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| 1 | +# SPDX-FileCopyrightText: 2023-present deepset GmbH <info@deepset.ai> |
| 2 | +# |
| 3 | +# SPDX-License-Identifier: Apache-2.0 |
| 4 | + |
| 5 | +from typing import TYPE_CHECKING, Any, Dict, List, Optional, Union |
| 6 | + |
| 7 | +from haystack import DeserializationError, Pipeline, default_from_dict, default_to_dict, logging, super_component |
| 8 | +from haystack.components.embedders.types import TextEmbedder |
| 9 | +from haystack.components.joiners import DocumentJoiner |
| 10 | +from haystack.components.joiners.document_joiner import JoinMode |
| 11 | +from haystack.core.serialization import component_from_dict, import_class_by_name |
| 12 | +from haystack.document_stores.types import FilterPolicy |
| 13 | + |
| 14 | +from haystack_integrations.components.retrievers.opensearch import OpenSearchBM25Retriever, OpenSearchEmbeddingRetriever |
| 15 | +from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore |
| 16 | + |
| 17 | +logger = logging.getLogger(__name__) |
| 18 | + |
| 19 | + |
| 20 | +@super_component |
| 21 | +class OpenSearchHybridRetriever: |
| 22 | + """ |
| 23 | + A hybrid retriever that combines embedding-based and keyword-based retrieval from OpenSearch. |
| 24 | +
|
| 25 | + Example usage: |
| 26 | +
|
| 27 | + Make sure you have "sentence-transformers>=3.0.0": |
| 28 | +
|
| 29 | + pip install haystack-ai datasets "sentence-transformers>=3.0.0" |
| 30 | +
|
| 31 | +
|
| 32 | + And OpenSearch running. You can run OpenSearch with Docker: |
| 33 | +
|
| 34 | + docker run -d --name opensearch-nosec -p 9200:9200 -p 9600:9600 -e "discovery.type=single-node" |
| 35 | + -e "DISABLE_SECURITY_PLUGIN=true" opensearchproject/opensearch:2.12.0 |
| 36 | +
|
| 37 | + ```python |
| 38 | + from haystack import Document |
| 39 | + from haystack.components.embedders import SentenceTransformersTextEmbedder, SentenceTransformersDocumentEmbedder |
| 40 | + from haystack_integrations.components.retrievers.opensearch import OpenSearchHybridRetriever |
| 41 | + from haystack_integrations.document_stores.opensearch import OpenSearchDocumentStore |
| 42 | +
|
| 43 | + # Initialize the document store |
| 44 | + doc_store = OpenSearchDocumentStore( |
| 45 | + hosts=["<http://localhost:9200>"], |
| 46 | + index="document_store", |
| 47 | + embedding_dim=384, |
| 48 | + ) |
| 49 | +
|
| 50 | + # Create some sample documents |
| 51 | + docs = [ |
| 52 | + Document(content="Machine learning is a subset of artificial intelligence."), |
| 53 | + Document(content="Deep learning is a subset of machine learning."), |
| 54 | + Document(content="Natural language processing is a field of AI."), |
| 55 | + Document(content="Reinforcement learning is a type of machine learning."), |
| 56 | + Document(content="Supervised learning is a type of machine learning."), |
| 57 | + ] |
| 58 | +
|
| 59 | + # Embed the documents and add them to the document store |
| 60 | + doc_embedder = SentenceTransformersDocumentEmbedder(model="sentence-transformers/all-MiniLM-L6-v2") |
| 61 | + doc_embedder.warm_up() |
| 62 | + docs = doc_embedder.run(docs) |
| 63 | + doc_store.write_documents(docs['documents']) |
| 64 | +
|
| 65 | + # Initialize some haystack text embedder, in this case the SentenceTransformersTextEmbedder |
| 66 | + embedder = SentenceTransformersTextEmbedder(model="sentence-transformers/all-MiniLM-L6-v2") |
| 67 | +
|
| 68 | + # Initialize the hybrid retriever |
| 69 | + retriever = OpenSearchHybridRetriever( |
| 70 | + document_store=doc_store, |
| 71 | + embedder=embedder, |
| 72 | + top_k_bm25=3, |
| 73 | + top_k_embedding=3, |
| 74 | + join_mode="reciprocal_rank_fusion" |
| 75 | + ) |
| 76 | +
|
| 77 | + # Run the retriever |
| 78 | + results = retriever.run(query="What is reinforcement learning?", filters_bm25=None, filters_embedding=None) |
| 79 | +
|
| 80 | + >> results['documents'] |
| 81 | + {'documents': [Document(id=..., content: 'Reinforcement learning is a type of machine learning.', score: 1.0), |
| 82 | + Document(id=..., content: 'Supervised learning is a type of machine learning.', score: 0.9760624679979518), |
| 83 | + Document(id=..., content: 'Deep learning is a subset of machine learning.', score: 0.4919354838709677), |
| 84 | + Document(id=..., content: 'Machine learning is a subset of artificial intelligence.', score: 0.4841269841269841)]} |
| 85 | + ``` |
| 86 | + """ |
| 87 | + |
| 88 | + def __init__( |
| 89 | + self, |
| 90 | + document_store: OpenSearchDocumentStore, |
| 91 | + *, |
| 92 | + embedder: TextEmbedder, |
| 93 | + # OpenSearchBM25Retriever |
| 94 | + filters_bm25: Optional[Dict[str, Any]] = None, |
| 95 | + fuzziness: Union[int, str] = "AUTO", |
| 96 | + top_k_bm25: int = 10, |
| 97 | + scale_score: bool = False, |
| 98 | + all_terms_must_match: bool = False, |
| 99 | + filter_policy_bm25: Union[str, FilterPolicy] = FilterPolicy.REPLACE, |
| 100 | + custom_query_bm25: Optional[Dict[str, Any]] = None, |
| 101 | + # OpenSearchEmbeddingRetriever |
| 102 | + filters_embedding: Optional[Dict[str, Any]] = None, |
| 103 | + top_k_embedding: int = 10, |
| 104 | + filter_policy_embedding: Union[str, FilterPolicy] = FilterPolicy.REPLACE, |
| 105 | + custom_query_embedding: Optional[Dict[str, Any]] = None, |
| 106 | + # DocumentJoiner |
| 107 | + join_mode: Union[str, JoinMode] = JoinMode.RECIPROCAL_RANK_FUSION, |
| 108 | + weights: Optional[List[float]] = None, |
| 109 | + top_k: Optional[int] = None, |
| 110 | + sort_by_score: bool = True, |
| 111 | + # extra kwargs |
| 112 | + **kwargs, |
| 113 | + ): |
| 114 | + """ |
| 115 | + Initialize the OpenSearchHybridRetriever, a super component to retrieve documents from OpenSearch using |
| 116 | + both embedding-based and keyword-based retrieval methods. |
| 117 | +
|
| 118 | + We don't explicitly define all the init parameters of the components in the constructor, for each |
| 119 | + of the components, since that would be around 20+ parameters. Instead, we define the most important ones |
| 120 | + and pass the rest as kwargs. This is to keep the constructor clean and easy to read. |
| 121 | +
|
| 122 | + If you need to pass extra parameters to the components, you can do so by passing them as kwargs. It expects |
| 123 | + a dictionary with the component name as the key and the parameters as the value. The component name should be: |
| 124 | +
|
| 125 | + - "bm25_retriever" -> OpenSearchBM25Retriever |
| 126 | + - "embedding_retriever" -> OpenSearchEmbeddingRetriever |
| 127 | +
|
| 128 | + :param document_store: |
| 129 | + The OpenSearchDocumentStore to use for retrieval. |
| 130 | + :param embedder: |
| 131 | + A TextEmbedder to use for embedding the query. |
| 132 | + See `haystack.components.embedders.types.protocol.TextEmbedder` for more information. |
| 133 | + :param filters_bm25: |
| 134 | + Filters for the BM25 retriever. |
| 135 | + :param fuzziness: |
| 136 | + The fuzziness for the BM25 retriever. |
| 137 | + :param top_k_bm25: |
| 138 | + The number of results to return from the BM25 retriever. |
| 139 | + :param scale_score: |
| 140 | + Whether to scale the score for the BM25 retriever. |
| 141 | + :param all_terms_must_match: |
| 142 | + Whether all terms must match for the BM25 retriever. |
| 143 | + :param filter_policy_bm25: |
| 144 | + The filter policy for the BM25 retriever. |
| 145 | + :param custom_query_bm25: |
| 146 | + A custom query for the BM25 retriever. |
| 147 | + :param filters_embedding: |
| 148 | + Filters for the embedding retriever. |
| 149 | + :param top_k_embedding: |
| 150 | + The number of results to return from the embedding retriever. |
| 151 | + :param filter_policy_embedding: |
| 152 | + The filter policy for the embedding retriever. |
| 153 | + :param custom_query_embedding: |
| 154 | + A custom query for the embedding retriever. |
| 155 | + :param join_mode: |
| 156 | + The mode to use for joining the results from the BM25 and embedding retrievers. |
| 157 | + :param weights: |
| 158 | + The weights for the joiner. |
| 159 | + :param top_k: |
| 160 | + The number of results to return from the joiner. |
| 161 | + :param sort_by_score: |
| 162 | + Whether to sort the results by score. |
| 163 | + :param **kwargs: |
| 164 | + Additional keyword arguments. Use the following keys to pass extra parameters to the retrievers: |
| 165 | + - "bm25_retriever" -> OpenSearchBM25Retriever |
| 166 | + - "embedding_retriever" -> OpenSearchEmbeddingRetriever |
| 167 | +
|
| 168 | +
|
| 169 | + """ |
| 170 | + self.document_store = document_store |
| 171 | + self.embedder = embedder |
| 172 | + |
| 173 | + # OpenSearchBM25Retriever |
| 174 | + self.filters_bm25 = filters_bm25 |
| 175 | + self.fuzziness = fuzziness |
| 176 | + self.top_k_bm25 = top_k_bm25 |
| 177 | + self.scale_score = scale_score |
| 178 | + self.all_terms_must_match = all_terms_must_match |
| 179 | + self.filter_policy_bm25 = filter_policy_bm25 |
| 180 | + self.custom_query_bm25 = custom_query_bm25 |
| 181 | + |
| 182 | + # OpenSearchEmbeddingRetriever |
| 183 | + self.filters_embedding = filters_embedding |
| 184 | + self.top_k_embedding = top_k_embedding |
| 185 | + self.filter_policy_embedding = filter_policy_embedding |
| 186 | + self.custom_query_embedding = custom_query_embedding |
| 187 | + |
| 188 | + # DocumentJoiner |
| 189 | + self.join_mode = join_mode |
| 190 | + self.weights = weights |
| 191 | + self.top_k = top_k |
| 192 | + self.sort_by_score = sort_by_score |
| 193 | + |
| 194 | + init_args = { |
| 195 | + "bm25_retriever": { |
| 196 | + "document_store": self.document_store, |
| 197 | + "filters": self.filters_bm25, |
| 198 | + "fuzziness": self.fuzziness, |
| 199 | + "top_k": self.top_k_bm25, |
| 200 | + "scale_score": self.scale_score, |
| 201 | + "all_terms_must_match": self.all_terms_must_match, |
| 202 | + "filter_policy": self.filter_policy_bm25, |
| 203 | + "custom_query": self.custom_query_bm25, |
| 204 | + }, |
| 205 | + "embedding_retriever": { |
| 206 | + "document_store": self.document_store, |
| 207 | + "filters": self.filters_embedding, |
| 208 | + "top_k": self.top_k_embedding, |
| 209 | + "filter_policy": self.filter_policy_embedding, |
| 210 | + "custom_query": self.custom_query_embedding, |
| 211 | + }, |
| 212 | + "document_joiner": { |
| 213 | + "join_mode": self.join_mode, |
| 214 | + "weights": self.weights, |
| 215 | + "top_k": self.top_k, |
| 216 | + "sort_by_score": self.sort_by_score, |
| 217 | + }, |
| 218 | + } |
| 219 | + |
| 220 | + for k in kwargs: |
| 221 | + if k not in ["bm25_retriever", "embedding_retriever"]: |
| 222 | + msg = f"valid extra args are only: 'bm25_retriever' and 'embedding_retriever'. Found: {k}" |
| 223 | + raise ValueError(msg) |
| 224 | + |
| 225 | + self.extra_args = kwargs |
| 226 | + |
| 227 | + # handle extra kwargs for the bm25 and embedding retrievers and the doc store as init param |
| 228 | + if "bm25_retriever" in kwargs: |
| 229 | + init_args["bm25_retriever"].update(kwargs["bm25_retriever"]) |
| 230 | + init_args["bm25_retriever"]["document_store"] = self.document_store |
| 231 | + if "embedding_retriever" in kwargs: |
| 232 | + init_args["embedding_retriever"].update(kwargs["embedding_retriever"]) |
| 233 | + init_args["embedding_retriever"]["document_store"] = self.document_store |
| 234 | + |
| 235 | + self.pipeline = self._create_pipeline(init_args) |
| 236 | + |
| 237 | + if TYPE_CHECKING: |
| 238 | + |
| 239 | + def warm_up(self) -> None: ... |
| 240 | + |
| 241 | + def run(self, query: str, filters_bm25=None, filters_embedding=None) -> Dict[str, Any]: ... |
| 242 | + |
| 243 | + def _create_pipeline(self, data: dict[str, Any]) -> Pipeline: |
| 244 | + """ |
| 245 | + Create the pipeline for the OpenSearchHybridRetriever. |
| 246 | + """ |
| 247 | + embedding_retriever = OpenSearchEmbeddingRetriever(**data["embedding_retriever"]) |
| 248 | + bm25_retriever = OpenSearchBM25Retriever(**data["bm25_retriever"]) |
| 249 | + document_joiner = DocumentJoiner(**data["document_joiner"]) |
| 250 | + |
| 251 | + hybrid_retrieval = Pipeline() |
| 252 | + hybrid_retrieval.add_component("text_embedder", self.embedder) |
| 253 | + hybrid_retrieval.add_component("embedding_retriever", embedding_retriever) |
| 254 | + hybrid_retrieval.add_component("bm25_retriever", bm25_retriever) |
| 255 | + hybrid_retrieval.add_component("document_joiner", document_joiner) |
| 256 | + |
| 257 | + hybrid_retrieval.connect("text_embedder.embedding", "embedding_retriever.query_embedding") |
| 258 | + hybrid_retrieval.connect("bm25_retriever", "document_joiner") |
| 259 | + hybrid_retrieval.connect("embedding_retriever", "document_joiner") |
| 260 | + |
| 261 | + # Define how pipeline inputs/outputs map to subcomponent inputs/outputs |
| 262 | + self.input_mapping = { |
| 263 | + # The pipeline input "query" feeds into each of the retrievers |
| 264 | + "query": ["text_embedder.text", "bm25_retriever.query"], |
| 265 | + } |
| 266 | + self.output_mapping = {"document_joiner.documents": "documents"} |
| 267 | + |
| 268 | + return hybrid_retrieval |
| 269 | + |
| 270 | + def to_dict(self): |
| 271 | + """ |
| 272 | + Serialize OpenSearchHybridRetriever to a dictionary. |
| 273 | +
|
| 274 | + :returns: |
| 275 | + Dictionary with serialized data. |
| 276 | + """ |
| 277 | + return default_to_dict( |
| 278 | + self, |
| 279 | + # DocumentStore |
| 280 | + document_store=self.document_store.to_dict(), |
| 281 | + embedder=self.embedder.to_dict(), |
| 282 | + filters_bm25=self.filters_bm25, |
| 283 | + fuzziness=self.fuzziness, |
| 284 | + top_k_bm25=self.top_k_bm25, |
| 285 | + scale_score=self.scale_score, |
| 286 | + all_terms_must_match=self.all_terms_must_match, |
| 287 | + filter_policy_bm25=self.filter_policy_bm25.value, |
| 288 | + custom_query_bm25=self.custom_query_bm25, |
| 289 | + # OpenSearchEmbeddingRetriever |
| 290 | + filters_embedding=self.filters_embedding, |
| 291 | + top_k_embedding=self.top_k_embedding, |
| 292 | + filter_policy_embedding=self.filter_policy_embedding.value, |
| 293 | + custom_query_embedding=self.custom_query_embedding, |
| 294 | + # DocumentJoiner |
| 295 | + join_mode=self.join_mode.value, |
| 296 | + weights=self.weights, |
| 297 | + top_k=self.top_k, |
| 298 | + sort_by_score=self.sort_by_score, |
| 299 | + # extra kwargs |
| 300 | + **self.extra_args, |
| 301 | + ) |
| 302 | + |
| 303 | + @classmethod |
| 304 | + def from_dict(cls, data): |
| 305 | + # deserialize the document store |
| 306 | + doc_store = OpenSearchDocumentStore.from_dict(data["init_parameters"]["document_store"]) |
| 307 | + data["init_parameters"]["document_store"] = doc_store |
| 308 | + |
| 309 | + # deserialize the embedder |
| 310 | + try: |
| 311 | + text_embedder_class = import_class_by_name(data["init_parameters"]["embedder"]["type"]) |
| 312 | + except ImportError as e: |
| 313 | + msg = f"Class '{data['init_parameters']['embedder']['type']}' not correctly imported" |
| 314 | + raise DeserializationError(msg) from e |
| 315 | + |
| 316 | + data["init_parameters"]["embedder"] = component_from_dict( |
| 317 | + cls=text_embedder_class, data=data["init_parameters"]["embedder"], name="embedder" |
| 318 | + ) |
| 319 | + |
| 320 | + # deserialize the embedders filtering policy |
| 321 | + if "filter_policy_bm25" in data["init_parameters"]: |
| 322 | + filter_policy_bm25 = FilterPolicy.from_str(data["init_parameters"]["filter_policy_bm25"]) |
| 323 | + data["init_parameters"]["filter_policy_bm25"] = filter_policy_bm25 |
| 324 | + |
| 325 | + if "filter_policy_embedding" in data["init_parameters"]: |
| 326 | + filter_policy_embedding = FilterPolicy.from_str(data["init_parameters"]["filter_policy_embedding"]) |
| 327 | + data["init_parameters"]["filter_policy_embedding"] = filter_policy_embedding |
| 328 | + |
| 329 | + if "join_mode" in data["init_parameters"]: |
| 330 | + join_mode = JoinMode.from_str(data["init_parameters"]["join_mode"]) |
| 331 | + data["init_parameters"]["join_mode"] = join_mode |
| 332 | + |
| 333 | + return default_from_dict(cls, data) |
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