Create app.py

app.py

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+from llama_index.core.base.embeddings.base import similarity
+from llama_index.llms.ollama import Ollama
+from llama_index.core import VectorStoreIndex, SimpleDirectoryReader, Settings
+from llama_index.core import StorageContext
+from llama_index.vector_stores.chroma import ChromaVectorStore
+from llama_index.embeddings.huggingface import HuggingFaceEmbedding
+from llama_index.core import load_index_from_storage
+import os
+from dotenv import load_dotenv
+from llama_index.core.callbacks import CallbackManager, LlamaDebugHandler, CBEventType
+from llama_index.core.node_parser import SentenceSplitter
+from llama_index.core.postprocessor import SimilarityPostprocessor
+from llama_index.llms.openai import OpenAI
+from llama_index.llms.groq import Groq
+from llama_parse import LlamaParse
+from llama_index.core.indices.query.query_transform.base import HyDEQueryTransform
+from llama_index.core.query_engine import TransformQueryEnginef
+from llama_index.core.extractors import (
+    SummaryExtractor,
+    QuestionsAnsweredExtractor,
+)
+from llama_index.core.schema import MetadataMode
+from llama_index.core.ingestion import IngestionPipeline
+
+load_dotenv()
+# OPENAI_API_KEY = os.getenv('OPENAI_API_KEY')
+GROQ_API_KEY = os.getenv('GROQ_API_KEY')
+LLAMAINDEX_API_KEY = os.getenv('LLAMAINDEX_API_KEY')
+
+llm = Groq(model="llama3-70b-8192")#"llama3-8b-8192")
+Settings.llm = llm
+
+# set up callback manager
+llama_debug = LlamaDebugHandler(print_trace_on_end=True)
+callback_manager = CallbackManager([llama_debug])
+Settings.callback_manager = callback_manager
+
+# converting documents into embeddings and indexing
+embed_model = HuggingFaceEmbedding(model_name="BAAI/bge-small-en-v1.5")
+Settings.embed_model = embed_model
+
+# create splitter
+splitter = SentenceSplitter(chunk_size=1024, chunk_overlap=20)
+Settings.transformations = [splitter]
+
+if os.path.exists("./vectordb"):
+    storage_context = StorageContext.from_defaults(persist_dir="./vectordb")
+    index = load_index_from_storage(storage_context)
+else:
+    parser = LlamaParse(
+        api_key=LLAMAINDEX_API_KEY, 
+        result_type="markdown",  # "markdown" and "text" are available
+        verbose=True,
+    )
+    filename_fn = lambda filename: {"file_name": filename}
+    required_exts = [".pdf",".docx"]
+    file_extractor = {".pdf": parser}
+    reader = SimpleDirectoryReader(
+        "./data", 
+        file_extractor=file_extractor,
+        required_exts=required_exts,
+        recursive=True,
+        file_metadata=filename_fn
+    )
+    documents = reader.load_data()
+    for doc in documents:
+        doc.text = str(doc.metadata) +' '+ doc.text 
+    print("index creating with `%d` documents", len(documents))
+    # index = VectorStoreIndex.from_documents(documents, embed_model=embed_model, text_splitter=splitter)
+    extractor_llm = Groq(model="llama3-70b-8192", temperature=0.1, max_tokens=512) #OpenAI(temperature=0.1, model="gpt-3.5-turbo", max_tokens=512)
+
+    node_parser = SentenceSplitter(chunk_size=512, chunk_overlap=20)
+    extractors = [
+        SummaryExtractor(summaries=["prev", "self", "next"], llm=extractor_llm),
+        QuestionsAnsweredExtractor(
+            questions=3, llm=extractor_llm, metadata_mode=MetadataMode.EMBED
+        ),
+    ]
+    nodes = node_parser.get_nodes_from_documents(documents)
+    nodes_extract_ls = []
+    print('extracting from:', len(nodes), ' nodes.')
+    import time
+    batch_size=5
+    for i in range(0, len(nodes), batch_size):
+        print(i)
+        nodes_batch_raw = nodes[i:i+batch_size]
+        try:
+            pipeline = IngestionPipeline(transformations=[node_parser, *extractors])
+            nodes_batch = pipeline.run(nodes=nodes_batch_raw, in_place=False, show_progress=True)
+            nodes_extract_ls.append(nodes_batch)
+        except:
+            time.sleep(30) # api call limit reach, sleep 30 seconds before trying
+    nodes_extract = [
+        x
+        for xs in nodes_extract_ls
+        for x in xs
+    ]
+    index = VectorStoreIndex(nodes_extract)
+
+    index.storage_context.persist(persist_dir="./vectordb")
+
+
+query_engine = index.as_query_engine(
+    similarity_top_k=5,
+    #node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.7)],
+    verbose=True,
+)
+
+# query_engine = index.as_query_engine(
+#     similarity_top_k=10,
+#     #node_postprocessors=[SimilarityPostprocessor(similarity_cutoff=0.7)],
+#     verbose=True,
+# )
+
+# # hyde: transform query into a temporary doc, and use doc to doc similarity match
+# hyde = HyDEQueryTransform(include_original=True)
+# hyde_query_engine = TransformQueryEngine(query_engine, query_transform=hyde)
+
+import gradio as gr
+
+def retreive(question):
+    qns_w_source = "Answer the following question: " + question + " Followed by providing the page and file name of the source document as well, thank you!"
+    streaming_response = query_engine.query(qns_w_source)
+    # sources = streaming_response.get_formatted_sources(length=5000)
+    return str(streaming_response) #+ "\n" + str(sources)
+
+demo = gr.Interface(fn=retreive, inputs="textbox", outputs="textbox")
+    
+demo.launch(share=True)  # Share your demo with just 1 extra parameter 🚀