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Fix imports related to the removal of dsp/* (#1937)
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* Fix dspy.dsp imports

* Update setup.py to exclude dsp/
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@okhat
okhat authored Dec 13, 2024
1 parent 65ba23a commit 1c4f050
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4 changes: 2 additions & 2 deletions dspy/__init__.py
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configure_dspy_loggers(__name__)

from dspy.dsp.modules.colbertv2 import ColBERTv2
# from dspy.dsp.modules.you import You
from dspy.dsp.colbertv2 import ColBERTv2
# from dspy.dsp.you import You

configure = settings.configure
context = settings.context
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0 dspy/dsp/modules/cache_utils.py → dspy/dsp/cache_utils.py
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2 changes: 1 addition & 1 deletion dspy/dsp/modules/colbertv2.py → dspy/dsp/colbertv2.py
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import requests

from dspy.dsp.modules.cache_utils import CacheMemory, NotebookCacheMemory
from dspy.dsp.cache_utils import CacheMemory, NotebookCacheMemory
from dspy.dsp.utils import dotdict

# TODO: Ideally, this takes the name of the index and looks up its port.
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312 changes: 156 additions & 156 deletions dspy/retrieve/faiss_rm.py
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"""Retriever model for faiss: https://github.com/facebookresearch/faiss.
Author: Jagane Sundar: https://github.com/jagane.
"""

import logging
from typing import Optional, Union

import numpy as np

import dspy
from dspy.dsp.modules.sentence_vectorizer import SentenceTransformersVectorizer
from dspy.dsp.utils import dotdict

try:
import faiss
except ImportError:
faiss = None

if faiss is None:
raise ImportError(
"""
The faiss package is required. Install it using `pip install dspy-ai[faiss-cpu]`
""",
)


logger = logging.getLogger(__name__)
class FaissRM(dspy.Retrieve):
"""A retrieval module that uses an in-memory Faiss to return the top passages for a given query.
Args:
document_chunks: the input text chunks
vectorizer: an object that is a subclass of BaseSentenceVectorizer
k (int, optional): The number of top passages to retrieve. Defaults to 3.
Returns:
dspy.Prediction: An object containing the retrieved passages.
Examples:
Below is a code snippet that shows how to use this as the default retriver:
```python
import dspy
from dspy.retrieve import faiss_rm
document_chunks = [
"The superbowl this year was played between the San Francisco 49ers and the Kanasas City Chiefs",
"Pop corn is often served in a bowl",
"The Rice Bowl is a Chinese Restaurant located in the city of Tucson, Arizona",
"Mars is the fourth planet in the Solar System",
"An aquarium is a place where children can learn about marine life",
"The capital of the United States is Washington, D.C",
"Rock and Roll musicians are honored by being inducted in the Rock and Roll Hall of Fame",
"Music albums were published on Long Play Records in the 70s and 80s",
"Sichuan cuisine is a spicy cuisine from central China",
"The interest rates for mortgages is considered to be very high in 2024",
]
frm = faiss_rm.FaissRM(document_chunks)
turbo = dspy.OpenAI(model="gpt-3.5-turbo")
dspy.settings.configure(lm=turbo, rm=frm)
print(frm(["I am in the mood for Chinese food"]))
```
Below is a code snippet that shows how to use this in the forward() function of a module
```python
self.retrieve = FaissRM(k=num_passages)
```
"""

def __init__(self, document_chunks, vectorizer=None, k: int = 3):
"""Inits the faiss retriever.
Args:
document_chunks: a list of input strings.
vectorizer: an object that is a subclass of BaseTransformersVectorizer.
k: number of matches to return.
"""
if vectorizer:
self._vectorizer = vectorizer
else:
self._vectorizer = SentenceTransformersVectorizer()
embeddings = self._vectorizer(document_chunks)
xb = np.array(embeddings)
d = len(xb[0])
logger.info(f"FaissRM: embedding size={d}")
if len(xb) < 100:
self._faiss_index = faiss.IndexFlatL2(d)
self._faiss_index.add(xb)
else:
# if we have at least 100 vectors, we use Voronoi cells
nlist = 100
quantizer = faiss.IndexFlatL2(d)
self._faiss_index = faiss.IndexIVFFlat(quantizer, d, nlist)
self._faiss_index.train(xb)
self._faiss_index.add(xb)

logger.info(f"{self._faiss_index.ntotal} vectors in faiss index")
self._document_chunks = document_chunks # save the input document chunks

super().__init__(k=k)

def _dump_raw_results(self, queries, index_list, distance_list) -> None:
for i in range(len(queries)):
indices = index_list[i]
distances = distance_list[i]
logger.debug(f"Query: {queries[i]}")
for j in range(len(indices)):
logger.debug(f" Hit {j} = {indices[j]}/{distances[j]}: {self._document_chunks[indices[j]]}")
return

def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = None, **kwargs) -> dspy.Prediction:
"""Search the faiss index for k or self.k top passages for query.
Args:
query_or_queries (Union[str, List[str]]): The query or queries to search for.
Returns:
dspy.Prediction: An object containing the retrieved passages.
"""
queries = [query_or_queries] if isinstance(query_or_queries, str) else query_or_queries
queries = [q for q in queries if q] # Filter empty queries
embeddings = self._vectorizer(queries)
emb_npa = np.array(embeddings)
# For single query, just look up the top k passages
if len(queries) == 1:
distance_list, index_list = self._faiss_index.search(emb_npa, k or self.k)
# self._dump_raw_results(queries, index_list, distance_list)
passages = [(self._document_chunks[ind], ind) for ind in index_list[0]]
return [dotdict({"long_text": passage[0], "index": passage[1]}) for passage in passages]

distance_list, index_list = self._faiss_index.search(emb_npa, (k or self.k) * 3, **kwargs)
# self._dump_raw_results(queries, index_list, distance_list)
passage_scores = {}
for emb in range(len(embeddings)):
indices = index_list[emb] # indices of neighbors for embeddings[emb] - this is an array of k*3 integers
distances = distance_list[
emb
] # distances of neighbors for embeddings[emb] - this is an array of k*3 floating point numbers
for res in range((k or self.k) * 3):
neighbor = indices[res]
distance = distances[res]
if neighbor in passage_scores:
passage_scores[neighbor].append(distance)
else:
passage_scores[neighbor] = [distance]
# Note re. sorting:
# first degree sort: number of queries that got a hit with any particular document chunk. More
# is a better match. This is len(queries)-len(x[1])
# second degree sort: sum of the distances of each hit returned by faiss. Smaller distance is a better match
sorted_passages = sorted(passage_scores.items(), key=lambda x: (len(queries) - len(x[1]), sum(x[1])))[
: k or self.k
]
return [
dotdict({"long_text": self._document_chunks[passage_index], "index": passage_index})
for passage_index, _ in sorted_passages
]
# """Retriever model for faiss: https://github.com/facebookresearch/faiss.
# Author: Jagane Sundar: https://github.com/jagane.
# """

# import logging
# from typing import Optional, Union

# import numpy as np

# import dspy
# from dspy.dsp.modules.sentence_vectorizer import SentenceTransformersVectorizer
# from dspy.dsp.utils import dotdict

# try:
# import faiss
# except ImportError:
# faiss = None

# if faiss is None:
# raise ImportError(
# """
# The faiss package is required. Install it using `pip install dspy-ai[faiss-cpu]`
# """,
# )


# logger = logging.getLogger(__name__)
# class FaissRM(dspy.Retrieve):
# """A retrieval module that uses an in-memory Faiss to return the top passages for a given query.

# Args:
# document_chunks: the input text chunks
# vectorizer: an object that is a subclass of BaseSentenceVectorizer
# k (int, optional): The number of top passages to retrieve. Defaults to 3.

# Returns:
# dspy.Prediction: An object containing the retrieved passages.

# Examples:
# Below is a code snippet that shows how to use this as the default retriver:
# ```python
# import dspy
# from dspy.retrieve import faiss_rm

# document_chunks = [
# "The superbowl this year was played between the San Francisco 49ers and the Kanasas City Chiefs",
# "Pop corn is often served in a bowl",
# "The Rice Bowl is a Chinese Restaurant located in the city of Tucson, Arizona",
# "Mars is the fourth planet in the Solar System",
# "An aquarium is a place where children can learn about marine life",
# "The capital of the United States is Washington, D.C",
# "Rock and Roll musicians are honored by being inducted in the Rock and Roll Hall of Fame",
# "Music albums were published on Long Play Records in the 70s and 80s",
# "Sichuan cuisine is a spicy cuisine from central China",
# "The interest rates for mortgages is considered to be very high in 2024",
# ]

# frm = faiss_rm.FaissRM(document_chunks)
# turbo = dspy.OpenAI(model="gpt-3.5-turbo")
# dspy.settings.configure(lm=turbo, rm=frm)
# print(frm(["I am in the mood for Chinese food"]))
# ```

# Below is a code snippet that shows how to use this in the forward() function of a module
# ```python
# self.retrieve = FaissRM(k=num_passages)
# ```
# """

# def __init__(self, document_chunks, vectorizer=None, k: int = 3):
# """Inits the faiss retriever.

# Args:
# document_chunks: a list of input strings.
# vectorizer: an object that is a subclass of BaseTransformersVectorizer.
# k: number of matches to return.
# """
# if vectorizer:
# self._vectorizer = vectorizer
# else:
# self._vectorizer = SentenceTransformersVectorizer()
# embeddings = self._vectorizer(document_chunks)
# xb = np.array(embeddings)
# d = len(xb[0])
# logger.info(f"FaissRM: embedding size={d}")
# if len(xb) < 100:
# self._faiss_index = faiss.IndexFlatL2(d)
# self._faiss_index.add(xb)
# else:
# # if we have at least 100 vectors, we use Voronoi cells
# nlist = 100
# quantizer = faiss.IndexFlatL2(d)
# self._faiss_index = faiss.IndexIVFFlat(quantizer, d, nlist)
# self._faiss_index.train(xb)
# self._faiss_index.add(xb)

# logger.info(f"{self._faiss_index.ntotal} vectors in faiss index")
# self._document_chunks = document_chunks # save the input document chunks

# super().__init__(k=k)

# def _dump_raw_results(self, queries, index_list, distance_list) -> None:
# for i in range(len(queries)):
# indices = index_list[i]
# distances = distance_list[i]
# logger.debug(f"Query: {queries[i]}")
# for j in range(len(indices)):
# logger.debug(f" Hit {j} = {indices[j]}/{distances[j]}: {self._document_chunks[indices[j]]}")
# return

# def forward(self, query_or_queries: Union[str, list[str]], k: Optional[int] = None, **kwargs) -> dspy.Prediction:
# """Search the faiss index for k or self.k top passages for query.

# Args:
# query_or_queries (Union[str, List[str]]): The query or queries to search for.

# Returns:
# dspy.Prediction: An object containing the retrieved passages.
# """
# queries = [query_or_queries] if isinstance(query_or_queries, str) else query_or_queries
# queries = [q for q in queries if q] # Filter empty queries
# embeddings = self._vectorizer(queries)
# emb_npa = np.array(embeddings)
# # For single query, just look up the top k passages
# if len(queries) == 1:
# distance_list, index_list = self._faiss_index.search(emb_npa, k or self.k)
# # self._dump_raw_results(queries, index_list, distance_list)
# passages = [(self._document_chunks[ind], ind) for ind in index_list[0]]
# return [dotdict({"long_text": passage[0], "index": passage[1]}) for passage in passages]

# distance_list, index_list = self._faiss_index.search(emb_npa, (k or self.k) * 3, **kwargs)
# # self._dump_raw_results(queries, index_list, distance_list)
# passage_scores = {}
# for emb in range(len(embeddings)):
# indices = index_list[emb] # indices of neighbors for embeddings[emb] - this is an array of k*3 integers
# distances = distance_list[
# emb
# ] # distances of neighbors for embeddings[emb] - this is an array of k*3 floating point numbers
# for res in range((k or self.k) * 3):
# neighbor = indices[res]
# distance = distances[res]
# if neighbor in passage_scores:
# passage_scores[neighbor].append(distance)
# else:
# passage_scores[neighbor] = [distance]
# # Note re. sorting:
# # first degree sort: number of queries that got a hit with any particular document chunk. More
# # is a better match. This is len(queries)-len(x[1])
# # second degree sort: sum of the distances of each hit returned by faiss. Smaller distance is a better match
# sorted_passages = sorted(passage_scores.items(), key=lambda x: (len(queries) - len(x[1]), sum(x[1])))[
# : k or self.k
# ]
# return [
# dotdict({"long_text": self._document_chunks[passage_index], "index": passage_index})
# for passage_index, _ in sorted_passages
# ]
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