Updated

pyproject.toml

@@ -9,6 +9,7 @@ dependencies = [
     "huggingface-hub>=0.30.2",
     "langchain>=0.3.24",
     "langchain-community>=0.3.23",
+    "langchain-google-genai>=2.1.4",
     "langchain-groq>=0.3.2",
     "langchain-huggingface>=0.1.2",
     "langchain-qdrant>=0.2.0",

src/embedding/embedding.py

@@ -1,39 +1,16 @@
-from langchain_huggingface import HuggingFaceEmbeddings
+from langchain_google_genai import GoogleGenerativeAIEmbeddings
+from src.settings import settings
 
+GOOGLE_API_KEY = settings.GOOGLE_API_KEY
 
-# embedding_instance = HuggingFaceEmbeddings(
-#     model_name="all-MiniLM-L6-v2",
-#     model_kwargs={"device": "cpu"},
-# )
 
-# def all_minilm_l6_v2():
-#     """
-#     Return the  embedding instance.
-#     """
-#     return embedding_instance
-
-
-# from sentence_transformers import SentenceTransformer
-# from langchain.embeddings import HuggingFaceEmbeddings
-
-# # Load the sentence-transformers model directly
-# model = SentenceTransformer("sentence-transformers/all-MiniLM-L6-v2")
-
-# # Wrap it in LangChain HuggingFaceEmbeddings by passing the model instance
-# embedding_instance = HuggingFaceEmbeddings(model=model)
-
-# def all_minilm_l6_v2():
-#     """
-#     Return the embedding instance.
-#     """
-#     return embedding_instance
 
 def all_minilm_l6_v2():
-    model_kwargs = {"device": "cpu"}
-    encode_kwargs = {"normalize_embeddings": True}
-    all_minilm_object = HuggingFaceEmbeddings(
-        model_name="sentence-transformers/all-MiniLM-L6-v2",  # or local path if manually downloaded
-        model_kwargs=model_kwargs,
-        encode_kwargs=encode_kwargs
-    )
-    return all_minilm_object
+    """
+    Return the  embedding instance.
+    """
+    embedding_instance = GoogleGenerativeAIEmbeddings(
+            google_api_key=GOOGLE_API_KEY, model="models/text-embedding-004",output_dimensionality=384
+        )
+    return embedding_instance
+

src/settings.py

@@ -8,6 +8,7 @@ class Settings(BaseSettings):
     QDRANT_URL:str
     QDRANT_API_KEY:str
     SERPER_API_KEY:str
+    GOOGLE_API_KEY:str
 
     model_config = SettingsConfigDict(
         env_file=".env",

uv.lock

@@ -179,6 +179,7 @@ dependencies = [
     { name = "huggingface-hub" },
     { name = "langchain" },
     { name = "langchain-community" },
+    { name = "langchain-google-genai" },
     { name = "langchain-groq" },
     { name = "langchain-huggingface" },
     { name = "langchain-qdrant" },
@@ -204,6 +205,7 @@ requires-dist = [
     { name = "huggingface-hub", specifier = ">=0.30.2" },
     { name = "langchain", specifier = ">=0.3.24" },
     { name = "langchain-community", specifier = ">=0.3.23" },
+    { name = "langchain-google-genai", specifier = ">=2.1.4" },
     { name = "langchain-groq", specifier = ">=0.3.2" },
     { name = "langchain-huggingface", specifier = ">=0.1.2" },
     { name = "langchain-qdrant", specifier = ">=0.2.0" },
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+version = "0.6.18"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
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+[package.optional-dependencies]
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+    { name = "grpcio-status" },
+]
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+[[package]]
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+version = "1.71.0"
+source = { registry = "https://pypi.org/simple" }
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+    { name = "grpcio" },
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+]
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+[[package]]
+name = "langchain-google-genai"
+version = "2.1.4"
+source = { registry = "https://pypi.org/simple" }
+dependencies = [
+    { name = "filetype" },
+    { name = "google-ai-generativelanguage" },
+    { name = "langchain-core" },
+    { name = "pydantic" },
+]
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