From 3e000ef4f22470d1f013c7cccc72ec0b7cde513b Mon Sep 17 00:00:00 2001 From: kingtroga Date: Sun, 20 Oct 2024 12:25:19 +0100 Subject: [PATCH 1/5] added Falkordb DSPY support --- .../retrieval_models_clients/FalkordbRM.md | 90 ++++++++ dspy/retrieve/falkordb_rm.py | 215 ++++++++++++++++++ poetry.lock | 27 ++- pyproject.toml | 1 + 4 files changed, 326 insertions(+), 7 deletions(-) create mode 100644 docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md create mode 100644 dspy/retrieve/falkordb_rm.py diff --git a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md new file mode 100644 index 0000000000..f6c5a5b957 --- /dev/null +++ b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md @@ -0,0 +1,90 @@ +# FalkordbRM + +### Constructor + +Initialize an instance of the `FalkordbRM` class. + +```python +FalkordbRM( + node_label: str, + text_node_property: str, + embedding_node_property: str, + k: int = 5, + retrieval_query: str, + embedding_provider: str = "openai", + embedding_model: str = "text-embedding-ada-002", +) +``` + +**Environment Variables:** + +You need to define the credentials as environment variables + +- `FALKORDB_HOST` (_str_): Specifies the host required for connecting with the Falkordb database. If not provided, the system will default to `localhost` + +- `FALKORDB_PORT` (_int_): Specifies the port required for connecting with the Falkordb database. If not provided, the system will default to `6379` + +- `FALKORDB_USERNAME` (_str_, optional): Specifies the username required for authenticating with a [Falkordb Cloud](https://app.falkordb.cloud/signin) database. + +- `FALKORDB_PASSWORD` (_str_, optional): Specifies the password required for authenticating with a [Falkordb Cloud](https://app.falkordb.cloud/signin) database. + +- `FALKORDB_DATABASE` (_str_, optional): Specifies the name of the database to connect to within the Falkordb instance. If not provided, the systems defaults to using a randomly generated four ascii_letters character string e.g "tari". + +- `OPENAI_API_KEY` (_str_): Specifies the API key required for authenticating with OpenAI's services. + +**Parameters:** + +- `node_label` (_str_): Specifies the label of the node to be used within Falkordb for organizing and querying data. +- `text_node_property` (_str_, _optional_): Defines the specific text property of the node that will be returned. +- `embedding_node_property` (_str_): Defines the specific embedding property of the node that will be used within Falkordb for querying data. +- `k` (_int_, _optional_): The number of top results to return from the retrieval operation. It defaults to 5 if not explicitly specified. +- `retrieval_query` (_str_, _optional_): A custom query string provided for retrieving data. If not provided, a default query tailored to the `text_node_property` will be used. +- `embedding_provider` (_str_, _optional_): The name of the service provider for generating embeddings. Only "openai" is supported. +- `embedding_model` (_str_, _optional_): The specific embedding model to use from the provider. By default, it uses the "text-embedding-ada-002" model from OpenAI. + + +### Methods + +#### `forward(self, query: [str], k: Optional[int] = None) -> dspy.Prediction` + +Search the neo4j vector index for the top `k` passages matching the given query or queries, using embeddings generated via the specified `embedding_model`. + +**Parameters:** + +- `query` (str\_): The query. +- `k` (_Optional[int]_, _optional_): The number of results to retrieve. If not specified, defaults to the value set during initialization. + +**Returns:** + +- `dspy.Prediction`: Contains the retrieved passages as a list of string with the prediction signature. + +ex: + +```python +Prediction( + passages=['Passage 1 Lorem Ipsum awesome', 'Passage 2 Lorem Ipsum Youppidoo', 'Passage 3 Lorem Ipsum Yassssss'] +) +``` + +### Quick Example how to use Neo4j in a local environment. + +```python +from dspy.retrieve.falkordb_rm import FalkordbRM +import os + + +os.environ["FALKORDB_HOST"] = 'localhost' +os.environ["FALKORDB_PORT"] = 6379 +os.environ["OPENAI_API_KEY"] = 'sk-' + +retriever_model = FalkordbRM( + node_label="myIndex", + text_node_property="text", + embedding_node_property="embedding" +) + +results = retriever_model("Explore the significance of quantum computing", k=3) + +for passage in results: + print("Document:", passage, "\n") +``` \ No newline at end of file diff --git a/dspy/retrieve/falkordb_rm.py b/dspy/retrieve/falkordb_rm.py new file mode 100644 index 0000000000..06c047765c --- /dev/null +++ b/dspy/retrieve/falkordb_rm.py @@ -0,0 +1,215 @@ +import os +from typing import List, Optional, Union +import string +import random + +import backoff +from openai import ( + APITimeoutError, + InternalServerError, + OpenAI, + RateLimitError, + UnprocessableEntityError, +) + +from dspy import Retrieve, Prediction +from dsp.utils.settings import settings +from dsp.utils import dotdict + +try: + import falkordb +except ImportError: + raise ImportError( + "Please install the falkordb package by running `pip install dspy-ai[falkordb]`" + ) +import redis.exceptions + + +def generate_random_string(length: int) -> str: + characters = string.ascii_letters + # Randomly choose 'length' characters from the pool of possible characters + random_string = "".join(random.choice(characters) for _ in range(length)) + return random_string + + +class Embedder: + def __init__(self, provider: str, model: str): + self.provider = provider + if self.provider == "openai": + api_key = os.getenv("OPENAI_API_KEY") + if not api_key: + raise ValueError( + "Environment variable OPENAI_API_KEY must be set to" + "use openai as embedding provider" + ) + self.client = OpenAI() + self.model = model + else: + raise ValueError(f"Unsupported provider: {provider}") + + @backoff.on_exception( + backoff.expo, + ( + APITimeoutError, + InternalServerError, + RateLimitError, + UnprocessableEntityError, + ), + max_time=settings.backoff_time, + ) + def __call__(self, queries: Union[str, List[str]]) -> List[List[float]]: + if isinstance(queries, str): + queries = [queries] + + if self.provider == "openai": + embedding = self.client.embeddings.create(input=queries, model=self.model) + return [result.embedding for result in embedding.data] + + +DEFAULT_INDEX_QUERY = "CALL db.idx.vector.queryNodes($node_label, $embedding_node_property, $k, vecf32($embedding)) YIELD node, score " + + +class FalkordbRM(Retrieve): + """ + Implements a retriever that utilizes FalkorDB for retrieving passages. + This class manages a connection to a FalkorDB database using official FalkorDB Python drivers and requires + the database credentials. That is, if using a local FalkorDB session, host and port else if using a FalkorDB cloud session, + host, port, username, and password to be set as environment variables and optionally the database name. + Additionally, it utilizes an embedding provider (defaulting to OpenAI's services) to compute query embeddings, + which are then used to find the most relevant nodes in the FalkorDB graph based on the specified node property or custom retrieval query. + + Returns a list of passages in the form of `dspy.Prediction` objects + + Args: + Args: + node_label (str): The label of the node in the FalkorDB database to query against + text_node_property (str): The property of the node containing the text. + embedding_node_property (List[float]): The property of the node containing the embeddings. + k (Optional[int]): The default number of top passages to retrieve. Defaults to 5. + retrieval_query (Optional[str]): Custom Cypher query for retrieving passages. + embedding_provider (str): The provider of the embedding service. Defaults to "openai". + embedding_model (str): The model identifier for generating embeddings. Defaults to "text-embedding-ada-002". + + Examples: + Below is a code snippet showcasing how to initialize FalkordbRM with environment variables for the database connection and OpenAI as the embedding provider: + + ```python + import os + + import dspy + import openai + + os.environ["FALKORDB_HOST"] = "localhost" + os.environ["FALORDB_PORT"] = "6379" + os.environ["OPENAI_API_KEY"] = "sk-" (Only if using openai as embedding's provider) + + # Uncomment and set the following if you are using FalkorDB cloud + # os.environ["FALKORDB_USERNAME"] = "falkordb" + # os.environ["FALKORDB_PASSWORD"] = "password" + + + falkordb_retriever = FalkordbRM( + node_label="myIndex", + text_node_property="text", + k=10, + embedding_provider="openai", + embedding_model="text-embedding-ada-002", + ) + + dspy.settings.configure(rm=falkordb_retriever) + ``` + + In this example, `FalkordbRM` is configured to retrieve nodes based on the "text" property from an index on a node labeled "myIndex", + using embeddings computed by OpenAI's "text-embedding-ada-002" model. + """ + + def __init__( + self, + node_label: str, + text_node_property: str = None, + embedding_node_property: str = None, + k: int = 5, + retrieval_query: Optional[str] = None, + embedding_provider: str = "openai", + embedding_model: str = "text-embedding-ada-002", + ): + super().__init__(k=k) + self.node_label = node_label + self.username = os.getenv("FALKORDB_USERNAME", None) + self.password = os.getenv("FALKORDB_PASSWORD", None) + self.host = os.getenv("FALKORDB_HOST", "localhost") + self.port = int(os.getenv("FALKORDB_PORT", 6379)) + + self.database = os.getenv("FALKORDB_DATABASE", generate_random_string(4)) + self.k = k + self.retrieval_query = retrieval_query + self.text_node_property = text_node_property + self.embedding_node_property = embedding_node_property + if not self.text_node_property and not self.retrieval_query: + raise ValueError( + "Either `text_node_property` or `retrieval_query` must be set" + ) + if not embedding_node_property: + raise ValueError("`embedding_node_property` must be set") + try: + self.driver = falkordb.FalkorDB( + host=self.host, + port=self.port, + username=self.username, + password=self.password, + ).select_graph(self.database) + + except ( + redis.exceptions.ConnectionError, + redis.exceptions.AuthenticationError, + ) as e: + raise ConnectionError("Failed to connect to FalkorDB database") from e + + self.embedder = Embedder(provider=embedding_provider, model=embedding_model) + + def forward( + self, query_or_queries: Union[str, List[str]], k: Optional[int] + ) -> Prediction: + if not isinstance(query_or_queries, list): + query_or_queries = [query_or_queries] + query_vectors = self.embedder(query_or_queries) + contents = [] + retrieval_query = ( + self.retrieval_query + or f"RETURN node.{self.text_node_property} AS text, score" + ) + if not k: + k = self.k + + for vector in query_vectors: + params = { + "embedding": vector, + "node_label": self.node_label, + "text_node_property": self.text_node_property, + "embedding_node_property": self.embedding_node_property, + "k": k, + } + print(params) + try: + records = self.driver.query( + DEFAULT_INDEX_QUERY + retrieval_query, + params=params, + ).result_set + except Exception as e: + if "Invalid arguments" in str(e): + raise ValueError( + f"There is no vector index on node label, {self.node_label}" + f" and node property, {self.embedding_node_property}" + ) + contents.extend( + [ + {"passage": dotdict({"long_text": r[1]}), "score": r[0]} + for r in records + ] + ) + sorted_passages = sorted( + contents, + key=lambda x: x["score"], + reverse=True, + )[:k] + return [el["passage"] for el in sorted_passages] diff --git a/poetry.lock b/poetry.lock index 639c1cd3b5..b6931c6481 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,4 +1,4 @@ -# This file is automatically @generated by Poetry 1.8.2 and should not be changed by hand. +# This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. [[package]] name = "aiohappyeyeballs" @@ -5172,6 +5172,24 @@ files = [ [package.extras] test = ["pytest (>=3.0)", "pytest-asyncio"] +[[package]] +name = "redis" +version = "5.1.1" +description = "Python client for Redis database and key-value store" +optional = false +python-versions = ">=3.8" +files = [ + {file = "redis-5.1.1-py3-none-any.whl", hash = "sha256:f8ea06b7482a668c6475ae202ed8d9bcaa409f6e87fb77ed1043d912afd62e24"}, + {file = "redis-5.1.1.tar.gz", hash = "sha256:f6c997521fedbae53387307c5d0bf784d9acc28d9f1d058abeac566ec4dbed72"}, +] + +[package.dependencies] +async-timeout = {version = ">=4.0.3", markers = "python_full_version < \"3.11.3\""} + +[package.extras] +hiredis = ["hiredis (>=3.0.0)"] +ocsp = ["cryptography (>=36.0.1)", "pyopenssl (==23.2.1)", "requests (>=2.31.0)"] + [[package]] name = "referencing" version = "0.35.1" @@ -6690,11 +6708,6 @@ files = [ {file = "triton-3.0.0-1-cp312-cp312-manylinux2014_x86_64.manylinux_2_17_x86_64.whl", hash = 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= "sha256:e8903767951bf86ec960b4fe4e21bc970055afc65e9d57e916d79ae3c93665e3"}, - {file = "triton-3.0.0-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41004fb1ae9a53fcb3e970745feb87f0e3c94c6ce1ba86e95fa3b8537894bef7"}, ] [package.dependencies] @@ -7624,4 +7637,4 @@ weaviate = ["weaviate-client"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "677d41c9c29f331d34b4b84b8181ba24855daaae3064469f4300a61ad41c4e3b" +content-hash = "21f766292b25fd511d9955521784585546af5feb9b075bcd2a29223b019e4ec1" diff --git a/pyproject.toml b/pyproject.toml index 4520293f6a..1de3c9a5a7 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -135,6 +135,7 @@ jinja2 = "^3.1.3" magicattr = "^0.1.6" litellm = "1.49.1" diskcache = "^5.6.0" +redis = "^5.1.1" [tool.poetry.group.dev.dependencies] From 976a08ea35847c98415fb11c9a75bec1113bb917 Mon Sep 17 00:00:00 2001 From: kingtroga Date: Sun, 20 Oct 2024 12:56:51 +0100 Subject: [PATCH 2/5] added Falkordbrm support --- .../retrieval_models_clients/FalkordbRM.md | 2 +- dspy/retrieve/falkordb_rm.py | 1 - poetry.lock | 15 ++++++++++++++- pyproject.toml | 1 + 4 files changed, 16 insertions(+), 3 deletions(-) diff --git a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md index f6c5a5b957..4a7c738497 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md @@ -62,7 +62,7 @@ ex: ```python Prediction( - passages=['Passage 1 Lorem Ipsum awesome', 'Passage 2 Lorem Ipsum Youppidoo', 'Passage 3 Lorem Ipsum Yassssss'] + passages=['Passage 1 Lorem Ipsum awesom', 'Passage 2 Lorem Ipsum Youppidoo', 'Passage 3 Lorem Ipsum Yassssss'] ) ``` diff --git a/dspy/retrieve/falkordb_rm.py b/dspy/retrieve/falkordb_rm.py index 06c047765c..a9bfc157bc 100644 --- a/dspy/retrieve/falkordb_rm.py +++ b/dspy/retrieve/falkordb_rm.py @@ -27,7 +27,6 @@ def generate_random_string(length: int) -> str: characters = string.ascii_letters - # Randomly choose 'length' characters from the pool of possible characters random_string = "".join(random.choice(characters) for _ in range(length)) return random_string diff --git a/poetry.lock b/poetry.lock index b6931c6481..ef21830c97 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1201,6 +1201,19 @@ files = [ [package.extras] tests = ["asttokens (>=2.1.0)", "coverage", "coverage-enable-subprocess", "ipython", "littleutils", "pytest", "rich"] +[[package]] +name = "falkordb" +version = "1.0.9" +description = "Python client for interacting with FalkorDB database" +optional = false +python-versions = "<4.0,>=3.8" +files = [ + {file = "falkordb-1.0.9.tar.gz", hash = "sha256:177008e63c7e4d9ebbdfeb8cad24b0e49175bb0f6e96cac9b4ffb641c0eff0f1"}, +] + +[package.dependencies] +redis = ">=5.0.1,<6.0.0" + [[package]] name = "fastapi" version = "0.115.0" @@ -7637,4 +7650,4 @@ weaviate = ["weaviate-client"] [metadata] lock-version = "2.0" python-versions = ">=3.9,<3.13" -content-hash = "21f766292b25fd511d9955521784585546af5feb9b075bcd2a29223b019e4ec1" +content-hash = "7fe655d9fef7cb91e261f27df95cbcb515dd50b6613088d74281dddd9ae43d60" diff --git a/pyproject.toml b/pyproject.toml index 1de3c9a5a7..0e04dac25f 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -136,6 +136,7 @@ magicattr = "^0.1.6" litellm = "1.49.1" diskcache = "^5.6.0" redis = "^5.1.1" +falkordb = "^1.0.9" [tool.poetry.group.dev.dependencies] From 63afc8a64101f116b5e52f7d6a29a0f75d99e55e Mon Sep 17 00:00:00 2001 From: kingtroga Date: Tue, 22 Oct 2024 16:14:46 +0100 Subject: [PATCH 3/5] removed print and changed "neo4j" to falkordb --- docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md | 4 ++-- dspy/retrieve/falkordb_rm.py | 1 - 2 files changed, 2 insertions(+), 3 deletions(-) diff --git a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md index 4a7c738497..ffb3202294 100644 --- a/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md +++ b/docs/docs/deep-dive/retrieval_models_clients/FalkordbRM.md @@ -47,7 +47,7 @@ You need to define the credentials as environment variables #### `forward(self, query: [str], k: Optional[int] = None) -> dspy.Prediction` -Search the neo4j vector index for the top `k` passages matching the given query or queries, using embeddings generated via the specified `embedding_model`. +Search the Falkordb vector index for the top `k` passages matching the given query or queries, using embeddings generated via the specified `embedding_model`. **Parameters:** @@ -66,7 +66,7 @@ Prediction( ) ``` -### Quick Example how to use Neo4j in a local environment. +### Quick Example how to use Falkordb in a local environment. ```python from dspy.retrieve.falkordb_rm import FalkordbRM diff --git a/dspy/retrieve/falkordb_rm.py b/dspy/retrieve/falkordb_rm.py index a9bfc157bc..f56144bb31 100644 --- a/dspy/retrieve/falkordb_rm.py +++ b/dspy/retrieve/falkordb_rm.py @@ -188,7 +188,6 @@ def forward( "embedding_node_property": self.embedding_node_property, "k": k, } - print(params) try: records = self.driver.query( DEFAULT_INDEX_QUERY + retrieval_query, From add793a7c1400ea3964fe08cd3ec9082db54b670 Mon Sep 17 00:00:00 2001 From: kingtroga Date: Mon, 4 Nov 2024 04:22:35 +0100 Subject: [PATCH 4/5] add falkordb dependencies to the extras --- poetry.lock | 1245 ++++++++++++++++++++++++++------------------------- setup.py | 3 +- 2 files changed, 640 insertions(+), 608 deletions(-) diff --git a/poetry.lock b/poetry.lock index c081a0a920..22832e722c 100644 --- a/poetry.lock +++ b/poetry.lock @@ -1,6 +1,5 @@ # This file is automatically @generated by Poetry 1.8.3 and should not be changed by hand. - [[package]] name = "aiohappyeyeballs" version = "2.4.3" @@ -181,13 +180,13 @@ files = [ [[package]] name = "anthropic" -version = "0.37.1" +version = "0.38.0" description = "The official Python library for the anthropic API" 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