[Question]: how to configure self-signed certificate for ingestion pipeline with OpenAI API about llama_index HOT 1 CLOSED

from llama_index.llms.openai import OpenAI
from llama_index.core import Settings
from llama_index.embeddings.openai import OpenAIEmbedding
import httpx

from llama_index.core import VectorStoreIndex, SimpleDirectoryReader
from llama_index.core.node_parser import SentenceSplitter
from llama_index.core.extractors import TitleExtractor
from llama_index.core.ingestion import IngestionPipeline
from llama_index.vector_stores.chroma import ChromaVectorStore
import chromadb
from llama_index.readers.file import PyMuPDFReader

# Configure HTTP clients with self-signed certificate
http_client = httpx.Client(verify="<path/to/certificate>")
async_http_client = httpx.AsyncClient(verify="<path/to/certificate>")

# Set the default LLM and embedding model with the configured HTTP clients
Settings.llm = OpenAI(
    http_client=http_client,
    async_http_client=async_http_client,
)

Settings.embed_model = OpenAIEmbedding(
    http_client=http_client,
)

# Load documents and create an index
documents = SimpleDirectoryReader("path/to/folder").load_data()
index = VectorStoreIndex.from_documents(documents, show_progress=True)

# Query the index
query_engine = index.as_query_engine()
response = query_engine.query("<my query>")
print(response)

# Configure Chroma vector store
chroma_client = chromadb.EphemeralClient()
chroma_collection = chroma_client.create_collection("ingestion_demo")
vector_store = ChromaVectorStore(chroma_collection=chroma_collection)

# Create an IngestionPipeline with the configured OpenAIEmbedding
pipeline = IngestionPipeline(
    transformations=[
        SentenceSplitter(chunk_size=300, chunk_overlap=100), 
        TitleExtractor(),  
        OpenAIEmbedding(http_client=http_client, async_http_client=async_http_client),  # Pass the configured HTTP clients
    ],
    vector_store=vector_store,
)

# Load documents and run the pipeline
documents = SimpleDirectoryReader(
    "<path/to/folder>",
    required_exts=[".pdf"],
    file_extractor={".pdf": PyMuPDFReader()},
).load_data()

pipeline.run(documents=documents, show_progress=True)
index = VectorStoreIndex.from_vector_store(vector_store, show_progress=True)
vector_retriever = index.as_retriever(similarity_top_k=2)