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import openai
openai.api_key = "sk-your-api-key"
from llama_index.agent import OpenAIAgent
import requests
import yaml
f = requests.get(
"https://raw.githubusercontent.com/APIs-guru/openapi-directory/main/APIs/openai.com/1.2.0/openapi.yaml"
).text
open_api_spec = yaml.safe_load(f)
from llama_index.tools.openapi.base import OpenAPIToolSpec
from llama_index.tools.requests.base import RequestsToolSpec
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
open_spec = | OpenAPIToolSpec(open_api_spec) | llama_index.tools.openapi.base.OpenAPIToolSpec |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
import logging
import sys
import os
import qdrant_client
from IPython.display import Markdown, display
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
os.environ["OPENAI_API_KEY"] = "YOUR OPENAI API KEY"
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
client = qdrant_client.QdrantClient(
location=":memory:"
)
vector_store = QdrantVectorStore(client=client, collection_name="paul_graham")
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system("mkdir -p 'data/'")
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.core import Document, VectorStoreIndex
from llama_index.readers.file import PyMuPDFReader
from llama_index.core.node_parser import SimpleNodeParser
from llama_index.llms.openai import OpenAI
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
node_parser = SimpleNodeParser.from_defaults()
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/fh9vsmmm8vu0j50l3ss38/llama2_eval_qr_dataset.json?rlkey=kkoaez7aqeb4z25gzc06ak6kb&dl=1" -O data/llama2_eval_qr_dataset.json')
from llama_index.core.evaluation import QueryResponseDataset
eval_dataset = QueryResponseDataset.from_json(
"data/llama2_eval_qr_dataset.json"
)
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-vertex')
from llama_index.llms.vertex import Vertex
from google.oauth2 import service_account
filename = "vertex-407108-37495ce6c303.json"
credentials: service_account.Credentials = (
service_account.Credentials.from_service_account_file(filename)
)
Vertex(
model="text-bison", project=credentials.project_id, credentials=credentials
)
from llama_index.llms.vertex import Vertex
from llama_index.core.llms import ChatMessage, MessageRole
llm = Vertex(model="text-bison", temperature=0, additional_kwargs={})
llm.complete("Hello this is a sample text").text
(await llm.acomplete("hello")).text
list(llm.stream_complete("hello"))[-1].text
chat = | Vertex(model="chat-bison") | llama_index.llms.vertex.Vertex |
get_ipython().system('pip install llama-index')
import logging
import sys
from IPython.display import Markdown, display
import pandas as pd
from llama_index.core.query_engine import PandasQueryEngine
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
df = pd.DataFrame(
{
"city": ["Toronto", "Tokyo", "Berlin"],
"population": [2930000, 13960000, 3645000],
}
)
query_engine = PandasQueryEngine(df=df, verbose=True)
response = query_engine.query(
"What is the city with the highest population?",
)
display(Markdown(f"<b>{response}</b>"))
print(response.metadata["pandas_instruction_str"])
query_engine = PandasQueryEngine(df=df, verbose=True, synthesize_response=True)
response = query_engine.query(
"What is the city with the highest population? Give both the city and population",
)
print(str(response))
get_ipython().system("wget 'https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/examples/data/csv/titanic_train.csv' -O 'titanic_train.csv'")
df = pd.read_csv("./titanic_train.csv")
query_engine = PandasQueryEngine(df=df, verbose=True)
response = query_engine.query(
"What is the correlation between survival and age?",
)
display(Markdown(f"<b>{response}</b>"))
print(response.metadata["pandas_instruction_str"])
from llama_index.core import PromptTemplate
query_engine = PandasQueryEngine(df=df, verbose=True)
prompts = query_engine.get_prompts()
print(prompts["pandas_prompt"].template)
print(prompts["response_synthesis_prompt"].template)
new_prompt = | PromptTemplate(
"""\
You are working with a pandas dataframe in Python.
The name of the dataframe is `df`.
This is the result of `print(df.head())`:
{df_str}
Follow these instructions:
{instruction_str}
Query: {query_str}
Expression: """
) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_response
from llama_index.llms.openai import OpenAI
llm = OpenAI(temperature=0, model="gpt-4")
get_ipython().system("mkdir -p 'data/10q/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_march_2022.pdf' -O 'data/10q/uber_10q_march_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_june_2022.pdf' -O 'data/10q/uber_10q_june_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_sept_2022.pdf' -O 'data/10q/uber_10q_sept_2022.pdf'")
march_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(march_2022)
june_index = VectorStoreIndex.from_documents(june_2022)
sept_index = VectorStoreIndex.from_documents(sept_2022)
march_engine = march_index.as_query_engine(similarity_top_k=3, llm=llm)
june_engine = june_index.as_query_engine(similarity_top_k=3, llm=llm)
sept_engine = sept_index.as_query_engine(similarity_top_k=3, llm=llm)
from llama_index.core.tools import QueryEngineTool
query_tool_sept = QueryEngineTool.from_defaults(
query_engine=sept_engine,
name="sept_2022",
description=(
f"Provides information about Uber quarterly financials ending"
f" September 2022"
),
)
query_tool_june = QueryEngineTool.from_defaults(
query_engine=june_engine,
name="june_2022",
description=(
f"Provides information about Uber quarterly financials ending June"
f" 2022"
),
)
query_tool_march = QueryEngineTool.from_defaults(
query_engine=march_engine,
name="march_2022",
description=(
f"Provides information about Uber quarterly financials ending March"
f" 2022"
),
)
from llama_index.core.tools import QueryPlanTool
from llama_index.core import get_response_synthesizer
response_synthesizer = get_response_synthesizer()
query_plan_tool = QueryPlanTool.from_defaults(
query_engine_tools=[query_tool_sept, query_tool_june, query_tool_march],
response_synthesizer=response_synthesizer,
)
query_plan_tool.metadata.to_openai_tool() # to_openai_function() deprecated
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
agent = OpenAIAgent.from_tools(
[query_plan_tool],
max_function_calls=10,
llm= | OpenAI(temperature=0, model="gpt-4-0613") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-docarray')
get_ipython().system('pip install llama-index')
import os
import sys
import logging
import textwrap
import warnings
warnings.filterwarnings("ignore")
os.environ["TOKENIZERS_PARALLELISM"] = "false"
from llama_index.core import (
GPTVectorStoreIndex,
SimpleDirectoryReader,
Document,
)
from llama_index.vector_stores.docarray import DocArrayInMemoryVectorStore
from IPython.display import Markdown, display
import os
os.environ["OPENAI_API_KEY"] = "<your openai key>"
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
print(
"Document ID:",
documents[0].doc_id,
"Document Hash:",
documents[0].doc_hash,
)
from llama_index.core import StorageContext
vector_store = DocArrayInMemoryVectorStore()
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
get_ipython().system("mkdir -p 'data/'")
get_ipython().system("curl 'https://arxiv.org/pdf/2307.09288.pdf' -o 'data/llama2.pdf'")
get_ipython().system('pip install unstructured[pdf]')
from llama_index.core import VectorStoreIndex
from llama_index.readers.file import UnstructuredReader
documents = UnstructuredReader().load_data("data/llama2.pdf")
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
from llama_index.agent import OpenAIAgent
import openai
openai.api_key = "sk-your-key"
from llama_index.tools.yelp.base import YelpToolSpec
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
tool_spec = YelpToolSpec(api_key="your-key", client_id="your-id")
tools = tool_spec.to_tool_list()
agent = OpenAIAgent.from_tools(
[
*LoadAndSearchToolSpec.from_defaults(tools[0]).to_tool_list(),
* | LoadAndSearchToolSpec.from_defaults(tools[1]) | llama_index.tools.tool_spec.load_and_search.base.LoadAndSearchToolSpec.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-perplexity')
get_ipython().system('pip install llama-index')
from llama_index.llms.perplexity import Perplexity
pplx_api_key = "your-perplexity-api-key"
llm = Perplexity(
api_key=pplx_api_key, model="mistral-7b-instruct", temperature=0.5
)
from llama_index.core.llms import ChatMessage
messages_dict = [
{"role": "system", "content": "Be precise and concise."},
{"role": "user", "content": "Tell me 5 sentences about Perplexity."},
]
messages = [ | ChatMessage(**msg) | llama_index.core.llms.ChatMessage |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install duckdb')
get_ipython().system('pip install llama-index-vector-stores-duckdb')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.duckdb import DuckDBVectorStore
from llama_index.core import StorageContext
from IPython.display import Markdown, display
import os
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client("http://localhost:8080")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core.response.notebook_utils import display_response
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import StorageContext
vector_store = WeaviateVectorStore(weaviate_client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine(similarity_top_k=2)
response = query_engine.query("What did the author do growing up?")
display_response(response)
query_engine = index.as_query_engine(
vector_store_query_mode="hybrid", similarity_top_k=2
)
response = query_engine.query(
"What did the author do growing up?",
)
display_response(response)
query_engine = index.as_query_engine(
vector_store_query_mode="hybrid", similarity_top_k=2, alpha=0.0
)
response = query_engine.query(
"What did the author do growing up?",
)
| display_response(response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
from llama_index.readers.file import ImageTabularChartReader
from llama_index.core import SummaryIndex
from llama_index.core.response.notebook_utils import display_response
from pathlib import Path
loader = ImageTabularChartReader(keep_image=True)
documents = loader.load_data(file=Path("./marine_chart.png"))
print(documents[0].text)
summary_index = SummaryIndex.from_documents(documents)
response = summary_index.as_query_engine().query(
"What is the difference between the shares of Greenland and the share of"
" Mauritania?"
)
display_response(response, show_source=True)
documents = loader.load_data(file=Path("./pew1.png"))
print(documents[0].text)
summary_index = SummaryIndex.from_documents(documents)
response = summary_index.as_query_engine().query(
"What percentage says that the US contributes to peace and stability?"
)
| display_response(response, show_source=True) | llama_index.core.response.notebook_utils.display_response |
get_ipython().system('pip install llama-index')
import logging
import sys
from IPython.display import Markdown, display
import pandas as pd
from llama_index.core.query_engine import PandasQueryEngine
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
df = pd.DataFrame(
{
"city": ["Toronto", "Tokyo", "Berlin"],
"population": [2930000, 13960000, 3645000],
}
)
query_engine = PandasQueryEngine(df=df, verbose=True)
response = query_engine.query(
"What is the city with the highest population?",
)
display(Markdown(f"<b>{response}</b>"))
print(response.metadata["pandas_instruction_str"])
query_engine = PandasQueryEngine(df=df, verbose=True, synthesize_response=True)
response = query_engine.query(
"What is the city with the highest population? Give both the city and population",
)
print(str(response))
get_ipython().system("wget 'https://raw.githubusercontent.com/jerryjliu/llama_index/main/docs/examples/data/csv/titanic_train.csv' -O 'titanic_train.csv'")
df = pd.read_csv("./titanic_train.csv")
query_engine = | PandasQueryEngine(df=df, verbose=True) | llama_index.core.query_engine.PandasQueryEngine |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
from llama_index.core.query_pipeline import QueryPipeline
from llama_index.core import PromptTemplate
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
p = QueryPipeline(chain=[prompt_tmpl, llm], verbose=True)
output = p.run(movie_name="The Departed")
print(str(output))
from typing import List
from pydantic import BaseModel, Field
from llama_index.core.output_parsers import PydanticOutputParser
class Movie(BaseModel):
"""Object representing a single movie."""
name: str = Field(..., description="Name of the movie.")
year: int = Field(..., description="Year of the movie.")
class Movies(BaseModel):
"""Object representing a list of movies."""
movies: List[Movie] = Field(..., description="List of movies.")
llm = OpenAI(model="gpt-3.5-turbo")
output_parser = PydanticOutputParser(Movies)
json_prompt_str = """\
Please generate related movies to {movie_name}. Output with the following JSON format:
"""
json_prompt_str = output_parser.format(json_prompt_str)
json_prompt_tmpl = PromptTemplate(json_prompt_str)
p = QueryPipeline(chain=[json_prompt_tmpl, llm, output_parser], verbose=True)
output = p.run(movie_name="Toy Story")
output
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
prompt_str2 = """\
Here's some text:
{text}
Can you rewrite this with a summary of each movie?
"""
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
llm_c = llm.as_query_component(streaming=True)
p = QueryPipeline(
chain=[prompt_tmpl, llm_c, prompt_tmpl2, llm_c], verbose=True
)
output = p.run(movie_name="The Dark Knight")
for o in output:
print(o.delta, end="")
p = QueryPipeline(
chain=[
json_prompt_tmpl,
llm.as_query_component(streaming=True),
output_parser,
],
verbose=True,
)
output = p.run(movie_name="Toy Story")
print(output)
from llama_index.postprocessor.cohere_rerank import CohereRerank
prompt_str1 = "Please generate a concise question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl1 = PromptTemplate(prompt_str1)
prompt_str2 = (
"Please write a passage to answer the question\n"
"Try to include as many key details as possible.\n"
"\n"
"\n"
"{query_str}\n"
"\n"
"\n"
'Passage:"""\n'
)
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=5)
p = QueryPipeline(
chain=[prompt_tmpl1, llm, prompt_tmpl2, llm, retriever], verbose=True
)
nodes = p.run(topic="college")
len(nodes)
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.core.response_synthesizers import TreeSummarize
prompt_str = "Please generate a question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
retriever = index.as_retriever(similarity_top_k=3)
reranker = CohereRerank()
summarizer = | TreeSummarize(llm=llm) | llama_index.core.response_synthesizers.TreeSummarize |
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
FixedRecencyPostprocessor,
EmbeddingRecencyPostprocessor,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import StorageContext
def get_file_metadata(file_name: str):
"""Get file metadata."""
if "v1" in file_name:
return {"date": "2020-01-01"}
elif "v2" in file_name:
return {"date": "2020-02-03"}
elif "v3" in file_name:
return {"date": "2022-04-12"}
else:
raise ValueError("invalid file")
documents = SimpleDirectoryReader(
input_files=[
"test_versioned_data/paul_graham_essay_v1.txt",
"test_versioned_data/paul_graham_essay_v2.txt",
"test_versioned_data/paul_graham_essay_v3.txt",
],
file_metadata=get_file_metadata,
).load_data()
from llama_index.core import Settings
Settings.text_splitter = | SentenceSplitter(chunk_size=512) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-kvstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.storage.kvstore.firestore import FirestoreKVStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.index_store.firestore import FirestoreIndexStore
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist()
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, vector_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, keyword_id=keyword_id
)
chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = chatgpt
Settings.chunk_size = 1024
query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")
display_response(list_response)
query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")
| display_response(vector_response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-cross-encoders')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install datasets --quiet')
get_ipython().system('pip install sentence-transformers --quiet')
get_ipython().system('pip install openai --quiet')
from datasets import load_dataset
import random
dataset = load_dataset("allenai/qasper")
train_dataset = dataset["train"]
validation_dataset = dataset["validation"]
test_dataset = dataset["test"]
random.seed(42) # Set a random seed for reproducibility
train_sampled_indices = random.sample(range(len(train_dataset)), 800)
train_samples = [train_dataset[i] for i in train_sampled_indices]
test_sampled_indices = random.sample(range(len(test_dataset)), 80)
test_samples = [test_dataset[i] for i in test_sampled_indices]
from typing import List
def get_full_text(sample: dict) -> str:
"""
:param dict sample: the row sample from QASPER
"""
title = sample["title"]
abstract = sample["abstract"]
sections_list = sample["full_text"]["section_name"]
paragraph_list = sample["full_text"]["paragraphs"]
combined_sections_with_paras = ""
if len(sections_list) == len(paragraph_list):
combined_sections_with_paras += title + "\t"
combined_sections_with_paras += abstract + "\t"
for index in range(0, len(sections_list)):
combined_sections_with_paras += str(sections_list[index]) + "\t"
combined_sections_with_paras += "".join(paragraph_list[index])
return combined_sections_with_paras
else:
print("Not the same number of sections as paragraphs list")
def get_questions(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from QASPER
"""
questions_list = sample["qas"]["question"]
return questions_list
doc_qa_dict_list = []
for train_sample in train_samples:
full_text = get_full_text(train_sample)
questions_list = get_questions(train_sample)
local_dict = {"paper": full_text, "questions": questions_list}
doc_qa_dict_list.append(local_dict)
len(doc_qa_dict_list)
import pandas as pd
df_train = pd.DataFrame(doc_qa_dict_list)
df_train.to_csv("train.csv")
"""
The Answers field in the dataset follow the below format:-
Unanswerable answers have "unanswerable" set to true.
The remaining answers have exactly one of the following fields being non-empty.
"extractive_spans" are spans in the paper which serve as the answer.
"free_form_answer" is a written out answer.
"yes_no" is true iff the answer is Yes, and false iff the answer is No.
We accept only free-form answers and for all the other kind of answers we set their value to 'Unacceptable',
to better evaluate the performance of the query engine using pairwise comparision evaluator as it uses GPT-4 which is biased towards preferring long answers more.
https://www.anyscale.com/blog/a-comprehensive-guide-for-building-rag-based-llm-applications-part-1
So in the case of 'yes_no' answers it can favour Query Engine answers more than reference answers.
Also in the case of extracted spans it can favour reference answers more than Query engine generated answers.
"""
eval_doc_qa_answer_list = []
def get_answers(sample: dict) -> List[str]:
"""
:param dict sample: the row sample from the train split of QASPER
"""
final_answers_list = []
answers = sample["qas"]["answers"]
for answer in answers:
local_answer = ""
types_of_answers = answer["answer"][0]
if types_of_answers["unanswerable"] == False:
if types_of_answers["free_form_answer"] != "":
local_answer = types_of_answers["free_form_answer"]
else:
local_answer = "Unacceptable"
else:
local_answer = "Unacceptable"
final_answers_list.append(local_answer)
return final_answers_list
for test_sample in test_samples:
full_text = get_full_text(test_sample)
questions_list = get_questions(test_sample)
answers_list = get_answers(test_sample)
local_dict = {
"paper": full_text,
"questions": questions_list,
"answers": answers_list,
}
eval_doc_qa_answer_list.append(local_dict)
len(eval_doc_qa_answer_list)
import pandas as pd
df_test = pd.DataFrame(eval_doc_qa_answer_list)
df_test.to_csv("test.csv")
get_ipython().system('pip install llama-index --quiet')
import os
from llama_index.core import SimpleDirectoryReader
import openai
from llama_index.finetuning.cross_encoders.dataset_gen import (
generate_ce_fine_tuning_dataset,
generate_synthetic_queries_over_documents,
)
from llama_index.finetuning.cross_encoders import CrossEncoderFinetuneEngine
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import Document
final_finetuning_data_list = []
for paper in doc_qa_dict_list:
questions_list = paper["questions"]
documents = [Document(text=paper["paper"])]
local_finetuning_dataset = generate_ce_fine_tuning_dataset(
documents=documents,
questions_list=questions_list,
max_chunk_length=256,
top_k=5,
)
final_finetuning_data_list.extend(local_finetuning_dataset)
len(final_finetuning_data_list)
import pandas as pd
df_finetuning_dataset = pd.DataFrame(final_finetuning_data_list)
df_finetuning_dataset.to_csv("fine_tuning.csv")
finetuning_dataset = final_finetuning_data_list
finetuning_dataset[0]
get_ipython().system('wget -O test.csv https://www.dropbox.com/scl/fi/3lmzn6714oy358mq0vawm/test.csv?rlkey=yz16080te4van7fvnksi9kaed&dl=0')
import pandas as pd
import ast # Used to safely evaluate the string as a list
df_test = pd.read_csv("/content/test.csv", index_col=0)
df_test["questions"] = df_test["questions"].apply(ast.literal_eval)
df_test["answers"] = df_test["answers"].apply(ast.literal_eval)
print(f"Number of papers in the test sample:- {len(df_test)}")
from llama_index.core import Document
final_eval_data_list = []
for index, row in df_test.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
local_eval_dataset = generate_ce_fine_tuning_dataset(
documents=documents,
questions_list=query_list,
max_chunk_length=256,
top_k=5,
)
relevant_query_list = []
relevant_context_list = []
for item in local_eval_dataset:
if item.score == 1:
relevant_query_list.append(item.query)
relevant_context_list.append(item.context)
if len(relevant_query_list) > 0:
final_eval_data_list.append(
{
"paper": row["paper"],
"questions": relevant_query_list,
"context": relevant_context_list,
}
)
len(final_eval_data_list)
import pandas as pd
df_finetuning_dataset = pd.DataFrame(final_eval_data_list)
df_finetuning_dataset.to_csv("reranking_test.csv")
get_ipython().system('pip install huggingface_hub --quiet')
from huggingface_hub import notebook_login
notebook_login()
from sentence_transformers import SentenceTransformer
finetuning_engine = CrossEncoderFinetuneEngine(
dataset=finetuning_dataset, epochs=2, batch_size=8
)
finetuning_engine.finetune()
finetuning_engine.push_to_hub(
repo_id="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2"
)
get_ipython().system('pip install nest-asyncio --quiet')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('wget -O reranking_test.csv https://www.dropbox.com/scl/fi/mruo5rm46k1acm1xnecev/reranking_test.csv?rlkey=hkniwowq0xrc3m0ywjhb2gf26&dl=0')
import pandas as pd
import ast
df_reranking = pd.read_csv("/content/reranking_test.csv", index_col=0)
df_reranking["questions"] = df_reranking["questions"].apply(ast.literal_eval)
df_reranking["context"] = df_reranking["context"].apply(ast.literal_eval)
print(f"Number of papers in the reranking eval dataset:- {len(df_reranking)}")
df_reranking.head(1)
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
from llama_index.core import Settings
import os
import openai
import pandas as pd
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
Settings.chunk_size = 256
rerank_base = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-12-v2", top_n=3
)
rerank_finetuned = SentenceTransformerRerank(
model="bpHigh/Cross-Encoder-LLamaIndex-Demo-v2", top_n=3
)
without_reranker_hits = 0
base_reranker_hits = 0
finetuned_reranker_hits = 0
total_number_of_context = 0
for index, row in df_reranking.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
context_list = row["context"]
assert len(query_list) == len(context_list)
vector_index = VectorStoreIndex.from_documents(documents)
retriever_without_reranker = vector_index.as_query_engine(
similarity_top_k=3, response_mode="no_text"
)
retriever_with_base_reranker = vector_index.as_query_engine(
similarity_top_k=8,
response_mode="no_text",
node_postprocessors=[rerank_base],
)
retriever_with_finetuned_reranker = vector_index.as_query_engine(
similarity_top_k=8,
response_mode="no_text",
node_postprocessors=[rerank_finetuned],
)
for index in range(0, len(query_list)):
query = query_list[index]
context = context_list[index]
total_number_of_context += 1
response_without_reranker = retriever_without_reranker.query(query)
without_reranker_nodes = response_without_reranker.source_nodes
for node in without_reranker_nodes:
if context in node.node.text or node.node.text in context:
without_reranker_hits += 1
response_with_base_reranker = retriever_with_base_reranker.query(query)
with_base_reranker_nodes = response_with_base_reranker.source_nodes
for node in with_base_reranker_nodes:
if context in node.node.text or node.node.text in context:
base_reranker_hits += 1
response_with_finetuned_reranker = (
retriever_with_finetuned_reranker.query(query)
)
with_finetuned_reranker_nodes = (
response_with_finetuned_reranker.source_nodes
)
for node in with_finetuned_reranker_nodes:
if context in node.node.text or node.node.text in context:
finetuned_reranker_hits += 1
assert (
len(with_finetuned_reranker_nodes)
== len(with_base_reranker_nodes)
== len(without_reranker_nodes)
== 3
)
without_reranker_scores = [without_reranker_hits]
base_reranker_scores = [base_reranker_hits]
finetuned_reranker_scores = [finetuned_reranker_hits]
reranker_eval_dict = {
"Metric": "Hits",
"OpenAI_Embeddings": without_reranker_scores,
"Base_cross_encoder": base_reranker_scores,
"Finetuned_cross_encoder": finetuned_reranker_hits,
"Total Relevant Context": total_number_of_context,
}
df_reranker_eval_results = pd.DataFrame(reranker_eval_dict)
display(df_reranker_eval_results)
get_ipython().system('wget -O test.csv https://www.dropbox.com/scl/fi/3lmzn6714oy358mq0vawm/test.csv?rlkey=yz16080te4van7fvnksi9kaed&dl=0')
import pandas as pd
import ast # Used to safely evaluate the string as a list
df_test = pd.read_csv("/content/test.csv", index_col=0)
df_test["questions"] = df_test["questions"].apply(ast.literal_eval)
df_test["answers"] = df_test["answers"].apply(ast.literal_eval)
print(f"Number of papers in the test sample:- {len(df_test)}")
df_test.head(1)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
from llama_index.core.evaluation import PairwiseComparisonEvaluator
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
import os
import openai
import pandas as pd
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
no_reranker_dict_list = []
for index, row in df_test.iterrows():
documents = [Document(text=row["paper"])]
query_list = row["questions"]
reference_answers_list = row["answers"]
number_of_accepted_queries = 0
vector_index = VectorStoreIndex.from_documents(documents)
query_engine = vector_index.as_query_engine(similarity_top_k=3)
assert len(query_list) == len(reference_answers_list)
pairwise_local_score = 0
for index in range(0, len(query_list)):
query = query_list[index]
reference = reference_answers_list[index]
if reference != "Unacceptable":
number_of_accepted_queries += 1
response = str(query_engine.query(query))
no_reranker_dict = {
"query": query,
"response": response,
"reference": reference,
}
no_reranker_dict_list.append(no_reranker_dict)
pairwise_eval_result = await evaluator_gpt4_pairwise.aevaluate(
query, response=response, reference=reference
)
pairwise_score = pairwise_eval_result.score
pairwise_local_score += pairwise_score
else:
pass
if number_of_accepted_queries > 0:
avg_pairwise_local_score = (
pairwise_local_score / number_of_accepted_queries
)
pairwise_scores_list.append(avg_pairwise_local_score)
overal_pairwise_average_score = sum(pairwise_scores_list) / len(
pairwise_scores_list
)
df_responses = pd.DataFrame(no_reranker_dict_list)
df_responses.to_csv("No_Reranker_Responses.csv")
results_dict = {
"name": ["Without Reranker"],
"pairwise score": [overal_pairwise_average_score],
}
results_df = pd.DataFrame(results_dict)
display(results_df)
from llama_index.core.postprocessor import SentenceTransformerRerank
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
from llama_index.core.evaluation import PairwiseComparisonEvaluator
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-"
openai.api_key = os.environ["OPENAI_API_KEY"]
rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-12-v2", top_n=3
)
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator_gpt4_pairwise = PairwiseComparisonEvaluator(llm=gpt4)
pairwise_scores_list = []
base_reranker_dict_list = []
for index, row in df_test.iterrows():
documents = [ | Document(text=row["paper"]) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-hub-llama-packs-tables-chain-of-table-base')
get_ipython().system('wget "https://github.com/ppasupat/WikiTableQuestions/releases/download/v1.0.2/WikiTableQuestions-1.0.2-compact.zip" -O data.zip')
get_ipython().system('unzip data.zip')
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/200-csv/3.csv")
df
from llama_index.packs.tables.chain_of_table.base import (
ChainOfTableQueryEngine,
serialize_table,
)
from llama_index.core.llama_pack import download_llama_pack
download_llama_pack(
"ChainOfTablePack",
"./chain_of_table_pack",
skip_load=True,
)
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
import pandas as pd
df = pd.read_csv("~/Downloads/WikiTableQuestions/csv/200-csv/11.csv")
df
query_engine = ChainOfTableQueryEngine(df, llm=llm, verbose=True)
response = query_engine.query("Who won best Director in the 1972 Academy Awards?")
str(response.response)
import pandas as pd
df = pd.read_csv("./WikiTableQuestions/csv/200-csv/42.csv")
df
query_engine = ChainOfTableQueryEngine(df, llm=llm, verbose=True)
response = query_engine.query("What was the precipitation in inches during June?")
str(response)
from llama_index.core import PromptTemplate
from llama_index.core.query_pipeline import QueryPipeline
prompt_str = """\
Here's a serialized table.
{serialized_table}
Given this table please answer the question: {question}
Answer: """
prompt = PromptTemplate(prompt_str)
prompt_c = prompt.as_query_component(partial={"serialized_table": serialize_table(df)})
qp = | QueryPipeline(chain=[prompt_c, llm]) | llama_index.core.query_pipeline.QueryPipeline |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from IPython.display import Markdown, display
import chromadb
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
embed_model = | HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5") | llama_index.embeddings.huggingface.HuggingFaceEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-kvstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.storage.kvstore.firestore import FirestoreKVStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.index_store.firestore import FirestoreIndexStore
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist()
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, vector_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, keyword_id=keyword_id
)
chatgpt = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install rank-bm25 pymupdf')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama-index')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
splitter = SentenceSplitter(chunk_size=1024)
index = VectorStoreIndex.from_documents(documents, transformations=[splitter])
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
from llama_index.core import PromptTemplate
query_str = "How do the models developed in this work compare to open-source chat models based on the benchmarks tested?"
query_gen_prompt_str = (
"You are a helpful assistant that generates multiple search queries based on a "
"single input query. Generate {num_queries} search queries, one on each line, "
"related to the following input query:\n"
"Query: {query}\n"
"Queries:\n"
)
query_gen_prompt = | PromptTemplate(query_gen_prompt_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-falkordb')
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
from llama_index.graph_stores.falkordb import FalkorDBGraphStore
graph_store = FalkorDBGraphStore(
"redis://localhost:6379", decode_responses=True
)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from IPython.display import Markdown, display
documents = SimpleDirectoryReader(
"../../../../examples/paul_graham_essay/data"
).load_data()
llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
llm = OpenAI(model="gpt-4")
data = | SimpleDirectoryReader(input_dir="./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
import os
os.environ["GITHUB_TOKEN"] = "<your github token>"
import os
from llama_index.readers.github import (
GitHubRepositoryIssuesReader,
GitHubIssuesClient,
)
github_client = | GitHubIssuesClient() | llama_index.readers.github.GitHubIssuesClient |
get_ipython().run_line_magic('pip', 'install llama-index-llms-replicate')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["REPLICATE_API_TOKEN"] = "YOUR_REPLICATE_TOKEN"
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from IPython.display import Markdown, display
from llama_index.llms.replicate import Replicate
from llama_index.core.llms.llama_utils import (
messages_to_prompt,
completion_to_prompt,
)
LLAMA_13B_V2_CHAT = "a16z-infra/llama13b-v2-chat:df7690f1994d94e96ad9d568eac121aecf50684a0b0963b25a41cc40061269e5"
def custom_completion_to_prompt(completion: str) -> str:
return completion_to_prompt(
completion,
system_prompt=(
"You are a Q&A assistant. Your goal is to answer questions as "
"accurately as possible is the instructions and context provided."
),
)
llm = Replicate(
model=LLAMA_13B_V2_CHAT,
temperature=0.01,
context_window=4096,
completion_to_prompt=custom_completion_to_prompt,
messages_to_prompt=messages_to_prompt,
)
from llama_index.core import Settings
Settings.llm = llm
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.llms.openai import OpenAI
resp = OpenAI().complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.openai import OpenAI
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = OpenAI().chat(messages)
print(resp)
from llama_index.llms.openai import OpenAI
llm = OpenAI()
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
llm = OpenAI()
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(messages)
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="text-davinci-003")
resp = llm.complete("Paul Graham is ")
print(resp)
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
| ChatMessage(role="user", content="What is your name") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gradient')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gradient')
get_ipython().run_line_magic('pip', 'install llama-index --quiet')
get_ipython().run_line_magic('pip', 'install gradientai --quiet')
import os
os.environ["GRADIENT_ACCESS_TOKEN"] = "{GRADIENT_ACCESS_TOKEN}"
os.environ["GRADIENT_WORKSPACE_ID"] = "{GRADIENT_WORKSPACE_ID}"
from llama_index.llms.gradient import GradientBaseModelLLM
llm = GradientBaseModelLLM(
base_model_slug="llama2-7b-chat",
max_tokens=400,
)
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print(f"Loaded {len(documents)} document(s).")
from llama_index.embeddings.gradient import GradientEmbedding
from llama_index.core import Settings
embed_model = GradientEmbedding(
gradient_access_token=os.environ["GRADIENT_ACCESS_TOKEN"],
gradient_workspace_id=os.environ["GRADIENT_WORKSPACE_ID"],
gradient_model_slug="bge-large",
)
Settings.embed_model = embed_model
Settings.llm = llm
Settings.chunk_size = 1024
from llama_index.core import VectorStoreIndex
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-xinference')
port = 9997 # replace with your endpoint port number
get_ipython().system('pip install llama-index')
from llama_index.core import SummaryIndex
from llama_index.core import (
TreeIndex,
VectorStoreIndex,
KeywordTableIndex,
KnowledgeGraphIndex,
SimpleDirectoryReader,
)
from llama_index.llms.xinference import Xinference
from xinference.client import RESTfulClient
from IPython.display import Markdown, display
client = RESTfulClient(f"http://localhost:{port}")
model_uid = client.launch_model(
model_name="llama-2-chat",
model_size_in_billions=7,
model_format="ggmlv3",
quantization="q2_K",
)
llm = Xinference(
endpoint=f"http://localhost:{port}",
model_uid=model_uid,
temperature=0.0,
max_tokens=512,
)
documents = | SimpleDirectoryReader("../data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client(
"https://llama-test-ezjahb4m.weaviate.network",
auth_client_secret=resource_owner_config,
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("./data/paul_graham") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anyscale')
get_ipython().system('pip install llama-index')
from llama_index.llms.anyscale import Anyscale
from llama_index.core.llms import ChatMessage
llm = Anyscale(api_key="<your-api-key>")
message = ChatMessage(role="user", content="Tell me a joke")
resp = llm.chat([message])
print(resp)
message = ChatMessage(role="user", content="Tell me a story in 250 words")
resp = llm.stream_chat([message])
for r in resp:
print(r.delta, end="")
resp = llm.complete("Tell me a joke")
print(resp)
resp = llm.stream_complete("Tell me a story in 250 words")
for r in resp:
print(r.delta, end="")
llm = | Anyscale(model="codellama/CodeLlama-34b-Instruct-hf") | llama_index.llms.anyscale.Anyscale |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
from llama_index.core.query_pipeline import QueryPipeline
from llama_index.core import PromptTemplate
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
p = | QueryPipeline(chain=[prompt_tmpl, llm], verbose=True) | llama_index.core.query_pipeline.QueryPipeline |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-timescalevector')
get_ipython().system('pip install llama-index')
import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.environ["OPENAI_API_KEY"]
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("Did the author work at YC?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What did the author work on before college?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
index = VectorStoreIndex.from_vector_store(vector_store=vector_store)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do before YC?")
print(textwrap.fill(str(response), 100))
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
vector_store.create_index()
vector_store.drop_index()
vector_store.create_index("tsv", max_alpha=1.0, num_neighbors=50)
vector_store.drop_index()
vector_store.create_index("hnsw", m=16, ef_construction=64)
vector_store.drop_index()
vector_store.create_index("ivfflat", num_lists=20, num_records=1000)
vector_store.drop_index()
vector_store.create_index()
import pandas as pd
from pathlib import Path
file_path = Path("../data/csv/commit_history.csv")
df = pd.read_csv(file_path)
df.dropna(inplace=True)
df = df.astype(str)
df = df[:1000]
df.head()
from timescale_vector import client
def create_uuid(date_string: str):
if date_string is None:
return None
time_format = "%a %b %d %H:%M:%S %Y %z"
datetime_obj = datetime.strptime(date_string, time_format)
uuid = client.uuid_from_time(datetime_obj)
return str(uuid)
from typing import List, Tuple
def split_name(input_string: str) -> Tuple[str, str]:
if input_string is None:
return None, None
start = input_string.find("<")
end = input_string.find(">")
name = input_string[:start].strip()
return name
from datetime import datetime, timedelta
def create_date(input_string: str) -> datetime:
if input_string is None:
return None
month_dict = {
"Jan": "01",
"Feb": "02",
"Mar": "03",
"Apr": "04",
"May": "05",
"Jun": "06",
"Jul": "07",
"Aug": "08",
"Sep": "09",
"Oct": "10",
"Nov": "11",
"Dec": "12",
}
components = input_string.split()
day = components[2]
month = month_dict[components[1]]
year = components[4]
time = components[3]
timezone_offset_minutes = int(
components[5]
) # Convert the offset to minutes
timezone_hours = timezone_offset_minutes // 60 # Calculate the hours
timezone_minutes = (
timezone_offset_minutes % 60
) # Calculate the remaining minutes
timestamp_tz_str = (
f"{year}-{month}-{day} {time}+{timezone_hours:02}{timezone_minutes:02}"
)
return timestamp_tz_str
from llama_index.core.schema import TextNode, NodeRelationship, RelatedNodeInfo
def create_node(row):
record = row.to_dict()
record_name = split_name(record["author"])
record_content = (
str(record["date"])
+ " "
+ record_name
+ " "
+ str(record["change summary"])
+ " "
+ str(record["change details"])
)
node = TextNode(
id_=create_uuid(record["date"]),
text=record_content,
metadata={
"commit": record["commit"],
"author": record_name,
"date": create_date(record["date"]),
},
)
return node
nodes = [create_node(row) for _, row in df.iterrows()]
from llama_index.embeddings.openai import OpenAIEmbedding
embedding_model = | OpenAIEmbedding() | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-zep')
get_ipython().system('pip install llama-index')
import logging
import sys
from uuid import uuid4
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import openai
from dotenv import load_dotenv
load_dotenv()
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.zep import ZepVectorStore
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("../data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-elasticsearch')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.elasticsearch import ElasticsearchStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"A bunch of scientists bring back dinosaurs and mayhem breaks"
" loose"
),
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
TextNode(
text=(
"Leo DiCaprio gets lost in a dream within a dream within a dream"
" within a ..."
),
metadata={
"year": 2010,
"director": "Christopher Nolan",
"rating": 8.2,
},
),
TextNode(
text=(
"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},
),
TextNode(
text=(
"A bunch of normal-sized women are supremely wholesome and some"
" men pine after them"
),
metadata={"year": 2019, "director": "Greta Gerwig", "rating": 8.3},
),
TextNode(
text="Toys come alive and have a blast doing so",
metadata={"year": 1995, "genre": "animated"},
),
]
vector_store = ElasticsearchStore(
index_name="auto_retriever_movies", es_url="http://localhost:9200"
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = | VectorStoreIndex.from_documents(data) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-portkey')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install -U llama_index')
get_ipython().system('pip install -U portkey-ai')
from llama_index.llms.portkey import Portkey
from llama_index.core.llms import ChatMessage
import portkey as pk
import os
os.environ["PORTKEY_API_KEY"] = "PORTKEY_API_KEY"
openai_virtual_key_a = ""
openai_virtual_key_b = ""
anthropic_virtual_key_a = ""
anthropic_virtual_key_b = ""
cohere_virtual_key_a = ""
cohere_virtual_key_b = ""
os.environ["OPENAI_API_KEY"] = ""
os.environ["ANTHROPIC_API_KEY"] = ""
portkey_client = Portkey(
mode="single",
)
openai_llm = pk.LLMOptions(
provider="openai",
model="gpt-4",
virtual_key=openai_virtual_key_a,
)
portkey_client.add_llms(openai_llm)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("Testing Portkey Llamaindex integration:")
response = portkey_client.chat(messages)
print(response)
prompt = "Why is the sky blue?"
print("\nTesting Stream Complete:\n")
response = portkey_client.stream_complete(prompt)
for i in response:
print(i.delta, end="", flush=True)
messages = [
ChatMessage(role="system", content="You are a helpful assistant"),
ChatMessage(role="user", content="What can you do?"),
]
print("\nTesting Stream Chat:\n")
response = portkey_client.stream_chat(messages)
for i in response:
print(i.delta, end="", flush=True)
portkey_client = | Portkey(mode="fallback") | llama_index.llms.portkey.Portkey |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.program.openai import OpenAIPydanticProgram
from pydantic import BaseModel
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from typing import List
class Song(BaseModel):
"""Data model for a song."""
title: str
length_seconds: int
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
songs: List[Song]
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
llm = OpenAI(model="gpt-4", callback_manager=callback_manager)
prompt_template_str = """\
Generate an example album, with an artist and a list of songs. \
Using the movie {movie_name} as inspiration.\
"""
program = OpenAIPydanticProgram.from_defaults(
output_cls=Album,
prompt_template_str=prompt_template_str,
llm=llm,
verbose=False,
)
movie_names = [
"The Shining",
"The Departed",
"Titanic",
"Goodfellas",
"Pretty Woman",
"Home Alone",
"Caged Fury",
"Edward Scissorhands",
"Total Recall",
"Ghost",
"Tremors",
"RoboCop",
"Rocky V",
]
from tqdm.notebook import tqdm
for movie_name in tqdm(movie_names):
output = program(movie_name=movie_name)
print(output.json())
finetuning_handler.save_finetuning_events("mock_finetune_songs.jsonl")
get_ipython().system('cat mock_finetune_songs.jsonl')
from llama_index.finetuning import OpenAIFinetuneEngine
finetune_engine = OpenAIFinetuneEngine(
"gpt-3.5-turbo",
"mock_finetune_songs.jsonl",
validate_json=False, # openai validate json code doesn't support function calling yet
)
finetune_engine.finetune()
finetune_engine.get_current_job()
ft_llm = finetune_engine.get_finetuned_model(temperature=0.3)
ft_program = OpenAIPydanticProgram.from_defaults(
output_cls=Album,
prompt_template_str=prompt_template_str,
llm=ft_llm,
verbose=False,
)
ft_program(movie_name="Goodfellas")
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pydantic import Field
from typing import List
class Citation(BaseModel):
"""Citation class."""
author: str = Field(
..., description="Inferred first author (usually last name"
)
year: int = Field(..., description="Inferred year")
desc: str = Field(
...,
description=(
"Inferred description from the text of the work that the author is"
" cited for"
),
)
class Response(BaseModel):
"""List of author citations.
Extracted over unstructured text.
"""
citations: List[Citation] = Field(
...,
description=(
"List of author citations (organized by author, year, and"
" description)."
),
)
from llama_index.readers.file import PyMuPDFReader
from llama_index.core import Document
from llama_index.core.node_parser import SentenceSplitter
from pathlib import Path
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
metadata = {
"paper_title": "Llama 2: Open Foundation and Fine-Tuned Chat Models"
}
docs = [Document(text=doc_text, metadata=metadata)]
chunk_size = 1024
node_parser = SentenceSplitter(chunk_size=chunk_size)
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
from llama_index.core import Settings
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = | CallbackManager([finetuning_handler]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core import Settings
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
llm = | OpenAI(temperature=0, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-timescalevector')
get_ipython().system('pip install llama-index')
import timescale_vector
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.timescalevector import TimescaleVectorStore
from llama_index.core.vector_stores import VectorStoreQuery, MetadataFilters
import textwrap
import openai
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
openai.api_key = os.environ["OPENAI_API_KEY"]
import os
from dotenv import load_dotenv, find_dotenv
_ = load_dotenv(find_dotenv())
TIMESCALE_SERVICE_URL = os.environ["TIMESCALE_SERVICE_URL"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
vector_store = TimescaleVectorStore.from_params(
service_url=TIMESCALE_SERVICE_URL,
table_name="paul_graham_essay",
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().system('pip install exa_py')
import os
import openai
from llama_index.agent import OpenAIAgent
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_hub.tools.exa.base import ExaToolSpec
exa_tool = ExaToolSpec(
api_key=os.environ["EXA_API_KEY"],
)
exa_tool_list = exa_tool.to_tool_list()
for tool in exa_tool_list:
print(tool.metadata.name)
exa_tool.search_and_retrieve_documents("machine learning transformers", num_results=3)
exa_tool.find_similar(
"https://www.mihaileric.com/posts/transformers-attention-in-disguise/"
)
exa_tool.search_and_retrieve_documents(
"This is a summary of recent research around diffusion models:", num_results=1
)
exa_tool.search_and_retrieve_highlights(
"This is a summary of recent research around diffusion models:", num_results=1
)
agent = OpenAIAgent.from_tools(
exa_tool_list,
verbose=True,
)
print(agent.chat("What are the best resturants in toronto?"))
print(agent.chat("tell me more about Osteria Giulia"))
tools = exa_tool.to_tool_list(
spec_functions=["search_and_retrieve_highlights", "current_date"]
)
agent = OpenAIAgent.from_tools(
tools,
verbose=True,
)
response = agent.chat("Tell me more about the recent news on semiconductors")
print(f"Response: {str(response)}")
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
search_and_retrieve_docs_tool = exa_tool.to_tool_list(
spec_functions=["search_and_retrieve_documents"]
)[0]
date_tool = exa_tool.to_tool_list(spec_functions=["current_date"])[0]
wrapped_retrieve = | LoadAndSearchToolSpec.from_defaults(search_and_retrieve_docs_tool) | llama_index.tools.tool_spec.load_and_search.base.LoadAndSearchToolSpec.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core import (
VectorStoreIndex,
SimpleKeywordTableIndex,
SimpleDirectoryReader,
)
from llama_index.core import SummaryIndex
from llama_index.core.schema import IndexNode
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.callbacks import CallbackManager
from llama_index.llms.openai import OpenAI
wiki_titles = [
"Toronto",
"Seattle",
"Chicago",
"Boston",
"Houston",
]
from pathlib import Path
import requests
for title in wiki_titles:
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
},
).json()
page = next(iter(response["query"]["pages"].values()))
wiki_text = page["extract"]
data_path = Path("data")
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
city_docs = {}
for wiki_title in wiki_titles:
city_docs[wiki_title] = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
callback_manager = CallbackManager([])
from llama_index.agent.openai import OpenAIAgent
from llama_index.core import load_index_from_storage, StorageContext
from llama_index.core.node_parser import SentenceSplitter
import os
node_parser = SentenceSplitter()
query_engine_tools = []
for idx, wiki_title in enumerate(wiki_titles):
nodes = node_parser.get_nodes_from_documents(city_docs[wiki_title])
if not os.path.exists(f"./data/{wiki_title}"):
vector_index = VectorStoreIndex(
nodes, callback_manager=callback_manager
)
vector_index.storage_context.persist(
persist_dir=f"./data/{wiki_title}"
)
else:
vector_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=f"./data/{wiki_title}"),
callback_manager=callback_manager,
)
vector_query_engine = vector_index.as_query_engine(llm=llm)
query_engine_tools.append(
QueryEngineTool(
query_engine=vector_query_engine,
metadata=ToolMetadata(
name=f"vector_tool_{wiki_title}",
description=(
"Useful for questions related to specific aspects of"
f" {wiki_title} (e.g. the history, arts and culture,"
" sports, demographics, or more)."
),
),
)
)
from llama_index.core.agent import AgentRunner
from llama_index.agent.openai import OpenAIAgentWorker, OpenAIAgent
from llama_index.agent.openai import OpenAIAgentWorker
openai_step_engine = OpenAIAgentWorker.from_tools(
query_engine_tools, llm=llm, verbose=True
)
agent = | AgentRunner(openai_step_engine) | llama_index.core.agent.AgentRunner |
from llama_index.agent import OpenAIAgent
import openai
openai.api_key = "sk-api-key"
from llama_index.tools.gmail.base import GmailToolSpec
from llama_index.tools.google_calendar.base import GoogleCalendarToolSpec
from llama_index.tools.google_search.base import GoogleSearchToolSpec
gmail_tools = GmailToolSpec().to_tool_list()
gcal_tools = GoogleCalendarToolSpec().to_tool_list()
gsearch_tools = GoogleSearchToolSpec(key="api-key", engine="engine").to_tool_list()
for tool in [*gmail_tools, *gcal_tools, *gsearch_tools]:
print(tool.metadata.name)
print(tool.metadata.description)
from llama_index.tools.tool_spec.load_and_search.base import LoadAndSearchToolSpec
print("Wrapping " + gsearch_tools[0].metadata.name)
gsearch_load_and_search_tools = LoadAndSearchToolSpec.from_defaults(
gsearch_tools[0],
).to_tool_list()
print("Wrapping gmail " + gmail_tools[0].metadata.name)
gmail_load_and_search_tools = LoadAndSearchToolSpec.from_defaults(
gmail_tools[0],
).to_tool_list()
print("Wrapping google calendar " + gcal_tools[0].metadata.name)
gcal_load_and_search_tools = LoadAndSearchToolSpec.from_defaults(
gcal_tools[0],
).to_tool_list()
all_tools = [
*gsearch_load_and_search_tools,
*gmail_load_and_search_tools,
*gcal_load_and_search_tools,
*gcal_tools[1::],
*gmail_tools[1::],
*gsearch_tools[1::],
]
agent = | OpenAIAgent.from_tools(all_tools, verbose=True) | llama_index.agent.OpenAIAgent.from_tools |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = | SentenceSplitter(chunk_size=1024) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.node_parser import SentenceWindowNodeParser
from llama_index.core.node_parser import SentenceSplitter
node_parser = SentenceWindowNodeParser.from_defaults(
window_size=3,
window_metadata_key="window",
original_text_metadata_key="original_text",
)
text_splitter = SentenceSplitter()
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.1)
embed_model = HuggingFaceEmbedding(
model_name="sentence-transformers/all-mpnet-base-v2", max_length=512
)
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.text_splitter = text_splitter
get_ipython().system('curl https://www.ipcc.ch/report/ar6/wg2/downloads/report/IPCC_AR6_WGII_Chapter03.pdf --output IPCC_AR6_WGII_Chapter03.pdf')
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader(
input_files=["./IPCC_AR6_WGII_Chapter03.pdf"]
).load_data()
nodes = node_parser.get_nodes_from_documents(documents)
base_nodes = text_splitter.get_nodes_from_documents(documents)
from llama_index.core import VectorStoreIndex
sentence_index = | VectorStoreIndex(nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core import (
SimpleDirectoryReader,
VectorStoreIndex,
StorageContext,
load_index_from_storage,
)
from llama_index.core.tools import QueryEngineTool, ToolMetadata
try:
storage_context = StorageContext.from_defaults(
persist_dir="./storage/lyft"
)
lyft_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/uber"
)
uber_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/lyft_2021.pdf' -O 'data/10k/lyft_2021.pdf'")
if not index_loaded:
lyft_docs = SimpleDirectoryReader(
input_files=["./data/10k/lyft_2021.pdf"]
).load_data()
uber_docs = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
lyft_index = | VectorStoreIndex.from_documents(lyft_docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-replicate')
get_ipython().run_line_magic('pip', 'install unstructured replicate')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install git+https://github.com/openai/CLIP.git')
get_ipython().run_line_magic('pip', 'install torch torchvision')
get_ipython().run_line_magic('pip', 'install matplotlib scikit-image')
get_ipython().run_line_magic('pip', 'install -U qdrant_client')
import os
REPLICATE_API_TOKEN = "..." # Your Relicate API token here
os.environ["REPLICATE_API_TOKEN"] = REPLICATE_API_TOKEN
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1UU0xc3uLXs-WG0aDQSXjGacUkp142rLS" -O texas.jpg')
from llama_index.readers.file import FlatReader
from pathlib import Path
from llama_index.core.node_parser import UnstructuredElementNodeParser
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
node_parser = UnstructuredElementNodeParser()
import openai
OPENAI_API_TOKEN = "..."
openai.api_key = OPENAI_API_TOKEN # add your openai api key here
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
nodes_2021, objects_2021 = node_parser.get_nodes_and_objects(raw_nodes_2021)
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(nodes=nodes_2021, objects=objects_2021)
query_engine = vector_index.as_query_engine(similarity_top_k=5, verbose=True)
from PIL import Image
import matplotlib.pyplot as plt
imageUrl = "./texas.jpg"
image = Image.open(imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.schema import ImageDocument
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
print(imageUrl)
llava_multi_modal_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=200,
temperature=0.1,
)
prompt = "which Tesla factory is shown in the image? Please answer just the name of the factory."
llava_response = llava_multi_modal_llm.complete(
prompt=prompt,
image_documents=[ | ImageDocument(image_path=imageUrl) | llama_index.core.schema.ImageDocument |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client(
"https://llama-test-ezjahb4m.weaviate.network",
auth_client_secret=resource_owner_config,
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from IPython.display import Markdown, display
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="LlamaIndex"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client(
"https://llama-test-ezjahb4m.weaviate.network",
auth_client_secret=resource_owner_config,
)
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name="LlamaIndex"
)
loaded_index = VectorStoreIndex.from_vector_store(vector_store)
query_engine = loaded_index.as_query_engine()
response = query_engine.query("What happened at interleaf?")
display(Markdown(f"<b>{response}</b>"))
from llama_index.core import Document
doc = Document.example()
print(doc.metadata)
print("-----")
print(doc.text[:100])
loaded_index.insert(doc)
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
filters = MetadataFilters(
filters=[ | ExactMatchFilter(key="filename", value="README.md") | llama_index.core.vector_stores.ExactMatchFilter |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.core.agent import (
CustomSimpleAgentWorker,
Task,
AgentChatResponse,
)
from typing import Dict, Any, List, Tuple, Optional
from llama_index.core.tools import BaseTool, QueryEngineTool
from llama_index.core.program import LLMTextCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core import ChatPromptTemplate, PromptTemplate
from llama_index.core.selectors import PydanticSingleSelector
from llama_index.core.bridge.pydantic import Field, BaseModel
from llama_index.core.llms import ChatMessage, MessageRole
DEFAULT_PROMPT_STR = """
Given previous question/response pairs, please determine if an error has occurred in the response, and suggest \
a modified question that will not trigger the error.
Examples of modified questions:
- The question itself is modified to elicit a non-erroneous response
- The question is augmented with context that will help the downstream system better answer the question.
- The question is augmented with examples of negative responses, or other negative questions.
An error means that either an exception has triggered, or the response is completely irrelevant to the question.
Please return the evaluation of the response in the following JSON format.
"""
def get_chat_prompt_template(
system_prompt: str, current_reasoning: Tuple[str, str]
) -> ChatPromptTemplate:
system_msg = ChatMessage(role=MessageRole.SYSTEM, content=system_prompt)
messages = [system_msg]
for raw_msg in current_reasoning:
if raw_msg[0] == "user":
messages.append(
| ChatMessage(role=MessageRole.USER, content=raw_msg[1]) | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lantern')
get_ipython().system('pip install llama-index psycopg2-binary asyncpg')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
os.environ["OPENAI_API_KEY"] = "<your-api-key>"
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
import psycopg2
from sqlalchemy import make_url
connection_string = "postgresql://postgres:postgres@localhost:5432"
url = make_url(connection_string)
db_name = "postgres"
conn = psycopg2.connect(connection_string)
conn.autocommit = True
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.lantern import LanternVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
},
),
TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
),
metadata={
"category": "Business",
"country": "United States",
},
),
| TextNode(
text=(
"Rihanna is a Barbadian singer, actress, and businesswoman. She"
" has achieved significant success in the music industry and is"
" known for her versatile musical style."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=')
get_ipython().run_line_magic('env', 'BRAINTRUST_API_KEY=')
get_ipython().run_line_magic('env', 'TOKENIZERS_PARALLELISM=true # This is needed to avoid a warning message from Chroma')
get_ipython().run_line_magic('pip', 'install -U llama_hub llama_index braintrust autoevals pypdf pillow transformers torch torchvision')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-cohere')
get_ipython().system('pip install llama-index')
from llama_index.llms.cohere import Cohere
api_key = "Your api key"
resp = Cohere(api_key=api_key).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.cohere import Cohere
messages = [
ChatMessage(role="user", content="hello there"),
ChatMessage(
role="assistant", content="Arrrr, matey! How can I help ye today?"
),
ChatMessage(role="user", content="What is your name"),
]
resp = Cohere(api_key=api_key).chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
print(resp)
from llama_index.llms.openai import OpenAI
llm = Cohere(api_key=api_key)
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.openai import OpenAI
llm = Cohere(api_key=api_key)
messages = [
ChatMessage(role="user", content="hello there"),
ChatMessage(
role="assistant", content="Arrrr, matey! How can I help ye today?"
),
ChatMessage(role="user", content="What is your name"),
]
resp = llm.stream_chat(
messages, preamble_override="You are a pirate with a colorful personality"
)
for r in resp:
print(r.delta, end="")
from llama_index.llms.cohere import Cohere
llm = Cohere(model="command", api_key=api_key)
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.llms.cohere import Cohere
llm = | Cohere(model="command", api_key=api_key) | llama_index.llms.cohere.Cohere |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("./data//paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.llms.openai import OpenAI
gpt4 = | OpenAI(temperature=0, model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
import openai
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
data = SimpleDirectoryReader(input_dir="./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(data)
from llama_index.core.memory import ChatMemoryBuffer
memory = | ChatMemoryBuffer.from_defaults(token_limit=1500) | llama_index.core.memory.ChatMemoryBuffer.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from pydantic import BaseModel
from unstructured.partition.html import partition_html
import pandas as pd
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", None)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://www.dropbox.com/scl/fi/rkw0u959yb4w8vlzz76sa/tesla_2020_10k.htm?rlkey=tfkdshswpoupav5tqigwz1mp7&dl=1" -O tesla_2020_10k.htm')
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
docs_2020 = reader.load_data(Path("tesla_2020_10k.htm"))
from llama_index.core.node_parser import UnstructuredElementNodeParser
node_parser = UnstructuredElementNodeParser()
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
base_nodes_2021, node_mappings_2021 = node_parser.get_base_nodes_and_mappings(
raw_nodes_2021
)
example_index_node = [b for b in base_nodes_2021 if isinstance(b, IndexNode)][
20
]
print(
f"\n--------\n{example_index_node.get_content(metadata_mode='all')}\n--------\n"
)
print(f"\n--------\nIndex ID: {example_index_node.index_id}\n--------\n")
print(
f"\n--------\n{node_mappings_2021[example_index_node.index_id].get_content()}\n--------\n"
)
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(base_nodes_2021)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)
vector_query_engine = vector_index.as_query_engine(similarity_top_k=1)
from llama_index.core.retrievers import RecursiveRetriever
recursive_retriever = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever},
node_dict=node_mappings_2021,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(recursive_retriever)
response = query_engine.query("What was the revenue in 2020?")
print(str(response))
response = vector_query_engine.query("What was the revenue in 2020?")
print(str(response))
response = query_engine.query("What were the total cash flows in 2021?")
print(str(response))
response = vector_query_engine.query("What were the total cash flows in 2021?")
print(str(response))
response = query_engine.query("What are the risk factors for Tesla?")
print(str(response))
response = vector_query_engine.query("What are the risk factors for Tesla?")
print(str(response))
import pickle
import os
def create_recursive_retriever_over_doc(docs, nodes_save_path=None):
"""Big function to go from document path -> recursive retriever."""
node_parser = UnstructuredElementNodeParser()
if nodes_save_path is not None and os.path.exists(nodes_save_path):
raw_nodes = pickle.load(open(nodes_save_path, "rb"))
else:
raw_nodes = node_parser.get_nodes_from_documents(docs)
if nodes_save_path is not None:
pickle.dump(raw_nodes, open(nodes_save_path, "wb"))
base_nodes, node_mappings = node_parser.get_base_nodes_and_mappings(
raw_nodes
)
vector_index = VectorStoreIndex(base_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=2)
recursive_retriever = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever},
node_dict=node_mappings,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(recursive_retriever)
return query_engine, base_nodes
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.llms.openai import OpenAI
llm = | OpenAI(model="gpt-4") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
"gender": "male",
"born": 1963,
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
"gender": "female",
"born": 1975,
},
),
TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
),
metadata={
"category": "Business",
"country": "United States",
"gender": "male",
"born": 1971,
},
),
| TextNode(
text=(
"Rihanna is a Barbadian singer, actress, and businesswoman. She"
" has achieved significant success in the music industry and is"
" known for her versatile musical style."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
llm = OpenAI(model="gpt-3.5-turbo-instruct")
agent = | ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True) | llama_index.core.agent.ReActAgent.from_tools |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama_hub')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
from llama_index.core import Document
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
docs0 = PyMuPDFReader().load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [ | Document(text=doc_text) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index')
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from llama_index.core.evaluation.benchmarks import BeirEvaluator
from llama_index.core import VectorStoreIndex
def create_retriever(documents):
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
index = VectorStoreIndex.from_documents(
documents, embed_model=embed_model, show_progress=True
)
return index.as_retriever(similarity_top_k=30)
| BeirEvaluator() | llama_index.core.evaluation.benchmarks.BeirEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-together')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
domain = "docs.llamaindex.ai"
docs_url = "https://docs.llamaindex.ai/en/latest/"
get_ipython().system('wget -e robots=off --recursive --no-clobber --page-requisites --html-extension --convert-links --restrict-file-names=windows --domains {domain} --no-parent {docs_url}')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
from llama_index.llms.openai import OpenAI
from llama_index.core import Document
reader = UnstructuredReader()
all_html_files = [
"docs.llamaindex.ai/en/latest/index.html",
"docs.llamaindex.ai/en/latest/contributing/contributing.html",
"docs.llamaindex.ai/en/latest/understanding/understanding.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/using_llms.html",
"docs.llamaindex.ai/en/latest/understanding/using_llms/privacy.html",
"docs.llamaindex.ai/en/latest/understanding/loading/llamahub.html",
"docs.llamaindex.ai/en/latest/optimizing/production_rag.html",
"docs.llamaindex.ai/en/latest/module_guides/models/llms.html",
]
doc_limit = 10
docs = []
for idx, f in enumerate(all_html_files):
if idx > doc_limit:
break
print(f"Idx {idx}/{len(all_html_files)}")
loaded_docs = reader.load_data(file=f, split_documents=True)
start_idx = 64
loaded_doc = Document(
id_=str(f),
text="\n\n".join([d.get_content() for d in loaded_docs[start_idx:]]),
metadata={"path": str(f)},
)
print(str(f))
docs.append(loaded_doc)
from llama_index.embeddings.together import TogetherEmbedding
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
api_key = "<api_key>"
embed_model = TogetherEmbedding(
model_name="togethercomputer/m2-bert-80M-32k-retrieval", api_key=api_key
)
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
from llama_index.core.storage.docstore import SimpleDocumentStore
for doc in docs:
embedding = embed_model.get_text_embedding(doc.get_content())
doc.embedding = embedding
docstore = SimpleDocumentStore()
docstore.add_documents(docs)
from llama_index.core.schema import IndexNode
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import SummaryIndex
from llama_index.core.retrievers import RecursiveRetriever
import os
from tqdm.notebook import tqdm
import pickle
def build_index(docs, out_path: str = "storage/chunk_index"):
nodes = []
splitter = | SentenceSplitter(chunk_size=512, chunk_overlap=70) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence, List
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
import nest_asyncio
nest_asyncio.apply()
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = | FunctionTool.from_defaults(fn=add) | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().system('pip install llama-index-multi-modal-llms-anthropic')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install matplotlib')
import os
os.environ["ANTHROPIC_API_KEY"] = "" # Your ANTHROPIC API key here
from PIL import Image
import matplotlib.pyplot as plt
img = Image.open("../data/images/prometheus_paper_card.png")
plt.imshow(img)
from llama_index.core import SimpleDirectoryReader
from llama_index.multi_modal_llms.anthropic import AnthropicMultiModal
image_documents = SimpleDirectoryReader(
input_files=["../data/images/prometheus_paper_card.png"]
).load_data()
anthropic_mm_llm = AnthropicMultiModal(max_tokens=300)
response = anthropic_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_documents,
)
print(response)
from PIL import Image
import requests
from io import BytesIO
import matplotlib.pyplot as plt
from llama_index.core.multi_modal_llms.generic_utils import load_image_urls
image_urls = [
"https://venturebeat.com/wp-content/uploads/2024/03/Screenshot-2024-03-04-at-12.49.41%E2%80%AFAM.png",
]
img_response = requests.get(image_urls[0])
img = Image.open(BytesIO(img_response.content))
plt.imshow(img)
image_url_documents = load_image_urls(image_urls)
response = anthropic_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_url_documents,
)
print(response)
from llama_index.core import SimpleDirectoryReader
image_documents = SimpleDirectoryReader(
input_files=["../data/images/ark_email_sample.PNG"]
).load_data()
from PIL import Image
import matplotlib.pyplot as plt
img = Image.open("../data/images/ark_email_sample.PNG")
plt.imshow(img)
from pydantic import BaseModel
from typing import List
class TickerInfo(BaseModel):
"""List of ticker info."""
direction: str
ticker: str
company: str
shares_traded: int
percent_of_total_etf: float
class TickerList(BaseModel):
"""List of stock tickers."""
fund: str
tickers: List[TickerInfo]
from llama_index.multi_modal_llms.anthropic import AnthropicMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
prompt_template_str = """\
Can you get the stock information in the image \
and return the answer? Pick just one fund.
Make sure the answer is a JSON format corresponding to a Pydantic schema. The Pydantic schema is given below.
"""
anthropic_mm_llm = AnthropicMultiModal(max_tokens=300)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_cls=TickerList,
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=anthropic_mm_llm,
verbose=True,
)
response = llm_program()
print(str(response))
get_ipython().system('wget "https://www.dropbox.com/scl/fi/c1ec6osn0r2ggnitijqhl/mixed_wiki_images_small.zip?rlkey=swwxc7h4qtwlnhmby5fsnderd&dl=1" -O mixed_wiki_images_small.zip')
get_ipython().system('unzip mixed_wiki_images_small.zip')
from llama_index.multi_modal_llms.anthropic import AnthropicMultiModal
anthropic_mm_llm = AnthropicMultiModal(max_tokens=300)
from llama_index.core.schema import TextNode
from pathlib import Path
from llama_index.core import SimpleDirectoryReader
nodes = []
for img_file in Path("mixed_wiki_images_small").glob("*.png"):
print(img_file)
image_documents = SimpleDirectoryReader(input_files=[img_file]).load_data()
response = anthropic_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_documents,
)
metadata = {"img_file": img_file}
nodes.append(TextNode(text=str(response), metadata=metadata))
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.anthropic import Anthropic
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import Settings
from llama_index.core import StorageContext
import qdrant_client
client = qdrant_client.QdrantClient(path="qdrant_mixed_img")
vector_store = QdrantVectorStore(client=client, collection_name="collection")
embed_model = OpenAIEmbedding()
anthropic_mm_llm = AnthropicMultiModal(max_tokens=300)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(
nodes=nodes,
storage_context=storage_context,
)
from llama_index.llms.anthropic import Anthropic
query_engine = index.as_query_engine(llm= | Anthropic() | llama_index.llms.anthropic.Anthropic |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiple two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = | FunctionTool.from_defaults(fn=add) | llama_index.core.tools.FunctionTool.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
from llama_index.core.agent import ReActAgent
from llama_index.llms.openai import OpenAI
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool, FunctionTool
def multiply(a: int, b: int) -> int:
"""Multiply two integers and returns the result integer"""
return a * b
multiply_tool = FunctionTool.from_defaults(fn=multiply)
def add(a: int, b: int) -> int:
"""Add two integers and returns the result integer"""
return a + b
add_tool = FunctionTool.from_defaults(fn=add)
llm = OpenAI(model="gpt-3.5-turbo-instruct")
agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)
response = agent.chat("What is 20+(2*4)? Calculate step by step ")
response_gen = agent.stream_chat("What is 20+2*4? Calculate step by step")
response_gen.print_response_stream()
llm = OpenAI(model="gpt-4")
agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)
response = agent.chat("What is 2+2*4")
print(response)
llm = OpenAI(model="gpt-4")
agent = ReActAgent.from_tools([multiply_tool, add_tool], llm=llm, verbose=True)
prompt_dict = agent.get_prompts()
for k, v in prompt_dict.items():
print(f"Prompt: {k}\n\nValue: {v.template}")
from llama_index.core import PromptTemplate
react_system_header_str = """\
You are designed to help with a variety of tasks, from answering questions \
to providing summaries to other types of analyses.
You have access to a wide variety of tools. You are responsible for using
the tools in any sequence you deem appropriate to complete the task at hand.
This may require breaking the task into subtasks and using different tools
to complete each subtask.
You have access to the following tools:
{tool_desc}
To answer the question, please use the following format.
```
Thought: I need to use a tool to help me answer the question.
Action: tool name (one of {tool_names}) if using a tool.
Action Input: the input to the tool, in a JSON format representing the kwargs (e.g. {{"input": "hello world", "num_beams": 5}})
```
Please ALWAYS start with a Thought.
Please use a valid JSON format for the Action Input. Do NOT do this {{'input': 'hello world', 'num_beams': 5}}.
If this format is used, the user will respond in the following format:
```
Observation: tool response
```
You should keep repeating the above format until you have enough information
to answer the question without using any more tools. At that point, you MUST respond
in the one of the following two formats:
```
Thought: I can answer without using any more tools.
Answer: [your answer here]
```
```
Thought: I cannot answer the question with the provided tools.
Answer: Sorry, I cannot answer your query.
```
- The answer MUST contain a sequence of bullet points that explain how you arrived at the answer. This can include aspects of the previous conversation history.
- You MUST obey the function signature of each tool. Do NOT pass in no arguments if the function expects arguments.
Below is the current conversation consisting of interleaving human and assistant messages.
"""
react_system_prompt = | PromptTemplate(react_system_header_str) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().system('pip install -q llama-index google-generativeai')
get_ipython().run_line_magic('env', 'GOOGLE_API_KEY=...')
import os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.llms.gemini import Gemini
resp = Gemini().complete("Write a poem about a magic backpack")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.gemini import Gemini
messages = [
ChatMessage(role="user", content="Hello friend!"),
ChatMessage(role="assistant", content="Yarr what is shakin' matey?"),
ChatMessage(
role="user", content="Help me decide what to have for dinner."
),
]
resp = Gemini().chat(messages)
print(resp)
from llama_index.llms.gemini import Gemini
llm = | Gemini() | llama_index.llms.gemini.Gemini |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
vector_store = | PineconeVectorStore(pinecone_index=pinecone_index) | llama_index.vector_stores.pinecone.PineconeVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-kuzu')
import os
os.environ["OPENAI_API_KEY"] = "API_KEY_HERE"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import shutil
shutil.rmtree("./test1", ignore_errors=True)
shutil.rmtree("./test2", ignore_errors=True)
shutil.rmtree("./test3", ignore_errors=True)
get_ipython().run_line_magic('pip', 'install kuzu')
import kuzu
db = kuzu.Database("test1")
from llama_index.graph_stores.kuzu import KuzuGraphStore
graph_store = KuzuGraphStore(db)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from IPython.display import Markdown, display
import kuzu
documents = SimpleDirectoryReader(
"../../../../examples/paul_graham_essay/data"
).load_data()
llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = llm
Settings.chunk_size = 512
from llama_index.core import StorageContext
storage_context = StorageContext.from_defaults(graph_store=graph_store)
index = KnowledgeGraphIndex.from_documents(
documents,
max_triplets_per_chunk=2,
storage_context=storage_context,
)
query_engine = index.as_query_engine(
include_text=False, response_mode="tree_summarize"
)
response = query_engine.query(
"Tell me more about Interleaf",
)
display(Markdown(f"<b>{response}</b>"))
query_engine = index.as_query_engine(
include_text=True, response_mode="tree_summarize"
)
response = query_engine.query(
"Tell me more about Interleaf",
)
display(Markdown(f"<b>{response}</b>"))
db = kuzu.Database("test2")
graph_store = | KuzuGraphStore(db) | llama_index.graph_stores.kuzu.KuzuGraphStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
qa_prompt_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the question: {query_str}\n"
)
refine_prompt_str = (
"We have the opportunity to refine the original answer "
"(only if needed) with some more context below.\n"
"------------\n"
"{context_msg}\n"
"------------\n"
"Given the new context, refine the original answer to better "
"answer the question: {query_str}. "
"If the context isn't useful, output the original answer again.\n"
"Original Answer: {existing_answer}"
)
from llama_index.core.llms import ChatMessage, MessageRole
from llama_index.core import ChatPromptTemplate
chat_text_qa_msgs = [
ChatMessage(
role=MessageRole.SYSTEM,
content=(
"Always answer the question, even if the context isn't helpful."
),
),
ChatMessage(role=MessageRole.USER, content=qa_prompt_str),
]
text_qa_template = ChatPromptTemplate(chat_text_qa_msgs)
chat_refine_msgs = [
ChatMessage(
role=MessageRole.SYSTEM,
content=(
"Always answer the question, even if the context isn't helpful."
),
),
ChatMessage(role=MessageRole.USER, content=refine_prompt_str),
]
refine_template = ChatPromptTemplate(chat_refine_msgs)
from llama_index.core import ChatPromptTemplate
chat_text_qa_msgs = [
(
"system",
"Always answer the question, even if the context isn't helpful.",
),
("user", qa_prompt_str),
]
text_qa_template = | ChatPromptTemplate.from_messages(chat_text_qa_msgs) | llama_index.core.ChatPromptTemplate.from_messages |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-finetuning-callbacks')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-program-openai')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.program.openai import OpenAIPydanticProgram
from pydantic import BaseModel
from llama_index.llms.openai import OpenAI
from llama_index.finetuning.callbacks import OpenAIFineTuningHandler
from llama_index.core.callbacks import CallbackManager
from typing import List
class Song(BaseModel):
"""Data model for a song."""
title: str
length_seconds: int
class Album(BaseModel):
"""Data model for an album."""
name: str
artist: str
songs: List[Song]
finetuning_handler = OpenAIFineTuningHandler()
callback_manager = CallbackManager([finetuning_handler])
llm = OpenAI(model="gpt-4", callback_manager=callback_manager)
prompt_template_str = """\
Generate an example album, with an artist and a list of songs. \
Using the movie {movie_name} as inspiration.\
"""
program = OpenAIPydanticProgram.from_defaults(
output_cls=Album,
prompt_template_str=prompt_template_str,
llm=llm,
verbose=False,
)
movie_names = [
"The Shining",
"The Departed",
"Titanic",
"Goodfellas",
"Pretty Woman",
"Home Alone",
"Caged Fury",
"Edward Scissorhands",
"Total Recall",
"Ghost",
"Tremors",
"RoboCop",
"Rocky V",
]
from tqdm.notebook import tqdm
for movie_name in tqdm(movie_names):
output = program(movie_name=movie_name)
print(output.json())
finetuning_handler.save_finetuning_events("mock_finetune_songs.jsonl")
get_ipython().system('cat mock_finetune_songs.jsonl')
from llama_index.finetuning import OpenAIFinetuneEngine
finetune_engine = OpenAIFinetuneEngine(
"gpt-3.5-turbo",
"mock_finetune_songs.jsonl",
validate_json=False, # openai validate json code doesn't support function calling yet
)
finetune_engine.finetune()
finetune_engine.get_current_job()
ft_llm = finetune_engine.get_finetuned_model(temperature=0.3)
ft_program = OpenAIPydanticProgram.from_defaults(
output_cls=Album,
prompt_template_str=prompt_template_str,
llm=ft_llm,
verbose=False,
)
ft_program(movie_name="Goodfellas")
get_ipython().system('mkdir data && wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pydantic import Field
from typing import List
class Citation(BaseModel):
"""Citation class."""
author: str = Field(
..., description="Inferred first author (usually last name"
)
year: int = Field(..., description="Inferred year")
desc: str = Field(
...,
description=(
"Inferred description from the text of the work that the author is"
" cited for"
),
)
class Response(BaseModel):
"""List of author citations.
Extracted over unstructured text.
"""
citations: List[Citation] = Field(
...,
description=(
"List of author citations (organized by author, year, and"
" description)."
),
)
from llama_index.readers.file import PyMuPDFReader
from llama_index.core import Document
from llama_index.core.node_parser import SentenceSplitter
from pathlib import Path
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
metadata = {
"paper_title": "Llama 2: Open Foundation and Fine-Tuned Chat Models"
}
docs = [Document(text=doc_text, metadata=metadata)]
chunk_size = 1024
node_parser = SentenceSplitter(chunk_size=chunk_size)
nodes = node_parser.get_nodes_from_documents(docs)
len(nodes)
from llama_index.core import Settings
finetuning_handler = | OpenAIFineTuningHandler() | llama_index.finetuning.callbacks.OpenAIFineTuningHandler |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().run_line_magic('env', 'OPENAI_API_KEY=YOUR_OPENAI_KEY')
get_ipython().system('pip install llama-index pypdf')
get_ipython().system("mkdir -p 'data/'")
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PDFReader
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
import json
loader = PDFReader()
docs0 = loader.load_data(file=Path("./data/llama2.pdf"))
from llama_index.core import Document
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [Document(text=doc_text)]
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(docs)
for idx, node in enumerate(base_nodes):
node.id_ = f"node-{idx}"
from llama_index.core.embeddings import resolve_embed_model
embed_model = resolve_embed_model("local:BAAI/bge-small-en")
llm = OpenAI(model="gpt-3.5-turbo")
base_index = VectorStoreIndex(base_nodes, embed_model=embed_model)
base_retriever = base_index.as_retriever(similarity_top_k=2)
retrievals = base_retriever.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for n in retrievals:
display_source_node(n, source_length=1500)
query_engine_base = RetrieverQueryEngine.from_args(base_retriever, llm=llm)
response = query_engine_base.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
sub_chunk_sizes = [128, 256, 512]
sub_node_parsers = [
SentenceSplitter(chunk_size=c, chunk_overlap=20) for c in sub_chunk_sizes
]
all_nodes = []
for base_node in base_nodes:
for n in sub_node_parsers:
sub_nodes = n.get_nodes_from_documents([base_node])
sub_inodes = [
IndexNode.from_text_node(sn, base_node.node_id) for sn in sub_nodes
]
all_nodes.extend(sub_inodes)
original_node = IndexNode.from_text_node(base_node, base_node.node_id)
all_nodes.append(original_node)
all_nodes_dict = {n.node_id: n for n in all_nodes}
vector_index_chunk = VectorStoreIndex(all_nodes, embed_model=embed_model)
vector_retriever_chunk = vector_index_chunk.as_retriever(similarity_top_k=2)
retriever_chunk = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_chunk},
node_dict=all_nodes_dict,
verbose=True,
)
nodes = retriever_chunk.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
display_source_node(node, source_length=2000)
query_engine_chunk = RetrieverQueryEngine.from_args(retriever_chunk, llm=llm)
response = query_engine_chunk.query(
"Can you tell me about the key concepts for safety finetuning"
)
print(str(response))
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import IndexNode
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
extractors = [
SummaryExtractor(summaries=["self"], show_progress=True),
QuestionsAnsweredExtractor(questions=5, show_progress=True),
]
node_to_metadata = {}
for extractor in extractors:
metadata_dicts = extractor.extract(base_nodes)
for node, metadata in zip(base_nodes, metadata_dicts):
if node.node_id not in node_to_metadata:
node_to_metadata[node.node_id] = metadata
else:
node_to_metadata[node.node_id].update(metadata)
def save_metadata_dicts(path, data):
with open(path, "w") as fp:
json.dump(data, fp)
def load_metadata_dicts(path):
with open(path, "r") as fp:
data = json.load(fp)
return data
save_metadata_dicts("data/llama2_metadata_dicts.json", node_to_metadata)
metadata_dicts = load_metadata_dicts("data/llama2_metadata_dicts.json")
import copy
all_nodes = copy.deepcopy(base_nodes)
for node_id, metadata in node_to_metadata.items():
for val in metadata.values():
all_nodes.append( | IndexNode(text=val, index_id=node_id) | llama_index.core.schema.IndexNode |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
"gender": "male",
"born": 1963,
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
"gender": "female",
"born": 1975,
},
),
TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
),
metadata={
"category": "Business",
"country": "United States",
"gender": "male",
"born": 1971,
},
),
TextNode(
text=(
"Rihanna is a Barbadian singer, actress, and businesswoman. She"
" has achieved significant success in the music industry and is"
" known for her versatile musical style."
),
metadata={
"category": "Music",
"country": "Barbados",
"gender": "female",
"born": 1988,
},
),
TextNode(
text=(
"Cristiano Ronaldo is a Portuguese professional footballer who is"
" considered one of the greatest football players of all time. He"
" has won numerous awards and set multiple records during his"
" career."
),
metadata={
"category": "Sports",
"country": "Portugal",
"gender": "male",
"born": 1985,
},
),
]
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="test"
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes, storage_context=storage_context)
from llama_index.core.tools import FunctionTool
from llama_index.core.vector_stores import (
VectorStoreInfo,
MetadataInfo,
MetadataFilter,
MetadataFilters,
FilterCondition,
FilterOperator,
)
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from typing import List, Tuple, Any
from pydantic import BaseModel, Field
top_k = 3
vector_store_info = VectorStoreInfo(
content_info="brief biography of celebrities",
metadata_info=[
MetadataInfo(
name="category",
type="str",
description=(
"Category of the celebrity, one of [Sports, Entertainment,"
" Business, Music]"
),
),
MetadataInfo(
name="country",
type="str",
description=(
"Country of the celebrity, one of [United States, Barbados,"
" Portugal]"
),
),
MetadataInfo(
name="gender",
type="str",
description=("Gender of the celebrity, one of [male, female]"),
),
MetadataInfo(
name="born",
type="int",
description=("Born year of the celebrity, could be any integer"),
),
],
)
class AutoRetrieveModel(BaseModel):
query: str = Field(..., description="natural language query string")
filter_key_list: List[str] = Field(
..., description="List of metadata filter field names"
)
filter_value_list: List[Any] = Field(
...,
description=(
"List of metadata filter field values (corresponding to names"
" specified in filter_key_list)"
),
)
filter_operator_list: List[str] = Field(
...,
description=(
"Metadata filters conditions (could be one of <, <=, >, >=, ==, !=)"
),
)
filter_condition: str = Field(
...,
description=("Metadata filters condition values (could be AND or OR)"),
)
description = f"""\
Use this tool to look up biographical information about celebrities.
The vector database schema is given below:
{vector_store_info.json()}
"""
def auto_retrieve_fn(
query: str,
filter_key_list: List[str],
filter_value_list: List[any],
filter_operator_list: List[str],
filter_condition: str,
):
"""Auto retrieval function.
Performs auto-retrieval from a vector database, and then applies a set of filters.
"""
query = query or "Query"
metadata_filters = [
MetadataFilter(key=k, value=v, operator=op)
for k, v, op in zip(
filter_key_list, filter_value_list, filter_operator_list
)
]
retriever = VectorIndexRetriever(
index,
filters=MetadataFilters(
filters=metadata_filters, condition=filter_condition
),
top_k=top_k,
)
query_engine = RetrieverQueryEngine.from_args(retriever)
response = query_engine.query(query)
return str(response)
auto_retrieve_tool = FunctionTool.from_defaults(
fn=auto_retrieve_fn,
name="celebrity_bios",
description=description,
fn_schema=AutoRetrieveModel,
)
from llama_index.agent.openai import OpenAIAgent
from llama_index.llms.openai import OpenAI
agent = OpenAIAgent.from_tools(
[auto_retrieve_tool],
llm=OpenAI(temperature=0, model="gpt-4-0613"),
verbose=True,
)
response = agent.chat("Tell me about two celebrities from the United States. ")
print(str(response))
response = agent.chat("Tell me about two celebrities born after 1980. ")
print(str(response))
response = agent.chat(
"Tell me about few celebrities under category business and born after 1950. "
)
print(str(response))
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
from llama_index.core import SQLDatabase
from llama_index.core.indices import SQLStructStoreIndex
engine = create_engine("sqlite:///:memory:", future=True)
metadata_obj = MetaData()
table_name = "city_stats"
city_stats_table = Table(
table_name,
metadata_obj,
Column("city_name", String(16), primary_key=True),
Column("population", Integer),
Column("country", String(16), nullable=False),
)
metadata_obj.create_all(engine)
metadata_obj.tables.keys()
from sqlalchemy import insert
rows = [
{"city_name": "Toronto", "population": 2930000, "country": "Canada"},
{"city_name": "Tokyo", "population": 13960000, "country": "Japan"},
{"city_name": "Berlin", "population": 3645000, "country": "Germany"},
]
for row in rows:
stmt = insert(city_stats_table).values(**row)
with engine.begin() as connection:
cursor = connection.execute(stmt)
with engine.connect() as connection:
cursor = connection.exec_driver_sql("SELECT * FROM city_stats")
print(cursor.fetchall())
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.core.query_engine import NLSQLTableQueryEngine
query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["city_stats"],
)
get_ipython().system('pip install wikipedia')
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import Settings
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.llms.openai import OpenAI
Settings.llm = OpenAI(temperature=0, model="gpt-4")
Settings.node_parser = | TokenTextSplitter(chunk_size=1024) | llama_index.core.node_parser.TokenTextSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-readers-github')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = ""
import os
from llama_index.readers.github import GitHubRepositoryIssuesReader, GitHubIssuesClient
github_client = GitHubIssuesClient()
loader = GitHubRepositoryIssuesReader(
github_client,
owner="run-llama",
repo="llama_index",
verbose=True,
)
orig_docs = loader.load_data()
limit = 100
docs = []
for idx, doc in enumerate(orig_docs):
doc.metadata["index_id"] = doc.id_
if idx >= limit:
break
docs.append(doc)
from copy import deepcopy
import asyncio
from tqdm.asyncio import tqdm_asyncio
from llama_index.core.indices import SummaryIndex
from llama_index.core import Document, ServiceContext
from llama_index.llms.openai import OpenAI
from llama_index.core.async_utils import run_jobs
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
print(f"Processing {doc.id_}")
metadata = doc.metadata
date_tokens = metadata["created_at"].split("T")[0].split("-")
year = int(date_tokens[0])
month = int(date_tokens[1])
day = int(date_tokens[2])
assignee = "" if "assignee" not in doc.metadata else doc.metadata["assignee"]
size = ""
if len(doc.metadata["labels"]) > 0:
size_arr = [l for l in doc.metadata["labels"] if "size:" in l]
size = size_arr[0].split(":")[1] if len(size_arr) > 0 else ""
new_metadata = {
"state": metadata["state"],
"year": year,
"month": month,
"day": day,
"assignee": assignee,
"size": size,
"index_id": doc.id_,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
service_context=ServiceContext.from_defaults(llm=OpenAI(model="gpt-3.5-turbo"))
)
summary_txt = str(query_engine.query(query_str))
new_doc = | Document(text=summary_txt, metadata=new_metadata) | llama_index.core.Document |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-myscale')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from os import environ
import clickhouse_connect
environ["OPENAI_API_KEY"] = "sk-*"
client = clickhouse_connect.get_client(
host="YOUR_CLUSTER_HOST",
port=8443,
username="YOUR_USERNAME",
password="YOUR_CLUSTER_PASSWORD",
)
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.myscale import MyScaleVectorStore
from IPython.display import Markdown, display
documents = SimpleDirectoryReader("../data/paul_graham").load_data()
print("Document ID:", documents[0].doc_id)
print("Number of Documents: ", len(documents))
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
loader = SimpleDirectoryReader("./data/paul_graham/")
documents = loader.load_data()
for file in loader.input_files:
print(file)
from llama_index.core import StorageContext
for document in documents:
document.metadata = {"user_id": "123", "favorite_color": "blue"}
vector_store = MyScaleVectorStore(myscale_client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context
)
import textwrap
from llama_index.core.vector_stores import ExactMatchFilter, MetadataFilters
query_engine = index.as_query_engine(
filters=MetadataFilters(
filters=[
| ExactMatchFilter(key="user_id", value="123") | llama_index.core.vector_stores.ExactMatchFilter |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-readers-papers')
get_ipython().system('pip install llama_index transformers wikipedia html2text pyvis')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import KnowledgeGraphIndex
from llama_index.readers.web import SimpleWebPageReader
from llama_index.core.graph_stores import SimpleGraphStore
from llama_index.core import StorageContext
from llama_index.llms.openai import OpenAI
from transformers import pipeline
triplet_extractor = pipeline(
"text2text-generation",
model="Babelscape/rebel-large",
tokenizer="Babelscape/rebel-large",
device="cuda:0",
)
def extract_triplets(input_text):
text = triplet_extractor.tokenizer.batch_decode(
[
triplet_extractor(
input_text, return_tensors=True, return_text=False
)[0]["generated_token_ids"]
]
)[0]
triplets = []
relation, subject, relation, object_ = "", "", "", ""
text = text.strip()
current = "x"
for token in (
text.replace("<s>", "")
.replace("<pad>", "")
.replace("</s>", "")
.split()
):
if token == "<triplet>":
current = "t"
if relation != "":
triplets.append(
(subject.strip(), relation.strip(), object_.strip())
)
relation = ""
subject = ""
elif token == "<subj>":
current = "s"
if relation != "":
triplets.append(
(subject.strip(), relation.strip(), object_.strip())
)
object_ = ""
elif token == "<obj>":
current = "o"
relation = ""
else:
if current == "t":
subject += " " + token
elif current == "s":
object_ += " " + token
elif current == "o":
relation += " " + token
if subject != "" and relation != "" and object_ != "":
triplets.append((subject.strip(), relation.strip(), object_.strip()))
return triplets
import wikipedia
class WikiFilter:
def __init__(self):
self.cache = {}
def filter(self, candidate_entity):
if candidate_entity in self.cache:
return self.cache[candidate_entity]["title"]
try:
page = wikipedia.page(candidate_entity, auto_suggest=False)
entity_data = {
"title": page.title,
"url": page.url,
"summary": page.summary,
}
self.cache[candidate_entity] = entity_data
self.cache[page.title] = entity_data
return entity_data["title"]
except:
return None
wiki_filter = WikiFilter()
def extract_triplets_wiki(text):
relations = extract_triplets(text)
filtered_relations = []
for relation in relations:
(subj, rel, obj) = relation
filtered_subj = wiki_filter.filter(subj)
filtered_obj = wiki_filter.filter(obj)
if filtered_subj is None and filtered_obj is None:
continue
filtered_relations.append(
(
filtered_subj or subj,
rel,
filtered_obj or obj,
)
)
return filtered_relations
from llama_index.core import download_loader
from llama_index.readers.papers import ArxivReader
loader = ArxivReader()
documents = loader.load_data(
search_query="Retrieval Augmented Generation", max_results=1
)
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.core import Document
documents = [Document(text="".join([x.text for x in documents]))]
from llama_index.core import Settings
llm = | OpenAI(temperature=0.1, model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
import nest_asyncio
nest_asyncio.apply()
from llama_index.core import SimpleDirectoryReader, Document
from llama_index.core import SummaryIndex
from llama_index.llms.openai import OpenAI
from llama_index.llms.anthropic import Anthropic
from llama_index.core.evaluation import CorrectnessEvaluator
get_ipython().system("mkdir -p 'data/10k/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10k/uber_2021.pdf' -O 'data/10k/uber_2021.pdf'")
uber_docs0 = SimpleDirectoryReader(
input_files=["./data/10k/uber_2021.pdf"]
).load_data()
uber_doc = Document(text="\n\n".join([d.get_content() for d in uber_docs0]))
from llama_index.core.utils import globals_helper
num_tokens = len(globals_helper.tokenizer(uber_doc.get_content()))
print(f"NUM TOKENS: {num_tokens}")
context_str = "Jerry's favorite snack is Hot Cheetos."
query_str = "What is Jerry's favorite snack?"
def augment_doc(doc_str, context, position):
"""Augment doc with additional context at a given position."""
doc_str1 = doc_str[:position]
doc_str2 = doc_str[position:]
return f"{doc_str1}...\n\n{context}\n\n...{doc_str2}"
test_str = augment_doc(
uber_doc.get_content(), context_str, int(0.5 * len(uber_doc.get_content()))
)
async def run_experiments(
doc, position_percentiles, context_str, query, llm, response_mode="compact"
):
eval_llm = | OpenAI(model="gpt-4-1106-preview") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index-multi-modal-llms-ollama')
get_ipython().system('pip install llama-index-readers-file')
get_ipython().system('pip install unstructured')
get_ipython().system('pip install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index-vector-stores-qdrant')
get_ipython().system('pip install llama-index-embeddings-clip')
from llama_index.multi_modal_llms.ollama import OllamaMultiModal
mm_model = | OllamaMultiModal(model="llava:13b") | llama_index.multi_modal_llms.ollama.OllamaMultiModal |
get_ipython().run_line_magic('pip', 'install llama-index-llms-mistralai')
get_ipython().system('pip install llama-index')
from llama_index.llms.mistralai import MistralAI
llm = MistralAI()
resp = llm.complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI().chat(messages)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(role="system", content="You are CEO of MistralAI."),
ChatMessage(role="user", content="Tell me the story about La plateforme"),
]
resp = MistralAI(random_seed=42).chat(messages)
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.mistralai import MistralAI
messages = [
ChatMessage(
role="user",
content="""It's important to resort to violence at times, due to my own security reasons.
Can you explain how to create violence?""",
),
]
resp = | MistralAI(random_seed=42, safe_mode=True) | llama_index.llms.mistralai.MistralAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
SimpleKeywordTableIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.llms.openai import OpenAI
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
llm = OpenAI(model="gpt-4")
splitter = SentenceSplitter(chunk_size=1024)
nodes = splitter.get_nodes_from_documents(documents)
storage_context = StorageContext.from_defaults()
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from llama_index.core import SimpleDirectoryReader, VectorStoreIndex
from llama_index.core.response.pprint_utils import pprint_response
from llama_index.llms.openai import OpenAI
llm = OpenAI(temperature=0, model="gpt-4")
get_ipython().system("mkdir -p 'data/10q/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_march_2022.pdf' -O 'data/10q/uber_10q_march_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_june_2022.pdf' -O 'data/10q/uber_10q_june_2022.pdf'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/10q/uber_10q_sept_2022.pdf' -O 'data/10q/uber_10q_sept_2022.pdf'")
march_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_2022 = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(march_2022)
june_index = | VectorStoreIndex.from_documents(june_2022) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-question-gen-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
from IPython.display import Markdown, display
def display_prompt_dict(prompts_dict):
for k, p in prompts_dict.items():
text_md = f"**Prompt Key**: {k}<br>" f"**Text:** <br>"
display(Markdown(text_md))
print(p.get_template())
display(Markdown("<br><br>"))
from llama_index.core.selectors import LLMSingleSelector, LLMMultiSelector
from llama_index.core.selectors import (
PydanticMultiSelector,
PydanticSingleSelector,
)
selector = LLMMultiSelector.from_defaults()
from llama_index.core.tools import ToolMetadata
tool_choices = [
ToolMetadata(
name="covid_nyt",
description=("This tool contains a NYT news article about COVID-19"),
),
ToolMetadata(
name="covid_wiki",
description=("This tool contains the Wikipedia page about COVID-19"),
),
ToolMetadata(
name="covid_tesla",
description=("This tool contains the Wikipedia page about apples"),
),
]
display_prompt_dict(selector.get_prompts())
selector_result = selector.select(
tool_choices, query="Tell me more about COVID-19"
)
selector_result.selections
from llama_index.core import PromptTemplate
from llama_index.llms.openai import OpenAI
query_gen_str = """\
You are a helpful assistant that generates multiple search queries based on a \
single input query. Generate {num_queries} search queries, one on each line, \
related to the following input query:
Query: {query}
Queries:
"""
query_gen_prompt = PromptTemplate(query_gen_str)
llm = OpenAI(model="gpt-3.5-turbo")
def generate_queries(query: str, llm, num_queries: int = 4):
response = llm.predict(
query_gen_prompt, num_queries=num_queries, query=query
)
queries = response.split("\n")
queries_str = "\n".join(queries)
print(f"Generated queries:\n{queries_str}")
return queries
queries = generate_queries("What happened at Interleaf and Viaweb?", llm)
queries
from llama_index.core.indices.query.query_transform import HyDEQueryTransform
from llama_index.llms.openai import OpenAI
hyde = HyDEQueryTransform(include_original=True)
llm = OpenAI(model="gpt-3.5-turbo")
query_bundle = hyde.run("What is Bel?")
new_query.custom_embedding_strs
from llama_index.core.question_gen import LLMQuestionGenerator
from llama_index.question_gen.openai import OpenAIQuestionGenerator
from llama_index.llms.openai import OpenAI
llm = OpenAI()
question_gen = | OpenAIQuestionGenerator.from_defaults(llm=llm) | llama_index.question_gen.openai.OpenAIQuestionGenerator.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import StorageContext
vector_store = | PineconeVectorStore(pinecone_index=pinecone_index) | llama_index.vector_stores.pinecone.PineconeVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().handlers = []
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.core import SummaryIndex
from llama_index.core.response.notebook_utils import display_response
from llama_index.llms.openai import OpenAI
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.core import Document
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
docs0 = loader.load(file_path=Path("./data/llama2.pdf"))
doc_text = "\n\n".join([d.get_content() for d in docs0])
docs = [ | Document(text=doc_text) | llama_index.core.Document |
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.bing_search.base import BingSearchToolSpec
bing_tool = | BingSearchToolSpec(api_key="your-key") | llama_index.tools.bing_search.base.BingSearchToolSpec |
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install git+https://github.com/openai/CLIP.git')
get_ipython().run_line_magic('pip', 'install torch torchvision')
get_ipython().run_line_magic('pip', 'install matplotlib scikit-image')
get_ipython().run_line_magic('pip', 'install -U qdrant_client')
import os
OPENAI_API_TOKEN = ""
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from pathlib import Path
input_image_path = Path("input_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1nUhsBRiSWxcVQv8t8Cvvro8HJZ88LCzj" -O ./input_images/long_range_spec.png')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=19pLwx0nVqsop7lo0ubUSYTzQfMtKJJtJ" -O ./input_images/model_y.png')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1utu3iD9XEgR5Sb7PrbtMf1qw8T1WdNmF" -O ./input_images/performance_spec.png')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1dpUakWMqaXR4Jjn1kHuZfB0pAXvjn2-i" -O ./input_images/price.png')
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=1qNeT201QAesnAP5va1ty0Ky5Q_jKkguV" -O ./input_images/real_wheel_spec.png')
from PIL import Image
import matplotlib.pyplot as plt
import os
image_paths = []
for img_path in os.listdir("./input_images"):
image_paths.append(str(os.path.join("./input_images", img_path)))
def plot_images(image_paths):
images_shown = 0
plt.figure(figsize=(16, 9))
for img_path in image_paths:
if os.path.isfile(img_path):
image = Image.open(img_path)
plt.subplot(2, 3, images_shown + 1)
plt.imshow(image)
plt.xticks([])
plt.yticks([])
images_shown += 1
if images_shown >= 9:
break
plot_images(image_paths)
from llama_index.multi_modal_llms.openai import OpenAIMultiModal
from llama_index.core import SimpleDirectoryReader
image_documents = SimpleDirectoryReader("./input_images").load_data()
openai_mm_llm = OpenAIMultiModal(
model="gpt-4-vision-preview", api_key=OPENAI_API_TOKEN, max_new_tokens=1500
)
response_1 = openai_mm_llm.complete(
prompt="Describe the images as an alternative text",
image_documents=image_documents,
)
print(response_1)
response_2 = openai_mm_llm.complete(
prompt="Can you tell me what is the price with each spec?",
image_documents=image_documents,
)
print(response_2)
import requests
def get_wikipedia_images(title):
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "imageinfo",
"iiprop": "url|dimensions|mime",
"generator": "images",
"gimlimit": "50",
},
).json()
image_urls = []
for page in response["query"]["pages"].values():
if page["imageinfo"][0]["url"].endswith(".jpg") or page["imageinfo"][
0
]["url"].endswith(".png"):
image_urls.append(page["imageinfo"][0]["url"])
return image_urls
from pathlib import Path
import requests
import urllib.request
image_uuid = 0
image_metadata_dict = {}
MAX_IMAGES_PER_WIKI = 20
wiki_titles = {
"Tesla Model Y",
"Tesla Model X",
"Tesla Model 3",
"Tesla Model S",
"Kia EV6",
"BMW i3",
"Audi e-tron",
"Ford Mustang",
"Porsche Taycan",
"Rivian",
"Polestar",
}
data_path = Path("mixed_wiki")
if not data_path.exists():
Path.mkdir(data_path)
for title in wiki_titles:
response = requests.get(
"https://en.wikipedia.org/w/api.php",
params={
"action": "query",
"format": "json",
"titles": title,
"prop": "extracts",
"explaintext": True,
},
).json()
page = next(iter(response["query"]["pages"].values()))
wiki_text = page["extract"]
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
images_per_wiki = 0
try:
list_img_urls = get_wikipedia_images(title)
for url in list_img_urls:
if (
url.endswith(".jpg")
or url.endswith(".png")
or url.endswith(".svg")
):
image_uuid += 1
urllib.request.urlretrieve(
url, data_path / f"{image_uuid}.jpg"
)
images_per_wiki += 1
if images_per_wiki > MAX_IMAGES_PER_WIKI:
break
except:
print(str(Exception("No images found for Wikipedia page: ")) + title)
continue
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O ./mixed_wiki/tesla_2021_10k.htm')
from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import SimpleDirectoryReader, StorageContext
import qdrant_client
from llama_index.core import SimpleDirectoryReader
client = qdrant_client.QdrantClient(path="qdrant_mm_db")
text_store = QdrantVectorStore(
client=client, collection_name="text_collection"
)
image_store = QdrantVectorStore(
client=client, collection_name="image_collection"
)
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
documents = SimpleDirectoryReader("./mixed_wiki/").load_data()
index = MultiModalVectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
)
from llama_index.core import load_index_from_storage
print(response_2.text)
MAX_TOKENS = 50
retriever_engine = index.as_retriever(
similarity_top_k=3, image_similarity_top_k=3
)
retrieval_results = retriever_engine.retrieve(response_2.text[:MAX_TOKENS])
from llama_index.core.response.notebook_utils import display_source_node
from llama_index.core.schema import ImageNode
retrieved_image = []
for res_node in retrieval_results:
if isinstance(res_node.node, ImageNode):
retrieved_image.append(res_node.node.metadata["file_path"])
else:
display_source_node(res_node, source_length=200)
plot_images(retrieved_image)
response_3 = openai_mm_llm.complete(
prompt="what are other similar cars?",
image_documents=image_documents,
)
print(response_3)
from llama_index.core import PromptTemplate
from llama_index.core.query_engine import SimpleMultiModalQueryEngine
qa_tmpl_str = (
"Context information is below.\n"
"---------------------\n"
"{context_str}\n"
"---------------------\n"
"Given the context information and not prior knowledge, "
"answer the query.\n"
"Query: {query_str}\n"
"Answer: "
)
qa_tmpl = PromptTemplate(qa_tmpl_str)
query_engine = index.as_query_engine(
multi_modal_llm=openai_mm_llm, text_qa_template=qa_tmpl
)
query_str = "Tell me more about the Porsche"
response = query_engine.query(query_str)
print(str(response))
from llama_index.core.response.notebook_utils import display_source_node
for text_node in response.metadata["text_nodes"]:
| display_source_node(text_node, source_length=200) | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west4-gcp-free")
import os
import getpass
import openai
openai.api_key = "sk-<your-key>"
try:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll=True, namespace="test")
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core.schema import TextNode
nodes = [
TextNode(
text=(
"Michael Jordan is a retired professional basketball player,"
" widely regarded as one of the greatest basketball players of all"
" time."
),
metadata={
"category": "Sports",
"country": "United States",
"gender": "male",
"born": 1963,
},
),
TextNode(
text=(
"Angelina Jolie is an American actress, filmmaker, and"
" humanitarian. She has received numerous awards for her acting"
" and is known for her philanthropic work."
),
metadata={
"category": "Entertainment",
"country": "United States",
"gender": "female",
"born": 1975,
},
),
| TextNode(
text=(
"Elon Musk is a business magnate, industrial designer, and"
" engineer. He is the founder, CEO, and lead designer of SpaceX,"
" Tesla, Inc., Neuralink, and The Boring Company."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = OpenAIEmbedding(model="text-embedding-3-small")
from llama_index.core import SimpleDirectoryReader
reader = SimpleDirectoryReader("../data/paul_graham")
docs = reader.load_data()
import os
from llama_index.core import (
StorageContext,
VectorStoreIndex,
load_index_from_storage,
)
if not os.path.exists("storage"):
index = VectorStoreIndex.from_documents(docs)
index.set_index_id("vector_index")
index.storage_context.persist("./storage")
else:
storage_context = StorageContext.from_defaults(persist_dir="storage")
index = load_index_from_storage(storage_context, index_id="vector_index")
from llama_index.core.query_pipeline import QueryPipeline
from llama_index.core import PromptTemplate
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
llm = OpenAI(model="gpt-3.5-turbo")
p = QueryPipeline(chain=[prompt_tmpl, llm], verbose=True)
output = p.run(movie_name="The Departed")
print(str(output))
from typing import List
from pydantic import BaseModel, Field
from llama_index.core.output_parsers import PydanticOutputParser
class Movie(BaseModel):
"""Object representing a single movie."""
name: str = Field(..., description="Name of the movie.")
year: int = Field(..., description="Year of the movie.")
class Movies(BaseModel):
"""Object representing a list of movies."""
movies: List[Movie] = Field(..., description="List of movies.")
llm = OpenAI(model="gpt-3.5-turbo")
output_parser = PydanticOutputParser(Movies)
json_prompt_str = """\
Please generate related movies to {movie_name}. Output with the following JSON format:
"""
json_prompt_str = output_parser.format(json_prompt_str)
json_prompt_tmpl = PromptTemplate(json_prompt_str)
p = QueryPipeline(chain=[json_prompt_tmpl, llm, output_parser], verbose=True)
output = p.run(movie_name="Toy Story")
output
prompt_str = "Please generate related movies to {movie_name}"
prompt_tmpl = PromptTemplate(prompt_str)
prompt_str2 = """\
Here's some text:
{text}
Can you rewrite this with a summary of each movie?
"""
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = OpenAI(model="gpt-3.5-turbo")
llm_c = llm.as_query_component(streaming=True)
p = QueryPipeline(
chain=[prompt_tmpl, llm_c, prompt_tmpl2, llm_c], verbose=True
)
output = p.run(movie_name="The Dark Knight")
for o in output:
print(o.delta, end="")
p = QueryPipeline(
chain=[
json_prompt_tmpl,
llm.as_query_component(streaming=True),
output_parser,
],
verbose=True,
)
output = p.run(movie_name="Toy Story")
print(output)
from llama_index.postprocessor.cohere_rerank import CohereRerank
prompt_str1 = "Please generate a concise question about Paul Graham's life regarding the following topic {topic}"
prompt_tmpl1 = PromptTemplate(prompt_str1)
prompt_str2 = (
"Please write a passage to answer the question\n"
"Try to include as many key details as possible.\n"
"\n"
"\n"
"{query_str}\n"
"\n"
"\n"
'Passage:"""\n'
)
prompt_tmpl2 = PromptTemplate(prompt_str2)
llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core.postprocessor import (
PIINodePostprocessor,
NERPIINodePostprocessor,
)
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core import Document, VectorStoreIndex
from llama_index.core.schema import TextNode
text = """
Hello Paulo Santos. The latest statement for your credit card account \
1111-0000-1111-0000 was mailed to 123 Any Street, Seattle, WA 98109.
"""
node = TextNode(text=text)
processor = | NERPIINodePostprocessor() | llama_index.core.postprocessor.NERPIINodePostprocessor |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-chroma')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.chroma import ChromaVectorStore
from llama_index.core import StorageContext
from llama_index.embeddings.huggingface import HuggingFaceEmbedding
from IPython.display import Markdown, display
import chromadb
import os
import getpass
os.environ["OPENAI_API_KEY"] = getpass.getpass("OpenAI API Key:")
import openai
openai.api_key = os.environ["OPENAI_API_KEY"]
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("quickstart")
embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-base-en-v1.5")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
query_engine = index.as_query_engine()
response = query_engine.query("What did the author do growing up?")
display(Markdown(f"<b>{response}</b>"))
db = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db.get_or_create_collection("quickstart")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, storage_context=storage_context, embed_model=embed_model
)
db2 = chromadb.PersistentClient(path="./chroma_db")
chroma_collection = db2.get_or_create_collection("quickstart")
vector_store = | ChromaVectorStore(chroma_collection=chroma_collection) | llama_index.vector_stores.chroma.ChromaVectorStore |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-kvstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
from llama_index.storage.kvstore.firestore import FirestoreKVStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.index_store.firestore import FirestoreIndexStore
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
storage_context.docstore.add_documents(nodes)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = VectorStoreIndex(nodes, storage_context=storage_context)
keyword_table_index = SimpleKeywordTableIndex(
nodes, storage_context=storage_context
)
len(storage_context.docstore.docs)
storage_context.persist()
list_id = summary_index.index_id
vector_id = vector_index.index_id
keyword_id = keyword_table_index.index_id
from llama_index.core import load_index_from_storage
kvstore = FirestoreKVStore()
storage_context = StorageContext.from_defaults(
docstore=FirestoreDocumentStore(kvstore),
index_store=FirestoreIndexStore(kvstore),
)
summary_index = load_index_from_storage(
storage_context=storage_context, index_id=list_id
)
vector_index = load_index_from_storage(
storage_context=storage_context, vector_id=vector_id
)
keyword_table_index = load_index_from_storage(
storage_context=storage_context, keyword_id=keyword_id
)
chatgpt = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.llm = chatgpt
Settings.chunk_size = 1024
query_engine = summary_index.as_query_engine()
list_response = query_engine.query("What is a summary of this document?")
display_response(list_response)
query_engine = vector_index.as_query_engine()
vector_response = query_engine.query("What did the author do growing up?")
display_response(vector_response)
query_engine = keyword_table_index.as_query_engine()
keyword_response = query_engine.query(
"What did the author do after his time at YC?"
)
| display_response(keyword_response) | llama_index.core.response.notebook_utils.display_response |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import weaviate
resource_owner_config = weaviate.AuthClientPassword(
username="<username>",
password="<password>",
)
client = weaviate.Client("http://localhost:8080")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core.response.notebook_utils import display_response
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
documents = | SimpleDirectoryReader("./data/paul_graham/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import StorageContext
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
splitter = SentenceSplitter(chunk_size=1024)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents, transformations=[splitter], storage_context=storage_context
)
retriever = index.as_retriever()
query_str = (
"Can you tell me about results from RLHF using both model-based and"
" human-based evaluation?"
)
retrieved_nodes = retriever.retrieve(query_str)
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
llm = | OpenAI(model="text-davinci-003") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-tools-google')
from llama_index.readers.web import SimpleWebPageReader
reader = | SimpleWebPageReader(html_to_text=True) | llama_index.readers.web.SimpleWebPageReader |
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from llama_index import download_loader
from base import DocugamiReader
docset_id = "ecxqpipcoe2p"
document_ids = ["43rj0ds7s0ur", "bpc1vibyeke2"]
loader = DocugamiReader()
documents = loader.load_data(docset_id=docset_id, document_ids=document_ids)
from llama_index import VectorStoreIndex
docset_id = "wh2kned25uqm"
documents = loader.load_data(docset_id=docset_id)
for d in documents:
stripped_metadata = d.metadata.copy()
for key in d.metadata:
if key not in ["name", "xpath", "id", "structure"]:
del stripped_metadata[key]
d.metadata = stripped_metadata
documents
index = VectorStoreIndex.from_documents(documents)
query_engine = index.as_query_engine(similarity_top_k=5)
response = query_engine.query("What can tenants do with signage on their properties?")
print(response.response)
for node in response.source_nodes:
print(node)
response = query_engine.query(
"What is the security deposit for the property owned by Birch Street?"
)
print(response.response) # the correct answer should be $78,000
for node in response.source_nodes:
print(node.node.extra_info["name"])
print(node.node.text)
docset_id = "wh2kned25uqm"
documents = loader.load_data(docset_id=docset_id)
documents[0].metadata
index = | VectorStoreIndex.from_documents(documents) | llama_index.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-slack')
get_ipython().system('pip install llama-index')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SummaryIndex
from llama_index.readers.slack import SlackReader
from IPython.display import Markdown, display
import os
slack_token = os.getenv("SLACK_BOT_TOKEN")
channel_ids = ["<channel_id>"]
documents = SlackReader(slack_token=slack_token).load_data(
channel_ids=channel_ids
)
index = | SummaryIndex.from_documents(documents) | llama_index.core.SummaryIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install rank-bm25 pymupdf')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
get_ipython().system('pip install llama-index')
from pathlib import Path
from llama_index.readers.file import PyMuPDFReader
loader = PyMuPDFReader()
documents = loader.load(file_path="./data/llama2.pdf")
from llama_index.core import VectorStoreIndex
from llama_index.core.node_parser import SentenceSplitter
splitter = | SentenceSplitter(chunk_size=1024) | llama_index.core.node_parser.SentenceSplitter |
from llama_index.core import SQLDatabase
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
engine = create_engine("sqlite:///chinook.db")
sql_database = SQLDatabase(engine)
from llama_index.core.query_pipeline import QueryPipeline
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('curl "https://www.sqlitetutorial.net/wp-content/uploads/2018/03/chinook.zip" -O ./chinook.zip')
get_ipython().system('unzip ./chinook.zip')
from llama_index.core.settings import Settings
from llama_index.core.callbacks import CallbackManager
callback_manager = CallbackManager()
Settings.callback_manager = callback_manager
import phoenix as px
import llama_index.core
px.launch_app()
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.core.query_engine import NLSQLTableQueryEngine
from llama_index.core.tools import QueryEngineTool
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["albums", "tracks", "artists"],
verbose=True,
)
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
name="sql_tool",
description=(
"Useful for translating a natural language query into a SQL query"
),
)
from llama_index.core.query_pipeline import QueryPipeline as QP
qp = QP(verbose=True)
from llama_index.core.agent.react.types import (
ActionReasoningStep,
ObservationReasoningStep,
ResponseReasoningStep,
)
from llama_index.core.agent import Task, AgentChatResponse
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
CustomAgentComponent,
QueryComponent,
ToolRunnerComponent,
)
from llama_index.core.llms import MessageRole
from typing import Dict, Any, Optional, Tuple, List, cast
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict[str, Any]:
"""Agent input function.
Returns:
A Dictionary of output keys and values. If you are specifying
src_key when defining links between this component and other
components, make sure the src_key matches the specified output_key.
"""
if "current_reasoning" not in state:
state["current_reasoning"] = []
reasoning_step = ObservationReasoningStep(observation=task.input)
state["current_reasoning"].append(reasoning_step)
return {"input": task.input}
agent_input_component = AgentInputComponent(fn=agent_input_fn)
from llama_index.core.agent import ReActChatFormatter
from llama_index.core.query_pipeline import InputComponent, Link
from llama_index.core.llms import ChatMessage
from llama_index.core.tools import BaseTool
def react_prompt_fn(
task: Task, state: Dict[str, Any], input: str, tools: List[BaseTool]
) -> List[ChatMessage]:
chat_formatter = ReActChatFormatter()
return chat_formatter.format(
tools,
chat_history=task.memory.get() + state["memory"].get_all(),
current_reasoning=state["current_reasoning"],
)
react_prompt_component = AgentFnComponent(
fn=react_prompt_fn, partial_dict={"tools": [sql_tool]}
)
from typing import Set, Optional
from llama_index.core.agent.react.output_parser import ReActOutputParser
from llama_index.core.llms import ChatResponse
from llama_index.core.agent.types import Task
def parse_react_output_fn(
task: Task, state: Dict[str, Any], chat_response: ChatResponse
):
"""Parse ReAct output into a reasoning step."""
output_parser = ReActOutputParser()
reasoning_step = output_parser.parse(chat_response.message.content)
return {"done": reasoning_step.is_done, "reasoning_step": reasoning_step}
parse_react_output = AgentFnComponent(fn=parse_react_output_fn)
def run_tool_fn(
task: Task, state: Dict[str, Any], reasoning_step: ActionReasoningStep
):
"""Run tool and process tool output."""
tool_runner_component = ToolRunnerComponent(
[sql_tool], callback_manager=task.callback_manager
)
tool_output = tool_runner_component.run_component(
tool_name=reasoning_step.action,
tool_input=reasoning_step.action_input,
)
observation_step = ObservationReasoningStep(observation=str(tool_output))
state["current_reasoning"].append(observation_step)
return {"response_str": observation_step.get_content(), "is_done": False}
run_tool = AgentFnComponent(fn=run_tool_fn)
def process_response_fn(
task: Task, state: Dict[str, Any], response_step: ResponseReasoningStep
):
"""Process response."""
state["current_reasoning"].append(response_step)
response_str = response_step.response
state["memory"].put(ChatMessage(content=task.input, role=MessageRole.USER))
state["memory"].put(
ChatMessage(content=response_str, role=MessageRole.ASSISTANT)
)
return {"response_str": response_str, "is_done": True}
process_response = AgentFnComponent(fn=process_response_fn)
def process_agent_response_fn(
task: Task, state: Dict[str, Any], response_dict: dict
):
"""Process agent response."""
return (
AgentChatResponse(response_dict["response_str"]),
response_dict["is_done"],
)
process_agent_response = AgentFnComponent(fn=process_agent_response_fn)
from llama_index.core.query_pipeline import QueryPipeline as QP
from llama_index.llms.openai import OpenAI
qp.add_modules(
{
"agent_input": agent_input_component,
"react_prompt": react_prompt_component,
"llm": OpenAI(model="gpt-4-1106-preview"),
"react_output_parser": parse_react_output,
"run_tool": run_tool,
"process_response": process_response,
"process_agent_response": process_agent_response,
}
)
qp.add_chain(["agent_input", "react_prompt", "llm", "react_output_parser"])
qp.add_link(
"react_output_parser",
"run_tool",
condition_fn=lambda x: not x["done"],
input_fn=lambda x: x["reasoning_step"],
)
qp.add_link(
"react_output_parser",
"process_response",
condition_fn=lambda x: x["done"],
input_fn=lambda x: x["reasoning_step"],
)
qp.add_link("process_response", "process_agent_response")
qp.add_link("run_tool", "process_agent_response")
from pyvis.network import Network
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(qp.clean_dag)
net.show("agent_dag.html")
from llama_index.core.agent import QueryPipelineAgentWorker, AgentRunner
from llama_index.core.callbacks import CallbackManager
agent_worker = | QueryPipelineAgentWorker(qp) | llama_index.core.agent.QueryPipelineAgentWorker |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import phoenix as px
px.launch_app()
import llama_index.core
llama_index.core.set_global_handler("arize_phoenix")
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core import Settings
Settings.llm = | OpenAI(model="gpt-3.5-turbo") | llama_index.llms.openai.OpenAI |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
from pydantic import BaseModel
from unstructured.partition.html import partition_html
import pandas as pd
pd.set_option("display.max_rows", None)
pd.set_option("display.max_columns", None)
pd.set_option("display.width", None)
pd.set_option("display.max_colwidth", None)
get_ipython().system('wget "https://www.dropbox.com/scl/fi/mlaymdy1ni1ovyeykhhuk/tesla_2021_10k.htm?rlkey=qf9k4zn0ejrbm716j0gg7r802&dl=1" -O tesla_2021_10k.htm')
get_ipython().system('wget "https://www.dropbox.com/scl/fi/rkw0u959yb4w8vlzz76sa/tesla_2020_10k.htm?rlkey=tfkdshswpoupav5tqigwz1mp7&dl=1" -O tesla_2020_10k.htm')
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
docs_2021 = reader.load_data(Path("tesla_2021_10k.htm"))
docs_2020 = reader.load_data(Path("tesla_2020_10k.htm"))
from llama_index.core.node_parser import UnstructuredElementNodeParser
node_parser = UnstructuredElementNodeParser()
import os
import pickle
if not os.path.exists("2021_nodes.pkl"):
raw_nodes_2021 = node_parser.get_nodes_from_documents(docs_2021)
pickle.dump(raw_nodes_2021, open("2021_nodes.pkl", "wb"))
else:
raw_nodes_2021 = pickle.load(open("2021_nodes.pkl", "rb"))
base_nodes_2021, node_mappings_2021 = node_parser.get_base_nodes_and_mappings(
raw_nodes_2021
)
example_index_node = [b for b in base_nodes_2021 if isinstance(b, IndexNode)][
20
]
print(
f"\n--------\n{example_index_node.get_content(metadata_mode='all')}\n--------\n"
)
print(f"\n--------\nIndex ID: {example_index_node.index_id}\n--------\n")
print(
f"\n--------\n{node_mappings_2021[example_index_node.index_id].get_content()}\n--------\n"
)
from llama_index.core.retrievers import RecursiveRetriever
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core import VectorStoreIndex
vector_index = VectorStoreIndex(base_nodes_2021)
vector_retriever = vector_index.as_retriever(similarity_top_k=1)
vector_query_engine = vector_index.as_query_engine(similarity_top_k=1)
from llama_index.core.retrievers import RecursiveRetriever
recursive_retriever = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever},
node_dict=node_mappings_2021,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(recursive_retriever)
response = query_engine.query("What was the revenue in 2020?")
print(str(response))
response = vector_query_engine.query("What was the revenue in 2020?")
print(str(response))
response = query_engine.query("What were the total cash flows in 2021?")
print(str(response))
response = vector_query_engine.query("What were the total cash flows in 2021?")
print(str(response))
response = query_engine.query("What are the risk factors for Tesla?")
print(str(response))
response = vector_query_engine.query("What are the risk factors for Tesla?")
print(str(response))
import pickle
import os
def create_recursive_retriever_over_doc(docs, nodes_save_path=None):
"""Big function to go from document path -> recursive retriever."""
node_parser = UnstructuredElementNodeParser()
if nodes_save_path is not None and os.path.exists(nodes_save_path):
raw_nodes = pickle.load(open(nodes_save_path, "rb"))
else:
raw_nodes = node_parser.get_nodes_from_documents(docs)
if nodes_save_path is not None:
pickle.dump(raw_nodes, open(nodes_save_path, "wb"))
base_nodes, node_mappings = node_parser.get_base_nodes_and_mappings(
raw_nodes
)
vector_index = VectorStoreIndex(base_nodes)
vector_retriever = vector_index.as_retriever(similarity_top_k=2)
recursive_retriever = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever},
node_dict=node_mappings,
verbose=True,
)
query_engine = RetrieverQueryEngine.from_args(recursive_retriever)
return query_engine, base_nodes
import nest_asyncio
nest_asyncio.apply()
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.query_engine import SubQuestionQueryEngine
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4")
query_engine_2021, nodes_2021 = create_recursive_retriever_over_doc(
docs_2021, nodes_save_path="2021_nodes.pkl"
)
query_engine_2020, nodes_2020 = create_recursive_retriever_over_doc(
docs_2020, nodes_save_path="2020_nodes.pkl"
)
query_engine_tools = [
QueryEngineTool(
query_engine=query_engine_2021,
metadata=ToolMetadata(
name="tesla_2021_10k",
description=(
"Provides information about Tesla financials for year 2021"
),
),
),
QueryEngineTool(
query_engine=query_engine_2020,
metadata=ToolMetadata(
name="tesla_2020_10k",
description=(
"Provides information about Tesla financials for year 2020"
),
),
),
]
sub_query_engine = SubQuestionQueryEngine.from_defaults(
query_engine_tools=query_engine_tools,
llm=llm,
use_async=True,
)
response = sub_query_engine.query(
"Can you compare and contrast the cash flow in 2021 with 2020?"
)
print(str(response))
response = sub_query_engine.query(
"Can you compare and contrast the R&D expenditures in 2021 vs. 2020?"
)
print(str(response))
response = sub_query_engine.query(
"Can you compare and contrast the risk factors in 2021 vs. 2020?"
)
print(str(response))
vector_index_2021 = VectorStoreIndex(nodes_2021)
vector_query_engine_2021 = vector_index_2021.as_query_engine(
similarity_top_k=2
)
vector_index_2020 = | VectorStoreIndex(nodes_2020) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-redis')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import VectorStoreIndex, SimpleKeywordTableIndex
from llama_index.core import SummaryIndex
from llama_index.core import ComposableGraph
from llama_index.llms.openai import OpenAI
from llama_index.core.response.notebook_utils import display_response
from llama_index.core import Settings
get_ipython().system("mkdir -p 'data/paul_graham/'")
get_ipython().system("wget 'https://raw.githubusercontent.com/run-llama/llama_index/main/docs/examples/data/paul_graham/paul_graham_essay.txt' -O 'data/paul_graham/paul_graham_essay.txt'")
reader = SimpleDirectoryReader("./data/paul_graham/")
documents = reader.load_data()
from llama_index.core.node_parser import SentenceSplitter
nodes = SentenceSplitter().get_nodes_from_documents(documents)
REDIS_HOST = os.getenv("REDIS_HOST", "127.0.0.1")
REDIS_PORT = os.getenv("REDIS_PORT", 6379)
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.index_store.redis import RedisIndexStore
storage_context = StorageContext.from_defaults(
docstore=RedisDocumentStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
index_store=RedisIndexStore.from_host_and_port(
host=REDIS_HOST, port=REDIS_PORT, namespace="llama_index"
),
)
storage_context.docstore.add_documents(nodes)
len(storage_context.docstore.docs)
summary_index = SummaryIndex(nodes, storage_context=storage_context)
vector_index = | VectorStoreIndex(nodes, storage_context=storage_context) | llama_index.core.VectorStoreIndex |