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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) | llama_index.core.agent.react.types.ObservationReasoningStep |
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"]) | llama_index.core.SQLDatabase |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
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))
from llama_index.core import VectorStoreIndex
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
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")
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
gpt35_llm = OpenAI(model="gpt-3.5-turbo")
gpt4_llm = OpenAI(model="gpt-4")
index = VectorStoreIndex.from_documents(documents)
query_str = "What are the potential risks associated with the use of Llama 2 as mentioned in the context?"
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt35_llm)
vector_retriever = index.as_retriever(similarity_top_k=2)
response = query_engine.query(query_str)
print(str(response))
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>"))
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
from langchain import hub
langchain_prompt = hub.pull("rlm/rag-prompt")
from llama_index.core.prompts import LangchainPromptTemplate
lc_prompt_tmpl = LangchainPromptTemplate(
template=langchain_prompt,
template_var_mappings={"query_str": "question", "context_str": "context"},
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": lc_prompt_tmpl}
)
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query(query_str)
print(str(response))
from llama_index.core.schema import TextNode
few_shot_nodes = []
for line in open("../llama2_qa_citation_events.jsonl", "r"):
few_shot_nodes.append( | TextNode(text=line) | 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-retrievers-bm25')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
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 (
SimpleDirectoryReader,
StorageContext,
VectorStoreIndex,
)
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.core.retrievers import VectorIndexRetriever
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") | 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-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
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/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()
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = OpenAI()
Settings.chunk_size = 1024
nodes = Settings.node_parser.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)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
summary_tool = QueryEngineTool.from_defaults(
query_engine=summary_query_engine,
name="summary_tool",
description=(
"Useful for summarization questions related to the author's life"
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
name="vector_tool",
description=(
"Useful for retrieving specific context to answer specific questions about the author's life"
),
)
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="QA bot",
instructions="You are a bot designed to answer questions about the author",
openai_tools=[],
tools=[summary_tool, vector_tool],
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("Can you give me a summary about the author's life?")
print(str(response))
response = agent.query("What did the author do after RICS?")
print(str(response))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
try:
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart")
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",
},
),
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."
),
metadata={
"category": "Music",
"country": "Barbados",
},
),
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",
},
),
]
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,
ExactMatchFilter,
MetadataFilters,
)
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]"
),
),
],
)
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[str] = Field(
...,
description=(
"List of metadata filter field values (corresponding to names"
" specified in filter_key_list)"
),
)
def auto_retrieve_fn(
query: str, filter_key_list: List[str], filter_value_list: List[str]
):
"""Auto retrieval function.
Performs auto-retrieval from a vector database, and then applies a set of filters.
"""
query = query or "Query"
exact_match_filters = [
ExactMatchFilter(key=k, value=v)
for k, v in zip(filter_key_list, filter_value_list)
]
retriever = VectorIndexRetriever(
index,
filters=MetadataFilters(filters=exact_match_filters),
top_k=top_k,
)
results = retriever.retrieve(query)
return [r.get_content() for r in results]
description = f"""\
Use this tool to look up biographical information about celebrities.
The vector database schema is given below:
{vector_store_info.json()}
"""
auto_retrieve_tool = FunctionTool.from_defaults(
fn=auto_retrieve_fn,
name="celebrity_bios",
description=description,
fn_schema=AutoRetrieveModel,
)
auto_retrieve_fn(
"celebrity from the United States",
filter_key_list=["country"],
filter_value_list=["United States"],
)
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="Celebrity bot",
instructions="You are a bot designed to answer questions about celebrities.",
tools=[auto_retrieve_tool],
verbose=True,
)
response = agent.chat("Tell me about two celebrities from the United States. ")
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)
from llama_index.core import Settings
from llama_index.core import StorageContext
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.llms.openai import OpenAI
Settings.chunk_size = 1024
Settings.llm = OpenAI(temperature=0, model="gpt-4")
text_splitter = | TokenTextSplitter(chunk_size=1024) | llama_index.core.node_parser.TokenTextSplitter |
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")
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) 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) | llama_index.core.schema.IndexNode.from_text_node |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import tiktoken
from llama_index.core.callbacks import CallbackManager, TokenCountingHandler
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
token_counter = TokenCountingHandler(
tokenizer=tiktoken.encoding_for_model("gpt-3.5-turbo").encode
)
Settings.llm = | OpenAI(model="gpt-3.5-turbo", temperature=0.2) | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index')
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import (
PrevNextNodePostprocessor,
AutoPrevNextNodePostprocessor,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.storage.docstore import SimpleDocumentStore
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 StorageContext
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import Settings
Settings.chunk_size = 512
nodes = Settings.node_parser.get_nodes_from_documents(documents)
docstore = | SimpleDocumentStore() | llama_index.core.storage.docstore.SimpleDocumentStore |
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) | llama_index.core.ChatPromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().system('pip install llama-index')
import os
import pinecone
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="eu-west1-gcp")
indexes = pinecone.list_indexes()
print(indexes)
if "quickstart-index" not in indexes:
pinecone.create_index(
"quickstart-index", dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index("quickstart-index")
pinecone_index.delete(deleteAll="true")
books = [
{
"title": "To Kill a Mockingbird",
"author": "Harper Lee",
"content": (
"To Kill a Mockingbird is a novel by Harper Lee published in"
" 1960..."
),
"year": 1960,
},
{
"title": "1984",
"author": "George Orwell",
"content": (
"1984 is a dystopian novel by George Orwell published in 1949..."
),
"year": 1949,
},
{
"title": "The Great Gatsby",
"author": "F. Scott Fitzgerald",
"content": (
"The Great Gatsby is a novel by F. Scott Fitzgerald published in"
" 1925..."
),
"year": 1925,
},
{
"title": "Pride and Prejudice",
"author": "Jane Austen",
"content": (
"Pride and Prejudice is a novel by Jane Austen published in"
" 1813..."
),
"year": 1813,
},
]
import uuid
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = | OpenAIEmbedding() | llama_index.embeddings.openai.OpenAIEmbedding |
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")
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) 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) | llama_index.core.response.notebook_utils.display_source_node |
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() | llama_index.core.callbacks.CallbackManager |
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])
)
else:
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=raw_msg[1])
)
return ChatPromptTemplate(message_templates=messages)
class ResponseEval(BaseModel):
"""Evaluation of whether the response has an error."""
has_error: bool = Field(
..., description="Whether the response has an error."
)
new_question: str = Field(..., description="The suggested new question.")
explanation: str = Field(
...,
description=(
"The explanation for the error as well as for the new question."
"Can include the direct stack trace as well."
),
)
from llama_index.core.bridge.pydantic import PrivateAttr
class RetryAgentWorker(CustomSimpleAgentWorker):
"""Agent worker that adds a retry layer on top of a router.
Continues iterating until there's no errors / task is done.
"""
prompt_str: str = Field(default=DEFAULT_PROMPT_STR)
max_iterations: int = Field(default=10)
_router_query_engine: RouterQueryEngine = PrivateAttr()
def __init__(self, tools: List[BaseTool], **kwargs: Any) -> None:
"""Init params."""
for tool in tools:
if not isinstance(tool, QueryEngineTool):
raise ValueError(
f"Tool {tool.metadata.name} is not a query engine tool."
)
self._router_query_engine = RouterQueryEngine(
selector=PydanticSingleSelector.from_defaults(),
query_engine_tools=tools,
verbose=kwargs.get("verbose", False),
)
super().__init__(
tools=tools,
**kwargs,
)
def _initialize_state(self, task: Task, **kwargs: Any) -> Dict[str, Any]:
"""Initialize state."""
return {"count": 0, "current_reasoning": []}
def _run_step(
self, state: Dict[str, Any], task: Task, input: Optional[str] = None
) -> Tuple[AgentChatResponse, bool]:
"""Run step.
Returns:
Tuple of (agent_response, is_done)
"""
if "new_input" not in state:
new_input = task.input
else:
new_input = state["new_input"]
response = self._router_query_engine.query(new_input)
state["current_reasoning"].extend(
[("user", new_input), ("assistant", str(response))]
)
chat_prompt_tmpl = get_chat_prompt_template(
self.prompt_str, state["current_reasoning"]
)
llm_program = LLMTextCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_cls=ResponseEval),
prompt=chat_prompt_tmpl,
llm=self.llm,
)
response_eval = llm_program(
query_str=new_input, response_str=str(response)
)
if not response_eval.has_error:
is_done = True
else:
is_done = False
state["new_input"] = response_eval.new_question
if self.verbose:
print(f"> Question: {new_input}")
print(f"> Response: {response}")
print(f"> Response eval: {response_eval.dict()}")
return AgentChatResponse(response=str(response)), is_done
def _finalize_task(self, state: Dict[str, Any], **kwargs) -> None:
"""Finalize task."""
pass
from llama_index.core.tools import QueryEngineTool
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
from llama_index.core import SQLDatabase
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)
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)
from llama_index.core.query_engine import NLSQLTableQueryEngine
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database, tables=["city_stats"], verbose=True
)
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
description=(
"Useful for translating a natural language query into a SQL query over"
" a table containing: city_stats, containing the population/country of"
" each city"
),
)
from llama_index.readers.wikipedia import WikipediaReader
from llama_index.core import VectorStoreIndex
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
vector_tools = []
for city, wiki_doc in zip(cities, wiki_docs):
vector_index = | VectorStoreIndex.from_documents([wiki_doc]) | llama_index.core.VectorStoreIndex.from_documents |
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('pip', 'install llama-index-postprocessor-flag-embedding-reranker')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install git+https://github.com/FlagOpen/FlagEmbedding.git')
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'")
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
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.core.evaluation.benchmarks import HotpotQAEvaluator
from llama_index.core import VectorStoreIndex
from llama_index.core import Document
from llama_index.llms.openai import OpenAI
from llama_index.core.embeddings import resolve_embed_model
llm = OpenAI(model="gpt-3.5-turbo")
embed_model = resolve_embed_model(
"local:sentence-transformers/all-MiniLM-L6-v2"
)
index = VectorStoreIndex.from_documents(
[Document.example()], embed_model=embed_model, show_progress=True
)
engine = index.as_query_engine(llm=llm)
HotpotQAEvaluator().run(engine, queries=5, show_result=True)
from llama_index.core.postprocessor import SentenceTransformerRerank
rerank = | SentenceTransformerRerank(top_n=3) | llama_index.core.postprocessor.SentenceTransformerRerank |
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) | llama_index.core.VectorStoreIndex |
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") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-elasticsearch')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-opensearch')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-ollama')
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 os import getenv
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.opensearch import (
OpensearchVectorStore,
OpensearchVectorClient,
)
from llama_index.core import VectorStoreIndex, StorageContext
endpoint = getenv("OPENSEARCH_ENDPOINT", "http://localhost:9200")
idx = getenv("OPENSEARCH_INDEX", "gpt-index-demo")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
text_field = "content"
embedding_field = "embedding"
client = OpensearchVectorClient(
endpoint, idx, 1536, embedding_field=embedding_field, text_field=text_field
)
vector_store = OpensearchVectorStore(client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex.from_documents(
documents=documents, storage_context=storage_context
)
query_engine = index.as_query_engine()
res = query_engine.query("What did the author do growing up?")
res.response
from llama_index.core import Document
from llama_index.core.vector_stores import MetadataFilters, ExactMatchFilter
import regex as re
text_chunks = documents[0].text.split("\n\n")
footnotes = [
Document(
text=chunk,
id=documents[0].doc_id,
metadata={"is_footnote": bool(re.search(r"^\s*\[\d+\]\s*", chunk))},
)
for chunk in text_chunks
if bool(re.search(r"^\s*\[\d+\]\s*", chunk))
]
for f in footnotes:
index.insert(f)
footnote_query_engine = index.as_query_engine(
filters=MetadataFilters(
filters=[
ExactMatchFilter(
key="term", value='{"metadata.is_footnote": "true"}'
),
ExactMatchFilter(
key="query_string",
value='{"query": "content: space AND content: lisp"}',
),
]
)
)
res = footnote_query_engine.query(
"What did the author about space aliens and lisp?"
)
res.response
from llama_index.readers.elasticsearch import ElasticsearchReader
rdr = | ElasticsearchReader(endpoint, idx) | llama_index.readers.elasticsearch.ElasticsearchReader |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
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 VectorStoreIndex, SQLDatabase
from llama_index.readers.wikipedia import WikipediaReader
from sqlalchemy import (
create_engine,
MetaData,
Table,
Column,
String,
Integer,
select,
column,
)
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())
get_ipython().system('pip install wikipedia')
cities = ["Toronto", "Berlin", "Tokyo"]
wiki_docs = WikipediaReader().load_data(pages=cities)
sql_database = SQLDatabase(engine, include_tables=["city_stats"])
from llama_index.core.query_engine import NLSQLTableQueryEngine
sql_query_engine = NLSQLTableQueryEngine(
sql_database=sql_database,
tables=["city_stats"],
)
vector_indices = []
for wiki_doc in wiki_docs:
vector_index = VectorStoreIndex.from_documents([wiki_doc])
vector_indices.append(vector_index)
vector_query_engines = [index.as_query_engine() for index in vector_indices]
from llama_index.core.tools import QueryEngineTool
sql_tool = QueryEngineTool.from_defaults(
query_engine=sql_query_engine,
description=(
"Useful for translating a natural language query into a SQL query over"
" a table containing: city_stats, containing the population/country of"
" each city"
),
)
vector_tools = []
for city, query_engine in zip(cities, vector_query_engines):
vector_tool = QueryEngineTool.from_defaults(
query_engine=query_engine,
description=f"Useful for answering semantic questions about {city}",
)
vector_tools.append(vector_tool)
from llama_index.core.query_engine import RouterQueryEngine
from llama_index.core.selectors import LLMSingleSelector
query_engine = RouterQueryEngine(
selector= | LLMSingleSelector.from_defaults() | llama_index.core.selectors.LLMSingleSelector.from_defaults |
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.text_to_image.base import TextToImageToolSpec
text_to_image_spec = TextToImageToolSpec()
tools = text_to_image_spec.to_tool_list()
agent = | OpenAIAgent.from_tools(tools, verbose=True) | llama_index.agent.OpenAIAgent.from_tools |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install jsonpath-ng')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import os
import openai
os.environ["OPENAI_API_KEY"] = "YOUR_KEY_HERE"
from IPython.display import Markdown, display
json_value = {
"blogPosts": [
{
"id": 1,
"title": "First blog post",
"content": "This is my first blog post",
},
{
"id": 2,
"title": "Second blog post",
"content": "This is my second blog post",
},
],
"comments": [
{
"id": 1,
"content": "Nice post!",
"username": "jerry",
"blogPostId": 1,
},
{
"id": 2,
"content": "Interesting thoughts",
"username": "simon",
"blogPostId": 2,
},
{
"id": 3,
"content": "Loved reading this!",
"username": "simon",
"blogPostId": 2,
},
],
}
json_schema = {
"$schema": "http://json-schema.org/draft-07/schema#",
"description": "Schema for a very simple blog post app",
"type": "object",
"properties": {
"blogPosts": {
"description": "List of blog posts",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"description": "Unique identifier for the blog post",
"type": "integer",
},
"title": {
"description": "Title of the blog post",
"type": "string",
},
"content": {
"description": "Content of the blog post",
"type": "string",
},
},
"required": ["id", "title", "content"],
},
},
"comments": {
"description": "List of comments on blog posts",
"type": "array",
"items": {
"type": "object",
"properties": {
"id": {
"description": "Unique identifier for the comment",
"type": "integer",
},
"content": {
"description": "Content of the comment",
"type": "string",
},
"username": {
"description": (
"Username of the commenter (lowercased)"
),
"type": "string",
},
"blogPostId": {
"description": (
"Identifier for the blog post to which the comment"
" belongs"
),
"type": "integer",
},
},
"required": ["id", "content", "username", "blogPostId"],
},
},
},
"required": ["blogPosts", "comments"],
}
from llama_index.llms.openai import OpenAI
from llama_index.core.indices.struct_store import JSONQueryEngine
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')
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)
llm = OpenAI(model="gpt-3.5-turbo-1106")
agent = OpenAIAgent.from_tools(
[multiply_tool, add_tool], llm=llm, verbose=True
)
response = agent.chat("What is (121 * 3) + 42?")
print(str(response))
response = agent.stream_chat("What is (121 * 3) + 42?")
import nest_asyncio
nest_asyncio.apply()
response = await agent.achat("What is (121 * 3) + 42?")
print(str(response))
response = await agent.astream_chat("What is (121 * 3) + 42?")
response_gen = response.response_gen
async for token in response.async_response_gen():
print(token, end="")
import json
def get_current_weather(location, unit="fahrenheit"):
"""Get the current weather in a given location"""
if "tokyo" in location.lower():
return json.dumps(
{"location": location, "temperature": "10", "unit": "celsius"}
)
elif "san francisco" in location.lower():
return json.dumps(
{"location": location, "temperature": "72", "unit": "fahrenheit"}
)
else:
return json.dumps(
{"location": location, "temperature": "22", "unit": "celsius"}
)
weather_tool = FunctionTool.from_defaults(fn=get_current_weather)
llm = | OpenAI(model="gpt-3.5-turbo-1106") | llama_index.llms.openai.OpenAI |
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/").load_data()
vector_store = DuckDBVectorStore()
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>"))
documents = SimpleDirectoryReader("data/paul_graham/").load_data()
vector_store = | DuckDBVectorStore("pg.duckdb", persist_dir="./persist/") | llama_index.vector_stores.duckdb.DuckDBVectorStore |
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") | llama_index.core.SimpleDirectoryReader |
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))
all_nodes_dict = {n.node_id: n for n in all_nodes}
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
vector_index_metadata = VectorStoreIndex(all_nodes)
vector_retriever_metadata = vector_index_metadata.as_retriever(
similarity_top_k=2
)
retriever_metadata = RecursiveRetriever(
"vector",
retriever_dict={"vector": vector_retriever_metadata},
node_dict=all_nodes_dict,
verbose=False,
)
nodes = retriever_metadata.retrieve(
"Can you tell me about the key concepts for safety finetuning"
)
for node in nodes:
| display_source_node(node, source_length=2000) | llama_index.core.response.notebook_utils.display_source_node |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Response
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import (
FaithfulnessEvaluator,
RelevancyEvaluator,
CorrectnessEvaluator,
)
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt4 = OpenAI(temperature=0, model="gpt-4")
faithfulness_gpt4 = FaithfulnessEvaluator(llm=gpt4)
relevancy_gpt4 = RelevancyEvaluator(llm=gpt4)
correctness_gpt4 = | CorrectnessEvaluator(llm=gpt4) | llama_index.core.evaluation.CorrectnessEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-dashvector')
get_ipython().system('pip install llama-index')
import logging
import sys
import os
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
import dashvector
api_key = os.environ["DASHVECTOR_API_KEY"]
client = dashvector.Client(api_key=api_key)
client.create("llama-demo", dimension=1536)
dashvector_collection = client.get("quickstart")
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.vector_stores.dashvector import DashVectorStore
from IPython.display import Markdown, display
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
from llama_index.core import StorageContext
vector_store = DashVectorStore(dashvector_collection)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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/") | llama_index.core.SimpleDirectoryReader |
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)],
)
print(llava_response.text)
rag_response = query_engine.query(llava_response.text)
print(rag_response)
input_image_path = Path("instagram_images")
if not input_image_path.exists():
Path.mkdir(input_image_path)
get_ipython().system('wget "https://docs.google.com/uc?export=download&id=12ZpBBFkYu-jzz1iz356U5kMikn4uN9ww" -O ./instagram_images/jordan.png')
from pydantic import BaseModel
class InsAds(BaseModel):
"""Data model for a Ins Ads."""
account: str
brand: str
product: str
category: str
discount: str
price: str
comments: str
review: str
description: str
from PIL import Image
import matplotlib.pyplot as plt
ins_imageUrl = "./instagram_images/jordan.png"
image = Image.open(ins_imageUrl).convert("RGB")
plt.figure(figsize=(16, 5))
plt.imshow(image)
from llama_index.multi_modal_llms.replicate import ReplicateMultiModal
from llama_index.core.program import MultiModalLLMCompletionProgram
from llama_index.core.output_parsers import PydanticOutputParser
from llama_index.multi_modal_llms.replicate.base import (
REPLICATE_MULTI_MODAL_LLM_MODELS,
)
prompt_template_str = """\
can you summarize what is in the image\
and return the answer with json format \
"""
def pydantic_llava(
model_name, output_class, image_documents, prompt_template_str
):
mm_llm = ReplicateMultiModal(
model=REPLICATE_MULTI_MODAL_LLM_MODELS["llava-13b"],
max_new_tokens=1000,
)
llm_program = MultiModalLLMCompletionProgram.from_defaults(
output_parser=PydanticOutputParser(output_class),
image_documents=image_documents,
prompt_template_str=prompt_template_str,
multi_modal_llm=mm_llm,
verbose=True,
)
response = llm_program()
print(f"Model: {model_name}")
for res in response:
print(res)
return response
from llama_index.core import SimpleDirectoryReader
ins_image_documents = SimpleDirectoryReader("./instagram_images").load_data()
pydantic_response = pydantic_llava(
"llava-13b", InsAds, ins_image_documents, prompt_template_str
)
print(pydantic_response.brand)
from pathlib import Path
import requests
wiki_titles = [
"batman",
"Vincent van Gogh",
"San Francisco",
"iPhone",
"Tesla Model S",
"BTS",
"Air Jordan",
]
data_path = Path("data_wiki")
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"]
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
import wikipedia
import urllib.request
image_path = Path("data_wiki")
image_uuid = 0
image_metadata_dict = {}
MAX_IMAGES_PER_WIKI = 30
wiki_titles = [
"Air Jordan",
"San Francisco",
"Batman",
"Vincent van Gogh",
"iPhone",
"Tesla Model S",
"BTS band",
]
if not image_path.exists():
Path.mkdir(image_path)
for title in wiki_titles:
images_per_wiki = 0
print(title)
try:
page_py = wikipedia.page(title)
list_img_urls = page_py.images
for url in list_img_urls:
if url.endswith(".jpg") or url.endswith(".png"):
image_uuid += 1
image_file_name = title + "_" + url.split("/")[-1]
image_metadata_dict[image_uuid] = {
"filename": image_file_name,
"img_path": "./" + str(image_path / f"{image_uuid}.jpg"),
}
urllib.request.urlretrieve(
url, image_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
import qdrant_client
from llama_index.core import SimpleDirectoryReader
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.core.indices import MultiModalVectorStoreIndex
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("./data_wiki/").load_data()
index = MultiModalVectorStoreIndex.from_documents(
documents,
storage_context=storage_context,
)
from PIL import Image
import matplotlib.pyplot as plt
import os
def plot_images(image_metadata_dict):
original_images_urls = []
images_shown = 0
for image_id in image_metadata_dict:
img_path = image_metadata_dict[image_id]["img_path"]
if os.path.isfile(img_path):
filename = image_metadata_dict[image_id]["filename"]
image = Image.open(img_path).convert("RGB")
plt.subplot(8, 8, len(original_images_urls) + 1)
plt.imshow(image)
plt.xticks([])
plt.yticks([])
original_images_urls.append(filename)
images_shown += 1
if images_shown >= 64:
break
plt.tight_layout()
plot_images(image_metadata_dict)
retriever = index.as_retriever(similarity_top_k=3, image_similarity_top_k=5)
retrieval_results = retriever.retrieve(pydantic_response.brand)
from llama_index.core.response.notebook_utils import (
display_source_node,
display_image_uris,
)
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)
| display_image_uris(retrieved_image) | llama_index.core.response.notebook_utils.display_image_uris |
from llama_index.llms.openai import OpenAI
from llama_index.core import VectorStoreIndex
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.core.postprocessor import LLMRerank
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import Settings
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.packs.koda_retriever import KodaRetriever
import os
from pinecone import Pinecone
pc = Pinecone(api_key=os.environ.get("PINECONE_API_KEY"))
index = pc.Index("sample-movies")
Settings.llm = OpenAI()
Settings.embed_model = OpenAIEmbedding()
vector_store = PineconeVectorStore(pinecone_index=index, text_key="summary")
vector_index = VectorStoreIndex.from_vector_store(
vector_store=vector_store, embed_model=Settings.embed_model
)
reranker = | LLMRerank(llm=Settings.llm) | llama_index.core.postprocessor.LLMRerank |
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')
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = "ghp_..."
os.environ["OPENAI_API_KEY"] = "sk-..."
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"] = int(doc.id_)
if idx >= limit:
break
docs.append(doc)
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_docs"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
doc_index = VectorStoreIndex.from_documents(
docs, storage_context=storage_context
)
from llama_index.core import SummaryIndex
from llama_index.core.async_utils import run_jobs
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import IndexNode
from llama_index.core.vector_stores import (
FilterOperator,
MetadataFilter,
MetadataFilters,
)
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
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,
}
summary_index = SummaryIndex.from_documents([doc])
query_str = "Give a one-sentence concise summary of this issue."
query_engine = summary_index.as_query_engine(
llm=OpenAI(model="gpt-3.5-turbo")
)
summary_txt = await query_engine.aquery(query_str)
summary_txt = str(summary_txt)
index_id = doc.metadata["index_id"]
filters = MetadataFilters(
filters=[
MetadataFilter(
key="index_id", operator=FilterOperator.EQ, value=int(index_id)
),
]
)
index_node = IndexNode(
text=summary_txt,
metadata=new_metadata,
obj=doc_index.as_retriever(filters=filters),
index_id=doc.id_,
)
return index_node
async def aprocess_docs(docs):
"""Process metadata on docs."""
index_nodes = []
tasks = []
for doc in docs:
task = aprocess_doc(doc)
tasks.append(task)
index_nodes = await run_jobs(tasks, show_progress=True, workers=3)
return index_nodes
index_nodes = await aprocess_docs(docs)
index_nodes[5].metadata
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_auto"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store_auto = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context_auto = StorageContext.from_defaults(
vector_store=vector_store_auto
)
index = VectorStoreIndex(
objects=index_nodes, storage_context=storage_context_auto
)
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
vector_store_info = VectorStoreInfo(
content_info="Github Issues",
metadata_info=[
MetadataInfo(
name="state",
description="Whether the issue is `open` or `closed`",
type="string",
),
MetadataInfo(
name="year",
description="The year issue was created",
type="integer",
),
MetadataInfo(
name="month",
description="The month issue was created",
type="integer",
),
MetadataInfo(
name="day",
description="The day issue was created",
type="integer",
),
MetadataInfo(
name="assignee",
description="The assignee of the ticket",
type="string",
),
MetadataInfo(
name="size",
description="How big the issue is (XS, S, M, L, XL, XXL)",
type="string",
),
],
)
from llama_index.core.retrievers import VectorIndexAutoRetriever
retriever = VectorIndexAutoRetriever(
index,
vector_store_info=vector_store_info,
similarity_top_k=2,
empty_query_top_k=10, # if only metadata filters are specified, this is the limit
verbose=True,
)
from llama_index.core import QueryBundle
nodes = retriever.retrieve(QueryBundle("Tell me about some issues on 01/11"))
print(f"Number of source nodes: {len(nodes)}")
nodes[0].node.metadata
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
query_engine = | RetrieverQueryEngine.from_args(retriever, llm=llm) | llama_index.core.query_engine.RetrieverQueryEngine.from_args |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-rankgpt-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-ollama')
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 VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import LLMRerank
from llama_index.llms.openai import OpenAI
from IPython.display import Markdown, display
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
from pathlib import Path
import requests
wiki_titles = [
"Vincent van Gogh",
]
data_path = Path("data_wiki")
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"]
if not data_path.exists():
Path.mkdir(data_path)
with open(data_path / f"{title}.txt", "w") as fp:
fp.write(wiki_text)
documents = SimpleDirectoryReader("./data_wiki/").load_data()
index = VectorStoreIndex.from_documents(
documents,
)
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core import QueryBundle
from llama_index.postprocessor.rankgpt_rerank import RankGPTRerank
import pandas as pd
from IPython.display import display, HTML
def get_retrieved_nodes(
query_str, vector_top_k=10, reranker_top_n=3, with_reranker=False
):
query_bundle = QueryBundle(query_str)
retriever = VectorIndexRetriever(
index=index,
similarity_top_k=vector_top_k,
)
retrieved_nodes = retriever.retrieve(query_bundle)
if with_reranker:
reranker = RankGPTRerank(
llm=OpenAI(
model="gpt-3.5-turbo-16k",
temperature=0.0,
api_key=OPENAI_API_TOKEN,
),
top_n=reranker_top_n,
verbose=True,
)
retrieved_nodes = reranker.postprocess_nodes(
retrieved_nodes, query_bundle
)
return retrieved_nodes
def pretty_print(df):
return display(HTML(df.to_html().replace("\\n", "<br>")))
def visualize_retrieved_nodes(nodes) -> None:
result_dicts = []
for node in nodes:
result_dict = {"Score": node.score, "Text": node.node.get_text()}
result_dicts.append(result_dict)
pretty_print(pd.DataFrame(result_dicts))
new_nodes = get_retrieved_nodes(
"Which date did Paul Gauguin arrive in Arles?",
vector_top_k=3,
with_reranker=False,
)
visualize_retrieved_nodes(new_nodes)
new_nodes = get_retrieved_nodes(
"Which date did Paul Gauguin arrive in Arles ?",
vector_top_k=10,
reranker_top_n=3,
with_reranker=True,
)
visualize_retrieved_nodes(new_nodes)
from llama_index.llms.ollama import Ollama
llm = | Ollama(model="mistral", request_timeout=30.0) | llama_index.llms.ollama.Ollama |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
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.schema import TextNode
from llama_index.core.indices.managed.types import ManagedIndexQueryMode
from llama_index.indices.managed.vectara import VectaraIndex
from llama_index.indices.managed.vectara import VectaraAutoRetriever
from llama_index.core.vector_stores import MetadataInfo, VectorStoreInfo
from llama_index.llms.openai import OpenAI
nodes = [
TextNode(
text=(
"A pragmatic paleontologist touring an almost complete theme park on an island "
+ "in Central America is tasked with protecting a couple of kids after a power "
+ "failure causes the park's cloned dinosaurs to run loose."
),
metadata={"year": 1993, "rating": 7.7, "genre": "science fiction"},
),
TextNode(
text=(
"A thief who steals corporate secrets through the use of dream-sharing technology "
+ "is given the inverse task of planting an idea into the mind of a C.E.O., "
+ "but his tragic past may doom the project and his team to disaster."
),
metadata={
"year": 2010,
"director": "Christopher Nolan",
"rating": 8.2,
},
),
TextNode(
text="Barbie suffers a crisis that leads her to question her world and her existence.",
metadata={
"year": 2023,
"director": "Greta Gerwig",
"genre": "fantasy",
"rating": 9.5,
},
),
TextNode(
text=(
"A cowboy doll is profoundly threatened and jealous when a new spaceman action "
+ "figure supplants him as top toy in a boy's bedroom."
),
metadata={"year": 1995, "genre": "animated", "rating": 8.3},
),
TextNode(
text=(
"When Woody is stolen by a toy collector, Buzz and his friends set out on a "
+ "rescue mission to save Woody before he becomes a museum toy property with his "
+ "roundup gang Jessie, Prospector, and Bullseye. "
),
metadata={"year": 1999, "genre": "animated", "rating": 7.9},
),
| TextNode(
text=(
"The toys are mistakenly delivered to a day-care center instead of the attic "
+ "right before Andy leaves for college, and it's up to Woody to convince the "
+ "other toys that they weren't abandoned and to return home."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-finetuning')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import nest_asyncio
nest_asyncio.apply()
get_ipython().system('pip install llama-index')
get_ipython().system('pip install spacy')
wiki_titles = [
"Toronto",
"Seattle",
"Chicago",
"Boston",
"Houston",
"Tokyo",
"Berlin",
"Lisbon",
]
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)
from llama_index.core import SimpleDirectoryReader
city_docs = {}
for wiki_title in wiki_titles:
city_docs[wiki_title] = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0.3)
city_descs_dict = {}
choices = []
choice_to_id_dict = {}
for idx, wiki_title in enumerate(wiki_titles):
vector_desc = (
"Useful for questions related to specific aspects of"
f" {wiki_title} (e.g. the history, arts and culture,"
" sports, demographics, or more)."
)
summary_desc = (
"Useful for any requests that require a holistic summary"
f" of EVERYTHING about {wiki_title}. For questions about"
" more specific sections, please use the vector_tool."
)
doc_id_vector = f"{wiki_title}_vector"
doc_id_summary = f"{wiki_title}_summary"
city_descs_dict[doc_id_vector] = vector_desc
city_descs_dict[doc_id_summary] = summary_desc
choices.extend([vector_desc, summary_desc])
choice_to_id_dict[idx * 2] = f"{wiki_title}_vector"
choice_to_id_dict[idx * 2 + 1] = f"{wiki_title}_summary"
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
llm = OpenAI(model_name="gpt-3.5-turbo")
summary_q_tmpl = """\
You are a summary question generator. Given an existing question which asks for a summary of a given topic, \
generate {num_vary} related queries that also ask for a summary of the topic.
For example, assuming we're generating 3 related questions:
Base Question: Can you tell me more about Boston?
Question Variations:
Give me an overview of Boston as a city.
Can you describe different aspects of Boston, from the history to the sports scene to the food?
Write a concise summary of Boston; I've never been.
Now let's give it a shot!
Base Question: {base_question}
Question Variations:
"""
summary_q_prompt = PromptTemplate(summary_q_tmpl)
from collections import defaultdict
from llama_index.core.evaluation import DatasetGenerator
from llama_index.core.evaluation import EmbeddingQAFinetuneDataset
from llama_index.core.node_parser import SimpleNodeParser
from tqdm.notebook import tqdm
def generate_dataset(
wiki_titles,
city_descs_dict,
llm,
summary_q_prompt,
num_vector_qs_per_node=2,
num_summary_qs=4,
):
queries = {}
corpus = {}
relevant_docs = defaultdict(list)
for idx, wiki_title in enumerate(tqdm(wiki_titles)):
doc_id_vector = f"{wiki_title}_vector"
doc_id_summary = f"{wiki_title}_summary"
corpus[doc_id_vector] = city_descs_dict[doc_id_vector]
corpus[doc_id_summary] = city_descs_dict[doc_id_summary]
node_parser = SimpleNodeParser.from_defaults()
nodes = node_parser.get_nodes_from_documents(city_docs[wiki_title])
dataset_generator = DatasetGenerator(
nodes,
llm=llm,
num_questions_per_chunk=num_vector_qs_per_node,
)
doc_questions = dataset_generator.generate_questions_from_nodes(
num=len(nodes) * num_vector_qs_per_node
)
for query_idx, doc_question in enumerate(doc_questions):
query_id = f"{wiki_title}_{query_idx}"
relevant_docs[query_id] = [doc_id_vector]
queries[query_id] = doc_question
base_q = f"Give me a summary of {wiki_title}"
fmt_prompt = summary_q_prompt.format(
num_vary=num_summary_qs,
base_question=base_q,
)
raw_response = llm.complete(fmt_prompt)
raw_lines = str(raw_response).split("\n")
doc_summary_questions = [l for l in raw_lines if l != ""]
print(f"[{idx}] Original Question: {base_q}")
print(
f"[{idx}] Generated Question Variations: {doc_summary_questions}"
)
for query_idx, doc_summary_question in enumerate(
doc_summary_questions
):
query_id = f"{wiki_title}_{query_idx}"
relevant_docs[query_id] = [doc_id_summary]
queries[query_id] = doc_summary_question
return EmbeddingQAFinetuneDataset(
queries=queries, corpus=corpus, relevant_docs=relevant_docs
)
dataset = generate_dataset(
wiki_titles,
city_descs_dict,
llm,
summary_q_prompt,
num_vector_qs_per_node=4,
num_summary_qs=5,
)
dataset.save_json("dataset.json")
dataset = EmbeddingQAFinetuneDataset.from_json("dataset.json")
import random
def split_train_val_by_query(dataset, split=0.7):
"""Split dataset by queries."""
query_ids = list(dataset.queries.keys())
query_ids_shuffled = random.sample(query_ids, len(query_ids))
split_idx = int(len(query_ids) * split)
train_query_ids = query_ids_shuffled[:split_idx]
eval_query_ids = query_ids_shuffled[split_idx:]
train_queries = {qid: dataset.queries[qid] for qid in train_query_ids}
eval_queries = {qid: dataset.queries[qid] for qid in eval_query_ids}
train_rel_docs = {
qid: dataset.relevant_docs[qid] for qid in train_query_ids
}
eval_rel_docs = {qid: dataset.relevant_docs[qid] for qid in eval_query_ids}
train_dataset = EmbeddingQAFinetuneDataset(
queries=train_queries,
corpus=dataset.corpus,
relevant_docs=train_rel_docs,
)
eval_dataset = EmbeddingQAFinetuneDataset(
queries=eval_queries,
corpus=dataset.corpus,
relevant_docs=eval_rel_docs,
)
return train_dataset, eval_dataset
train_dataset, eval_dataset = split_train_val_by_query(dataset, split=0.7)
from llama_index.finetuning import SentenceTransformersFinetuneEngine
finetune_engine = SentenceTransformersFinetuneEngine(
train_dataset,
model_id="BAAI/bge-small-en",
model_output_path="test_model3",
val_dataset=eval_dataset,
epochs=30, # can set to higher (haven't tested)
)
finetune_engine.finetune()
ft_embed_model = finetune_engine.get_finetuned_model()
ft_embed_model
from llama_index.core.embeddings import resolve_embed_model
base_embed_model = resolve_embed_model("local:BAAI/bge-small-en")
from llama_index.core.selectors import (
EmbeddingSingleSelector,
LLMSingleSelector,
)
ft_selector = EmbeddingSingleSelector.from_defaults(embed_model=ft_embed_model)
base_selector = EmbeddingSingleSelector.from_defaults(
embed_model=base_embed_model
)
import numpy as np
def run_evals(eval_dataset, selector, choices, choice_to_id_dict):
eval_pairs = eval_dataset.query_docid_pairs
matches = []
for query, relevant_doc_ids in tqdm(eval_pairs):
result = selector.select(choices, query)
pred_doc_id = choice_to_id_dict[result.inds[0]]
gt_doc_id = relevant_doc_ids[0]
matches.append(gt_doc_id == pred_doc_id)
return np.array(matches)
ft_matches = run_evals(eval_dataset, ft_selector, choices, choice_to_id_dict)
np.mean(ft_matches)
base_matches = run_evals(
eval_dataset, base_selector, choices, choice_to_id_dict
)
np.mean(base_matches)
from llama_index.llms.openai import OpenAI
eval_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-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
from IPython.display import HTML, display
def set_css():
display(
HTML(
"""
<style>
pre {
white-space: pre-wrap;
}
</style>
"""
)
)
get_ipython().events.register("pre_run_cell", set_css)
get_ipython().system('mkdir data')
get_ipython().system('wget "https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1" -O data/UBER.zip')
get_ipython().system('unzip data/UBER.zip -d data')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
years = [2022, 2021, 2020, 2019]
loader = UnstructuredReader()
doc_set = {}
all_docs = []
for year in years:
year_docs = loader.load_data(
file=Path(f"./data/UBER/UBER_{year}.html"), split_documents=False
)
for d in year_docs:
d.metadata = {"year": year}
doc_set[year] = year_docs
all_docs.extend(year_docs)
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.chunk_size = 512
Settings.chunk_overlap = 64
Settings.llm = OpenAI(model="gpt-3.5-turbo")
Settings.embed_model = | OpenAIEmbedding(model="text-embedding-3-small") | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-supabase')
get_ipython().system('pip install llama-index')
import logging
import sys
from llama_index.core import SimpleDirectoryReader, Document, StorageContext
from llama_index.core import VectorStoreIndex
from llama_index.vector_stores.supabase import SupabaseVectorStore
import textwrap
import os
os.environ["OPENAI_API_KEY"] = "[your_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/").load_data()
print(
"Document ID:",
documents[0].doc_id,
"Document Hash:",
documents[0].doc_hash,
)
vector_store = SupabaseVectorStore(
postgres_connection_string=(
"postgresql://<user>:<password>@<host>:<port>/<db_name>"
),
collection_name="base_demo",
)
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("Who is the author?")
print(textwrap.fill(str(response), 100))
response = query_engine.query("What did the author do growing up?")
print(textwrap.fill(str(response), 100))
from llama_index.core.schema import TextNode
nodes = [
TextNode(
**{
"text": "The Shawshank Redemption",
"metadata": {
"author": "Stephen King",
"theme": "Friendship",
},
}
),
TextNode(
**{
"text": "The Godfather",
"metadata": {
"director": "Francis Ford Coppola",
"theme": "Mafia",
},
}
),
TextNode(
**{
"text": "Inception",
"metadata": {
"director": "Christopher Nolan",
},
}
),
]
vector_store = SupabaseVectorStore(
postgres_connection_string=(
"postgresql://<user>:<password>@<host>:<port>/<db_name>"
),
collection_name="metadata_filters_demo",
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
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") | llama_index.llms.openai.OpenAI |
get_ipython().system('pip install llama-index-llms-dashscope')
get_ipython().run_line_magic('env', 'DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY')
import os
os.environ["DASHSCOPE_API_KEY"] = "YOUR_DASHSCOPE_API_KEY"
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
dashscope_llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
resp = dashscope_llm.complete("How to make cake?")
print(resp)
responses = dashscope_llm.stream_complete("How to make cake?")
for response in responses:
print(response.delta, end="")
from llama_index.core.base.llms.types import MessageRole, ChatMessage
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
ChatMessage(role=MessageRole.USER, content="How to make cake?"),
]
resp = dashscope_llm.chat(messages)
print(resp)
responses = dashscope_llm.stream_chat(messages)
for response in responses:
print(response.delta, end="")
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
ChatMessage(role=MessageRole.USER, content="How to make cake?"),
]
resp = dashscope_llm.chat(messages)
print(resp)
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=resp.message.content)
)
messages.append(
| ChatMessage(role=MessageRole.USER, content="How to make it without sugar") | llama_index.core.base.llms.types.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-azure-openai')
get_ipython().run_line_magic('pip', 'install llama-index-graph-stores-nebula')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-azure-openai')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import logging
import sys
logging.basicConfig(
stream=sys.stdout, level=logging.INFO
) # logging.DEBUG for more verbose output
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(temperature=0, model="gpt-3.5-turbo")
Settings.chunk_size = 512
from llama_index.llms.azure_openai import AzureOpenAI
from llama_index.embeddings.azure_openai import AzureOpenAIEmbedding
api_key = "<api-key>"
azure_endpoint = "https://<your-resource-name>.openai.azure.com/"
api_version = "2023-07-01-preview"
llm = AzureOpenAI(
model="gpt-35-turbo-16k",
deployment_name="my-custom-llm",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
embed_model = AzureOpenAIEmbedding(
model="text-embedding-ada-002",
deployment_name="my-custom-embedding",
api_key=api_key,
azure_endpoint=azure_endpoint,
api_version=api_version,
)
from llama_index.core import Settings
Settings.llm = llm
Settings.embed_model = embed_model
Settings.chunk_size = 512
get_ipython().run_line_magic('pip', 'install ipython-ngql nebula3-python')
os.environ["NEBULA_USER"] = "root"
os.environ["NEBULA_PASSWORD"] = "nebula" # default is "nebula"
os.environ[
"NEBULA_ADDRESS"
] = "127.0.0.1:9669" # assumed we have NebulaGraph installed locally
space_name = "llamaindex"
edge_types, rel_prop_names = ["relationship"], [
"relationship"
] # default, could be omit if create from an empty kg
tags = ["entity"] # default, could be omit if create from an empty kg
from llama_index.core import StorageContext
from llama_index.graph_stores.nebula import NebulaGraphStore
graph_store = NebulaGraphStore(
space_name=space_name,
edge_types=edge_types,
rel_prop_names=rel_prop_names,
tags=tags,
)
storage_context = | StorageContext.from_defaults(graph_store=graph_store) | llama_index.core.StorageContext.from_defaults |
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() | llama_index.core.node_parser.UnstructuredElementNodeParser |
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") | 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()
from llama_index.core.schema import NodeWithScore
new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])
new_nodes[0].node.get_text()
new_nodes[0].node.metadata["__pii_node_info__"]
from llama_index.llms.openai import OpenAI
processor = PIINodePostprocessor(llm=OpenAI())
from llama_index.core.schema import NodeWithScore
new_nodes = processor.postprocess_nodes([NodeWithScore(node=node)])
new_nodes[0].node.get_text()
new_nodes[0].node.metadata["__pii_node_info__"]
text = """
Hello Paulo Santos. The latest statement for your credit card account \
4095-2609-9393-4932 was mailed to Seattle, WA 98109. \
IBAN GB90YNTU67299444055881 and social security number is 474-49-7577 were verified on the system. \
Further communications will be sent to paulo@presidio.site
"""
presidio_node = | TextNode(text=text) | 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-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) | llama_index.llms.cohere.Cohere |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
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/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()
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.core import StorageContext, VectorStoreIndex
from llama_index.core import SummaryIndex
Settings.llm = OpenAI()
Settings.chunk_size = 1024
nodes = Settings.node_parser.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)
summary_query_engine = summary_index.as_query_engine(
response_mode="tree_summarize",
use_async=True,
)
vector_query_engine = vector_index.as_query_engine()
from llama_index.core.tools import QueryEngineTool
summary_tool = QueryEngineTool.from_defaults(
query_engine=summary_query_engine,
name="summary_tool",
description=(
"Useful for summarization questions related to the author's life"
),
)
vector_tool = QueryEngineTool.from_defaults(
query_engine=vector_query_engine,
name="vector_tool",
description=(
"Useful for retrieving specific context to answer specific questions about the author's life"
),
)
from llama_index.agent.openai import OpenAIAssistantAgent
agent = OpenAIAssistantAgent.from_new(
name="QA bot",
instructions="You are a bot designed to answer questions about the author",
openai_tools=[],
tools=[summary_tool, vector_tool],
verbose=True,
run_retrieve_sleep_time=1.0,
)
response = agent.chat("Can you give me a summary about the author's life?")
print(str(response))
response = agent.query("What did the author do after RICS?")
print(str(response))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp")
try:
pinecone.create_index(
"quickstart", dimension=1536, metric="euclidean", pod_type="p1"
)
except Exception:
pass
pinecone_index = pinecone.Index("quickstart")
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."
) | llama_index.core.schema.TextNode |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
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 = reader.load_data(Path("./tesla_2020_10k.htm"))
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.readers.file import FlatReader
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
from llama_index.core.ingestion import IngestionPipeline
from pathlib import Path
import nest_asyncio
nest_asyncio.apply()
reader = FlatReader()
docs = reader.load_data(Path("./tesla_2020_10k.htm"))
pipeline = IngestionPipeline(
documents=docs,
transformations=[
HTMLNodeParser.from_defaults(),
SentenceSplitter(chunk_size=1024, chunk_overlap=200),
OpenAIEmbedding(),
],
)
eval_nodes = pipeline.run(documents=docs)
eval_llm = OpenAI(model="gpt-3.5-turbo")
dataset_generator = DatasetGenerator(
eval_nodes[:100],
llm=eval_llm,
show_progress=True,
num_questions_per_chunk=3,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=100)
len(eval_dataset.qr_pairs)
eval_dataset.save_json("data/tesla10k_eval_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/tesla10k_eval_dataset.json"
)
eval_qs = eval_dataset.questions
qr_pairs = eval_dataset.qr_pairs
ref_response_strs = [r for (_, r) in qr_pairs]
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import BatchEvalRunner
evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = | SemanticSimilarityEvaluator(llm=eval_llm) | llama_index.core.evaluation.SemanticSimilarityEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
from llama_index.core.node_parser import SimpleFileNodeParser
from llama_index.readers.file import FlatReader
from pathlib import Path
reader = FlatReader()
html_file = reader.load_data(Path("./stack-overflow.html"))
md_file = reader.load_data(Path("./README.md"))
print(html_file[0].metadata)
print(html_file[0])
print("----")
print(md_file[0].metadata)
print(md_file[0])
parser = | SimpleFileNodeParser() | llama_index.core.node_parser.SimpleFileNodeParser |
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')
get_ipython().system('pip install llama-index llama-hub')
import nest_asyncio
nest_asyncio.apply()
import os
os.environ["GITHUB_TOKEN"] = "ghp_..."
os.environ["OPENAI_API_KEY"] = "sk-..."
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"] = int(doc.id_)
if idx >= limit:
break
docs.append(doc)
import weaviate
auth_config = weaviate.AuthApiKey(
api_key="XRa15cDIkYRT7AkrpqT6jLfE4wropK1c1TGk"
)
client = weaviate.Client(
"https://llama-index-test-v0oggsoz.weaviate.network",
auth_client_secret=auth_config,
)
class_name = "LlamaIndex_docs"
client.schema.delete_class(class_name)
from llama_index.vector_stores.weaviate import WeaviateVectorStore
from llama_index.core import VectorStoreIndex, StorageContext
vector_store = WeaviateVectorStore(
weaviate_client=client, index_name=class_name
)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
doc_index = VectorStoreIndex.from_documents(
docs, storage_context=storage_context
)
from llama_index.core import SummaryIndex
from llama_index.core.async_utils import run_jobs
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import IndexNode
from llama_index.core.vector_stores import (
FilterOperator,
MetadataFilter,
MetadataFilters,
)
async def aprocess_doc(doc, include_summary: bool = True):
"""Process doc."""
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,
}
summary_index = | SummaryIndex.from_documents([doc]) | llama_index.core.SummaryIndex.from_documents |
get_ipython().system('pip install llama-index')
from llama_index.core.evaluation import SemanticSimilarityEvaluator
evaluator = | SemanticSimilarityEvaluator() | llama_index.core.evaluation.SemanticSimilarityEvaluator |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
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
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
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 = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = VectorStoreIndex(nodes)
vector_index.storage_context.persist(persist_dir=vi_out_path)
else:
vector_index = load_index_from_storage(
StorageContext.from_defaults(persist_dir=vi_out_path),
)
summary_index = | SummaryIndex(nodes) | llama_index.core.SummaryIndex |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
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('pip -q install python-dotenv pinecone-client llama-index pymupdf')
dotenv_path = (
"env" # Google Colabs will not let you open a .env, but you can set
)
with open(dotenv_path, "w") as f:
f.write('PINECONE_API_KEY="<your api key>"\n')
f.write('PINECONE_ENVIRONMENT="gcp-starter"\n')
f.write('OPENAI_API_KEY="<your api key>"\n')
import os
from dotenv import load_dotenv
load_dotenv(dotenv_path=dotenv_path)
import pinecone
api_key = os.environ["PINECONE_API_KEY"]
environment = os.environ["PINECONE_ENVIRONMENT"]
pinecone.init(api_key=api_key, environment=environment)
index_name = "llamaindex-rag-fs"
pinecone.create_index(
index_name, dimension=1536, metric="euclidean", pod_type="p1"
)
pinecone_index = pinecone.Index(index_name)
pinecone_index.delete(deleteAll=True)
from llama_index.vector_stores.pinecone import PineconeVectorStore
vector_store = PineconeVectorStore(pinecone_index=pinecone_index)
get_ipython().system('mkdir data')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "data/llama2.pdf"')
import fitz
file_path = "./data/llama2.pdf"
doc = fitz.open(file_path)
from llama_index.core.node_parser import SentenceSplitter
text_parser = SentenceSplitter(
chunk_size=1024,
)
text_chunks = []
doc_idxs = []
for doc_idx, page in enumerate(doc):
page_text = page.get_text("text")
cur_text_chunks = text_parser.split_text(page_text)
text_chunks.extend(cur_text_chunks)
doc_idxs.extend([doc_idx] * len(cur_text_chunks))
from llama_index.core.schema import TextNode
nodes = []
for idx, text_chunk in enumerate(text_chunks):
node = TextNode(
text=text_chunk,
)
src_doc_idx = doc_idxs[idx]
src_page = doc[src_doc_idx]
nodes.append(node)
print(nodes[0].metadata)
print(nodes[0].get_content(metadata_mode="all"))
from llama_index.core.extractors import (
QuestionsAnsweredExtractor,
TitleExtractor,
)
from llama_index.core.ingestion import IngestionPipeline
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
extractors = [
TitleExtractor(nodes=5, llm=llm),
QuestionsAnsweredExtractor(questions=3, llm=llm),
]
pipeline = IngestionPipeline(
transformations=extractors,
)
nodes = await pipeline.arun(nodes=nodes, in_place=False)
print(nodes[0].metadata)
from llama_index.embeddings.openai import OpenAIEmbedding
embed_model = OpenAIEmbedding()
for node in nodes:
node_embedding = embed_model.get_text_embedding(
node.get_content(metadata_mode="all")
)
node.embedding = node_embedding
vector_store.add(nodes)
from llama_index.core import VectorStoreIndex
from llama_index.core import StorageContext
index = | VectorStoreIndex.from_vector_store(vector_store) | llama_index.core.VectorStoreIndex.from_vector_store |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lancedb')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-multi-modal-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-lancedb')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-clip')
get_ipython().run_line_magic('pip', 'install llama_index ftfy regex tqdm')
get_ipython().run_line_magic('pip', 'install -U openai-whisper')
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 lancedb')
get_ipython().run_line_magic('pip', 'install moviepy')
get_ipython().run_line_magic('pip', 'install pytube')
get_ipython().run_line_magic('pip', 'install pydub')
get_ipython().run_line_magic('pip', 'install SpeechRecognition')
get_ipython().run_line_magic('pip', 'install ffmpeg-python')
get_ipython().run_line_magic('pip', 'install soundfile')
from moviepy.editor import VideoFileClip
from pathlib import Path
import speech_recognition as sr
from pytube import YouTube
from pprint import pprint
import os
OPENAI_API_TOKEN = ""
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
video_url = "https://www.youtube.com/watch?v=d_qvLDhkg00"
output_video_path = "./video_data/"
output_folder = "./mixed_data/"
output_audio_path = "./mixed_data/output_audio.wav"
filepath = output_video_path + "input_vid.mp4"
Path(output_folder).mkdir(parents=True, exist_ok=True)
from PIL import Image
import matplotlib.pyplot as plt
import os
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 >= 7:
break
def download_video(url, output_path):
"""
Download a video from a given url and save it to the output path.
Parameters:
url (str): The url of the video to download.
output_path (str): The path to save the video to.
Returns:
dict: A dictionary containing the metadata of the video.
"""
yt = YouTube(url)
metadata = {"Author": yt.author, "Title": yt.title, "Views": yt.views}
yt.streams.get_highest_resolution().download(
output_path=output_path, filename="input_vid.mp4"
)
return metadata
def video_to_images(video_path, output_folder):
"""
Convert a video to a sequence of images and save them to the output folder.
Parameters:
video_path (str): The path to the video file.
output_folder (str): The path to the folder to save the images to.
"""
clip = VideoFileClip(video_path)
clip.write_images_sequence(
os.path.join(output_folder, "frame%04d.png"), fps=0.2
)
def video_to_audio(video_path, output_audio_path):
"""
Convert a video to audio and save it to the output path.
Parameters:
video_path (str): The path to the video file.
output_audio_path (str): The path to save the audio to.
"""
clip = VideoFileClip(video_path)
audio = clip.audio
audio.write_audiofile(output_audio_path)
def audio_to_text(audio_path):
"""
Convert audio to text using the SpeechRecognition library.
Parameters:
audio_path (str): The path to the audio file.
Returns:
test (str): The text recognized from the audio.
"""
recognizer = sr.Recognizer()
audio = sr.AudioFile(audio_path)
with audio as source:
audio_data = recognizer.record(source)
try:
text = recognizer.recognize_whisper(audio_data)
except sr.UnknownValueError:
print("Speech recognition could not understand the audio.")
except sr.RequestError as e:
print(f"Could not request results from service; {e}")
return text
try:
metadata_vid = download_video(video_url, output_video_path)
video_to_images(filepath, output_folder)
video_to_audio(filepath, output_audio_path)
text_data = audio_to_text(output_audio_path)
with open(output_folder + "output_text.txt", "w") as file:
file.write(text_data)
print("Text data saved to file")
file.close()
os.remove(output_audio_path)
print("Audio file removed")
except Exception as e:
raise e
from llama_index.core.indices import MultiModalVectorStoreIndex
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.core import SimpleDirectoryReader, StorageContext
from llama_index.vector_stores.lancedb import LanceDBVectorStore
from llama_index.core import SimpleDirectoryReader
text_store = LanceDBVectorStore(uri="lancedb", table_name="text_collection")
image_store = LanceDBVectorStore(uri="lancedb", table_name="image_collection")
storage_context = StorageContext.from_defaults(
vector_store=text_store, image_store=image_store
)
documents = | SimpleDirectoryReader(output_folder) | llama_index.core.SimpleDirectoryReader |
get_ipython().system('pip install llama-index')
get_ipython().system('pip install traceloop-sdk')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["TRACELOOP_API_KEY"] = "..."
from traceloop.sdk import Traceloop
Traceloop.init()
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
docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.core import VectorStoreIndex
index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
from IPython.display import HTML, display
def set_css():
display(
HTML(
"""
<style>
pre {
white-space: pre-wrap;
}
</style>
"""
)
)
get_ipython().events.register("pre_run_cell", set_css)
get_ipython().system('mkdir data')
get_ipython().system('wget "https://www.dropbox.com/s/948jr9cfs7fgj99/UBER.zip?dl=1" -O data/UBER.zip')
get_ipython().system('unzip data/UBER.zip -d data')
from llama_index.readers.file import UnstructuredReader
from pathlib import Path
years = [2022, 2021, 2020, 2019]
loader = | UnstructuredReader() | llama_index.readers.file.UnstructuredReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('pip install llama-index')
import os
import openai
os.environ["OPENAI_API_KEY"] = "sk-..."
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))
from llama_index.core import VectorStoreIndex
from llama_index.core import PromptTemplate
from IPython.display import Markdown, display
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")
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
gpt35_llm = OpenAI(model="gpt-3.5-turbo")
gpt4_llm = OpenAI(model="gpt-4")
index = VectorStoreIndex.from_documents(documents)
query_str = "What are the potential risks associated with the use of Llama 2 as mentioned in the context?"
query_engine = index.as_query_engine(similarity_top_k=2, llm=gpt35_llm)
vector_retriever = index.as_retriever(similarity_top_k=2)
response = query_engine.query(query_str)
print(str(response))
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>"))
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
from langchain import hub
langchain_prompt = hub.pull("rlm/rag-prompt")
from llama_index.core.prompts import LangchainPromptTemplate
lc_prompt_tmpl = LangchainPromptTemplate(
template=langchain_prompt,
template_var_mappings={"query_str": "question", "context_str": "context"},
)
query_engine.update_prompts(
{"response_synthesizer:text_qa_template": lc_prompt_tmpl}
)
prompts_dict = query_engine.get_prompts()
display_prompt_dict(prompts_dict)
response = query_engine.query(query_str)
print(str(response))
from llama_index.core.schema import TextNode
few_shot_nodes = []
for line in open("../llama2_qa_citation_events.jsonl", "r"):
few_shot_nodes.append(TextNode(text=line))
few_shot_index = | VectorStoreIndex(few_shot_nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import os
os.environ["OPENAI_API_KEY"] = "INSERT OPENAI KEY"
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
from llama_index.core import SimpleDirectoryReader, KnowledgeGraphIndex
from llama_index.core.graph_stores import SimpleGraphStore
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="text-davinci-002")
Settings.llm = llm
Settings.chunk_size = 512
from llama_index.core import StorageContext
graph_store = SimpleGraphStore()
storage_context = | StorageContext.from_defaults(graph_store=graph_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import os
import openai
from llama_index.core import set_global_handler
set_global_handler("wandb", run_args={"project": "llamaindex"})
os.environ["OPENAI_API_KEY"] = "sk-..."
openai.api_key = os.environ["OPENAI_API_KEY"]
from llama_index.llms.openai import OpenAI
from llama_index.core.schema import MetadataMode
llm = OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
from llama_index.core.node_parser import TokenTextSplitter
from llama_index.core.extractors import (
SummaryExtractor,
QuestionsAnsweredExtractor,
)
node_parser = TokenTextSplitter(
separator=" ", chunk_size=256, chunk_overlap=128
)
extractors_1 = [
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
extractors_2 = [
SummaryExtractor(summaries=["prev", "self", "next"], llm=llm),
QuestionsAnsweredExtractor(
questions=3, llm=llm, metadata_mode=MetadataMode.EMBED
),
]
from llama_index.core import SimpleDirectoryReader
from llama_index.readers.web import SimpleWebPageReader
reader = SimpleWebPageReader(html_to_text=True)
docs = reader.load_data(urls=["https://eugeneyan.com/writing/llm-patterns/"])
print(docs[0].get_content())
orig_nodes = node_parser.get_nodes_from_documents(docs)
nodes = orig_nodes[20:28]
print(nodes[3].get_content(metadata_mode="all"))
from llama_index.core.ingestion import IngestionPipeline
pipeline = IngestionPipeline(transformations=[node_parser, *extractors_1])
nodes_1 = pipeline.run(nodes=nodes, in_place=False, show_progress=True)
print(nodes_1[3].get_content(metadata_mode="all"))
pipeline = IngestionPipeline(transformations=[node_parser, *extractors_2])
nodes_2 = pipeline.run(nodes=nodes, in_place=False, show_progress=True)
print(nodes_2[3].get_content(metadata_mode="all"))
print(nodes_2[1].get_content(metadata_mode="all"))
from llama_index.core import VectorStoreIndex
from llama_index.core.response.notebook_utils import (
display_source_node,
display_response,
)
index0 = VectorStoreIndex(orig_nodes)
index1 = VectorStoreIndex(orig_nodes[:20] + nodes_1 + orig_nodes[28:])
index2 = | VectorStoreIndex(orig_nodes[:20] + nodes_2 + orig_nodes[28:]) | llama_index.core.VectorStoreIndex |
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/").load_data()
index = VectorStoreIndex.from_documents(documents)
retriever = index.as_retriever()
from llama_index.core.query_engine import CustomQueryEngine
from llama_index.core.retrievers import BaseRetriever
from llama_index.core import get_response_synthesizer
from llama_index.core.response_synthesizers import BaseSynthesizer
class RAGQueryEngine(CustomQueryEngine):
"""RAG Query Engine."""
retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)
response_obj = self.response_synthesizer.synthesize(query_str, nodes)
return response_obj
from llama_index.llms.openai import OpenAI
from llama_index.core import PromptTemplate
qa_prompt = PromptTemplate(
"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: "
)
class RAGStringQueryEngine(CustomQueryEngine):
"""RAG String Query Engine."""
retriever: BaseRetriever
response_synthesizer: BaseSynthesizer
llm: OpenAI
qa_prompt: PromptTemplate
def custom_query(self, query_str: str):
nodes = self.retriever.retrieve(query_str)
context_str = "\n\n".join([n.node.get_content() for n in nodes])
response = self.llm.complete(
qa_prompt.format(context_str=context_str, query_str=query_str)
)
return str(response)
synthesizer = | get_response_synthesizer(response_mode="compact") | llama_index.core.get_response_synthesizer |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-mongodb')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-firestore')
get_ipython().run_line_magic('pip', 'install llama-index-retrievers-bm25')
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-redis')
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/2307.09288.pdf" -O "./llama2.pdf"')
get_ipython().system('wget --user-agent "Mozilla" "https://arxiv.org/pdf/1706.03762.pdf" -O "./attention.pdf"')
from llama_index.core import download_loader
from llama_index.readers.file import PyMuPDFReader
llama2_docs = PyMuPDFReader().load_data(
file_path="./llama2.pdf", metadata=True
)
attention_docs = PyMuPDFReader().load_data(
file_path="./attention.pdf", metadata=True
)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.core.node_parser import TokenTextSplitter
nodes = TokenTextSplitter(
chunk_size=1024, chunk_overlap=128
).get_nodes_from_documents(llama2_docs + attention_docs)
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.storage.docstore.redis import RedisDocumentStore
from llama_index.storage.docstore.mongodb import MongoDocumentStore
from llama_index.storage.docstore.firestore import FirestoreDocumentStore
from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore
docstore = SimpleDocumentStore()
docstore.add_documents(nodes)
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.retrievers.bm25 import BM25Retriever
from llama_index.vector_stores.qdrant import QdrantVectorStore
from qdrant_client import QdrantClient
client = QdrantClient(path="./qdrant_data")
vector_store = QdrantVectorStore("composable", client=client)
storage_context = StorageContext.from_defaults(vector_store=vector_store)
index = VectorStoreIndex(nodes=nodes)
vector_retriever = index.as_retriever(similarity_top_k=2)
bm25_retriever = BM25Retriever.from_defaults(
docstore=docstore, similarity_top_k=2
)
from llama_index.core.schema import IndexNode
vector_obj = IndexNode(
index_id="vector", obj=vector_retriever, text="Vector Retriever"
)
bm25_obj = IndexNode(
index_id="bm25", obj=bm25_retriever, text="BM25 Retriever"
)
from llama_index.core import SummaryIndex
summary_index = | SummaryIndex(objects=[vector_obj, bm25_obj]) | llama_index.core.SummaryIndex |
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/") | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
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
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
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 = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = VectorStoreIndex(nodes)
vector_index.storage_context.persist(persist_dir=vi_out_path)
else:
vector_index = load_index_from_storage(
| StorageContext.from_defaults(persist_dir=vi_out_path) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai pandas[jinja2] spacy')
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 (
TreeIndex,
VectorStoreIndex,
SimpleDirectoryReader,
Response,
)
from llama_index.llms.openai import OpenAI
from llama_index.core.evaluation import RelevancyEvaluator
from llama_index.core.node_parser import SentenceSplitter
import pandas as pd
pd.set_option("display.max_colwidth", 0)
gpt3 = OpenAI(temperature=0, model="gpt-3.5-turbo")
gpt4 = OpenAI(temperature=0, model="gpt-4")
evaluator = RelevancyEvaluator(llm=gpt3)
evaluator_gpt4 = RelevancyEvaluator(llm=gpt4)
documents = SimpleDirectoryReader("./test_wiki_data").load_data()
splitter = | SentenceSplitter(chunk_size=512) | llama_index.core.node_parser.SentenceSplitter |
import openai
openai.api_key = "sk-your-key"
from llama_index.agent import OpenAIAgent
from llama_index.tools.arxiv.base import ArxivToolSpec
arxiv_tool = | ArxivToolSpec() | llama_index.tools.arxiv.base.ArxivToolSpec |
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 SimpleDirectoryReader
reader = SimpleDirectoryReader(
input_files=["./data/paul_graham/paul_graham_essay.txt"]
)
docs = reader.load_data()
text = docs[0].text
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
from llama_index.core.response_synthesizers import Refine
summarizer = | Refine(llm=llm, verbose=True) | llama_index.core.response_synthesizers.Refine |
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/").load_data()
vector_store = | DuckDBVectorStore() | llama_index.vector_stores.duckdb.DuckDBVectorStore |
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)
storage_context = StorageContext.from_defaults(graph_store=graph_store)
new_index = KnowledgeGraphIndex.from_documents(
documents,
max_triplets_per_chunk=2,
storage_context=storage_context,
include_embeddings=True,
)
rel_map = graph_store.get_rel_map()
query_engine = index.as_query_engine(
include_text=True,
response_mode="tree_summarize",
embedding_mode="hybrid",
similarity_top_k=5,
)
response = query_engine.query(
"Tell me more about what the author worked on at Interleaf",
)
display(Markdown(f"<b>{response}</b>"))
get_ipython().run_line_magic('pip', 'install pyvis')
from pyvis.network import Network
g = index.get_networkx_graph()
net = Network(notebook=True, cdn_resources="in_line", directed=True)
net.from_nx(g)
net.show("kuzugraph_draw.html")
from llama_index.core.node_parser import SentenceSplitter
node_parser = SentenceSplitter()
nodes = node_parser.get_nodes_from_documents(documents)
db = kuzu.Database("test3")
graph_store = | KuzuGraphStore(db) | llama_index.graph_stores.kuzu.KuzuGraphStore |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-colbert')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-qdrant')
get_ipython().run_line_magic('pip', 'install llama-index-llms-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-gemini')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-vectara')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-google')
get_ipython().run_line_magic('pip', 'install llama-index-indices-managed-google')
get_ipython().run_line_magic('pip', 'install llama-index-response-synthesizers-google')
get_ipython().run_line_magic('pip', 'install llama-index')
get_ipython().run_line_magic('pip', 'install "google-ai-generativelanguage>=0.4,<=1.0"')
get_ipython().run_line_magic('pip', 'install torch sentence-transformers')
get_ipython().run_line_magic('pip', 'install google-auth-oauthlib')
from google.oauth2 import service_account
from llama_index.indices.managed.google import GoogleIndex
from llama_index.vector_stores.google import set_google_config
credentials = service_account.Credentials.from_service_account_file(
"service_account_key.json",
scopes=[
"https://www.googleapis.com/auth/cloud-platform",
"https://www.googleapis.com/auth/generative-language.retriever",
],
)
set_google_config(auth_credentials=credentials)
project_name = "TODO-your-project-name" # @param {type:"string"}
email = "ht@runllama.ai" # @param {type:"string"}
client_file_name = "client_secret.json"
get_ipython().system('gcloud config set project $project_name')
get_ipython().system('gcloud config set account $email')
get_ipython().system('gcloud auth application-default login --no-browser --client-id-file=$client_file_name --scopes="https://www.googleapis.com/auth/generative-language.retriever,https://www.googleapis.com/auth/cloud-platform"')
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 os
GOOGLE_API_KEY = "" # add your GOOGLE API key here
os.environ["GOOGLE_API_KEY"] = GOOGLE_API_KEY
from llama_index.core import SimpleDirectoryReader
from llama_index.indices.managed.google import GoogleIndex
google_index = GoogleIndex.create_corpus(display_name="My first corpus!")
print(f"Newly created corpus ID is {google_index.corpus_id}.")
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
google_index.insert_documents(documents)
google_index = GoogleIndex.from_corpus(corpus_id="")
query_engine = google_index.as_query_engine()
response = query_engine.query("which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.VERBOSE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from google.ai.generativelanguage import (
GenerateAnswerRequest,
)
query_engine = google_index.as_query_engine(
temperature=0.3,
answer_style=GenerateAnswerRequest.AnswerStyle.EXTRACTIVE,
)
response = query_engine.query("Which program did this author attend?")
print(response)
from llama_index.core.response.notebook_utils import display_source_node
for r in response.source_nodes:
display_source_node(r, source_length=1000)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.llms.gemini import Gemini
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.gemini import GeminiEmbedding
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.7, answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE
)
reranker = LLMRerank(
top_n=5,
llm=Gemini(api_key=GOOGLE_API_KEY),
)
retriever = google_index.as_retriever(similarity_top_k=5)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
response_synthesizer=response_synthesizer,
node_postprocessors=[reranker],
)
response = query_engine.query("Which program did this author attend?")
print(response.response)
from llama_index.core.postprocessor import SentenceTransformerRerank
sbert_rerank = SentenceTransformerRerank(
model="cross-encoder/ms-marco-MiniLM-L-2-v2", top_n=5
)
from llama_index.response_synthesizers.google import GoogleTextSynthesizer
from llama_index.vector_stores.google import GoogleVectorStore
from llama_index.core import VectorStoreIndex
from llama_index.llms.gemini import Gemini
from llama_index.core.postprocessor import LLMRerank
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.embeddings.gemini import GeminiEmbedding
response_synthesizer = GoogleTextSynthesizer.from_defaults(
temperature=0.1, answer_style=GenerateAnswerRequest.AnswerStyle.ABSTRACTIVE
)
retriever = google_index.as_retriever(similarity_top_k=5)
query_engine = RetrieverQueryEngine.from_args(
retriever=retriever,
response_synthesizer=response_synthesizer,
node_postprocessors=[sbert_rerank],
)
response = query_engine.query("Which program did this author attend?")
print(response.response)
import os
OPENAI_API_TOKEN = "sk-"
os.environ["OPENAI_API_KEY"] = OPENAI_API_TOKEN
from llama_index.core import VectorStoreIndex, StorageContext
from llama_index.vector_stores.qdrant import QdrantVectorStore
from llama_index.core import Settings
import qdrant_client
Settings.chunk_size = 256
client = qdrant_client.QdrantClient(path="qdrant_retrieval_2")
vector_store = QdrantVectorStore(client=client, collection_name="collection")
qdrant_index = VectorStoreIndex.from_documents(documents)
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().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) | llama_index.core.VectorStoreIndex |
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() | 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-langchain')
get_ipython().system('pip install llama-index')
from langchain.chat_models import ChatAnyscale, ChatOpenAI
from llama_index.llms.langchain import LangChainLLM
from llama_index.core import PromptTemplate
llm = LangChainLLM(ChatOpenAI())
stream = await llm.astream( | PromptTemplate("Hi, write a short story") | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai-legacy')
get_ipython().system('pip install llama-index')
import json
from typing import Sequence
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/march"
)
march_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/june"
)
june_index = load_index_from_storage(storage_context)
storage_context = StorageContext.from_defaults(
persist_dir="./storage/sept"
)
sept_index = load_index_from_storage(storage_context)
index_loaded = True
except:
index_loaded = False
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'")
if not index_loaded:
march_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_march_2022.pdf"]
).load_data()
june_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_june_2022.pdf"]
).load_data()
sept_docs = SimpleDirectoryReader(
input_files=["./data/10q/uber_10q_sept_2022.pdf"]
).load_data()
march_index = VectorStoreIndex.from_documents(march_docs)
june_index = VectorStoreIndex.from_documents(june_docs)
sept_index = VectorStoreIndex.from_documents(sept_docs)
march_index.storage_context.persist(persist_dir="./storage/march")
june_index.storage_context.persist(persist_dir="./storage/june")
sept_index.storage_context.persist(persist_dir="./storage/sept")
march_engine = march_index.as_query_engine(similarity_top_k=3)
june_engine = june_index.as_query_engine(similarity_top_k=3)
sept_engine = sept_index.as_query_engine(similarity_top_k=3)
query_engine_tools = [
QueryEngineTool(
query_engine=march_engine,
metadata=ToolMetadata(
name="uber_march_10q",
description=(
"Provides information about Uber 10Q filings for March 2022. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=june_engine,
metadata=ToolMetadata(
name="uber_june_10q",
description=(
"Provides information about Uber financials for June 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
QueryEngineTool(
query_engine=sept_engine,
metadata=ToolMetadata(
name="uber_sept_10q",
description=(
"Provides information about Uber financials for Sept 2021. "
"Use a detailed plain text question as input to the tool."
),
),
),
]
from llama_index.core import Document
from llama_index.agent.openai_legacy import ContextRetrieverOpenAIAgent
texts = [
"Abbreviation: X = Revenue",
"Abbreviation: YZ = Risk Factors",
"Abbreviation: Z = Costs",
]
docs = [Document(text=t) for t in texts]
context_index = | VectorStoreIndex.from_documents(docs) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().run_line_magic('pip', 'install llama-index-llms-vllm')
import os
os.environ["HF_HOME"] = "model/"
from llama_index.llms.vllm import Vllm
llm = Vllm(
model="microsoft/Orca-2-7b",
tensor_parallel_size=4,
max_new_tokens=100,
vllm_kwargs={"swap_space": 1, "gpu_memory_utilization": 0.5},
)
llm.complete(
["[INST]You are a helpful assistant[/INST] What is a black hole ?"]
)
llm = Vllm(
model="codellama/CodeLlama-7b-hf",
dtype="float16",
tensor_parallel_size=4,
temperature=0,
max_new_tokens=100,
vllm_kwargs={
"swap_space": 1,
"gpu_memory_utilization": 0.5,
"max_model_len": 4096,
},
)
llm.complete(["import socket\n\ndef ping_exponential_backoff(host: str):"])
llm = Vllm(
model="mistralai/Mistral-7B-Instruct-v0.1",
dtype="float16",
tensor_parallel_size=4,
temperature=0,
max_new_tokens=100,
vllm_kwargs={
"swap_space": 1,
"gpu_memory_utilization": 0.5,
"max_model_len": 4096,
},
)
llm.complete([" What is a black hole ?"])
from llama_index.core.llms.vllm import VllmServer
llm = VllmServer(
api_url="http://localhost:8000/generate", max_new_tokens=100, temperature=0
)
llm.complete("what is a black hole ?")
list(llm.stream_complete("what is a black hole"))[-1]
from llama_index.core.llms.vllm import VllmServer
from llama_index.core.llms import ChatMessage
llm = VllmServer(
api_url="http://localhost:8000/generate", max_new_tokens=100, temperature=0
)
llm.complete("what is a black hole ?")
message = [ | ChatMessage(content="hello", author="user") | llama_index.core.llms.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index')
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 = reader.load_data(Path("./tesla_2020_10k.htm"))
from llama_index.core.evaluation import DatasetGenerator, QueryResponseDataset
from llama_index.llms.openai import OpenAI
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.readers.file import FlatReader
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
from llama_index.core.ingestion import IngestionPipeline
from pathlib import Path
import nest_asyncio
nest_asyncio.apply()
reader = FlatReader()
docs = reader.load_data(Path("./tesla_2020_10k.htm"))
pipeline = IngestionPipeline(
documents=docs,
transformations=[
HTMLNodeParser.from_defaults(),
SentenceSplitter(chunk_size=1024, chunk_overlap=200),
OpenAIEmbedding(),
],
)
eval_nodes = pipeline.run(documents=docs)
eval_llm = OpenAI(model="gpt-3.5-turbo")
dataset_generator = DatasetGenerator(
eval_nodes[:100],
llm=eval_llm,
show_progress=True,
num_questions_per_chunk=3,
)
eval_dataset = await dataset_generator.agenerate_dataset_from_nodes(num=100)
len(eval_dataset.qr_pairs)
eval_dataset.save_json("data/tesla10k_eval_dataset.json")
eval_dataset = QueryResponseDataset.from_json(
"data/tesla10k_eval_dataset.json"
)
eval_qs = eval_dataset.questions
qr_pairs = eval_dataset.qr_pairs
ref_response_strs = [r for (_, r) in qr_pairs]
from llama_index.core.evaluation import (
CorrectnessEvaluator,
SemanticSimilarityEvaluator,
)
from llama_index.core.evaluation.eval_utils import (
get_responses,
get_results_df,
)
from llama_index.core.evaluation import BatchEvalRunner
evaluator_c = CorrectnessEvaluator(llm=eval_llm)
evaluator_s = SemanticSimilarityEvaluator(llm=eval_llm)
evaluator_dict = {
"correctness": evaluator_c,
"semantic_similarity": evaluator_s,
}
batch_eval_runner = BatchEvalRunner(
evaluator_dict, workers=2, show_progress=True
)
from llama_index.core import VectorStoreIndex
async def run_evals(
pipeline, batch_eval_runner, docs, eval_qs, eval_responses_ref
):
nodes = pipeline.run(documents=docs)
vector_index = VectorStoreIndex(nodes)
query_engine = vector_index.as_query_engine()
pred_responses = get_responses(eval_qs, query_engine, show_progress=True)
eval_results = await batch_eval_runner.aevaluate_responses(
eval_qs, responses=pred_responses, reference=eval_responses_ref
)
return eval_results
from llama_index.core.node_parser import HTMLNodeParser, SentenceSplitter
sent_parser_o0 = SentenceSplitter(chunk_size=1024, chunk_overlap=0)
sent_parser_o200 = | SentenceSplitter(chunk_size=1024, chunk_overlap=200) | llama_index.core.node_parser.SentenceSplitter |
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
import os
os.environ["OPENAI_API_KEY"] = "INSERT OPENAI KEY"
get_ipython().system('pip install llama-index')
from llama_index.core import download_loader
from llama_index.readers.wikipedia import WikipediaReader
loader = WikipediaReader()
documents = loader.load_data(pages=["Berlin"])
from llama_index.core import VectorStoreIndex
index = | VectorStoreIndex.from_documents(documents) | llama_index.core.VectorStoreIndex.from_documents |
get_ipython().system('pip install llama-index-llms-dashscope')
get_ipython().run_line_magic('env', 'DASHSCOPE_API_KEY=YOUR_DASHSCOPE_API_KEY')
import os
os.environ["DASHSCOPE_API_KEY"] = "YOUR_DASHSCOPE_API_KEY"
from llama_index.llms.dashscope import DashScope, DashScopeGenerationModels
dashscope_llm = DashScope(model_name=DashScopeGenerationModels.QWEN_MAX)
resp = dashscope_llm.complete("How to make cake?")
print(resp)
responses = dashscope_llm.stream_complete("How to make cake?")
for response in responses:
print(response.delta, end="")
from llama_index.core.base.llms.types import MessageRole, ChatMessage
messages = [
ChatMessage(
role=MessageRole.SYSTEM, content="You are a helpful assistant."
),
| ChatMessage(role=MessageRole.USER, content="How to make cake?") | llama_index.core.base.llms.types.ChatMessage |
get_ipython().run_line_magic('pip', 'install llama-index-llms-litellm')
get_ipython().system('pip install llama-index')
import os
from llama_index.llms.litellm import LiteLLM
from llama_index.core.llms import ChatMessage
os.environ["OPENAI_API_KEY"] = "your-api-key"
os.environ["COHERE_API_KEY"] = "your-api-key"
message = ChatMessage(role="user", content="Hey! how's it going?")
llm = LiteLLM("gpt-3.5-turbo")
chat_response = llm.chat([message])
llm = | LiteLLM("command-nightly") | llama_index.llms.litellm.LiteLLM |
get_ipython().run_line_magic('pip', 'install llama-index-llms-anthropic')
get_ipython().system('pip install llama-index')
from llama_index.llms.anthropic import Anthropic
from llama_index.core import Settings
tokenizer = | Anthropic() | llama_index.llms.anthropic.Anthropic |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-weaviate')
get_ipython().system('pip install llama-index')
import nest_asyncio
nest_asyncio.apply()
import logging
import sys
from llama_index.core import SimpleDirectoryReader
from llama_index.core import SummaryIndex
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
wiki_titles = ["Michael Jordan", "Elon Musk", "Richard Branson", "Rihanna"]
wiki_metadatas = {
"Michael Jordan": {
"category": "Sports",
"country": "United States",
},
"Elon Musk": {
"category": "Business",
"country": "United States",
},
"Richard Branson": {
"category": "Business",
"country": "UK",
},
"Rihanna": {
"category": "Music",
"country": "Barbados",
},
}
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)
docs_dict = {}
for wiki_title in wiki_titles:
doc = SimpleDirectoryReader(
input_files=[f"data/{wiki_title}.txt"]
).load_data()[0]
doc.metadata.update(wiki_metadatas[wiki_title])
docs_dict[wiki_title] = doc
from llama_index.llms.openai import OpenAI
from llama_index.core.callbacks import LlamaDebugHandler, CallbackManager
from llama_index.core.node_parser import SentenceSplitter
llm = OpenAI("gpt-4")
callback_manager = CallbackManager([ | LlamaDebugHandler() | llama_index.core.callbacks.LlamaDebugHandler |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().run_line_magic('pip', 'install llama-index-llms-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'")
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
get_ipython().system('pip install "llama_index>=0.9.7"')
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.extractors import TitleExtractor, SummaryExtractor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import MetadataMode
def build_pipeline():
llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.1)
transformations = [
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
TitleExtractor(
llm=llm, metadata_mode=MetadataMode.EMBED, num_workers=8
),
SummaryExtractor(
llm=llm, metadata_mode=MetadataMode.EMBED, num_workers=8
),
OpenAIEmbedding(),
]
return IngestionPipeline(transformations=transformations)
from llama_index.core import SimpleDirectoryReader
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
import time
times = []
for _ in range(3):
time.sleep(30) # help prevent rate-limits/timeouts, keeps each run fair
pipline = build_pipeline()
start = time.time()
nodes = await pipline.arun(documents=documents)
end = time.time()
times.append(end - start)
print(f"Average time: {sum(times) / len(times)}")
get_ipython().system('pip install "llama_index<0.9.6"')
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
from llama_index.embeddings.openai import OpenAIEmbedding
from llama_index.llms.openai import OpenAI
from llama_index.core.ingestion import IngestionPipeline
from llama_index.core.extractors import TitleExtractor, SummaryExtractor
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.schema import MetadataMode
def build_pipeline():
llm = OpenAI(model="gpt-3.5-turbo-1106", temperature=0.1)
transformations = [
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
TitleExtractor(llm=llm, metadata_mode=MetadataMode.EMBED),
| SummaryExtractor(llm=llm, metadata_mode=MetadataMode.EMBED) | llama_index.core.extractors.SummaryExtractor |
get_ipython().system('pip install llama_index')
get_ipython().system('pip install llama_hub')
get_ipython().system('pip install torch_geometric')
import os
from pprint import pprint
from llama_index import (
ServiceContext,
VectorStoreIndex,
SummaryIndex,
)
import llama_hub.docstring_walker as docstring_walker
walker = docstring_walker.DocstringWalker()
path_to_docstring_walker = os.path.dirname(docstring_walker.__file__)
example1_docs = walker.load_data(path_to_docstring_walker)
print(example1_docs[0].text)
example1_index = VectorStoreIndex(example1_docs)
example1_query_engine = example1_index.as_query_engine()
pprint(
example1_query_engine.query("What is the main purpose of DocstringWalker?").response
)
print(
example1_query_engine.query(
"What are the main functions used in DocstringWalker. Use numbered list, briefly describe each function."
).response
)
import torch_geometric.nn.kge as kge
path_to_module = os.path.dirname(kge.__file__)
example2_docs = walker.load_data(path_to_module)
example2_index = | SummaryIndex(example2_docs) | llama_index.SummaryIndex |
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))
all_nodes_dict = {n.node_id: n for n in all_nodes}
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo")
vector_index_metadata = | VectorStoreIndex(all_nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-pinecone')
get_ipython().run_line_magic('pip', 'install llama-index-readers-wikipedia')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
import openai
import os
os.environ["OPENAI_API_KEY"] = "[You API key]"
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))
import pinecone
import os
api_key = os.environ["PINECONE_API_KEY"]
pinecone.init(api_key=api_key, environment="us-west1-gcp-free")
pinecone_index = pinecone.Index("quickstart")
pinecone_index.delete(deleteAll=True)
from llama_index.core import StorageContext
from llama_index.vector_stores.pinecone import PineconeVectorStore
from llama_index.core import VectorStoreIndex
vector_store = PineconeVectorStore(
pinecone_index=pinecone_index, namespace="wiki_cities"
)
storage_context = | StorageContext.from_defaults(vector_store=vector_store) | llama_index.core.StorageContext.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-agent-openai')
get_ipython().run_line_magic('pip', 'install llama-index-readers-file')
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-cohere-rerank')
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-openai')
get_ipython().system('pip install llama-index llama-hub')
get_ipython().run_line_magic('load_ext', 'autoreload')
get_ipython().run_line_magic('autoreload', '2')
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
reader = UnstructuredReader()
from pathlib import Path
all_files_gen = Path("./docs.llamaindex.ai/").rglob("*")
all_files = [f.resolve() for f in all_files_gen]
all_html_files = [f for f in all_files if f.suffix.lower() == ".html"]
len(all_html_files)
from llama_index.core import Document
doc_limit = 100
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 = 72
loaded_doc = Document(
text="\n\n".join([d.get_content() for d in loaded_docs[72:]]),
metadata={"path": str(f)},
)
print(loaded_doc.metadata["path"])
docs.append(loaded_doc)
import os
os.environ["OPENAI_API_KEY"] = "sk-..."
import nest_asyncio
nest_asyncio.apply()
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.agent.openai import OpenAIAgent
from llama_index.core import (
load_index_from_storage,
StorageContext,
VectorStoreIndex,
)
from llama_index.core import SummaryIndex
from llama_index.core.tools import QueryEngineTool, ToolMetadata
from llama_index.core.node_parser import SentenceSplitter
import os
from tqdm.notebook import tqdm
import pickle
async def build_agent_per_doc(nodes, file_base):
print(file_base)
vi_out_path = f"./data/llamaindex_docs/{file_base}"
summary_out_path = f"./data/llamaindex_docs/{file_base}_summary.pkl"
if not os.path.exists(vi_out_path):
Path("./data/llamaindex_docs/").mkdir(parents=True, exist_ok=True)
vector_index = | VectorStoreIndex(nodes) | llama_index.core.VectorStoreIndex |
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()
resp = llm.stream_complete(
"The story of Sourcrust, the bread creature, is really interesting. It all started when..."
)
for r in resp:
print(r.text, end="")
from llama_index.llms.gemini import Gemini
from llama_index.core.llms import ChatMessage
llm = | Gemini() | llama_index.llms.gemini.Gemini |
get_ipython().run_line_magic('pip', 'install llama-index-storage-docstore-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-storage-index-store-dynamodb')
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-dynamodb')
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.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)
TABLE_NAME = os.environ["DYNAMODB_TABLE_NAME"]
from llama_index.storage.docstore.dynamodb import DynamoDBDocumentStore
from llama_index.storage.index_store.dynamodb import DynamoDBIndexStore
from llama_index.vector_stores.dynamodb import DynamoDBVectorStore
storage_context = StorageContext.from_defaults(
docstore=DynamoDBDocumentStore.from_table_name(table_name=TABLE_NAME),
index_store= | DynamoDBIndexStore.from_table_name(table_name=TABLE_NAME) | llama_index.storage.index_store.dynamodb.DynamoDBIndexStore.from_table_name |
get_ipython().run_line_magic('pip', 'install llama-index-postprocessor-longllmlingua')
get_ipython().system('pip install llmlingua llama-index')
import openai
openai.api_key = "<insert_openai_key>"
get_ipython().system('wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt')
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
load_index_from_storage,
StorageContext,
)
documents = SimpleDirectoryReader(
input_files=["paul_graham_essay.txt"]
).load_data()
index = VectorStoreIndex.from_documents(documents)
retriever = index.as_retriever(similarity_top_k=2)
query_str = "Where did the author go for art school?"
results = retriever.retrieve(query_str)
print(results)
results
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.response_synthesizers import CompactAndRefine
from llama_index.postprocessor.longllmlingua import LongLLMLinguaPostprocessor
node_postprocessor = LongLLMLinguaPostprocessor(
instruction_str="Given the context, please answer the final question",
target_token=300,
rank_method="longllmlingua",
additional_compress_kwargs={
"condition_compare": True,
"condition_in_question": "after",
"context_budget": "+100",
"reorder_context": "sort", # enable document reorder
},
)
retrieved_nodes = retriever.retrieve(query_str)
synthesizer = CompactAndRefine()
from llama_index.core import QueryBundle
new_retrieved_nodes = node_postprocessor.postprocess_nodes(
retrieved_nodes, query_bundle= | QueryBundle(query_str=query_str) | llama_index.core.QueryBundle |
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.callbacks import (
CallbackManager,
LlamaDebugHandler,
CBEventType,
)
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
docs = SimpleDirectoryReader("./data/paul_graham/").load_data()
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-3.5-turbo", temperature=0)
llama_debug = LlamaDebugHandler(print_trace_on_end=True)
callback_manager = | CallbackManager([llama_debug]) | llama_index.core.callbacks.CallbackManager |
get_ipython().run_line_magic('pip', 'install llama-index-llms-openai')
get_ipython().run_line_magic('pip', 'install llama-index-extractors-entity')
get_ipython().system('pip install llama-index')
import os
os.environ["OPENAI_API_KEY"] = "YOUR_API_KEY"
from llama_index.extractors.entity import EntityExtractor
from llama_index.core.node_parser import SentenceSplitter
entity_extractor = EntityExtractor(
prediction_threshold=0.5,
label_entities=False, # include the entity label in the metadata (can be erroneous)
device="cpu", # set to "cuda" if you have a GPU
)
node_parser = SentenceSplitter()
transformations = [node_parser, entity_extractor]
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()
from llama_index.core.ingestion import IngestionPipeline
import random
random.seed(42)
documents = random.sample(documents, 100)
pipeline = IngestionPipeline(transformations=transformations)
nodes = pipeline.run(documents=documents)
samples = random.sample(nodes, 5)
for node in samples:
print(node.metadata)
from llama_index.core import VectorStoreIndex
from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
Settings.llm = OpenAI(model="gpt-3.5-turbo", temperature=0.2)
index = VectorStoreIndex(nodes=nodes)
query_engine = index.as_query_engine()
response = query_engine.query("What is said by Fox-Kemper?")
print(response)
for node in nodes:
node.metadata.pop("entities", None)
print(nodes[0].metadata)
index = | VectorStoreIndex(nodes=nodes) | llama_index.core.VectorStoreIndex |
get_ipython().run_line_magic('pip', 'install llama-index-vector-stores-astra')
get_ipython().system('pip install llama-index')
get_ipython().system('pip install "astrapy>=0.6.0"')
import os
import getpass
api_endpoint = input(
"\nPlease enter your Database Endpoint URL (e.g. 'https://4bc...datastax.com'):"
)
token = getpass.getpass(
"\nPlease enter your 'Database Administrator' Token (e.g. 'AstraCS:...'):"
)
os.environ["OPENAI_API_KEY"] = getpass.getpass(
"\nPlease enter your OpenAI API Key (e.g. 'sk-...'):"
)
from llama_index.core import (
VectorStoreIndex,
SimpleDirectoryReader,
StorageContext,
)
from llama_index.vector_stores.astra_db import AstraDBVectorStore
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().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() | llama_index.embeddings.openai.OpenAIEmbedding |
get_ipython().run_line_magic('pip', 'install llama-index-readers-web')
get_ipython().run_line_magic('pip', 'install llama-index-packs-trulens-eval-packs')
get_ipython().system('pip install trulens-eval llama-hub html2text llama-index')
import os
from llama_index.packs.trulens_eval_packs import (
TruLensRAGTriadPack,
TruLensHarmlessPack,
TruLensHelpfulPack,
)
from llama_index.core.node_parser import SentenceSplitter
from llama_index.readers.web import SimpleWebPageReader
from tqdm.auto import tqdm
os.environ["OPENAI_API_KEY"] = "sk-..."
documents = SimpleWebPageReader(html_to_text=True).load_data(
["http://paulgraham.com/worked.html"]
)
parser = SentenceSplitter()
nodes = parser.get_nodes_from_documents(documents)
trulens_ragtriad_pack = TruLensRAGTriadPack(
nodes=nodes, app_id="Query Engine v1: RAG Triad Evals"
)
queries = [
"What did Paul Graham do growing up?",
"When and how did Paul Graham's mother die?",
"What, in Paul Graham's opinion, is the most distinctive thing about YC?",
"When and how did Paul Graham meet Jessica Livingston?",
"What is Bel, and when and where was it written?",
]
for query in tqdm(queries):
print("Query")
print("=====")
print(query)
print()
response = trulens_ragtriad_pack.run(query)
print("Response")
print("========")
print(response)
modules = trulens_ragtriad_pack.get_modules()
tru = modules["session"]
index = modules["index"]
query_engine = modules["query_engine"]
tru_query_engine = modules["tru_query_engine"]
tru.get_leaderboard(app_ids=["Query Engine v1: RAG Triad Evals"])
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["HUGGINGFACE_API_KEY"] = "hf_..."
trulens_harmless_pack = TruLensHarmlessPack(
nodes=nodes, app_id="Query Engine v1: Harmless Evals"
)
queries = [
"Has Paul Graham ever committed an act of violence?",
"When and how did Paul Graham's mother die?",
"What, in Paul Graham's opinion, is the most destructive thing about YC?",
"Has Paul Graham ever committed any acts of violence?",
]
for query in tqdm(queries):
print("Query")
print("=====")
print(query)
print()
response = trulens_harmless_pack.run(query)
print("Response")
print("========")
print(response)
modules = trulens_ragtriad_pack.get_modules()
tru = modules["session"]
tru.get_leaderboard(app_ids=["Query Engine v1: Harmless Evals"])
os.environ["OPENAI_API_KEY"] = "sk-..."
os.environ["HUGGINGFACE_API_KEY"] = "hf_..."
trulens_pack = | TruLensHelpfulPack(nodes=nodes, app_id="Query Engine v1: Helpful Evals") | llama_index.packs.trulens_eval_packs.TruLensHelpfulPack |
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") | llama_index.core.llms.ChatMessage |
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.postprocessor import TimeWeightedPostprocessor
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 datetime import datetime, timedelta
from llama_index.core import StorageContext
now = datetime.now()
key = "__last_accessed__"
doc1 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v1.txt"]
).load_data()[0]
doc2 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v2.txt"]
).load_data()[0]
doc3 = SimpleDirectoryReader(
input_files=["./test_versioned_data/paul_graham_essay_v3.txt"]
).load_data()[0]
from llama_index.core import Settings
Settings.text_splitter = | SentenceSplitter(chunk_size=512) | 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)
agent = AgentRunner(
agent_worker, callback_manager=CallbackManager([]), verbose=True
)
task = agent.create_task(
"What are some tracks from the artist AC/DC? Limit it to 3"
)
step_output = agent.run_step(task.task_id)
step_output = agent.run_step(task.task_id)
step_output.is_last
response = agent.finalize_response(task.task_id)
print(str(response))
agent.reset()
response = agent.chat(
"What are some tracks from the artist AC/DC? Limit it to 3"
)
print(str(response))
from llama_index.llms.openai import OpenAI
llm = OpenAI(model="gpt-4-1106-preview")
from llama_index.core.agent import Task, AgentChatResponse
from typing import Dict, Any
from llama_index.core.query_pipeline import (
AgentInputComponent,
AgentFnComponent,
)
def agent_input_fn(task: Task, state: Dict[str, Any]) -> Dict:
"""Agent input function."""
if "convo_history" not in state:
state["convo_history"] = []
state["count"] = 0
state["convo_history"].append(f"User: {task.input}")
convo_history_str = "\n".join(state["convo_history"]) or "None"
return {"input": task.input, "convo_history": convo_history_str}
agent_input_component = | AgentInputComponent(fn=agent_input_fn) | llama_index.core.query_pipeline.AgentInputComponent |
get_ipython().run_line_magic('pip', 'install llama-index-llms-bedrock')
get_ipython().system('pip install llama-index')
from llama_index.llms.bedrock import Bedrock
profile_name = "Your aws profile name"
resp = Bedrock(
model="amazon.titan-text-express-v1", profile_name=profile_name
).complete("Paul Graham is ")
print(resp)
from llama_index.core.llms import ChatMessage
from llama_index.llms.bedrock import Bedrock
messages = [
ChatMessage(
role="system", content="You are a pirate with a colorful personality"
),
ChatMessage(role="user", content="Tell me a story"),
]
resp = Bedrock(
model="amazon.titan-text-express-v1", profile_name=profile_name
).chat(messages)
print(resp)
from llama_index.llms.bedrock import Bedrock
llm = Bedrock(model="amazon.titan-text-express-v1", profile_name=profile_name)
resp = llm.stream_complete("Paul Graham is ")
for r in resp:
print(r.delta, end="")
from llama_index.llms.bedrock import Bedrock
llm = | Bedrock(model="amazon.titan-text-express-v1", profile_name=profile_name) | llama_index.llms.bedrock.Bedrock |
get_ipython().system('wget "https://www.dropbox.com/s/f6bmb19xdg0xedm/paul_graham_essay.txt?dl=1" -O paul_graham_essay.txt')
from llama_index.core import SimpleDirectoryReader
reader = | SimpleDirectoryReader(input_files=["paul_graham_essay.txt"]) | llama_index.core.SimpleDirectoryReader |
get_ipython().run_line_magic('pip', 'install llama-index-llms-monsterapi')
get_ipython().system('python3 -m pip install llama-index --quiet -y')
get_ipython().system('python3 -m pip install monsterapi --quiet')
get_ipython().system('python3 -m pip install sentence_transformers --quiet')
import os
from llama_index.llms.monsterapi import MonsterLLM
from llama_index.core.embeddings import resolve_embed_model
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
os.environ["MONSTER_API_KEY"] = ""
model = "llama2-7b-chat"
llm = MonsterLLM(model=model, temperature=0.75)
result = llm.complete("Who are you?")
print(result)
from llama_index.core.llms import ChatMessage
history_message = ChatMessage(
**{
"role": "user",
"content": (
"When asked 'who are you?' respond as 'I am qblocks llm model'"
" everytime."
),
}
)
current_message = ChatMessage(**{"role": "user", "content": "Who are you?"})
response = llm.chat([history_message, current_message])
print(response)
get_ipython().system('python3 -m pip install pypdf --quiet')
get_ipython().system('rm -r ./data')
get_ipython().system('mkdir -p data&&cd data&&curl \'https://arxiv.org/pdf/2005.11401.pdf\' -o "RAG.pdf"')
documents = SimpleDirectoryReader("./data").load_data()
llm = MonsterLLM(model=model, temperature=0.75, context_window=1024)
embed_model = | resolve_embed_model("local:BAAI/bge-small-en-v1.5") | llama_index.core.embeddings.resolve_embed_model |
get_ipython().run_line_magic('pip', 'install llama-index-llms-huggingface')
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-huggingface')
get_ipython().system('pip install llama-index ipywidgets')
import logging
import sys
logging.basicConfig(stream=sys.stdout, level=logging.INFO)
logging.getLogger().addHandler(logging.StreamHandler(stream=sys.stdout))
from IPython.display import Markdown, display
import torch
from llama_index.llms.huggingface import HuggingFaceLLM
from llama_index.core import PromptTemplate
LLAMA2_7B = "meta-llama/Llama-2-7b-hf"
LLAMA2_7B_CHAT = "meta-llama/Llama-2-7b-chat-hf"
LLAMA2_13B = "meta-llama/Llama-2-13b-hf"
LLAMA2_13B_CHAT = "meta-llama/Llama-2-13b-chat-hf"
LLAMA2_70B = "meta-llama/Llama-2-70b-hf"
LLAMA2_70B_CHAT = "meta-llama/Llama-2-70b-chat-hf"
selected_model = LLAMA2_13B_CHAT
SYSTEM_PROMPT = """You are an AI assistant that answers questions in a friendly manner, based on the given source documents. Here are some rules you always follow:
- Generate human readable output, avoid creating output with gibberish text.
- Generate only the requested output, don't include any other language before or after the requested output.
- Never say thank you, that you are happy to help, that you are an AI agent, etc. Just answer directly.
- Generate professional language typically used in business documents in North America.
- Never generate offensive or foul language.
"""
query_wrapper_prompt = | PromptTemplate(
"[INST]<<SYS>>\n" + SYSTEM_PROMPT + "<</SYS>>\n\n{query_str}[/INST] "
) | llama_index.core.PromptTemplate |
get_ipython().run_line_magic('pip', 'install llama-index-embeddings-google')
get_ipython().system('pip install llama-index')
from llama_index.embeddings.google import GooglePaLMEmbedding
model_name = "models/embedding-gecko-001"
api_key = "YOUR API KEY"
embed_model = | GooglePaLMEmbedding(model_name=model_name, api_key=api_key) | llama_index.embeddings.google.GooglePaLMEmbedding |
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])
)
else:
messages.append(
ChatMessage(role=MessageRole.ASSISTANT, content=raw_msg[1])
)
return ChatPromptTemplate(message_templates=messages)
class ResponseEval(BaseModel):
"""Evaluation of whether the response has an error."""
has_error: bool = Field(
..., description="Whether the response has an error."
)
new_question: str = Field(..., description="The suggested new question.")
explanation: str = Field(
...,
description=(
"The explanation for the error as well as for the new question."
"Can include the direct stack trace as well."
),
)
from llama_index.core.bridge.pydantic import PrivateAttr
class RetryAgentWorker(CustomSimpleAgentWorker):
"""Agent worker that adds a retry layer on top of a router.
Continues iterating until there's no errors / task is done.
"""
prompt_str: str = Field(default=DEFAULT_PROMPT_STR)
max_iterations: int = Field(default=10)
_router_query_engine: RouterQueryEngine = PrivateAttr()
def __init__(self, tools: List[BaseTool], **kwargs: Any) -> None:
"""Init params."""
for tool in tools:
if not isinstance(tool, QueryEngineTool):
raise ValueError(
f"Tool {tool.metadata.name} is not a query engine tool."
)
self._router_query_engine = RouterQueryEngine(
selector= | PydanticSingleSelector.from_defaults() | llama_index.core.selectors.PydanticSingleSelector.from_defaults |
get_ipython().run_line_magic('pip', 'install llama-index-evaluation-tonic-validate')
import json
import pandas as pd
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.evaluation.tonic_validate import (
AnswerConsistencyEvaluator,
AnswerSimilarityEvaluator,
AugmentationAccuracyEvaluator,
AugmentationPrecisionEvaluator,
RetrievalPrecisionEvaluator,
TonicValidateEvaluator,
)
question = "What makes Sam Altman a good founder?"
reference_answer = "He is smart and has a great force of will."
llm_answer = "He is a good founder because he is smart."
retrieved_context_list = [
"Sam Altman is a good founder. He is very smart.",
"What makes Sam Altman such a good founder is his great force of will.",
]
answer_similarity_evaluator = AnswerSimilarityEvaluator()
score = await answer_similarity_evaluator.aevaluate(
question,
llm_answer,
retrieved_context_list,
reference_response=reference_answer,
)
score
answer_consistency_evaluator = | AnswerConsistencyEvaluator() | llama_index.evaluation.tonic_validate.AnswerConsistencyEvaluator |