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# Copyright 2019 The Feast Authors
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Any, Callable, Optional
import numpy as np
from transformers import (
PreTrainedModel,
PreTrainedTokenizer,
RagRetriever,
)
from feast import FeatureStore, FeatureView
from feast.torch_wrapper import get_torch
from feast.vector_store import FeastVectorStore
class FeastIndex:
"""Dummy index required by HuggingFace's RagRetriever."""
def __init__(self):
"""Initialize the Feast index."""
pass
class FeastRAGRetriever(RagRetriever):
"""RAG retriever implementation that uses Feast as a backend."""
VALID_SEARCH_TYPES = {"text", "vector", "hybrid"}
def __init__(
self,
question_encoder_tokenizer: PreTrainedTokenizer,
question_encoder: PreTrainedModel,
generator_tokenizer: PreTrainedTokenizer,
generator_model: PreTrainedModel,
feast_repo_path: str,
feature_view: FeatureView,
features: list[str],
search_type: str,
config: dict[str, Any],
index: FeastIndex,
format_document: Optional[Callable[[dict[str, Any]], str]] = None,
id_field: str = "",
text_field: str = "",
**kwargs,
):
"""Initialize the Feast RAG retriever.
Args:
question_encoder_tokenizer: Tokenizer for encoding questions
question_encoder: Model for encoding questions
generator_tokenizer: Tokenizer for the generator model
generator_model: The generator model
feast_repo_path: Path to the Feast repository
feature_view: Feast FeatureView containing the document data
features: List of feature names to use from the feature view
search_type: Type of search to perform (text, vector, or hybrid)
config: Configuration for the retriever
index: Index instance (must be FeastIndex)
format_document: Optional function to format retrieved documents
id_field: Field to use as document ID
text_field: Field to use as text field name
**kwargs: Additional arguments passed to RagRetriever
"""
if search_type.lower() not in self.VALID_SEARCH_TYPES:
raise ValueError(
f"Unsupported search_type {search_type}. "
f"Must be one of: {self.VALID_SEARCH_TYPES}"
)
# move to gpu if available
torch = get_torch()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.question_encoder = question_encoder.to(self.device) # type: ignore
self.generator_model = generator_model.to(self.device) # type: ignore
self.question_encoder_tokenizer = question_encoder_tokenizer
self.generator_tokenizer = generator_tokenizer
super().__init__(
config=config,
question_encoder_tokenizer=self.question_encoder_tokenizer,
generator_tokenizer=self.generator_tokenizer,
index=index,
init_retrieval=False,
**kwargs,
)
self.feast_repo_path = feast_repo_path
self.search_type = search_type.lower()
self.format_document = format_document or self._default_format_document
self.id_field = id_field
self.text_field = text_field
self.feature_view = feature_view
self.features = features
# Initialize these lazily for compatability with distributed training/tuning
self._feature_store = None
self._vector_store = None
@property
def feature_store(self):
# Initialize FeatureStore lazily
if self._feature_store is None:
self._feature_store = FeatureStore(repo_path=self.feast_repo_path)
return self._feature_store
@property
def vector_store(self):
# Initialize FeastVectorStore lazily
if self._vector_store is None:
self._vector_store = FeastVectorStore(
repo_path=self.feast_repo_path,
rag_view=self.feature_view,
features=self.features,
)
return self._vector_store
def retrieve( # type: ignore [override]
self,
question_hidden_states: np.ndarray,
n_docs: int,
query: Optional[str] = None,
) -> tuple[np.ndarray, np.ndarray, list[dict]]:
"""
Overrides the base `retrieve` method to fetch documents from Feast.
This method processes a batch of questions, queries the vector store,
and formats the results into the 3-part tuple expected by the RAG model:
(document_embeddings, document_ids, document_dictionaries).
Args:
question_hidden_states (np.ndarray):
Hidden state representation of the question from the encoder.
Expected shape is (batch_size, seq_len, hidden_dim).
n_docs (int):
Number of top documents to retrieve.
query (Optional[str]):
Optional raw query string. If not provided and search_type is "text" or "hybrid",
it will be decoded from question_hidden_states.
Returns:
Tuple containing:
- retrieved_doc_embeds (np.ndarray):
Embeddings of the retrieved documents with shape (batch_size, n_docs, embed_dim).
- doc_ids (np.ndarray):
Array of document IDs or passage identifiers with shape (batch_size, n_docs).
- doc_dicts (list[dict]):
List of dictionaries containing document metadata and text.
Each dictionary has keys "text", "id", and "title".
"""
batch_size = question_hidden_states.shape[0]
# Convert the question hidden states into a list of 1D query vectors.
pooled_query_vectors = []
for i in range(batch_size):
pooled = question_hidden_states[i]
# Perform normalization to create a unit vector.
norm = np.linalg.norm(pooled)
if norm > 0:
pooled = pooled / norm
pooled_query_vectors.append(pooled)
# Determine embedding dimension for padding
emb_dim = (
pooled_query_vectors[0].shape[-1]
if pooled_query_vectors and pooled_query_vectors[0] is not None
else self.config.retrieval_vector_size
)
# Retrieve documents for each query in batch
batch_embeddings, batch_doc_ids, batch_metadata = [], [], []
for i in range(batch_size):
query_vector = pooled_query_vectors[i]
if isinstance(query, list):
query_text = query[i] if i < len(query) else None
else:
query_text = query
# Query Feast to get the raw document data
response = self.vector_store.query(
query_vector=query_vector if self.search_type != "text" else None,
query_string=query_text if self.search_type != "vector" else None,
top_k=n_docs,
)
results_dict = response.to_dict()
# Dynamically get data using the configured feature names
texts = results_dict.get(self.text_field, [])
ids = results_dict.get(self.id_field, [])
num_retrieved = len(texts)
embeddings = []
embedding_field_name = None
for field in self.feature_view.schema:
if hasattr(field, "vector_index") and field.vector_index:
embedding_field_name = field.name
break
if embedding_field_name:
embeddings = results_dict.get(embedding_field_name, [])
else:
raise ValueError(
"Warning: No field with 'vector_index=True' found in schema, cannot extract embeddings"
)
# Initialize lists for the current query's results
current_query_embeddings, current_query_ids, current_query_texts = (
[],
[],
[],
)
# Process retrieved documents up to n_docs
emb_array = np.array([])
if num_retrieved > 0:
emb_array = np.array(
[np.asarray(emb, dtype=np.float32) for emb in embeddings]
)
if emb_array.ndim == 1: # Reshape if it's a flat array
emb_array = emb_array.reshape(num_retrieved, -1)
for k in range(n_docs):
if k < num_retrieved:
# Append actual retrieved data
current_query_texts.append(texts[k])
current_query_embeddings.append(emb_array[k])
try:
current_query_ids.append(int(ids[k]))
except (ValueError, TypeError):
current_query_ids.append(-1) # Use placeholder for invalid IDs
else:
# Pad with empty/zero values if fewer than n_docs were found
current_query_texts.append("")
current_query_embeddings.append(np.zeros(emb_dim, dtype=np.float32))
current_query_ids.append(-1)
# Collate results for the current query
batch_embeddings.append(np.array(current_query_embeddings))
batch_doc_ids.append(np.array(current_query_ids))
batch_metadata.append(
{
"text": current_query_texts,
"id": current_query_ids,
"title": [""]
* n_docs, # RAG model expects a "title" key during Forward pass
}
)
# Return the Collated Batch Results
# Convert lists of arrays into single batch arrays.
return (
np.array(batch_embeddings, dtype=np.float32),
np.array(batch_doc_ids, dtype=np.int64),
batch_metadata,
)
def generate_answer(
self, query: str, top_k: int = 5, max_new_tokens: int = 100
) -> str:
"""A helper method to generate an answer for a single query string.
Args:
query: The query to answer
top_k: Number of documents to retrieve
max_new_tokens: Maximum number of tokens to generate
Returns:
Generated answer string
"""
if not self.question_encoder or not self.generator_model:
raise ValueError(
"`question_encoder` and `generator_model` must be provided to use `generate_answer`."
)
torch = get_torch()
inputs = self.question_encoder_tokenizer(query, return_tensors="pt").to(
self.question_encoder.device
)
question_embeddings = self.question_encoder(**inputs).pooler_output
question_embeddings = (
question_embeddings.detach().cpu().to(torch.float32).numpy()
)
_, _, doc_batch = self.retrieve(question_embeddings, n_docs=top_k, query=query)
contexts = doc_batch[0]["text"] if doc_batch else []
context_str = "\n\n".join(filter(None, contexts))
prompt = f"Context: {context_str}\n\nQuestion: {query}\n\nAnswer:"
generator_inputs = self.generator_tokenizer(prompt, return_tensors="pt").to(
self.generator_model.device
)
output_ids = self.generator_model.generate(
**generator_inputs, max_new_tokens=max_new_tokens
)
return self.generator_tokenizer.decode(output_ids[0], skip_special_tokens=True)
def _default_format_document(self, doc: dict[str, Any]) -> str:
"""Default document formatting function.
Args:
doc: Document dictionary to format
Returns:
Formatted document string
"""
lines = []
for key, value in doc.items():
# Skip vectors by checking for long float lists
if (
isinstance(value, list)
and len(value) > 10
and all(isinstance(x, (float, int)) for x in value)
):
continue
lines.append(f"{key.replace('_', ' ').capitalize()}: {value}")
return "\n".join(lines)