Use GIST Embeddings (#28)

* Use langchain-community Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com> * Update qdrant langchain_community Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com> * Use GIST Embedding for docs Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com> * Use GIST embedding for indicators Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com> --------- Signed-off-by: Aivin V. Solatorio <avsolatorio@gmail.com>
worldbank · Feb 26, 2024 · a4ab398 · a4ab398
1 parent 5e000f5
commit a4ab398
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Showing 4 changed files with 19 additions and 9 deletions.
diff --git a/llm4data/embeddings/base.py b/llm4data/embeddings/base.py
@@ -1,6 +1,6 @@
 """Base classes for embedding models."""
 from typing import Union, Optional
-from langchain import embeddings as langchain_embeddings
+from langchain_community import embeddings as langchain_embeddings
 from pydantic.main import ModelMetaclass
 from qdrant_client.http import models
 from pydantic.main import ModelMetaclass
@@ -17,6 +17,10 @@ class BaseEmbeddingModel:
         "instruct": 768,
         "all-MiniLM-L6-v2": 384,
         "multi-qa-mpnet-base-dot-v1": 768,
+        "avsolatorio/GIST-all-MiniLM-L6-v2": 384,
+        "avsolatorio/GIST-small-Embedding-v0": 384,
+        "avsolatorio/GIST-Embedding-v0": 768,
+        "avsolatorio/GIST-large-Embedding-v0": 384,
     }
     model_name: str
     distance: Union[str, models.Distance]
@@ -36,7 +40,7 @@ class BaseEmbeddingModel:
 
     @property
     def model_id(self):
-        return f"{self.data_type}_{self.model_name}_{self.collection_name}_{self.distance}_{self.size}_{self.max_tokens}_{self.is_instruct}"
+        return f"{self.data_type}_{self.model_name.replace('/', '_')}_{self.collection_name}_{self.distance}_{self.size}_{self.max_tokens}_{self.is_instruct}"
 
     def dict(self):
         return asdict(self)

diff --git a/llm4data/embeddings/docs.py b/llm4data/embeddings/docs.py
@@ -15,7 +15,7 @@ def get_docs_embeddings():
 
     if DOCS_EMBEDDINGS is None:
         DOCS_EMBEDDINGS = DocsEmbedding(
-            model_name="all-MiniLM-L6-v2",
+            model_name="avsolatorio/GIST-small-Embedding-v0",
             distance="Cosine",
             embedding_cls="HuggingFaceEmbeddings",
             is_instruct=False,

diff --git a/llm4data/embeddings/indicators.py b/llm4data/embeddings/indicators.py
@@ -14,13 +14,19 @@ def get_indicators_embeddings():
     global INDICATORS_EMBEDDINGS
 
     if INDICATORS_EMBEDDINGS is None:
+        # INDICATORS_EMBEDDINGS = IndicatorsEmbedding(
+        #     model_name="instruct",
+        #     distance="Cosine",
+        #     embedding_cls="HuggingFaceInstructEmbeddings",
+        #     is_instruct=True,
+        #     embed_instruction="Represent the Economic Development description for retrieval; Input: ",
+        #     query_instruction="Represent the Economic Development prompt for retrieving descriptions; Input: ",
+        # )
         INDICATORS_EMBEDDINGS = IndicatorsEmbedding(
-            model_name="instruct",
+            model_name="avsolatorio/GIST-small-Embedding-v0",
             distance="Cosine",
-            embedding_cls="HuggingFaceInstructEmbeddings",
-            is_instruct=True,
-            embed_instruction="Represent the Economic Development description for retrieval; Input: ",
-            query_instruction="Represent the Economic Development prompt for retrieving descriptions; Input: ",
+            embedding_cls="HuggingFaceEmbeddings",
+            is_instruct=False,
         )
 
     return INDICATORS_EMBEDDINGS
diff --git a/llm4data/index/qdrant.py b/llm4data/index/qdrant.py
@@ -1,6 +1,6 @@
 import os
 from typing import Optional, Union
-from langchain.vectorstores import Qdrant
+from langchain_community.vectorstores import Qdrant
 import qdrant_client
 from qdrant_client.http import models