corpus_query.py

from llama_index.core.vector_stores import SimpleVectorStore
from llama_index.core.storage.storage_context import StorageContext
from llama_index.core.storage.index_store import SimpleIndexStore
from llama_index.core.storage.docstore import SimpleDocumentStore
from llama_index.core import load_index_from_storage
from llama_index.core import PromptTemplate
from llama_index.core import get_response_synthesizer
from llama_index.core.retrievers import VectorIndexRetriever
from llama_index.core.query_engine import RetrieverQueryEngine

STORE_DIR = "openworm.ai_store"
SOURCE_DOCUMENT = "source document"

LLM_GPT4o = "GPT4o"


def print_(text):
    print(text)

from openworm_ai.graphrag.GraphRAG_test import load_index
'''
def load_index(model):
    OLLAMA_MODEL = model.replace("Ollama:", "") if model is not LLM_GPT4o else None

    print_("Creating a storage context for %s" % model)

    STORE_SUBFOLDER = (
        "" if OLLAMA_MODEL is None else "/%s" % OLLAMA_MODEL.replace(":", "_")
    )

    # index_reloaded =SimpleIndexStore.from_persist_dir(persist_dir=INDEX_STORE_DIR)
    storage_context = StorageContext.from_defaults(
        docstore=SimpleDocumentStore.from_persist_dir(
            persist_dir=STORE_DIR + STORE_SUBFOLDER
        ),
        vector_store=SimpleVectorStore.from_persist_dir(
            persist_dir=STORE_DIR + STORE_SUBFOLDER
        ),
        index_store=SimpleIndexStore.from_persist_dir(
            persist_dir=STORE_DIR + STORE_SUBFOLDER
        ),
    )
    print_("Reloading index for %s" % model)

    index_reloaded = load_index_from_storage(storage_context)

    return index_reloaded'''


def get_query_engine(index_reloaded, model, similarity_top_k=4):
    OLLAMA_MODEL = model.replace("Ollama:", "") if model is not LLM_GPT4o else None

    print_("Creating query engine for %s" % model)

    # Based on: https://docs.llamaindex.ai/en/stable/examples/customization/prompts/completion_prompts/

    text_qa_template_str = (
        "Context information is"
        " below.\n---------------------\n{context_str}\n---------------------\nUsing"
        " both the context information and also using your own knowledge, answer"
        " the question: {query_str}\nIf the context isn't helpful, you can also"
        " answer the question on your own.\n"
    )
    text_qa_template = PromptTemplate(text_qa_template_str)

    refine_template_str = (
        "The original question is as follows: {query_str}\nWe have provided an"
        " existing answer: {existing_answer}\nWe have the opportunity to refine"
        " the existing answer (only if needed) with some more context"
        " below.\n------------\n{context_msg}\n------------\nUsing both the new"
        " context and your own knowledge, update or repeat the existing answer.\n"
    )
    refine_template = PromptTemplate(refine_template_str)

    # create a query engine for the index
    if OLLAMA_MODEL is not None:
        llm = Ollama(model=OLLAMA_MODEL)

        ollama_embedding = OllamaEmbedding(
            model_name=OLLAMA_MODEL,
        )

        query_engine = index_reloaded.as_query_engine(
            llm=llm,
            text_qa_template=text_qa_template,
            refine_template=refine_template,
            embed_model=ollama_embedding,
        )

        query_engine.retriever.similarity_top_k = similarity_top_k

    else:  # use OpenAI...
        # configure retriever
        retriever = VectorIndexRetriever(
            index=index_reloaded,
            similarity_top_k=similarity_top_k,
        )

        # configure response synthesizer
        response_synthesizer = get_response_synthesizer(
            response_mode="refine",
            text_qa_template=text_qa_template,
            refine_template=refine_template,
        )

        query_engine = RetrieverQueryEngine(
            retriever=retriever,
            response_synthesizer=response_synthesizer,
        )

    return query_engine


llm_ver = LLM_GPT4o
index_reloaded = load_index(llm_ver)
query_engine = get_query_engine(index_reloaded, llm_ver)


def process_query(query, model=llm_ver):
    response = query_engine.query(query)

    response_text = str(response)

    if "<think>" in response_text:  # Give deepseek a fighting chance...
        response_text = (
            response_text[0 : response_text.index("<think>")]
            + response_text[response_text.index("</think>") + 8 :]
        )

    metadata = response.metadata
    cutoff = 0.2
    files_used = []
    for sn in response.source_nodes:
        # print(sn)
        print(sn.metadata)
        sd = sn.metadata["source document"]
        if "et_al_" in sd:
            sd = sd.replace("WormAtlas Handbook:", "Paper: ")

        if sd not in files_used:
            if len(files_used) == 0 or sn.score >= cutoff:
                files_used.append(f"{sd} (score: {sn.score})")

    file_info = ",\n   ".join(files_used)
    print_(f"""
===============================================================================
QUERY: {query}
MODEL: {model}
-------------------------------------------------------------------------------
RESPONSE: {response_text}
SOURCES: 
   {file_info}
===============================================================================
""")

    return response_text, metadata


def run_query(query):
    response_text, metadata = process_query(query, "GPT4o")
    files_used = []

    for k in metadata:
        v = metadata[k]
        if SOURCE_DOCUMENT in v:
            if v[SOURCE_DOCUMENT] not in files_used:
                sd = v[SOURCE_DOCUMENT]
                if "et_al_" in sd:
                    sd = sd.replace("WormAtlas Handbook:", "Paper: ")
                files_used.append(sd)

    srcs = "\n- ".join(files_used)
    answer = f"""{response_text}

SOURCES OF ANSWER: 
 - {srcs}"""

    return answer


if __name__ == "__main__":
    print("Running queries")

    queries = [
        "What are the main types of neurons and muscles in the C. elegans pharynx?",
        "Tell me about the egg laying apparatus",
        "Tell me about locomotion in C. elegans",
    ]

    for query in queries:
        print("-------------------")
        print("Q: %s" % query)
        # answer = docs.query(query)
        answer = run_query(query)

        print("A: %s" % answer)