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22af93d851
This addresses #6291 adding support for using Cassandra (and compatible databases, such as DataStax Astra DB) as a [Vector Store](https://cwiki.apache.org/confluence/display/CASSANDRA/CEP-30%3A+Approximate+Nearest+Neighbor(ANN)+Vector+Search+via+Storage-Attached+Indexes). A new class `Cassandra` is introduced, which complies with the contract and interface for a vector store, along with the corresponding integration test, a sample notebook and modified dependency toml. Dependencies: the implementation relies on the library `cassio`, which simplifies interacting with Cassandra for ML- and LLM-oriented workloads. CassIO, in turn, uses the `cassandra-driver` low-lever drivers to communicate with the database. The former is added as optional dependency (+ in `extended_testing`), the latter was already in the project. Integration testing relies on a locally-running instance of Cassandra. [Here](https://cassio.org/more_info/#use-a-local-vector-capable-cassandra) a detailed description can be found on how to compile and run it (at the time of writing the feature has not made it yet to a release). During development of the integration tests, I added a new "fake embedding" class for what I consider a more controlled way of testing the MMR search method. Likewise, I had to amend what looked like a glitch in the behaviour of `ConsistentFakeEmbeddings` whereby an `embed_query` call would have bypassed storage of the requested text in the class cache for use in later repeated invocations. @dev2049 might be the right person to tag here for a review. Thank you! --------- Co-authored-by: rlm <pexpresss31@gmail.com>
75 lines
2.7 KiB
Python
75 lines
2.7 KiB
Python
"""Fake Embedding class for testing purposes."""
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import math
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from typing import List
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from langchain.embeddings.base import Embeddings
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fake_texts = ["foo", "bar", "baz"]
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class FakeEmbeddings(Embeddings):
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"""Fake embeddings functionality for testing."""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Return simple embeddings.
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Embeddings encode each text as its index."""
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return [[float(1.0)] * 9 + [float(i)] for i in range(len(texts))]
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def embed_query(self, text: str) -> List[float]:
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"""Return constant query embeddings.
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Embeddings are identical to embed_documents(texts)[0].
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Distance to each text will be that text's index,
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as it was passed to embed_documents."""
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return [float(1.0)] * 9 + [float(0.0)]
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class ConsistentFakeEmbeddings(FakeEmbeddings):
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"""Fake embeddings which remember all the texts seen so far to return consistent
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vectors for the same texts."""
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def __init__(self) -> None:
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self.known_texts: List[str] = []
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""Return consistent embeddings for each text seen so far."""
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out_vectors = []
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for text in texts:
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if text not in self.known_texts:
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self.known_texts.append(text)
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vector = [float(1.0)] * 9 + [float(self.known_texts.index(text))]
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out_vectors.append(vector)
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return out_vectors
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def embed_query(self, text: str) -> List[float]:
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"""Return consistent embeddings for the text, if seen before, or a constant
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one if the text is unknown."""
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if text not in self.known_texts:
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return [float(1.0)] * 9 + [float(0.0)]
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return [float(1.0)] * 9 + [float(self.known_texts.index(text))]
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class AngularTwoDimensionalEmbeddings(Embeddings):
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"""
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From angles (as strings in units of pi) to unit embedding vectors on a circle.
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"""
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def embed_documents(self, texts: List[str]) -> List[List[float]]:
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"""
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Make a list of texts into a list of embedding vectors.
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"""
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return [self.embed_query(text) for text in texts]
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def embed_query(self, text: str) -> List[float]:
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"""
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Convert input text to a 'vector' (list of floats).
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If the text is a number, use it as the angle for the
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unit vector in units of pi.
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Any other input text becomes the singular result [0, 0] !
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"""
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try:
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angle = float(text)
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return [math.cos(angle * math.pi), math.sin(angle * math.pi)]
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except ValueError:
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# Assume: just test string, no attention is paid to values.
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return [0.0, 0.0]
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