feat: ability to use local embeddings model (sBERT)

requirements.txt

@@ -17,4 +17,4 @@ orjson
 Pillow
 coverage
 flake8
-numpy
+sentence_transformers

scripts/config.py

@@ -82,6 +82,7 @@ class Config(metaclass=Singleton):
         # Note that indexes must be created on db 0 in redis, this is not configurable.
 
         self.memory_backend = os.getenv("MEMORY_BACKEND", 'local')
+        self.memory_embeder = os.getenv("MEMORY_EMBEDER", 'ada')
         # Initialize the OpenAI API client
         openai.api_key = self.openai_api_key
 

scripts/memory/base.py

@@ -3,16 +3,28 @@ import abc
 from config import AbstractSingleton, Config
 import openai
 
+try:
+    from sentence_transformers import SentenceTransformer
+except ImportError:
+    SentenceTransformer = None
+    if cfg.memory_embeder == "sbert":
+        print("Error: Sentence Transformers is not installed. Please install sentence_transformers"
+            " to use BERT as an embeder. Defaulting to Ada.")
+        cfg.memory_embeder = "ada"
+
+
 cfg = Config()
 
-
-def get_ada_embedding(text):
+def get_embedding(text):
     text = text.replace("\n", " ")
-    if cfg.use_azure:
-        return openai.Embedding.create(input=[text], engine=cfg.get_azure_deployment_id_for_model("text-embedding-ada-002"))["data"][0]["embedding"]
-    else:
-        return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
 
+    if cfg.memory_embeder == "sbert":
+        embedding = SentenceTransformer("sentence-transformers/all-mpnet-base-v2", device="cpu").encode(text, show_progress_bar=False)
+    else:
+        embedding = openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
+    
+    return embedding
+    
 
 class MemoryProviderSingleton(AbstractSingleton):
     @abc.abstractmethod

scripts/memory/local.py

@@ -3,10 +3,12 @@ import orjson
 from typing import Any, List, Optional
 import numpy as np
 import os
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from memory.base import MemoryProviderSingleton, get_embedding
+from config import Config 
 
+cfg = Config()
 
-EMBED_DIM = 1536
+EMBED_DIM = 1536 if cfg.memory_embeder == "ada" else 768
 SAVE_OPTIONS = orjson.OPT_SERIALIZE_NUMPY | orjson.OPT_SERIALIZE_DATACLASS
 
 
@@ -58,7 +60,7 @@ class LocalCache(MemoryProviderSingleton):
             return ""
         self.data.texts.append(text)
 
-        embedding = get_ada_embedding(text)
+        embedding = get_embedding(text)
 
         vector = np.array(embedding).astype(np.float32)
         vector = vector[np.newaxis, :]
@@ -109,7 +111,7 @@ class LocalCache(MemoryProviderSingleton):
 
         Returns: List[str]
         """
-        embedding = get_ada_embedding(text)
+        embedding = get_embedding(text)
 
         scores = np.dot(self.data.embeddings, embedding)
 

scripts/memory/pinecone.py

@@ -1,17 +1,16 @@
 
 import pinecone
 
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from memory.base import MemoryProviderSingleton, get_embedding
 from logger import logger
 from colorama import Fore, Style
 
-
 class PineconeMemory(MemoryProviderSingleton):
     def __init__(self, cfg):
         pinecone_api_key = cfg.pinecone_api_key
         pinecone_region = cfg.pinecone_region
         pinecone.init(api_key=pinecone_api_key, environment=pinecone_region)
-        dimension = 1536
+        dimension = 1536 if cfg.memory_embeder == "ada" else 768
         metric = "cosine"
         pod_type = "p1"
         table_name = "auto-gpt"
@@ -33,7 +32,7 @@ class PineconeMemory(MemoryProviderSingleton):
         self.index = pinecone.Index(table_name)
 
     def add(self, data):
-        vector = get_ada_embedding(data)
+        vector = get_embedding(data)
         # no metadata here. We may wish to change that long term.
         resp = self.index.upsert([(str(self.vec_num), vector, {"raw_text": data})])
         _text = f"Inserting data into memory at index: {self.vec_num}:\n data: {data}"
@@ -53,7 +52,7 @@ class PineconeMemory(MemoryProviderSingleton):
         :param data: The data to compare to.
         :param num_relevant: The number of relevant data to return. Defaults to 5
         """
-        query_embedding = get_ada_embedding(data)
+        query_embedding = get_embedding(data)
         results = self.index.query(query_embedding, top_k=num_relevant, include_metadata=True)
         sorted_results = sorted(results.matches, key=lambda x: x.score)
         return [str(item['metadata']["raw_text"]) for item in sorted_results]

scripts/memory/redismem.py

@@ -6,10 +6,14 @@ from redis.commands.search.query import Query
 from redis.commands.search.indexDefinition import IndexDefinition, IndexType
 import numpy as np
 
-from memory.base import MemoryProviderSingleton, get_ada_embedding
+from memory.base import MemoryProviderSingleton, get_embedding
 from logger import logger
 from colorama import Fore, Style
+from config import Config 
 
+cfg = Config()
+
+EMBED_DIM = 1536 if cfg.memory_embeder == "ada" else 768
 
 SCHEMA = [
     TextField("data"),
@@ -18,7 +22,7 @@ SCHEMA = [
         "HNSW",
         {
             "TYPE": "FLOAT32",
-            "DIM": 1536,
+            "DIM": EMBED_DIM,
             "DISTANCE_METRIC": "COSINE"
         }
     ),
@@ -38,7 +42,6 @@ class RedisMemory(MemoryProviderSingleton):
         redis_host = cfg.redis_host
         redis_port = cfg.redis_port
         redis_password = cfg.redis_password
-        self.dimension = 1536
         self.redis = redis.Redis(
             host=redis_host,
             port=redis_port,
@@ -83,7 +86,7 @@ class RedisMemory(MemoryProviderSingleton):
         """
         if 'Command Error:' in data:
             return ""
-        vector = get_ada_embedding(data)
+        vector = get_embedding(data)
         vector = np.array(vector).astype(np.float32).tobytes()
         data_dict = {
             b"data": data,
@@ -131,7 +134,7 @@ class RedisMemory(MemoryProviderSingleton):
 
         Returns: A list of the most relevant data.
         """
-        query_embedding = get_ada_embedding(data)
+        query_embedding = get_embedding(data)
         base_query = f"*=>[KNN {num_relevant} @embedding $vector AS vector_score]"
         query = Query(base_query).return_fields(
             "data",