hybrid retrieval

buster/busterbot.py

@@ -1,11 +1,11 @@
 import logging
 from dataclasses import dataclass, field
-from typing import Any, Optional
+from typing import Optional
 
 import pandas as pd
 
 from buster.completers import Completion, DocumentAnswerer, UserInputs
-from buster.llm_utils import QuestionReformulator
+from buster.llm_utils import QuestionReformulator, get_openai_embedding
 from buster.retriever import Retriever
 from buster.validators import Validator
 
@@ -33,7 +33,7 @@ class BusterConfig:
             "max_tokens": 3000,
             "top_k": 3,
             "thresh": 0.7,
-            "embedding_model": "text-embedding-ada-002",
+            "embedding_fn": get_openai_embedding,
         }
     )
     prompt_formatter_cfg: dict = field(

buster/documents_manager/base.py

@@ -2,8 +2,9 @@
 import time
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from typing import Optional
+from typing import Callable, Optional
 
+import numpy as np
 import pandas as pd
 from tqdm import tqdm
 
@@ -63,7 +64,8 @@ def add(
         self,
         df: pd.DataFrame,
         num_workers: int = 16,
-        embedding_fn: callable = get_openai_embedding,
+        embedding_fn: Callable[[str], np.ndarray] = get_openai_embedding,
+        sparse_embedding_fn: Callable[[str], dict[str, list[float]]] = None,
         csv_filename: Optional[str] = None,
         csv_overwrite: bool = True,
         **add_kwargs,
@@ -81,6 +83,8 @@ def add(
             num_workers (int, optional): The number of parallel workers to use for computing embeddings. Default is 32.
             embedding_fn (callable, optional): A function that computes embeddings for a given input string.
                 Default is 'get_embedding_openai' which uses the text-embedding-ada-002 model.
+            sparse_embedding_fn (callable, optional): A function that computes sparse embeddings for a given input string.
+                Default is None. Only use if you want sparse embeddings.
             csv_filename (str, optional): Path to save a copy of the dataframe with computed embeddings for later use.
             csv_overwrite (bool, optional): Whether to overwrite the file with a new file. Defaults to True.
             **add_kwargs: Additional keyword arguments to be passed to the '_add_documents' method.
@@ -92,6 +96,8 @@ def add(
         # Check if embeddings are present, computes them if not
         if "embedding" not in df.columns:
             df["embedding"] = compute_embeddings_parallelized(df, embedding_fn=embedding_fn, num_workers=num_workers)
+        if "sparse_embedding" not in df.columns and sparse_embedding_fn is not None:
+            df["sparse_embedding"] = sparse_embedding_fn(df.content.to_list())
 
         if csv_filename is not None:
             self._checkpoint_csv(df, csv_filename=csv_filename, csv_overwrite=csv_overwrite)
@@ -104,7 +110,8 @@ def batch_add(
         batch_size: int = 3000,
         min_time_interval: int = 60,
         num_workers: int = 16,
-        embedding_fn: callable = get_openai_embedding,
+        embedding_fn: Callable[[str], np.ndarray] = get_openai_embedding,
+        sparse_embedding_fn: Callable[[str], dict[str, list[float]]] = None,
         csv_filename: Optional[str] = None,
         csv_overwrite: bool = False,
         **add_kwargs,
@@ -125,6 +132,8 @@ def batch_add(
                                         Defaults to 32.
             embedding_fn (callable, optional): A function that computes embeddings for a given input string.
                 Default is 'get_embedding_openai' which uses the text-embedding-ada-002 model.
+            sparse_embedding_fn (callable, optional): A function that computes sparse embeddings for a given input string.
+                Default is None. Only use if you want sparse embeddings.
             csv_filename (str, optional): Path to save a copy of the dataframe with computed embeddings for later use.
             csv_overwrite (bool, optional): Whether to overwrite the file with a new file. Defaults to False.
                 When using batches, set to False to keep all embeddings in the same file. You may want to manually remove the file if experimenting.
@@ -151,6 +160,7 @@ def batch_add(
                 csv_filename=csv_filename,
                 csv_overwrite=csv_overwrite,
                 embedding_fn=embedding_fn,
+                sparse_embedding_fn=sparse_embedding_fn,
                 **add_kwargs,
             )
 

buster/documents_manager/service.py

@@ -67,6 +67,10 @@ def _add_documents(self, df: pd.DataFrame):
         Args:
             df: The dataframe containing the documents.
         """
+        use_sparse_vector = "sparse_embedding" in df.columns
+        if use_sparse_vector:
+            logger.info("Uploading sparse embeddings too.")
+
         for source in df.source.unique():
             source_exists = self.db.sources.find_one({"name": source})
             if source_exists is None:
@@ -75,14 +79,32 @@ def _add_documents(self, df: pd.DataFrame):
             source_id = self.get_source_id(source)
 
             df_source = df[df.source == source]
+            to_upsert = []
             for row in df_source.to_dict(orient="records"):
                 embedding = row["embedding"].tolist()
+                if use_sparse_vector:
+                    sparse_embedding = row["sparse_embedding"]
+
                 document = row.copy()
                 document.pop("embedding")
+                if use_sparse_vector:
+                    document.pop("sparse_embedding")
                 document["source_id"] = source_id
 
                 document_id = str(self.db.documents.insert_one(document).inserted_id)
-                self.index.upsert([(document_id, embedding, {"source": source})], namespace=self.namespace)
+                vector = {"id": document_id, "values": embedding, "metadata": {"source": source}}
+                if use_sparse_vector:
+                    vector["sparse_values"] = sparse_embedding
+
+                to_upsert.append(vector)
+
+            # Current (November 2023) Pinecone upload rules:
+            # - Max 1000 vectors per batch
+            # - Max 2 MB per batch
+            # Sparse vectors are heavier, so we reduce the batch size when using them.
+            MAX_PINECONE_BATCH_SIZE = 100 if use_sparse_vector else 1000
+            for i in range(0, len(to_upsert), MAX_PINECONE_BATCH_SIZE):
+                self.index.upsert(vectors=to_upsert[i : i + MAX_PINECONE_BATCH_SIZE], namespace=self.namespace)
 
     def update_source(self, source: str, display_name: str = None, note: str = None):
         """Update the display name and/or note of a source. Also create the source if it does not exist.

buster/llm_utils/__init__.py

@@ -1,4 +1,5 @@
 from buster.llm_utils.embeddings import (
+    BM25,
     compute_embeddings_parallelized,
     cosine_similarity,
     get_openai_embedding,
@@ -12,4 +13,5 @@
     get_openai_embedding,
     compute_embeddings_parallelized,
     get_openai_embedding_constructor,
+    BM25,
 ]

buster/llm_utils/embeddings.py

@@ -5,6 +5,7 @@
 import numpy as np
 import pandas as pd
 from openai import OpenAI
+from pinecone_text.sparse import BM25Encoder
 from tqdm.contrib.concurrent import thread_map
 
 logger = logging.getLogger(__name__)
@@ -64,3 +65,23 @@ def compute_embeddings_parallelized(df: pd.DataFrame, embedding_fn: callable, nu
 
     logger.info(f"Finished computing embeddings")
     return embeddings
+
+
+class BM25:
+    def __init__(self, path_to_params: str = None) -> None:
+        self.encoder = BM25Encoder()
+
+        if path_to_params:
+            self.encoder.load(path_to_params)
+
+    def fit(self, df: pd.DataFrame):
+        self.encoder.fit(df.content.to_list())
+
+    def dump_params(self, path: str):
+        self.encoder.dump(path)
+
+    def get_sparse_embedding_fn(self):
+        def sparse_embedding_fn(query: str):
+            return self.encoder.encode_queries(query)
+
+        return sparse_embedding_fn

buster/retriever/base.py

@@ -1,7 +1,6 @@
 import logging
 from abc import ABC, abstractmethod
 from dataclasses import dataclass
-from functools import lru_cache
 from typing import Callable, Optional
 
 import numpy as np
@@ -18,13 +17,22 @@
 
 @dataclass
 class Retriever(ABC):
-    def __init__(self, top_k: int, thresh: float, embedding_fn: Callable[[str], np.array] = None, *args, **kwargs):
+    def __init__(
+        self,
+        top_k: int,
+        thresh: float,
+        embedding_fn: Callable[[str], np.ndarray] = None,
+        sparse_embedding_fn: Callable[[str], dict[str, list[float]]] = None,
+        *args,
+        **kwargs,
+    ):
         """Initializes a Retriever instance.
 
         Args:
           top_k: The maximum number of documents to retrieve.
           thresh: The similarity threshold for document retrieval.
           embedding_fn: The function to compute document embeddings.
+          embedding_fn: (Optional) The function to compute sparse document embeddings.
           *args, **kwargs: Additional arguments and keyword arguments.
         """
         if embedding_fn is None:
@@ -33,6 +41,7 @@ def __init__(self, top_k: int, thresh: float, embedding_fn: Callable[[str], np.a
         self.top_k = top_k
         self.thresh = thresh
         self.embedding_fn = embedding_fn
+        self.sparse_embedding_fn = sparse_embedding_fn
 
         # Add your access to documents in your own init
 
@@ -62,18 +71,6 @@ def get_source_display_name(self, source: str) -> str:
         """
         ...
 
-    def get_embedding(self, query: str) -> np.ndarray:
-        """Generates the embedding of a query.
-
-        Args:
-          query: The query for which to generate the embedding.
-
-        Returns:
-          The embedding of the query as a NumPy array.
-        """
-        logger.info("generating embedding")
-        return self.embedding_fn(query)
-
     @abstractmethod
     def get_topk_documents(self, query: str, source: Optional[str] = None, top_k: Optional[int] = None) -> pd.DataFrame:
         """Get the topk documents matching a user's query.

buster/retriever/deeplake.py

@@ -169,7 +169,7 @@ def get_topk_documents(
           The DataFrame containing the matched documents.
         """
         if query is not None:
-            query_embedding = self.get_embedding(query)
+            query_embedding = self.embedding_fn(query)
         elif embedding is not None:
             query_embedding = embedding
         else:

buster/retriever/service.py

@@ -121,15 +121,21 @@ def get_topk_documents(self, query: str, sources: Optional[List[str]], top_k: in
                 logger.warning(f"Sources {sources} do not exist. Returning empty dataframe.")
                 return pd.DataFrame()
 
-        query_embedding = self.get_embedding(query)
+        query_embedding = self.embedding_fn(query)
+        sparse_query_embedding = self.sparse_embedding_fn(query) if self.sparse_embedding_fn is not None else None
 
         if isinstance(query_embedding, np.ndarray):
             # pinecone expects a list of floats, so convert from ndarray if necessary
             query_embedding = query_embedding.tolist()
 
         # Pinecone retrieval
         matches = self.index.query(
-            query_embedding, top_k=top_k, filter=filter, include_values=True, namespace=self.namespace
+            vector=query_embedding,
+            sparse_vector=sparse_query_embedding,
+            top_k=top_k,
+            filter=filter,
+            include_values=True,
+            namespace=self.namespace,
         )["matches"]
         matching_ids = [ObjectId(match.id) for match in matches]
         matching_scores = {match.id: match.score for match in matches}

requirements.txt

@@ -7,6 +7,7 @@ numpy
 openai>=1.0
 pandas
 pinecone-client
+pinecone-text
 pymongo
 pytest
 tabulate

tests/test_chatbot.py

@@ -12,6 +12,7 @@
 from buster.documents_manager import DeepLakeDocumentsManager
 from buster.formatters.documents import DocumentsFormatterHTML
 from buster.formatters.prompts import PromptFormatter
+from buster.llm_utils import get_openai_embedding
 from buster.retriever import DeepLakeRetriever, Retriever
 from buster.tokenizers.gpt import GPTTokenizer
 from buster.validators import Validator
@@ -63,7 +64,7 @@
         "top_k": 3,
         "thresh": 0.7,
         "max_tokens": 2000,
-        "embedding_model": "text-embedding-ada-002",
+        "embedding_fn": get_openai_embedding,
     },
     prompt_formatter_cfg={
         "max_tokens": 3500,
@@ -130,6 +131,8 @@ def __init__(self, **kwargs):
             }
         )
 
+        self.embedding_fn = get_fake_embedding
+
     def get_documents(self, source):
         return self.documents
 
@@ -139,9 +142,6 @@ def get_topk_documents(self, query: str, sources: list[str] = None, top_k: int =
         documents["similarity"] = [np.random.random() for _ in range(len(documents))]
         return documents
 
-    def get_embedding(self, query, engine):
-        return get_fake_embedding()
-
     def get_source_display_name(self, source):
         return source
 
@@ -258,7 +258,7 @@ def test_chatbot_real_data__no_docs_found(vector_store_path):
         buster_cfg = copy.deepcopy(buster_cfg_template)
         buster_cfg.retriever_cfg = {
             "path": vector_store_path,
-            "embedding_model": "text-embedding-ada-002",
+            "embedding_fn": get_openai_embedding,
             "top_k": 3,
             "thresh": 1,  # Set threshold very high to be sure no docs are matched
             "max_tokens": 3000,

tests/test_documents.py

@@ -27,7 +27,7 @@ def test_write_read(tmp_path, documents_manager, retriever):
         "top_k": 3,
         "thresh": 0.7,
         "max_tokens": 2000,
-        "embedding_model": "text-embedding-ada-002",
+        "embedding_fn": get_openai_embedding,
     }
     dm_path = tmp_path / "tmp_dir_2"
     retriever_cfg["path"] = dm_path
@@ -66,7 +66,7 @@ def test_write_write_read(tmp_path, documents_manager, retriever):
         "top_k": 3,
         "thresh": 0.7,
         "max_tokens": 2000,
-        "embedding_model": "text-embedding-ada-002",
+        "embedding_fn": get_openai_embedding,
     }
     db_path = tmp_path / "tmp_dir"
     retriever_cfg["path"] = db_path
@@ -123,7 +123,7 @@ def test_generate_embeddings(tmp_path, monkeypatch):
         "top_k": 3,
         "thresh": 0.85,
         "max_tokens": 3000,
-        "embedding_model": "fake-embedding",
+        "embedding_fn": get_fake_embedding,
     }
     read_df = DeepLakeRetriever(**retriever_cfg).get_documents("my_source")