rag_chain/retrievers_setup.py

import os
from functools import cache

import qdrant_client
import torch
from langchain.retrievers import ContextualCompressionRetriever
from langchain.retrievers.document_compressors import EmbeddingsFilter
from langchain_community.retrievers import QdrantSparseVectorRetriever
from langchain_community.vectorstores import Qdrant
from langchain_openai.embeddings import OpenAIEmbeddings
from transformers import AutoModelForMaskedLM, AutoTokenizer


class DenseRetrieverClient:
    """Inititalize the dense retriever using OpenAI text embeddings and Qdrant vector database.

    Attributes:
        embeddings_model (str): The embeddings model to use. Right now only OpenAI text embeddings.
        collection_name (str): Qdrant collection name.
        client (QdrantClient): Qdrant client.
        qdrant_collection (Qdrant): Qdrant collection.
    """

    def __init__(self, embeddings_model: str = "text-embedding-ada-002", collection_name: str = "practitioners_db"):
        self.validate_environment_variables()
        self.embeddings_model = embeddings_model
        self.collection_name = collection_name
        self.client = qdrant_client.QdrantClient(
            url=os.getenv("QDRANT_URL"),
            api_key=os.getenv("QDRANT_API_KEY"),
        )
        self.qdrant_collection = self.load_qdrant_collection()

    def validate_environment_variables(self):
        """ Check if the Qdrant environment variables are set."""
        required_vars = ["QDRANT_API_KEY", "QDRANT_URL"]
        for var in required_vars:
            if not os.getenv(var):
                raise EnvironmentError(f"Missing environment variable: {var}")

    def set_qdrant_collection(self, embeddings):
        """Prepare the Qdrant collection for the embeddings model."""
        return Qdrant(client=self.client,
                      collection_name=self.collection_name,
                      embeddings=embeddings)

    @cache
    def load_qdrant_collection(self):
        """Load Qdrant collection for a given embeddings model."""
        # TODO: Test new OpenAI text embeddings models
        openai_text_embeddings = ["text-embedding-ada-002"]
        if self.embeddings_model in openai_text_embeddings:
            self.qdrant_collection = self.set_qdrant_collection(
                OpenAIEmbeddings(model=self.embeddings_model))
        else:
            raise ValueError(
                f"Invalid embeddings model: {self.embeddings_model}. Valid options are {', '.join(openai_text_embeddings)}.")

        return self.qdrant_collection

    def get_dense_retriever(self, search_type: str = "similarity", k: int = 4):
        """Set up retrievers (Qdrant vectorstore as retriever).

        Args:
            search_type (str, optional): similarity or mmr. Defaults to "similarity".
            k (int, optional): Number of documents retrieved. Defaults to 4.

        Returns:
            Retriever: Vectorstore as a retriever
        """
        dense_retriever = self.qdrant_collection.as_retriever(search_type=search_type,
                                                              search_kwargs={
                                                                  "k": k}
                                                              )
        return dense_retriever


class SparseRetrieverClient:
    """Inititalize the sparse retriever using the SPLADE neural retrieval model and Qdrant vector database.

    Attributes:
        collection_name (str): Qdrant collection name.
        vector_name (str): Qdrant vector name.
        splade_model_id (str): The SPLADE neural retrieval model id.
        k (int): Number of documents retrieved.
        client (QdrantClient): Qdrant client.
    """

    def __init__(self, collection_name: str, vector_name: str, splade_model_id: str = "naver/splade-cocondenser-ensembledistil", k: int = 15):
        self.validate_environment_variables()
        self.client = qdrant_client.QdrantClient(url=os.getenv(
            "QDRANT_URL"), api_key=os.getenv("QDRANT_API_KEY"))
        self.model_id = splade_model_id
        self.collection_name = collection_name
        self.vector_name = vector_name
        self.k = k

    def validate_environment_variables(self):
        required_vars = ["QDRANT_API_KEY", "QDRANT_URL"]
        for var in required_vars:
            if not os.getenv(var):
                raise EnvironmentError(f"Missing environment variable: {var}")

    @cache
    def set_tokenizer_config(self):
        """Initialize the tokenizer and the SPLADE neural retrieval model.
        See to https://huggingface.co./naver/splade-cocondenser-ensembledistil for more details.
        """
        tokenizer = AutoTokenizer.from_pretrained(self.model_id)
        model = AutoModelForMaskedLM.from_pretrained(self.model_id)
        return tokenizer, model

    def sparse_encoder(self, text: str) -> tuple[list[int], list[float]]:
        """This function encodes the input text into a sparse vector. The encoder is required for the QdrantSparseVectorRetriever.
        Adapted from the Qdrant documentation: Computing the Sparse Vector code.

        Args:
            text (str): Text to encode

        Returns:
            tuple[list[int], list[float]]: Indices and values of the sparse vector
        """
        tokenizer, model = self.set_tokenizer_config()
        tokens = tokenizer(text, return_tensors="pt",
                           max_length=512, padding="max_length", truncation=True)
        output = model(**tokens)
        logits, attention_mask = output.logits, tokens.attention_mask
        relu_log = torch.log(1 + torch.relu(logits))
        weighted_log = relu_log * attention_mask.unsqueeze(-1)
        max_val, _ = torch.max(weighted_log, dim=1)
        vec = max_val.squeeze()
        indices = vec.nonzero().numpy().flatten()
        values = vec.detach().numpy()[indices]
        return indices.tolist(), values.tolist()

    def get_sparse_retriever(self):

        sparse_retriever = QdrantSparseVectorRetriever(
            client=self.client,
            collection_name=self.collection_name,
            sparse_vector_name=self.vector_name,
            sparse_encoder=self.sparse_encoder,
            k=self.k,
        )

        return sparse_retriever


def compression_retriever_setup(base_retriever, embeddings_model: str = "text-embedding-ada-002", similarity_threshold: float = 0.76) -> ContextualCompressionRetriever:
    """
    Creates a ContextualCompressionRetriever with a base retriever and a similarity threshold.

    The ContextualCompressionRetriever uses an EmbeddingsFilter with OpenAIEmbeddings to filter out documents 
    with a similarity score below the given threshold.

    Args:
        base_retriever: Retriever to be filtered.
        similarity_threshold (float, optional): The similarity threshold for the EmbeddingsFilter. 
            Documents with a similarity score below this threshold will be filtered out. Defaults to 0.76 (Obtained by experimenting with text-embeddings-ada-002).
            ** Be careful with this parameter, as it can have a big impact on the results and highly depends on the embeddings model used.

    Returns:
        ContextualCompressionRetriever: The created ContextualCompressionRetriever.
    """

    # Set up compression retriever (filter out documents with low similarity)
    relevant_filter = EmbeddingsFilter(embeddings=OpenAIEmbeddings(model=embeddings_model),
                                       similarity_threshold=similarity_threshold)

    compression_retriever = ContextualCompressionRetriever(
        base_compressor=relevant_filter, base_retriever=base_retriever
    )

    return compression_retriever