added support for multiple memory provider and added weaviate integration

.env.template

@@ -9,4 +9,10 @@ CUSTOM_SEARCH_ENGINE_ID=
 USE_AZURE=False
 OPENAI_API_BASE=your-base-url-for-azure
 OPENAI_API_VERSION=api-version-for-azure
-OPENAI_DEPLOYMENT_ID=deployment-id-for-azure
+OPENAI_DEPLOYMENT_ID=deployment-id-for-azure
+WEAVIATE_HOST="http://127.0.0.1"
+WEAVIATE_PORT="8080"
+WEAVIATE_USERNAME=
+WEAVIATE_PASSWORD=
+WEAVIATE_INDEX="Autogpt"
+MEMORY_PROVIDER="weaviate"

README.md

@@ -140,7 +140,13 @@ export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"
 
 ```
 
-## 🌲 Pinecone API Key Setup
+## Vector based memory provider
+Auto-GPT supports two providers for vector-based memory, [Pinecone](https://www.pinecone.io/) and [Weaviate](https://weaviate.io/). To select the provider to use, specify the following in your `.env`:
+
+```
+MEMORY_PROVIDER="pinecone" # change to "weaviate" to use weaviate as the memory provider
+```
+### 🌲 Pinecone API Key Setup
 
 Pinecone enable a vector based memory so a vast memory can be stored and only relevant memories
 are loaded for the agent at any given time.
@@ -149,7 +155,7 @@ are loaded for the agent at any given time.
 2. Choose the `Starter` plan to avoid being charged.
 3. Find your API key and region under the default project in the left sidebar.
 
-### Setting up environment variables
+#### Setting up environment variables
    For Windows Users:
 ```
 setx PINECONE_API_KEY "YOUR_PINECONE_API_KEY"
@@ -165,6 +171,22 @@ export PINECONE_ENV="Your pinecone region" # something like: us-east4-gcp
 
 Or you can set them in the `.env` file.
 
+### Weaviate Setup
+
+[Weaviate](https://weaviate.io/) is an open-source vector database. It allows to store data objects and vector embeddings from ML-models and scales seamlessly to billion of data objects. [An instance of Weaviate can be created locally (using Docker), on Kubernetes or using Weaviate Cloud Services](https://weaviate.io/developers/weaviate/quickstart). 
+
+#### Setting up enviornment variables
+
+In your `.env` file set the following:
+
+```
+WEAVIATE_HOST="http://127.0.0.1" # the URL of the running Weaviate instance
+WEAVIATE_PORT="8080" 
+WEAVIATE_USERNAME="your username"
+WEAVIATE_PASSWORD="your password"
+WEAVIATE_INDEX="Autogpt" # name of the index to create for the application
+```
+
 ## View Memory Usage
 
 1. View memory usage by using the `--debug` flag :)

requirements.txt

@@ -12,3 +12,4 @@ docker
 duckduckgo-search
 google-api-python-client #(https://developers.google.com/custom-search/v1/overview)
 pinecone-client==2.2.1
+weaviate-client==3.15.4

scripts/commands.py

@@ -1,6 +1,6 @@
 import browse
 import json
-from memory import PineconeMemory
+from factory import MemoryFactory
 import datetime
 import agent_manager as agents
 import speak
@@ -52,7 +52,7 @@ def get_command(response):
 
 
 def execute_command(command_name, arguments):
-    memory = PineconeMemory()
+    memory = MemoryFactory.get_memory(cfg.memory_provider)
     try:
         if command_name == "google":
             

scripts/config.py

@@ -50,9 +50,17 @@ class Config(metaclass=Singleton):
         self.google_api_key = os.getenv("GOOGLE_API_KEY")
         self.custom_search_engine_id = os.getenv("CUSTOM_SEARCH_ENGINE_ID")
 
+        self.memory_provider = os.getenv("MEMORY_PROVIDER", 'pinecone')
         self.pinecone_api_key = os.getenv("PINECONE_API_KEY")
         self.pinecone_region = os.getenv("PINECONE_ENV")
 
+        self.weaviate_host  = os.getenv("WEAVIATE_HOST")
+        self.weaviate_port = os.getenv("WEAVIATE_PORT")
+        self.weaviate_username = os.getenv("WEAVIATE_USERNAME", None)
+        self.weaviate_password = os.getenv("WEAVIATE_PASSWORD", None)
+        self.weaviate_scopes = os.getenv("WEAVIATE_SCOPES", None)
+        self.weaviate_index = os.getenv("WEAVIATE_INDEX", 'auto-gpt')
+
         # User agent headers to use when browsing web
         # Some websites might just completely deny request with an error code if no user agent was found.
         self.user_agent_header = {"User-Agent":"Mozilla/5.0 (Macintosh; Intel Mac OS X 10_15_4) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/83.0.4103.97 Safari/537.36"}

scripts/factory.py

@@ -0,0 +1,11 @@
+from providers.pinecone import PineconeMemory
+from providers.weaviate import WeaviateMemory
+
+class MemoryFactory:
+    @staticmethod
+    def get_memory(mem_type):
+        if mem_type == 'pinecone':
+            return PineconeMemory()
+
+        if mem_type == 'weaviate':
+            return WeaviateMemory()

scripts/main.py

@@ -1,7 +1,7 @@
 import json
 import random
 import commands as cmd
-from memory import PineconeMemory
+from factory import MemoryFactory
 import data
 import chat
 from colorama import Fore, Style
@@ -283,7 +283,7 @@ user_input = "Determine which next command to use, and respond using the format
 
 # Initialize memory and make sure it is empty.
 # this is particularly important for indexing and referencing pinecone memory
-memory = PineconeMemory()
+memory = MemoryFactory.get_memory(cfg.memory_provider)
 memory.clear()
 
 print('Using memory of type: ' + memory.__class__.__name__)

scripts/providers/memory.py

@@ -0,0 +1,26 @@
+from config import Singleton
+import openai
+
+def get_ada_embedding(text):
+    text = text.replace("\n", " ")
+    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
+
+
+def get_text_from_embedding(embedding):
+    return openai.Embedding.retrieve(embedding, model="text-embedding-ada-002")["data"][0]["text"]
+
+class Memory(metaclass=Singleton):
+    def add(self, data):
+        raise NotImplementedError()
+
+    def get(self, data):
+        raise NotImplementedError()
+
+    def clear(self):
+        raise NotImplementedError()
+
+    def get_relevant(self, data, num_relevant=5):
+        raise NotImplementedError()
+
+    def get_stats(self):
+        raise NotImplementedError()

scripts/providers/pinecone.py

@@ -1,20 +1,10 @@
-from config import Config, Singleton
+from config import Config
+from providers.memory import Memory, get_ada_embedding
 import pinecone
-import openai
 
 cfg = Config()
 
-
-def get_ada_embedding(text):
-    text = text.replace("\n", " ")
-    return openai.Embedding.create(input=[text], model="text-embedding-ada-002")["data"][0]["embedding"]
-
-
-def get_text_from_embedding(embedding):
-    return openai.Embedding.retrieve(embedding, model="text-embedding-ada-002")["data"][0]["text"]
-
-
-class PineconeMemory(metaclass=Singleton):
+class PineconeMemory(Memory):
     def __init__(self):
         pinecone_api_key = cfg.pinecone_api_key
         pinecone_region = cfg.pinecone_region
@@ -58,4 +48,4 @@ class PineconeMemory(metaclass=Singleton):
         return [str(item['metadata']["raw_text"]) for item in sorted_results]
 
     def get_stats(self):
-        return self.index.describe_index_stats()
+        return self.index.describe_index_stats()

scripts/providers/weaviate.py

@@ -0,0 +1,100 @@
+from config import Config
+from providers.memory import Memory, get_ada_embedding
+from weaviate import Client
+import weaviate
+import uuid
+
+from weaviate.util import generate_uuid5
+
+cfg = Config()
+
+SCHEMA = {
+    "class": cfg.weaviate_index,
+    "properties": [
+        {
+            "name": "raw_text",
+            "dataType": ["text"],
+            "description": "original text for the embedding"
+        }
+    ],
+}
+
+class WeaviateMemory(Memory):
+
+    def __init__(self):
+        auth_credentials = self._build_auth_credentials()
+
+        url = f'{cfg.weaviate_host}:{cfg.weaviate_port}'
+
+        self.client = Client(url, auth_client_secret=auth_credentials)
+
+        self._create_schema()
+
+    def _create_schema(self):
+        if not self.client.schema.contains(SCHEMA):
+            self.client.schema.create_class(SCHEMA)
+
+    @staticmethod
+    def _build_auth_credentials():
+        if cfg.weaviate_username and cfg.weaviate_password:
+            return weaviate_auth.AuthClientPassword(cfg.weaviate_username, cfg.weaviate_password)
+        else:
+            return None
+
+    def add(self, data):
+        vector = get_ada_embedding(data)
+
+        doc_uuid = generate_uuid5(data, cfg.weaviate_index)
+        data_object = {
+            'class': cfg.weaviate_index,
+            'raw_text': data
+        }
+
+        with self.client.batch as batch:
+            batch.add_data_object(
+                uuid=doc_uuid,
+                data_object=data_object,
+                class_name=cfg.weaviate_index,
+                vector=vector
+            )
+
+            batch.flush()
+
+        return f"Inserting data into memory at uuid: {doc_uuid}:\n data: {data}"
+
+
+    def get(self, data):
+        return self.get_relevant(data, 1)
+
+
+    def clear(self):
+        self.client.schema.delete_all()
+
+        # weaviate does not yet have a neat way to just remove the items in an index
+        # without removing the entire schema, therefore we need to re-create it 
+        # after a call to delete_all
+        self._create_schema()
+
+        return 'Obliterated'
+
+    def get_relevant(self, data, num_relevant=5):
+        query_embedding = get_ada_embedding(data)
+        try:
+            results = self.client.query.get(cfg.weaviate_index, ['raw_text']) \
+                          .with_near_vector({'vector': query_embedding, 'certainty': 0.7}) \
+                          .with_limit(num_relevant)  \
+                          .do()
+
+            print(results)
+             
+            if len(results['data']['Get'][cfg.weaviate_index]) > 0:
+                return [str(item['raw_text']) for item in results['data']['Get'][cfg.weaviate_index]]
+            else:
+                return []
+
+        except Exception as err:
+            print(f'Unexpected error {err=}, {type(err)=}')
+            return []
+
+    def get_stats(self):
+        return self.client.index_stats.get(cfg.weaviate_index)