Using MongoDBLoader for Mistral 7B LLM model

Victor_Iglesias_Gonzalez · 2024-03-07T10:33:34.971Z

0

I’ve been working lately with MongoDB VectorStore and MongoDb Atlas Search Index for storing data for my LLM model Mistral 7B.

I’ve been loading simple data files like small *txt files or PDFs. However, my main approach is to provide my LLM with a MongoDB database to ask questions about it.

So, I have tried with the Langchain MongoDBLoader but I did not receive the results I expected.

First of all, am I loading correctly the database? Do I have to make changes on my search_index? I beleive the error is in the retriever, but I just don’t know how to fix it, Is there any other method to create a retriever?

Thank you guys.

Here is the loader code:

client = pymongo.MongoClient("mongodb+srv://xxxxxx:xxxxxx@prueba1.hdlxqaf.mongodb.net/")
dbName = "LLM2"
collectionName = "Mistral2"
collection = client[dbName][collectionName]

loader = MongodbLoader(
    connection_string="mongodb+srv://xxxxxx:xxxxxx@prueba1.hdlxqaf.mongodb.net/",
    db_name = "sample_restaurants",
    collection_name="restaurants",
    filter_criteria={"borough": "Bronx", "cuisine": "Bakery"}
)

doc = loader.load()

splitter = RecursiveCharacterTextSplitter(
    chunk_size = 300,
    chunk_overlap = 50,
)
data = splitter.split_documents(doc)

embeddings = HuggingFaceBgeEmbeddings(
    model_name = "sentence-transformers/paraphrase-MiniLM-L6-v2",
)

vectorStore = MongoDBAtlasVectorSearch.from_documents( data, embeddings, collection=collection, index_name = "Model" )

When I check on Compass if all the data has been uploaded, everything looks fine. So I guess the problem is not here.

I’m using the following search_index

{
  "fields": [
    {
      "numDimensions": 384,
      "path": "embedding",
      "similarity": "cosine",
      "type": "vector"
    }
  ]
}

Then, I applied the standard RAG architecture:

def query_data(query):   

    docs = vectorStore.similarity_search(query, top_k=1)
    as_output = docs[0].page_content

    llm = CTransformers(model = "./mistral-7b-instruct-v0.1.Q4_0.gguf",
                        model_type = "llama",
                        #config = {'max_new_tokens': 400, 'temperature': 0.01}
                        )

    retriever = vectorStore.as_retriever()

    QA_CHAIN_PROMPT = PromptTemplate.from_template(template)

    qa = RetrievalQA.from_chain_type(llm, chain_type="stuff", retriever=retriever, chain_type_kwargs = {'prompt': QA_CHAIN_PROMPT})

    retriever_output = qa.invoke(query)

    return as_output, retriever_output

But when I ask my model about how many restaurants does he have information about, he answers me with only 4 restaurants and they are never the same ones. The filter criteria involves 70 restaurants.

The same happens when I ask specific information about one restaurant: It returns me wrong data or it just tells me it does not have information about that restaurant, when it should have it.

Etimbuk_Afia · 2024-05-20T13:08:23.669Z

what if I want the document id to be in the metadata too?