Removed unneeded references to LangChain (#185)

* Removed uneeded references to LangChain

* Remove unneeded langchain deps

Author: alexthomas93

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")

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 <retriever-configuration>`) and lets you write your own if none of the provided implementations matches your needs (see :ref:`how to write a custom retriever <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
 --------------------

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)
 

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(

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)

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)

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)