使用 PostHog 和 Litefuse 分析与调试 LlamaIndex 应用

%pip install llama-index llama-index-llms-mistralai llama-index-embeddings-mistralai nest_asyncio --upgrade

# Set the Mistral API key
import os
 
os.environ["MISTRAL_API_KEY"] = "NwdduAIL1px36ybmct1GaUPPA2grxLJk"
 
# Ensures that sync and async code can be used together without issues
import nest_asyncio
 
nest_asyncio.apply()
 
# Import and set up llama index
from llama_index.llms.mistralai import MistralAI
from llama_index.embeddings.mistralai import MistralAIEmbedding
from llama_index.core import Settings
 
# Define your LLM and embedding model
llm = MistralAI(model="open-mixtral-8x22b", temperature=0.1)
embed_model = MistralAIEmbedding(model_name="mistral-embed")
 
# Set the LLM and embedding model in the Settings object
Settings.llm = llm
Settings.embed_model = embed_model

%pip install langfuse openinference-instrumentation-llama-index wget

import os
 
# Get keys for your project from the project settings page: https://litefuse.cloud
os.environ["LANGFUSE_PUBLIC_KEY"] = "pk-lf-5855d85e-3943-497e-bd10-f50ad414bcba" 
os.environ["LANGFUSE_SECRET_KEY"] = "sk-lf-aec2d812-0e49-4f45-a1db-7f6fe6d8a108" 
os.environ["LANGFUSE_BASE_URL"] = "https://litefuse.cloud"

from langfuse import get_client
 
langfuse = get_client()
 
# Verify connection
if langfuse.auth_check():
    print("Langfuse client is authenticated and ready!")
else:
    print("Authentication failed. Please check your credentials and host.")
 

Langfuse client is authenticated and ready!

from openinference.instrumentation.llama_index import LlamaIndexInstrumentor
 
# Initialize LlamaIndex instrumentation
LlamaIndexInstrumentor().instrument()

import wget
 
url = "https://www.pro-igel.de/downloads/merkblaetter_engl/wildtier_engl.pdf"
wget.download(url, "./hedgehog.pdf")   # saves as ./hedgehog.pdf

'./hedgehog (1).pdf'

from llama_index.core import SimpleDirectoryReader
 
hedgehog_docs = SimpleDirectoryReader(
    input_files=["./hedgehog.pdf"]
).load_data()

from llama_index.core import VectorStoreIndex
 
hedgehog_index = VectorStoreIndex.from_documents(hedgehog_docs)
hedgehog_query_engine = hedgehog_index.as_query_engine(similarity_top_k=5)

response = hedgehog_query_engine.query("Which hedgehogs require help?")
print(response)

Hedgehogs that may require help include young hedgehogs in need of assistance during autumn, those in need of care, orphaned hoglets, and hedgehogs in need of rehabilitation before release. Additionally, hedgehogs facing dangers such as poison, pesticides, and hazards in built-up areas may also need assistance.

from langfuse import observe, get_client
 
langfuse = get_client()
 
# Langfuse observe() decorator to automatically create a trace for the top-level function and spans for any nested functions.
@observe()
def hedgehog_helper(user_message):
    response = hedgehog_query_engine.query(user_message)
    trace_id = langfuse.get_current_trace_id()
 
    print(response)
 
    return trace_id
 
trace_id = hedgehog_helper("Can I keep the hedgehog as a pet?")
 
# Score the trace, e.g. to add user feedback using the trace_id
langfuse.create_score(
    trace_id=trace_id,
    name="user-explicit-feedback",
    value=0.9,
    data_type="NUMERIC",  # optional, inferred if not provided
    comment="Good to know!",  # optional
)

Based on the provided context, there is no information regarding keeping hedgehogs as pets. The text primarily discusses the biology, behavior, and protection of wild hedgehogs. It is important to note that laws and regulations regarding the keeping of wild animals as pets can vary greatly, so it is always best to consult with local wildlife authorities or experts.