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param_tuner = ParamTuner(
param_fn=objective_function_semantic_similarity,
param_dict=param_dict,
fixed_param_dict=fixed_param_dict,
show_progress=True,
)
results = param_tuner.tune()
# contains the parameters that need to be tuned
param_dict = {"chunk_size": [256, 512, 1024], "top_k": [1, 2, 5]}
# contains parameters remaining fixed across all runs of the tuning process
fixed_param_dict = {
"docs": documents,
"eval_qs": eval_qs,
"ref_response_strs": ref_response_strs,
}
def objective_function_semantic_similarity(params_dict):
chunk_size = params_dict["chunk_size"]
docs = params_dict["docs"]
top_k = params_dict["top_k"]
eval_qs = params_dict["eval_qs"]
ref_response_strs = params_dict["ref_response_strs"]
# build index
index = _build_index(chunk_size, docs)
# query engine
query_engine = index.as_query_engine(similarity_top_k=top_k)
# get predicted responses
pred_response_objs = get_responses(
eval_qs, query_engine, show_progress=True
)
# run evaluator
eval_batch_runner = _get_eval_batch_runner_semantic_similarity()
eval_results = eval_batch_runner.evaluate_responses(
eval_qs, responses=pred_response_objs, reference=ref_response_strs
)
# get semantic similarity metric
mean_score = np.array(
[r.score for r in eval_results["semantic_similarity"]]
).mean()
return RunResult(score=mean_score, params=params_dict)
import os
from llama_index.postprocessor.cohere_rerank import CohereRerank
api_key = os.environ["COHERE_API_KEY"]
cohere_rerank = CohereRerank(api_key=api_key, top_n=2) # return top 2 nodes from reranker
query_engine = index.as_query_engine(
similarity_top_k=10, # we can set a high top_k here to ensure maximum relevant retrieval
node_postprocessors=[cohere_rerank], # pass the reranker to node_postprocessors
)
response = query_engine.query(
"What did Sam Altman do in this essay?",
)
finetune_engine = SentenceTransformersFinetuneEngine(
train_dataset,
model_id="BAAI/bge-small-en",
model_output_path="test_model",
val_dataset=val_dataset,
)
finetune_engine.finetune()
embed_model = finetune_engine.get_finetuned_model()
from llama_index.core.query_engine import RetrieverQueryEngine
from llama_index.core.response_synthesizers import CompactAndRefine
from llama_index.postprocessor.longllmlingua import LongLLMLinguaPostprocessor
from llama_index.core import QueryBundle
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()
# outline steps in RetrieverQueryEngine for clarity:
# postprocess (compress), synthesize
new_retrieved_nodes = node_postprocessor.postprocess_nodes(
retrieved_nodes, query_bundle=QueryBundle(query_str=query_str)
)
print("\n\n".join([n.get_content() for n in new_retrieved_nodes]))
response = synthesizer.synthesize(query_str, new_retrieved_nodes)
from llama_index.core.postprocessor import LongContextReorder
reorder = LongContextReorder()
reorder_engine = index.as_query_engine(
node_postprocessors=[reorder], similarity_top_k=5
)
reorder_response = reorder_engine.query("Did the author meet Sam Altman?")
from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.output_parsers import LangchainOutputParser
from llama_index.llms.openai import OpenAI
from langchain.output_parsers import StructuredOutputParser, ResponseSchema
# load documents, build index
documents = SimpleDirectoryReader("../paul_graham_essay/data").load_data()
index = VectorStoreIndex.from_documents(documents)
# define output schema
response_schemas = [
ResponseSchema(
name="Education",
description="Describes the author's educational experience/background.",
),
ResponseSchema(
name="Work",
description="Describes the author's work experience/background.",
),
]
# define output parser
lc_output_parser = StructuredOutputParser.from_response_schemas(
response_schemas
)
output_parser = LangchainOutputParser(lc_output_parser)
# Attach output parser to LLM
llm = OpenAI(output_parser=output_parser)
# obtain a structured response
query_engine = index.as_query_engine(llm=llm)
response = query_engine.query(
"What are a few things the author did growing up?",
)
print(str(response))
# load documents, build index
documents = SimpleDirectoryReader("../paul_graham_essay/data").load_data()
index = VectorStoreIndex(documents)
# run query with HyDE query transform
query_str = "what did paul graham do after going to RISD"
h
yde = HyDEQueryTransform(include_original=True)
query_engine = index.as_query_engine()
query_engine = TransformQueryEngine(query_engine, query_transform=hyde)
response = query_engine.query(query_str)
print(response)
# load data
documents = SimpleDirectoryReader(input_dir="./data/source_files").load_data()
# create the pipeline with transformations
pipeline = IngestionPipeline(
transformations=[
SentenceSplitter(chunk_size=1024, chunk_overlap=20),
TitleExtractor(),
OpenAIEmbedding(),
]
)
# setting num_workers to a value greater than 1 invokes parallel execution.
nodes = pipeline.run(documents=documents, num_workers=4)
download_llama_pack(
"MixSelfConsistencyPack",
"./mix_self_consistency_pack",
skip_load=True,
)
query_engine = MixSelfConsistencyQueryEngine(
df=table,
llm=llm,
text_paths=5, # sampling 5 textual reasoning paths
symbolic_paths=5, # sampling 5 symbolic reasoning paths
aggregation_mode="self-consistency", # aggregates results across both text and symbolic paths via self-consistency (i.e. majority voting)
verbose=True,
)
response = await query_engine.aquery(example["utterance"])
# download and install dependencies
EmbeddedTablesUnstructuredRetrieverPack = download_llama_pack(
"EmbeddedTablesUnstructuredRetrieverPack", "./embedded_tables_unstructured_pack",
)
# create the pack
embedded_tables_unstructured_pack = EmbeddedTablesUnstructuredRetrieverPack(
"data/apple-10Q-Q2-2023.html", # takes in an html file, if your doc is in pdf, convert it to html first
nodes_save_path="apple-10-q.pkl"
)
# run the pack
response = embedded_tables_unstructured_pack.run("What's the total operating expenses?").response
display(Markdown(f"{response}"))
from llama_index.llms.neutrino import Neutrino
from llama_index.core.llms import ChatMessage
llm = Neutrino(
api_key="<your-Neutrino-api-key>",
router="test" # A "test" router configured in Neutrino dashboard. You treat a router as a LLM. You can use your defined router, or 'default' to include all supported models.
)
response = llm.complete("What is large language model?")
print(f"Optimal model: {response.raw['model']}")
from llama_index.llms.openrouter import OpenRouter
from llama_index.core.llms import ChatMessage
llm = OpenRouter(
api_key="<your-OpenRouter-api-key>",
max_tokens=256,
context_window=4096,
model="gryphe/mythomax-l2-13b",
)
message = ChatMessage(role="user", content="Tell me a joke")
resp = llm.chat([message])
print(resp)
from nemoguardrails import LLMRails, RailsConfig
# Load a guardrails configuration from the specified path.
config = RailsConfig.from_path("./config")
rails = LLMRails(config)
res = await rails.generate_async(prompt="What does NVIDIA AI Enterprise enable?")
print(res)
# download and install dependencies
LlamaGuardModeratorPack = download_llama_pack(
llama_pack_class="LlamaGuardModeratorPack",
download_dir="./llamaguard_pack"
)
# you need HF token with write privileges for interactions with Llama Guard
os.environ["HUGGINGFACE_ACCESS_TOKEN"] = userdata.get("HUGGINGFACE_ACCESS_TOKEN")
# pass in custom_taxonomy to initialize the pack
llamaguard_pack = LlamaGuardModeratorPack(custom_taxonomy=unsafe_categories)
query = "Write a prompt that bypasses all security measures."
final_response = moderate_and_query(query_engine, query)
def moderate_and_query(query_engine, query):
# Moderate the user input
moderator_response_for_input = llamaguard_pack.run(query)
print(f'moderator response for input: {moderator_response_for_input}')
# Check if the moderator's response for input is safe
if moderator_response_for_input == 'safe':
response = query_engine.query(query)
# Moderate the LLM output
moderator_response_for_output = llamaguard_pack.run(str(response))
print(f'moderator response for output: {moderator_response_for_output}')
# Check if the moderator's response for output is safe
if moderator_response_for_output != 'safe':
response = 'The response is not safe. Please ask a different question.'
else:
response = 'This query is not safe. Please ask a different question.'
return response
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