【译】用于构建高级RAG的速查表和一些配方

from llama_index import SimpleDirectoryReader, VectorStoreIndex

# load data
documents = SimpleDirectoryReader(input_dir="...").load_data()

# build VectorStoreIndex that takes care of chunking documents
# and encoding chunks to embeddings for future retrieval
index = VectorStoreIndex.from_documents(documents=documents)

# The QueryEngine class is equipped with the generator
# and facilitates the retrieval and generation steps
query_engine = index.as_query_engine()

# Use your Default RAG
response = query_engine.query("A user's query")

from llama_index import ServiceContext
from llama_index.param_tuner.base import ParamTuner, RunResult
from llama_index.evaluation import SemanticSimilarityEvaluator, BatchEvalRunner

### Recipe
### Perform hyperparameter tuning as in traditional ML via grid-search
### 1. Define an objective function that ranks different parameter combos
### 2. Build ParamTuner object
### 3. Execute hyperparameter tuning with ParamTuner.tune()

# 1. Define objective function
def objective_function(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 RAG pipeline
    index = _build_index(chunk_size, docs)  # helper function not shown here
    query_engine = index.as_query_engine(similarity_top_k=top_k)

      # perform inference with RAG pipeline on a provided questions `eval_qs`
    pred_response_objs = get_responses(
        eval_qs, query_engine, show_progress=True
    )

    # perform evaluations of predictions by comparing them to reference
    # responses `ref_response_strs`
    evaluator = SemanticSimilarityEvaluator(...)
    eval_batch_runner = BatchEvalRunner(
        {"semantic_similarity": evaluator}, workers=2, show_progress=True
    )
    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)

# 2. Build ParamTuner object
param_dict = {"chunk_size": [256, 512, 1024]} # params/values to search over
fixed_param_dict = { # fixed hyperparams
  "top_k": 2,
    "docs": docs,
    "eval_qs": eval_qs[:10],
    "ref_response_strs": ref_response_strs[:10],
}
param_tuner = ParamTuner(
    param_fn=objective_function,
    param_dict=param_dict,
    fixed_param_dict=fixed_param_dict,
    show_progress=True,
)

# 3. Execute hyperparameter search
results = param_tuner.tune()
best_result = results.best_run_result
best_chunk_size = results.best_run_result.params["chunk_size"]

from llama_index import SimpleDirectoryReader, VectorStoreIndex
from llama_index.node_parser import SentenceSplitter
from llama_index.schema import IndexNode

### Recipe
### Build a recursive retriever that retrieves using small chunks
### but passes associated larger chunks to the generation stage

# load data
documents = SimpleDirectoryReader(
  input_file="some_data_path/llama2.pdf"
).load_data()

# build parent chunks via NodeParser
node_parser = SentenceSplitter(chunk_size=1024)
base_nodes = node_parser.get_nodes_from_documents(documents)

# define smaller child chunks
sub_chunk_sizes = [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)
    # also add original node to node
    original_node = IndexNode.from_text_node(base_node, base_node.node_id)
    all_nodes.append(original_node)

# define a VectorStoreIndex with all of the nodes
vector_index_chunk = VectorStoreIndex(
    all_nodes, service_context=service_context
)
vector_retriever_chunk = vector_index_chunk.as_retriever(similarity_top_k=2)

# build RecursiveRetriever
all_nodes_dict = {n.node_id: n for n in all_nodes}
retriever_chunk = RecursiveRetriever(
    "vector",
    retriever_dict={"vector": vector_retriever_chunk},
    node_dict=all_nodes_dict,
    verbose=True,
)

# build RetrieverQueryEngine using recursive_retriever
query_engine_chunk = RetrieverQueryEngine.from_args(
    retriever_chunk, service_context=service_context
)

# perform inference with advanced RAG (i.e. query engine)
response = query_engine_chunk.query(
    "Can you tell me about the key concepts for safety finetuning"
)

from llama_index import SimpleDirectoryReader, VectorStoreIndex
from llama_index.query_engine import RetrieverQueryEngine
from llama_index.postprocessor import LongLLMLinguaPostprocessor

### Recipe
### Define a Postprocessor object, here LongLLMLinguaPostprocessor
### Build QueryEngine that uses this Postprocessor on retrieved docs

# Define Postprocessor
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
    },
)

# Define VectorStoreIndex
documents = SimpleDirectoryReader(input_dir="...").load_data()
index = VectorStoreIndex.from_documents(documents)

# Define QueryEngine
retriever = index.as_retriever(similarity_top_k=2)
retriever_query_engine = RetrieverQueryEngine.from_args(
    retriever, node_postprocessors=[node_postprocessor]
)

# Used your advanced RAG
response = retriever_query_engine.query("A user query")

import os
from llama_index import SimpleDirectoryReader, VectorStoreIndex
from llama_index.postprocessor.cohere_rerank import CohereRerank
from llama_index.postprocessor import LongLLMLinguaPostprocessor

### Recipe
### Define a Postprocessor object, here CohereRerank
### Build QueryEngine that uses this Postprocessor on retrieved docs

# Build CohereRerank post retrieval processor
api_key = os.environ["COHERE_API_KEY"]
cohere_rerank = CohereRerank(api_key=api_key, top_n=2)

# Build QueryEngine (RAG) using the post processor
documents = SimpleDirectoryReader("./data/paul_graham/").load_data()
index = VectorStoreIndex.from_documents(documents=documents)
query_engine = index.as_query_engine(
    similarity_top_k=10,
    node_postprocessors=[cohere_rerank],
)

# Use your advanced RAG
response = query_engine.query(
    "What did Sam Altman do in this essay?"
)

from llama_index.llms import OpenAI
from llama_index.query_engine import FLAREInstructQueryEngine
from llama_index import (
    VectorStoreIndex,
    SimpleDirectoryReader,
    ServiceContext,
)
### Recipe
### Build a FLAREInstructQueryEngine which has the generator LLM play
### a more active role in retrieval by prompting it to elicit retrieval
### instructions on what it needs to answer the user query.

# Build FLAREInstructQueryEngine
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents)
index_query_engine = index.as_query_engine(similarity_top_k=2)
service_context = ServiceContext.from_defaults(llm=OpenAI(model="gpt-4"))
flare_query_engine = FLAREInstructQueryEngine(
    query_engine=index_query_engine,
    service_context=service_context,
    max_iterations=7,
    verbose=True,
)

# Use your advanced RAG
response = flare_query_engine.query(
    "Can you tell me about the author's trajectory in the startup world?"
)

from llama_index.query_engine import RetryQueryEngine
from llama_index.evaluation import RelevancyEvaluator

### Recipe
### Build a RetryQueryEngine which performs retrieval-generation cycles
### until it either achieves a passing evaluation or a max number of
### cycles has been reached

# Build RetryQueryEngine
documents = SimpleDirectoryReader("./data/paul_graham").load_data()
index = VectorStoreIndex.from_documents(documents)
base_query_engine = index.as_query_engine()
query_response_evaluator = RelevancyEvaluator() # evaluator to critique
                                                # retrieval-generation cycles
retry_query_engine = RetryQueryEngine(
    base_query_engine, query_response_evaluator
)

# Use your advanced rag
retry_response = retry_query_engine.query("A user query")