From 789fb353eac9f92043050fd85467adfdcad14e3c Mon Sep 17 00:00:00 2001 From: Alex Thomas Date: Wed, 16 Oct 2024 16:23:01 +0100 Subject: [PATCH] Removed unneeded references to LangChain (#185) * Removed uneeded references to LangChain * Remove unneeded langchain deps --- docs/source/index.rst | 2 +- docs/source/user_guide_rag.rst | 5 +- examples/graphrag_with_langchain_llm.py | 6 +- examples/openai_search.py | 4 +- examples/pinecone/text_search.py | 9 +- examples/qdrant/text_search.py | 8 +- .../weaviate/text_search_local_embedder.py | 8 +- poetry.lock | 2235 +++++++++-------- pyproject.toml | 4 +- .../components/text_splitters/llamaindex.py | 4 +- tests/e2e/pinecone_e2e/test_pinecone_e2e.py | 10 +- tests/e2e/qdrant_e2e/test_qdrant_e2e.py | 10 +- tests/e2e/weaviate_e2e/test_weaviate_e2e.py | 10 +- 13 files changed, 1195 insertions(+), 1120 deletions(-) diff --git a/docs/source/index.rst b/docs/source/index.rst index 85a9a5e7..c4744cc8 100644 --- a/docs/source/index.rst +++ b/docs/source/index.rst @@ -152,8 +152,8 @@ While the library has more retrievers than shown here, the following examples sh .. code:: python from neo4j import GraphDatabase + from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings from neo4j_graphrag.retrievers import VectorRetriever - from langchain_openai import OpenAIEmbeddings URI = "neo4j://localhost:7687" AUTH = ("neo4j", "password") diff --git a/docs/source/user_guide_rag.rst b/docs/source/user_guide_rag.rst index 38f772e3..84585bac 100644 --- a/docs/source/user_guide_rag.rst +++ b/docs/source/user_guide_rag.rst @@ -15,7 +15,7 @@ To perform a GraphRAG query using the `neo4j-graphrag` package, a few components 1. A Neo4j driver: used to query your Neo4j database. 2. A Retriever: the `neo4j-graphrag` package provides some implementations (see the :ref:`dedicated section `) and lets you write your own if none of the provided implementations matches your needs (see :ref:`how to write a custom retriever `). -3. An LLM: to generate the answer, we need to call an LLM model. The neo4j-graphrag package currently only provides implementation for the OpenAI LLMs, but its interface is compatible with LangChain and let developers write their own interface if needed. +3. An LLM: to generate the answer, we need to call an LLM model. The neo4j-graphrag package's LLM interface is compatible with LangChain. Developers can also write their own interface if needed. In practice, it's done with only a few lines of code: @@ -223,8 +223,7 @@ Its interface is compatible with our `GraphRAG` interface, facilitating integrat print(response.answer) -It is however not mandatory to use LangChain. The alternative is to implement -a custom model. +It is however not mandatory to use LangChain. Using a Custom Model -------------------- diff --git a/examples/graphrag_with_langchain_llm.py b/examples/graphrag_with_langchain_llm.py index e3f11f7a..3fe87c2f 100644 --- a/examples/graphrag_with_langchain_llm.py +++ b/examples/graphrag_with_langchain_llm.py @@ -10,9 +10,9 @@ import logging import neo4j -from langchain.chat_models import ChatOpenAI -from langchain.embeddings import OpenAIEmbeddings +from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings from neo4j_graphrag.generation import GraphRAG +from neo4j_graphrag.llm import OpenAILLM from neo4j_graphrag.retrievers import VectorCypherRetriever from neo4j_graphrag.types import RetrieverResultItem @@ -48,7 +48,7 @@ def formatter(record: neo4j.Record) -> RetrieverResultItem: embedder=embedder, # type: ignore ) -llm = ChatOpenAI(model_name="gpt-4o", temperature=0) # type: ignore +llm = OpenAILLM(model_name="gpt-4o", model_params={"temperature": 0}) rag = GraphRAG(retriever=retriever, llm=llm) diff --git a/examples/openai_search.py b/examples/openai_search.py index cbbaeed6..f4d7e2ae 100644 --- a/examples/openai_search.py +++ b/examples/openai_search.py @@ -1,7 +1,7 @@ from random import random -from langchain_openai import OpenAIEmbeddings from neo4j import GraphDatabase +from neo4j_graphrag.embeddings.openai import OpenAIEmbeddings from neo4j_graphrag.indexes import create_vector_index from neo4j_graphrag.retrievers import VectorRetriever @@ -19,7 +19,7 @@ embedder = OpenAIEmbeddings(model="text-embedding-3-large") # Initialize the retriever -retriever = VectorRetriever(driver, INDEX_NAME, embedder) # type: ignore +retriever = VectorRetriever(driver, INDEX_NAME, embedder) # Creating the index create_vector_index( diff --git a/examples/pinecone/text_search.py b/examples/pinecone/text_search.py index b48e187f..606e361e 100644 --- a/examples/pinecone/text_search.py +++ b/examples/pinecone/text_search.py @@ -1,5 +1,7 @@ -from langchain_huggingface.embeddings import HuggingFaceEmbeddings from neo4j import GraphDatabase +from neo4j_graphrag.embeddings.sentence_transformers import ( + SentenceTransformerEmbeddings, +) from neo4j_graphrag.retrievers import PineconeNeo4jRetriever from pinecone import Pinecone @@ -11,14 +13,13 @@ def main() -> None: with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver: pc_client = Pinecone(PC_API_KEY) - embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") - + embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") retriever = PineconeNeo4jRetriever( driver=neo4j_driver, client=pc_client, index_name="jeopardy", id_property_neo4j="id", - embedder=embedder, # type: ignore + embedder=embedder, ) res = retriever.search(query_text="biology", top_k=2) diff --git a/examples/qdrant/text_search.py b/examples/qdrant/text_search.py index 5c79fcee..7f5e3003 100644 --- a/examples/qdrant/text_search.py +++ b/examples/qdrant/text_search.py @@ -1,5 +1,7 @@ -from langchain_huggingface.embeddings import HuggingFaceEmbeddings from neo4j import GraphDatabase +from neo4j_graphrag.embeddings.sentence_transformers import ( + SentenceTransformerEmbeddings, +) from neo4j_graphrag.retrievers import QdrantNeo4jRetriever from qdrant_client import QdrantClient @@ -9,14 +11,14 @@ def main() -> None: with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver: - embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") retriever = QdrantNeo4jRetriever( driver=neo4j_driver, client=QdrantClient(url="http://localhost:6333"), collection_name="Jeopardy", id_property_external="neo4j_id", id_property_neo4j="id", - embedder=embedder, # type: ignore + embedder=embedder, ) res = retriever.search(query_text="biology", top_k=2) diff --git a/examples/weaviate/text_search_local_embedder.py b/examples/weaviate/text_search_local_embedder.py index ad80d9c5..5767ad23 100644 --- a/examples/weaviate/text_search_local_embedder.py +++ b/examples/weaviate/text_search_local_embedder.py @@ -1,5 +1,7 @@ -from langchain_huggingface.embeddings import HuggingFaceEmbeddings from neo4j import GraphDatabase +from neo4j_graphrag.embeddings.sentence_transformers import ( + SentenceTransformerEmbeddings, +) from neo4j_graphrag.retrievers import WeaviateNeo4jRetriever from weaviate.connect.helpers import connect_to_local @@ -10,14 +12,14 @@ def main() -> None: with GraphDatabase.driver(NEO4J_URL, auth=NEO4J_AUTH) as neo4j_driver: with connect_to_local() as w_client: - embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") + embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") retriever = WeaviateNeo4jRetriever( driver=neo4j_driver, client=w_client, collection="Jeopardy", id_property_external="neo4j_id", id_property_neo4j="id", - embedder=embedder, # type: ignore + embedder=embedder, ) res = retriever.search(query_text="biology", top_k=2) diff --git a/poetry.lock b/poetry.lock index ff6b82ba..c1aa00c4 100644 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b/src/neo4j_graphrag/experimental/components/text_splitters/llamaindex.py @@ -32,7 +32,9 @@ class LlamaIndexTextSplitterAdapter(TextSplitter): .. code-block:: python from llama_index.core.node_parser.text.sentence import SentenceSplitter - from neo4j_graphrag.experimental.components.text_splitters.langchain import LangChainTextSplitterAdapter + from neo4j_graphrag.experimental.components.text_splitters.llamaindex import ( + LlamaIndexTextSplitterAdapter, + ) from neo4j_graphrag.experimental.pipeline import Pipeline pipeline = Pipeline() diff --git a/tests/e2e/pinecone_e2e/test_pinecone_e2e.py b/tests/e2e/pinecone_e2e/test_pinecone_e2e.py index 2d00d9db..be1c0d2c 100644 --- a/tests/e2e/pinecone_e2e/test_pinecone_e2e.py +++ b/tests/e2e/pinecone_e2e/test_pinecone_e2e.py @@ -18,8 +18,10 @@ from unittest.mock import MagicMock import pytest -from langchain_huggingface.embeddings import HuggingFaceEmbeddings from neo4j_graphrag.embeddings.base import Embedder +from neo4j_graphrag.embeddings.sentence_transformers import ( + SentenceTransformerEmbeddings, +) from neo4j_graphrag.retrievers import PineconeNeo4jRetriever from neo4j_graphrag.types import RetrieverResult, RetrieverResultItem from pinecone import Pinecone @@ -28,8 +30,10 @@ @pytest.fixture(scope="module") -def sentence_transformer_embedder() -> Generator[HuggingFaceEmbeddings, Any, Any]: - embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") +def sentence_transformer_embedder() -> ( + Generator[SentenceTransformerEmbeddings, Any, Any] +): + embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") yield embedder diff --git a/tests/e2e/qdrant_e2e/test_qdrant_e2e.py b/tests/e2e/qdrant_e2e/test_qdrant_e2e.py index bb7f0d95..8355abd7 100644 --- a/tests/e2e/qdrant_e2e/test_qdrant_e2e.py +++ b/tests/e2e/qdrant_e2e/test_qdrant_e2e.py @@ -17,9 +17,11 @@ from typing import Any, Generator import pytest -from langchain_huggingface.embeddings import HuggingFaceEmbeddings from neo4j import Driver from neo4j_graphrag.embeddings.base import Embedder +from neo4j_graphrag.embeddings.sentence_transformers import ( + SentenceTransformerEmbeddings, +) from neo4j_graphrag.retrievers import QdrantNeo4jRetriever from neo4j_graphrag.types import RetrieverResult, RetrieverResultItem from qdrant_client import QdrantClient @@ -29,8 +31,10 @@ @pytest.fixture(scope="module") -def sentence_transformer_embedder() -> Generator[HuggingFaceEmbeddings, Any, Any]: - embedder = HuggingFaceEmbeddings(model_name="all-MiniLM-L6-v2") +def sentence_transformer_embedder() -> ( + Generator[SentenceTransformerEmbeddings, Any, Any] +): + embedder = SentenceTransformerEmbeddings(model="all-MiniLM-L6-v2") yield embedder diff --git a/tests/e2e/weaviate_e2e/test_weaviate_e2e.py b/tests/e2e/weaviate_e2e/test_weaviate_e2e.py index 102253e0..c5583172 100644 --- a/tests/e2e/weaviate_e2e/test_weaviate_e2e.py +++ b/tests/e2e/weaviate_e2e/test_weaviate_e2e.py @@ -17,9 +17,11 @@ from typing import Any, 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