milvus-blog-answer-relevance.py

# Question/answer relevance between overall question and answer.
f_qa_relevance = Feedback(openai_gpt4.relevance_with_cot_reasons, name = "Answer Relevance").on_input_output()

milvus-blog-context-relevance.py

# Question/statement relevance between question and each context chunk.
f_context_relevance = Feedback(openai_gpt4.qs_relevance_with_cot_reasons, name = "Context Relevance").on_input().on(
    TruLlama.select_source_nodes().node.text
).aggregate(np.max)

milvus-blog-evaluators-init.py

# Initialize OpenAI-based feedback function collection class:
openai_gpt4 = feedback.OpenAI()

milvus-blog-groundedness.py

# Define groundedness
grounded = Groundedness(groundedness_provider=openai_gpt4)
f_groundedness = Feedback(grounded.groundedness_measure_with_cot_reasons, name = "Groundedness").on(
    TruLlama.select_source_nodes().node.text # context
).on_output().aggregate(grounded.grounded_statements_aggregator)

milvus-blog-index.py

vector_store = MilvusVectorStore(index_params={
        "index_type": index_param,
        "metric_type": "L2"
        },
        search_params={"nprobe": 20},
        overwrite=True)
llm = OpenAI(model="gpt-3.5-turbo")
storage_context = StorageContext.from_defaults(vector_store = vector_store)
service_context = ServiceContext.from_defaults(embed_model = embed_model, llm = llm, chunk_size=chunk_size)
index = VectorStoreIndex.from_documents(wiki_docs,
        service_context=service_context,
        storage_context=storage_context)

milvus-blog-load-data.py

from llama_index import WikipediaReader

cities = [
    "Los Angeles", "Houston", "Honolulu", "Tucson", "Mexico City", 
    "Cincinatti", "Chicago"
]

wiki_docs = []
for city in cities:
    try:
        doc = WikipediaReader().load_data(pages=[city])
        wiki_docs.extend(doc)
    except Exception as e:
        print(f"Error loading page for city {city}: {e}")

milvus-blog-query-engine.py

query_engine = index.as_query_engine(similarity_top_k = top_k)

milvus-blog-tenacity.py

@retry(stop=stop_after_attempt(10), wait=wait_exponential(multiplier=1, min=4, max=10))
def call_tru_query_engine(prompt):
    return tru_query_engine.query(prompt)
for prompt in test_prompts:
    call_tru_query_engine(prompt)

milvus-blog.py

tru_query_engine = TruLlama(query_engine,
                feedbacks=[f_groundedness, f_qa_relevance, f_qs_relevance],
                metadata={
                   'index_param':index_param,
                   'embed_model':embed_model_name,
                   'top_k':top_k,
                   'chunk_size':chunk_size
                   })