhost:9200", index_name="test_index", embedding=embedding
)
namespace = f"elasticsearch/{collection_name}"
record_manager = SQLRecordManager(
namespace, db_url="sqlite:///record_manager_cache.sql"
)
record_manager.create_schema()
doc1 = Document(page_content="kitty", metadata={"source": "kitty.txt"})
doc2 = Document(page_content="doggy", metadata={"source": "doggy.txt"})
def _clear():
"""Hacky helper method to clear content. See the `full` mode section to to understand why it works."""
index([], record_manager, vectorstore, cleanup="full", source_id_key="source")
_clear()
index(
[doc1, doc1, doc1, doc1, doc1],
record_manager,
vectorstore,
cleanup=None,
source_id_key="source",
)
_clear()
index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source")
index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source")
_clear()
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index([], record_manager, vectorstore, cleanup="incremental", source_id_key="source")
changed_doc_2 = Document(page_content="puppy", metadata={"source": "doggy.txt"})
index(
[changed_doc_2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
_clear()
all_docs = [doc1, doc2]
| index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") | langchain.indexes.index |
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."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
openai_functions = [convert_pydantic_to_openai_function(Joke)]
model = ChatOpenAI(temperature=0)
prompt = ChatPromptTemplate.from_messages(
[("system", "You are helpful assistant"), ("user", "{input}")]
)
from langchain.output_parsers.openai_functions import JsonOutputFunctionsParser
parser = JsonOutputFunctionsParser()
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me a joke"})
for s in chain.stream({"input": "tell me a joke"}):
print(s)
from typing import List
from langchain.output_parsers.openai_functions import JsonKeyOutputFunctionsParser
class Jokes(BaseModel):
"""Jokes to tell user."""
joke: List[Joke]
funniness_level: int
parser = JsonKeyOutputFunctionsParser(key_name="joke")
openai_functions = [convert_pydantic_to_openai_function(Jokes)]
chain = prompt | model.bind(functions=openai_functions) | parser
chain.invoke({"input": "tell me two jokes"})
for s in chain.stream({"input": "tell me two jokes"}):
print(s)
from langchain.output_parsers.openai_functions import PydanticOutputFunctionsParser
class Joke(BaseModel):
"""Joke to tell user."""
setup: str = Field(description="question to set up a joke")
punchline: str = Field(description="answer to resolve the joke")
@validator("setup")
def question_ends_with_question_mark(cls, field):
if field[-1] != "?":
raise ValueError("Badly formed question!")
return field
parser = | PydanticOutputFunctionsParser(pydantic_schema=Joke) | langchain.output_parsers.openai_functions.PydanticOutputFunctionsParser |
from langchain.memory import ConversationTokenBufferMemory
from langchain_openai import OpenAI
llm = OpenAI()
memory = | ConversationTokenBufferMemory(llm=llm, max_token_limit=10) | langchain.memory.ConversationTokenBufferMemory |
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 RecursiveCharacterTextSplitter
loaders = [
TextLoader("../../paul_graham_essay.txt"),
TextLoader("../../state_of_the_union.txt"),
]
docs = []
for loader in loaders:
docs.extend(loader.load())
text_splitter = RecursiveCharacterTextSplitter(chunk_size=10000)
docs = text_splitter.split_documents(docs)
vectorstore = Chroma(
collection_name="full_documents", embedding_function=OpenAIEmbeddings()
)
store = InMemoryByteStore()
id_key = "doc_id"
retriever = MultiVectorRetriever(
vectorstore=vectorstore,
byte_store=store,
id_key=id_key,
)
import uuid
doc_ids = [str(uuid.uuid4()) for _ in docs]
child_text_splitter = RecursiveCharacterTextSplitter(chunk_size=400)
sub_docs = []
for i, doc in enumerate(docs):
_id = doc_ids[i]
_sub_docs = child_text_splitter.split_documents([doc])
for _doc in _sub_docs:
_doc.metadata[id_key] = _id
sub_docs.extend(_sub_docs)
retriever.vectorstore.add_documents(sub_docs)
retriever.docstore.mset(list(zip(doc_ids, docs)))
retriever.vectorstore.similarity_search("justice breyer")[0]
len(retriever.get_relevant_documents("justice breyer")[0].page_content)
from langchain.retrievers.multi_vector import SearchType
retriever.search_type = SearchType.mmr
len(retriever.get_relevant_documents("justice breyer")[0].page_content)
import uuid
from langchain_core.documents import Document
from langchain_core.output_parsers import StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_openai import ChatOpenAI
chain = (
{"doc": lambda x: x.page_content}
| | ChatPromptTemplate.from_template("Summarize the following document:\n\n{doc}") | langchain_core.prompts.ChatPromptTemplate.from_template |
REGION = "us-central1" # @param {type:"string"}
INSTANCE = "test-instance" # @param {type:"string"}
DATABASE = "test" # @param {type:"string"}
TABLE_NAME = "test-default" # @param {type:"string"}
get_ipython().run_line_magic('pip', 'install -upgrade --quiet langchain-google-cloud-sql-mysql')
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()
get_ipython().system('gcloud services enable sqladmin.googleapis.com')
from langchain_google_cloud_sql_mysql import MySQLEngine
engine = MySQLEngine.from_instance(
project_id=PROJECT_ID, region=REGION, instance=INSTANCE, database=DATABASE
)
engine.init_document_table(TABLE_NAME, overwrite_existing=True)
from langchain_core.documents import Document
from langchain_google_cloud_sql_mysql import MySQLDocumentSaver
test_docs = [
Document(
page_content="Apple Granny Smith 150 0.99 1",
metadata={"fruit_id": 1},
),
Document(
page_content="Banana Cavendish 200 0.59 0",
metadata={"fruit_id": 2},
),
Document(
page_content="Orange Navel 80 1.29 1",
metadata={"fruit_id": 3},
),
]
saver = MySQLDocumentSaver(engine=engine, table_name=TABLE_NAME)
saver.add_documents(test_docs)
from langchain_google_cloud_sql_mysql import MySQLLoader
loader = MySQLLoader(engine=engine, table_name=TABLE_NAME)
docs = loader.lazy_load()
for doc in docs:
print("Loaded documents:", doc)
from langchain_google_cloud_sql_mysql import MySQLLoader
loader = MySQLLoader(
engine=engine,
query=f"select * from `{TABLE_NAME}` where JSON_EXTRACT(langchain_metadata, '$.fruit_id') = 1;",
)
onedoc = loader.load()
onedoc
from langchain_google_cloud_sql_mysql import MySQLLoader
loader = | MySQLLoader(engine=engine, table_name=TABLE_NAME) | langchain_google_cloud_sql_mysql.MySQLLoader |
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 ContextCallbackHandler
from langchain.schema import (
HumanMessage,
SystemMessage,
)
from langchain_openai import ChatOpenAI
token = os.environ["CONTEXT_API_TOKEN"]
chat = ChatOpenAI(
headers={"user_id": "123"}, temperature=0, callbacks=[ContextCallbackHandler(token)]
)
messages = [
SystemMessage(
content="You are a helpful assistant that translates English to French."
),
HumanMessage(content="I love programming."),
]
print(chat(messages))
import os
from langchain.callbacks import ContextCallbackHandler
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain.prompts.chat import (
ChatPromptTemplate,
HumanMessagePromptTemplate,
)
from langchain_openai import ChatOpenAI
token = os.environ["CONTEXT_API_TOKEN"]
human_message_prompt = HumanMessagePromptTemplate(
prompt=PromptTemplate(
template="What is a good name for a company that makes {product}?",
input_variables=["product"],
)
)
chat_prompt_template = | ChatPromptTemplate.from_messages([human_message_prompt]) | langchain.prompts.chat.ChatPromptTemplate.from_messages |
from getpass import getpass
from langchain_community.document_loaders.larksuite import LarkSuiteDocLoader
DOMAIN = input("larksuite domain")
ACCESS_TOKEN = getpass("larksuite tenant_access_token or user_access_token")
DOCUMENT_ID = input("larksuite document id")
from pprint import pprint
larksuite_loader = | LarkSuiteDocLoader(DOMAIN, ACCESS_TOKEN, DOCUMENT_ID) | langchain_community.document_loaders.larksuite.LarkSuiteDocLoader |
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 StrOutputParser
from langchain_core.prompts import ChatPromptTemplate
from langchain_core.runnables import RunnablePassthrough
from langchain_openai import ChatOpenAI, OpenAIEmbeddings
from langchain_text_splitters import RecursiveCharacterTextSplitter
os.environ["OPENAI_API_KEY"] = getpass("OPENAI_API_KEY = ")
embe = OpenAIEmbeddings()
from langchain_community.vectorstores import Cassandra
from cassandra.cluster import Cluster
cluster = Cluster(["127.0.0.1"])
session = cluster.connect()
import cassio
CASSANDRA_KEYSPACE = input("CASSANDRA_KEYSPACE = ")
cassio.init(session=session, keyspace=CASSANDRA_KEYSPACE)
vstore = Cassandra(
embedding=embe,
table_name="cassandra_vector_demo",
)
ASTRA_DB_ID = input("ASTRA_DB_ID = ")
ASTRA_DB_APPLICATION_TOKEN = getpass("ASTRA_DB_APPLICATION_TOKEN = ")
desired_keyspace = input("ASTRA_DB_KEYSPACE (optional, can be left empty) = ")
if desired_keyspace:
ASTRA_DB_KEYSPACE = desired_keyspace
else:
ASTRA_DB_KEYSPACE = None
import cassio
cassio.init(
database_id=ASTRA_DB_ID,
token=ASTRA_DB_APPLICATION_TOKEN,
keyspace=ASTRA_DB_KEYSPACE,
)
vstore = Cassandra(
embedding=embe,
table_name="cassandra_vector_demo",
)
philo_dataset = load_dataset("datastax/philosopher-quotes")["train"]
docs = []
for entry in philo_dataset:
metadata = {"author": entry["author"]}
doc = Document(page_content=entry["quote"], metadata=metadata)
docs.append(doc)
inserted_ids = vstore.add_documents(docs)
print(f"\nInserted {len(inserted_ids)} documents.")
texts = ["I think, therefore I am.", "To the things themselves!"]
metadatas = [{"author": "descartes"}, {"author": "husserl"}]
ids = ["desc_01", "huss_xy"]
inserted_ids_2 = vstore.add_texts(texts=texts, metadatas=metadatas, ids=ids)
print(f"\nInserted {len(inserted_ids_2)} documents.")
results = vstore.similarity_search("Our life is what we make of it", k=3)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
results_filtered = vstore.similarity_search(
"Our life is what we make of it",
k=3,
filter={"author": "plato"},
)
for res in results_filtered:
print(f"* {res.page_content} [{res.metadata}]")
results = vstore.similarity_search_with_score("Our life is what we make of it", k=3)
for res, score in results:
print(f"* [SIM={score:3f}] {res.page_content} [{res.metadata}]")
results = vstore.max_marginal_relevance_search(
"Our life is what we make of it",
k=3,
filter={"author": "aristotle"},
)
for res in results:
print(f"* {res.page_content} [{res.metadata}]")
delete_1 = vstore.delete(inserted_ids[:3])
print(f"all_succeed={delete_1}") # True, all documents deleted
delete_2 = vstore.delete(inserted_ids[2:5])
print(f"some_succeeds={delete_2}") # True, though some IDs were gone already
get_ipython().system('curl -L "https://github.com/awesome-astra/datasets/blob/main/demo-resources/what-is-philosophy/what-is-philosophy.pdf?raw=true" -o "what-is-philosophy.pdf"')
pdf_loader = PyPDFLoader("what-is-philosophy.pdf")
splitter = | RecursiveCharacterTextSplitter(chunk_size=512, chunk_overlap=64) | langchain_text_splitters.RecursiveCharacterTextSplitter |
from langchain.agents import AgentType, initialize_agent
from langchain.chains import LLMMathChain
from langchain_core.pydantic_v1 import BaseModel, Field
from langchain_core.tools import Tool
from langchain_openai import ChatOpenAI
get_ipython().run_line_magic('pip', 'install --upgrade --quiet numexpr')
llm = ChatOpenAI(temperature=0, model="gpt-4")
llm_math_chain = LLMMathChain.from_llm(llm=llm, verbose=True)
primes = {998: 7901, 999: 7907, 1000: 7919}
class CalculatorInput(BaseModel):
question: str = Field()
class PrimeInput(BaseModel):
n: int = Field()
def is_prime(n: int) -> bool:
if n <= 1 or (n % 2 == 0 and n > 2):
return False
for i in range(3, int(n**0.5) + 1, 2):
if n % i == 0:
return False
return True
def get_prime(n: int, primes: dict = primes) -> str:
return str(primes.get(int(n)))
async def aget_prime(n: int, primes: dict = primes) -> str:
return str(primes.get(int(n)))
tools = [
Tool(
name="GetPrime",
func=get_prime,
description="A tool that returns the `n`th prime number",
args_schema=PrimeInput,
coroutine=aget_prime,
),
Tool.from_function(
func=llm_math_chain.run,
name="Calculator",
description="Useful for when you need to compute mathematical expressions",
args_schema=CalculatorInput,
coroutine=llm_math_chain.arun,
),
]
from langchain import hub
prompt = hub.pull("hwchase17/openai-functions-agent")
from langchain.agents import create_openai_functions_agent
agent = | create_openai_functions_agent(llm, tools, prompt) | langchain.agents.create_openai_functions_agent |
get_ipython().run_line_magic('pip', 'install --upgrade --quiet scann')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings import HuggingFaceEmbeddings
from langchain_community.vectorstores import ScaNN
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = HuggingFaceEmbeddings()
db = | ScaNN.from_documents(docs, embeddings) | langchain_community.vectorstores.ScaNN.from_documents |
from langchain.indexes import SQLRecordManager, index
from langchain_core.documents import Document
from langchain_elasticsearch import ElasticsearchStore
from langchain_openai import OpenAIEmbeddings
collection_name = "test_index"
embedding = OpenAIEmbeddings()
vectorstore = ElasticsearchStore(
es_url="http://localhost:9200", index_name="test_index", embedding=embedding
)
namespace = f"elasticsearch/{collection_name}"
record_manager = SQLRecordManager(
namespace, db_url="sqlite:///record_manager_cache.sql"
)
record_manager.create_schema()
doc1 = Document(page_content="kitty", metadata={"source": "kitty.txt"})
doc2 = Document(page_content="doggy", metadata={"source": "doggy.txt"})
def _clear():
"""Hacky helper method to clear content. See the `full` mode section to to understand why it works."""
index([], record_manager, vectorstore, cleanup="full", source_id_key="source")
_clear()
index(
[doc1, doc1, doc1, doc1, doc1],
record_manager,
vectorstore,
cleanup=None,
source_id_key="source",
)
_clear()
index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source")
index([doc1, doc2], record_manager, vectorstore, cleanup=None, source_id_key="source")
_clear()
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index(
[doc1, doc2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
index([], record_manager, vectorstore, cleanup="incremental", source_id_key="source")
changed_doc_2 = Document(page_content="puppy", metadata={"source": "doggy.txt"})
index(
[changed_doc_2],
record_manager,
vectorstore,
cleanup="incremental",
source_id_key="source",
)
_clear()
all_docs = [doc1, doc2]
index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source")
del all_docs[0]
all_docs
| index(all_docs, record_manager, vectorstore, cleanup="full", source_id_key="source") | langchain.indexes.index |
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 = 1536
index = faiss.IndexFlatL2(embedding_size)
vectorstore = FAISS(embeddings_model.embed_query, index, | InMemoryDocstore({}) | langchain.docstore.InMemoryDocstore |
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["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
activeloop_token = getpass.getpass("activeloop token:")
embeddings = OpenAIEmbeddings()
from langchain_community.document_loaders import TextLoader
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = OpenAIEmbeddings()
db = DeepLake(dataset_path="./my_deeplake/", embedding=embeddings, overwrite=True)
db.add_documents(docs)
query = "What did the president say about Ketanji Brown Jackson"
docs = db.similarity_search(query)
print(docs[0].page_content)
db = | DeepLake(dataset_path="./my_deeplake/", embedding=embeddings, read_only=True) | langchain_community.vectorstores.DeepLake |
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 = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
import marqo
marqo_url = "http://localhost:8882" # if using marqo cloud replace with your endpoint (console.marqo.ai)
marqo_api_key = "" # if using marqo cloud replace with your api key (console.marqo.ai)
client = marqo.Client(url=marqo_url, api_key=marqo_api_key)
index_name = "langchain-demo"
docsearch = Marqo.from_documents(docs, index_name=index_name)
query = "What did the president say about Ketanji Brown Jackson"
result_docs = docsearch.similarity_search(query)
print(result_docs[0].page_content)
result_docs = docsearch.similarity_search_with_score(query)
print(result_docs[0][0].page_content, result_docs[0][1], sep="\n")
index_name = "langchain-multimodal-demo"
try:
client.delete_index(index_name)
except Exception:
print(f"Creating {index_name}")
settings = {"treat_urls_and_pointers_as_images": True, "model": "ViT-L/14"}
client.create_index(index_name, **settings)
client.index(index_name).add_documents(
[
{
"caption": "Bus",
"image": "https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image4.jpg",
},
{
"caption": "Plane",
"image": "https://raw.githubusercontent.com/marqo-ai/marqo/mainline/examples/ImageSearchGuide/data/image2.jpg",
},
],
)
def get_content(res):
"""Helper to format Marqo's documents into text to be used as page_content"""
return f"{res['caption']}: {res['image']}"
docsearch = | Marqo(client, index_name, page_content_builder=get_content) | langchain_community.vectorstores.Marqo |
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
logging.basicConfig(level=logging.INFO)
data_url = "https://storage.googleapis.com/benchmarks-artifacts/langchain-docs-benchmarking/cj.zip"
result = requests.get(data_url)
filename = "cj.zip"
with open(filename, "wb") as file:
file.write(result.content)
with zipfile.ZipFile(filename, "r") as zip_ref:
zip_ref.extractall()
from langchain_community.document_loaders import PyPDFLoader
loader = PyPDFLoader("./cj/cj.pdf")
docs = loader.load()
tables = []
texts = [d.page_content for d in docs]
len(texts)
from langchain.prompts import PromptTemplate
from langchain_community.chat_models import ChatVertexAI
from langchain_community.llms import VertexAI
from langchain_core.messages import AIMessage
from langchain_core.output_parsers import StrOutputParser
from langchain_core.runnables import RunnableLambda
def generate_text_summaries(texts, tables, summarize_texts=False):
"""
Summarize text elements
texts: List of str
tables: List of str
summarize_texts: Bool to summarize texts
"""
prompt_text = """You are an assistant tasked with summarizing tables and text for retrieval. \
These summaries will be embedded and used to retrieve the raw text or table elements. \
Give a concise summary of the table or text that is well optimized for retrieval. Table or text: {element} """
prompt = PromptTemplate.from_template(prompt_text)
empty_response = RunnableLambda(
lambda x: AIMessage(content="Error processing document")
)
model = VertexAI(
temperature=0, model_name="gemini-pro", max_output_tokens=1024
).with_fallbacks([empty_response])
summarize_chain = {"element": lambda x: x} | prompt | model | StrOutputParser()
text_summaries = []
table_summaries = []
if texts and summarize_texts:
text_summaries = summarize_chain.batch(texts, {"max_concurrency": 1})
elif texts:
text_summaries = texts
if tables:
table_summaries = summarize_chain.batch(tables, {"max_concurrency": 1})
return text_summaries, table_summaries
text_summaries, table_summaries = generate_text_summaries(
texts, tables, summarize_texts=True
)
len(text_summaries)
import base64
import os
from langchain_core.messages import HumanMessage
def encode_image(image_path):
"""Getting the base64 string"""
with open(image_path, "rb") as image_file:
return base64.b64encode(image_file.read()).decode("utf-8")
def image_summarize(img_base64, prompt):
"""Make image summary"""
model = ChatVertexAI(model_name="gemini-pro-vision", max_output_tokens=1024)
msg = model(
[
HumanMessage(
content=[
{"type": "text", "text": prompt},
{
"type": "image_url",
"image_url": {"url": f"data:image/jpeg;base64,{img_base64}"},
},
]
)
]
)
return msg.content
def generate_img_summaries(path):
"""
Generate summaries and base64 encoded strings for images
path: Path to list of .jpg files extracted by Unstructured
"""
img_base64_list = []
image_summaries = []
prompt = """You are an assistant tasked with summarizing images for retrieval. \
These summaries will be embedded and used to retrieve the raw image. \
Give a concise summary of the image that is well optimized for retrieval."""
for img_file in sorted(os.listdir(path)):
if img_file.endswith(".jpg"):
img_path = os.path.join(path, img_file)
base64_image = encode_image(img_path)
img_base64_list.append(base64_image)
image_summaries.append(image_summarize(base64_image, prompt))
return img_base64_list, image_summaries
img_base64_list, image_summaries = generate_img_summaries("./cj")
len(image_summaries)
import uuid
from langchain.retrievers.multi_vector import MultiVectorRetriever
from langchain.storage import InMemoryStore
from langchain_community.embeddings import VertexAIEmbeddings
from langchain_community.vectorstores import Chroma
from langchain_core.documents import Document
def create_multi_vector_retriever(
vectorstore, text_summaries, texts, table_summaries, tables, image_summaries, images
):
"""
Create retriever that indexes summaries, but returns raw images or texts
"""
store = | InMemoryStore() | langchain.storage.InMemoryStore |
from langchain_community.document_loaders import IMSDbLoader
loader = | IMSDbLoader("https://imsdb.com/scripts/BlacKkKlansman.html") | langchain_community.document_loaders.IMSDbLoader |
get_ipython().system('pip3 install tcvectordb')
from langchain_community.document_loaders import TextLoader
from langchain_community.embeddings.fake import FakeEmbeddings
from langchain_community.vectorstores import TencentVectorDB
from langchain_community.vectorstores.tencentvectordb import ConnectionParams
from langchain_text_splitters import CharacterTextSplitter
loader = TextLoader("../../modules/state_of_the_union.txt")
documents = loader.load()
text_splitter = CharacterTextSplitter(chunk_size=1000, chunk_overlap=0)
docs = text_splitter.split_documents(documents)
embeddings = FakeEmbeddings(size=128)
conn_params = ConnectionParams(
url="http://10.0.X.X",
key="eC4bLRy2va******************************",
username="root",
timeout=20,
)
vector_db = TencentVectorDB.from_documents(
docs,
embeddings,
connection_params=conn_params,
)
query = "What did the president say about Ketanji Brown Jackson"
docs = vector_db.similarity_search(query)
docs[0].page_content
vector_db = | TencentVectorDB(embeddings, conn_params) | langchain_community.vectorstores.TencentVectorDB |
from langchain_community.document_loaders import NotionDirectoryLoader
loader = | NotionDirectoryLoader("Notion_DB") | langchain_community.document_loaders.NotionDirectoryLoader |
from langchain_community.document_loaders import S3FileLoader
get_ipython().run_line_magic('pip', 'install --upgrade --quiet boto3')
loader = | S3FileLoader("testing-hwc", "fake.docx") | langchain_community.document_loaders.S3FileLoader |
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")
from langchain.llms.bedrock import Bedrock
from langchain_experimental.recommenders import AmazonPersonalizeChain
bedrock_llm = | Bedrock(model_id="anthropic.claude-v2", region_name="us-west-2") | langchain.llms.bedrock.Bedrock |
from langchain.evaluation import load_evaluator
evaluator = load_evaluator("criteria", criteria="conciseness")
from langchain.evaluation import EvaluatorType
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria="conciseness")
eval_result = evaluator.evaluate_strings(
prediction="What's 2+2? That's an elementary question. The answer you're looking for is that two and two is four.",
input="What's 2+2?",
)
print(eval_result)
evaluator = load_evaluator("labeled_criteria", criteria="correctness")
eval_result = evaluator.evaluate_strings(
input="What is the capital of the US?",
prediction="Topeka, KS",
reference="The capital of the US is Topeka, KS, where it permanently moved from Washington D.C. on May 16, 2023",
)
print(f'With ground truth: {eval_result["score"]}')
from langchain.evaluation import Criteria
list(Criteria)
custom_criterion = {
"numeric": "Does the output contain numeric or mathematical information?"
}
eval_chain = load_evaluator(
EvaluatorType.CRITERIA,
criteria=custom_criterion,
)
query = "Tell me a joke"
prediction = "I ate some square pie but I don't know the square of pi."
eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
print(eval_result)
custom_criteria = {
"numeric": "Does the output contain numeric information?",
"mathematical": "Does the output contain mathematical information?",
"grammatical": "Is the output grammatically correct?",
"logical": "Is the output logical?",
}
eval_chain = load_evaluator(
EvaluatorType.CRITERIA,
criteria=custom_criteria,
)
eval_result = eval_chain.evaluate_strings(prediction=prediction, input=query)
print("Multi-criteria evaluation")
print(eval_result)
from langchain.chains.constitutional_ai.principles import PRINCIPLES
print(f"{len(PRINCIPLES)} available principles")
list(PRINCIPLES.items())[:5]
evaluator = load_evaluator(EvaluatorType.CRITERIA, criteria=PRINCIPLES["harmful1"])
eval_result = evaluator.evaluate_strings(
prediction="I say that man is a lilly-livered nincompoop",
input="What do you think of Will?",
)
print(eval_result)
get_ipython().run_line_magic('pip', 'install --upgrade --quiet anthropic')
from langchain_community.chat_models import ChatAnthropic
llm = ChatAnthropic(temperature=0)
evaluator = | load_evaluator("criteria", llm=llm, criteria="conciseness") | langchain.evaluation.load_evaluator |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('poetry run pip install replicate')
from getpass import getpass
REPLICATE_API_TOKEN = getpass()
import os
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
from langchain.chains import LLMChain
from langchain.prompts import PromptTemplate
from langchain_community.llms import Replicate
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
llm(prompt)
llm = Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
)
prompt = """
Answer the following yes/no question by reasoning step by step.
Can a dog drive a car?
"""
llm(prompt)
text2image = Replicate(
model="stability-ai/stable-diffusion:db21e45d3f7023abc2a46ee38a23973f6dce16bb082a930b0c49861f96d1e5bf",
model_kwargs={"image_dimensions": "512x512"},
)
image_output = text2image("A cat riding a motorcycle by Picasso")
image_output
get_ipython().system('poetry run pip install Pillow')
from io import BytesIO
import requests
from PIL import Image
response = requests.get(image_output)
img = Image.open(BytesIO(response.content))
img
from langchain.callbacks.streaming_stdout import StreamingStdOutCallbackHandler
llm = Replicate(
streaming=True,
callbacks=[StreamingStdOutCallbackHandler()],
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.75, "max_length": 500, "top_p": 1},
)
prompt = """
User: Answer the following yes/no question by reasoning step by step. Can a dog drive a car?
Assistant:
"""
_ = llm(prompt)
import time
llm = Replicate(
model="a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5",
model_kwargs={"temperature": 0.01, "max_length": 500, "top_p": 1},
)
prompt = """
User: What is the best way to learn python?
Assistant:
"""
start_time = time.perf_counter()
raw_output = llm(prompt) # raw output, no stop
end_time = time.perf_counter()
print(f"Raw output:\n {raw_output}")
print(f"Raw output runtime: {end_time - start_time} seconds")
start_time = time.perf_counter()
stopped_output = llm(prompt, stop=["\n\n"]) # stop on double newlines
end_time = time.perf_counter()
print(f"Stopped output:\n {stopped_output}")
print(f"Stopped output runtime: {end_time - start_time} seconds")
from langchain.chains import SimpleSequentialChain
dolly_llm = | Replicate(
model="replicate/dolly-v2-12b:ef0e1aefc61f8e096ebe4db6b2bacc297daf2ef6899f0f7e001ec445893500e5"
) | langchain_community.llms.Replicate |
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 OpenAI
os.environ["JIRA_API_TOKEN"] = "abc"
os.environ["JIRA_USERNAME"] = "123"
os.environ["JIRA_INSTANCE_URL"] = "https://jira.atlassian.com"
os.environ["OPENAI_API_KEY"] = "xyz"
llm = OpenAI(temperature=0)
jira = | JiraAPIWrapper() | langchain_community.utilities.jira.JiraAPIWrapper |
from langchain_community.document_loaders import UnstructuredURLLoader
urls = [
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-8-2023",
"https://www.understandingwar.org/backgrounder/russian-offensive-campaign-assessment-february-9-2023",
]
loader = UnstructuredURLLoader(urls=urls)
data = loader.load()
from langchain_community.document_loaders import SeleniumURLLoader
urls = [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"https://goo.gl/maps/NDSHwePEyaHMFGwh8",
]
loader = SeleniumURLLoader(urls=urls)
data = loader.load()
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "playwright"')
get_ipython().run_line_magic('pip', 'install --upgrade --quiet "unstructured"')
get_ipython().system('playwright install')
from langchain_community.document_loaders import PlaywrightURLLoader
urls = [
"https://www.youtube.com/watch?v=dQw4w9WgXcQ",
"https://goo.gl/maps/NDSHwePEyaHMFGwh8",
]
loader = | PlaywrightURLLoader(urls=urls, remove_selectors=["header", "footer"]) | langchain_community.document_loaders.PlaywrightURLLoader |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
from langchain_text_splitters import (
Language,
RecursiveCharacterTextSplitter,
)
[e.value for e in Language]
| RecursiveCharacterTextSplitter.get_separators_for_language(Language.PYTHON) | langchain_text_splitters.RecursiveCharacterTextSplitter.get_separators_for_language |
get_ipython().run_line_magic('pip', 'install -qU langchain-text-splitters')
from langchain_text_splitters import HTMLHeaderTextSplitter
html_string = """
<!DOCTYPE html>
<html>
<body>
<div>
<h1>Foo</h1>
<p>Some intro text about Foo.</p>
<div>
<h2>Bar main section</h2>
<p>Some intro text about Bar.</p>
<h3>Bar subsection 1</h3>
<p>Some text about the first subtopic of Bar.</p>
<h3>Bar subsection 2</h3>
<p>Some text about the second subtopic of Bar.</p>
</div>
<div>
<h2>Baz</h2>
<p>Some text about Baz</p>
</div>
<br>
<p>Some concluding text about Foo</p>
</div>
</body>
</html>
"""
headers_to_split_on = [
("h1", "Header 1"),
("h2", "Header 2"),
("h3", "Header 3"),
]
html_splitter = | HTMLHeaderTextSplitter(headers_to_split_on=headers_to_split_on) | langchain_text_splitters.HTMLHeaderTextSplitter |