Feature/weaviate memory

.env.template

@@ -74,6 +74,27 @@ REDIS_PASSWORD=
 WIPE_REDIS_ON_START=False
 MEMORY_INDEX=auto-gpt
 
+### WEAVIATE
+# MEMORY_BACKEND - Use 'weaviate' to use Weaviate vector storage
+# WEAVIATE_HOST - Weaviate host IP
+# WEAVIATE_PORT - Weaviate host port
+# WEAVIATE_PROTOCOL - Weaviate host protocol (e.g. 'http')
+# USE_WEAVIATE_EMBEDDED - Whether to use Embedded Weaviate
+# WEAVIATE_EMBEDDED_PATH - File system path were to persist data when running Embedded Weaviate
+# WEAVIATE_USERNAME - Weaviate username
+# WEAVIATE_PASSWORD - Weaviate password
+# WEAVIATE_API_KEY - Weaviate API key if using API-key-based authentication
+# MEMORY_INDEX - Name of index to create in Weaviate
+WEAVIATE_HOST="127.0.0.1"
+WEAVIATE_PORT=8080
+WEAVIATE_PROTOCOL="http"
+USE_WEAVIATE_EMBEDDED=False
+WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate"
+WEAVIATE_USERNAME=
+WEAVIATE_PASSWORD=
+WEAVIATE_API_KEY=
+MEMORY_INDEX=AutoGpt
+
 ### MILVUS
 # MILVUS_ADDR - Milvus remote address (e.g. localhost:19530)
 # MILVUS_COLLECTION - Milvus collection, 

README.md

@@ -59,6 +59,7 @@ Development of this free, open-source project is made possible by all the <a hre
     - [Redis Setup](#redis-setup)
     - [🌲 Pinecone API Key Setup](#-pinecone-api-key-setup)
     - [Milvus Setup](#milvus-setup)
+    - [Weaviate Setup](#weaviate-setup)
     - [Setting up environment variables](#setting-up-environment-variables-1)
   - [Setting Your Cache Type](#setting-your-cache-type)
   - [View Memory Usage](#view-memory-usage)
@@ -267,7 +268,19 @@ export GOOGLE_API_KEY="YOUR_GOOGLE_API_KEY"
 export CUSTOM_SEARCH_ENGINE_ID="YOUR_CUSTOM_SEARCH_ENGINE_ID"
 ```
 
-## Redis Setup
+## Setting Your Cache Type
+
+By default, Auto-GPT is going to use LocalCache instead of redis or Pinecone.
+
+To switch to either, change the `MEMORY_BACKEND` env variable to the value that you want:
+
+* `local` (default) uses a local JSON cache file
+* `pinecone` uses the Pinecone.io account you configured in your ENV settings
+* `redis` will use the redis cache that you configured
+* `milvus` will use the milvus cache that you configured
+* `weaviate` will use the weaviate cache that you configured
+
+### Redis Setup
 > _**CAUTION**_ \
 This is not intended to be publicly accessible and lacks security measures. Therefore, avoid exposing Redis to the internet without a password or at all
 1. Install docker desktop
@@ -306,20 +319,6 @@ Pinecone enables the storage of vast amounts of vector-based memory, allowing fo
 2. Choose the `Starter` plan to avoid being charged.
 3. Find your API key and region under the default project in the left sidebar.
 
-### Milvus Setup
-
-[Milvus](https://milvus.io/) is a open-source, high scalable vector database to storage huge amount of vector-based memory and provide fast relevant search.
-
-- setup milvus database, keep your pymilvus version and milvus version same to avoid compatible issues.
-  - setup by open source [Install Milvus](https://milvus.io/docs/install_standalone-operator.md)
-  - or setup by [Zilliz Cloud](https://zilliz.com/cloud)
-- set `MILVUS_ADDR` in `.env` to your milvus address `host:ip`.
-- set `MEMORY_BACKEND` in `.env` to `milvus` to enable milvus as backend.
-- optional
-  - set `MILVUS_COLLECTION` in `.env` to change milvus collection name as you want, `autogpt` is the default name.
-
-### Setting up environment variables
-
 In the `.env` file set:
 - `PINECONE_API_KEY`
 - `PINECONE_ENV` (example: _"us-east4-gcp"_)
@@ -343,16 +342,52 @@ export PINECONE_ENV="<YOUR_PINECONE_REGION>" # e.g: "us-east4-gcp"
 export MEMORY_BACKEND="pinecone"
 ```
 
-## Setting Your Cache Type
+### Milvus Setup
 
-By default, Auto-GPT is going to use LocalCache instead of redis or Pinecone.
+[Milvus](https://milvus.io/) is a open-source, high scalable vector database to storage huge amount of vector-based memory and provide fast relevant search.
 
-To switch to either, change the `MEMORY_BACKEND` env variable to the value that you want:
+- setup milvus database, keep your pymilvus version and milvus version same to avoid compatible issues.
+  - setup by open source [Install Milvus](https://milvus.io/docs/install_standalone-operator.md)
+  - or setup by [Zilliz Cloud](https://zilliz.com/cloud)
+- set `MILVUS_ADDR` in `.env` to your milvus address `host:ip`.
+- set `MEMORY_BACKEND` in `.env` to `milvus` to enable milvus as backend.
+- optional
+  - set `MILVUS_COLLECTION` in `.env` to change milvus collection name as you want, `autogpt` is the default name.
 
-* `local` (default) uses a local JSON cache file
-* `pinecone` uses the Pinecone.io account you configured in your ENV settings
-* `redis` will use the redis cache that you configured
 
+### 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). 
+Although still experimental, [Embedded Weaviate](https://weaviate.io/developers/weaviate/installation/embedded) is supported which allows the Auto-GPT process itself to start a Weaviate instance. To enable it, set `USE_WEAVIATE_EMBEDDED` to `True` and make sure you `pip install "weaviate-client>=3.15.4"`. 
+
+#### Setting up environment variables
+
+In your `.env` file set the following:
+
+```
+MEMORY_BACKEND=weaviate
+WEAVIATE_HOST="127.0.0.1" # the IP or domain of the running Weaviate instance
+WEAVIATE_PORT="8080" 
+WEAVIATE_PROTOCOL="http"
+WEAVIATE_USERNAME="your username"
+WEAVIATE_PASSWORD="your password"
+WEAVIATE_API_KEY="your weaviate API key if you have one"
+WEAVIATE_EMBEDDED_PATH="/home/me/.local/share/weaviate" # this is optional and indicates where the data should be persisted when running an embedded instance
+USE_WEAVIATE_EMBEDDED=False # set to True to run Embedded Weaviate
+MEMORY_INDEX="Autogpt" # name of the index to create for the application
+```
+
+### Milvus Setup
+
+[Milvus](https://milvus.io/) is a open-source, high scalable vector database to storage huge amount of vector-based memory and provide fast relevant search.
+
+- setup milvus database, keep your pymilvus version and milvus version same to avoid compatible issues.
+  - setup by open source [Install Milvus](https://milvus.io/docs/install_standalone-operator.md)
+  - or setup by [Zilliz Cloud](https://zilliz.com/cloud)
+- set `MILVUS_ADDR` in `.env` to your milvus address `host:ip`.
+- set `MEMORY_BACKEND` in `.env` to `milvus` to enable milvus as backend.
+- optional
+  - set `MILVUS_COLLECTION` in `.env` to change milvus collection name as you want, `autogpt` is the default name.
+
 ## View Memory Usage
 
 1. View memory usage by using the `--debug` flag :)

autogpt/config/config.py

@@ -66,6 +66,16 @@ def __init__(self) -> None:
         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_protocol = os.getenv("WEAVIATE_PROTOCOL", "http")
+        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_embedded_path = os.getenv("WEAVIATE_EMBEDDED_PATH")
+        self.weaviate_api_key = os.getenv("WEAVIATE_API_KEY", None)
+        self.use_weaviate_embedded = os.getenv("USE_WEAVIATE_EMBEDDED", "False") == "True"
+
         # milvus configuration, e.g., localhost:19530.
         self.milvus_addr = os.getenv("MILVUS_ADDR", "localhost:19530")
         self.milvus_collection = os.getenv("MILVUS_COLLECTION", "autogpt")

autogpt/memory/__init__.py

@@ -21,6 +21,12 @@
     print("Pinecone not installed. Skipping import.")
     PineconeMemory = None
 
+try:
+    from autogpt.memory.weaviate import WeaviateMemory
+except ImportError:
+    print("Weaviate not installed. Skipping import.")
+    WeaviateMemory = None
+
 try:
     from autogpt.memory.milvus import MilvusMemory
 except ImportError:
@@ -48,6 +54,12 @@ def get_memory(cfg, init=False):
             )
         else:
             memory = RedisMemory(cfg)
+    elif cfg.memory_backend == "weaviate":
+        if not WeaviateMemory:
+            print("Error: Weaviate is not installed. Please install weaviate-client to"
+                  " use Weaviate as a memory backend.")
+        else:
+            memory = WeaviateMemory(cfg)
     elif cfg.memory_backend == "milvus":
         if not MilvusMemory:
             print(
@@ -77,4 +89,5 @@ def get_supported_memory_backends():
     "PineconeMemory",
     "NoMemory",
     "MilvusMemory",
+    "WeaviateMemory"
 ]

autogpt/memory/weaviate.py

@@ -0,0 +1,110 @@
+from autogpt.config import Config
+from autogpt.memory.base import MemoryProviderSingleton, get_ada_embedding
+import uuid
+import weaviate
+from weaviate import Client
+from weaviate.embedded import EmbeddedOptions
+from weaviate.util import generate_uuid5
+
+
+def default_schema(weaviate_index):
+    return {
+        "class": weaviate_index,
+        "properties": [
+            {
+                "name": "raw_text",
+                "dataType": ["text"],
+                "description": "original text for the embedding"
+            }
+        ],
+    }
+
+
+class WeaviateMemory(MemoryProviderSingleton):
+    def __init__(self, cfg):
+        auth_credentials = self._build_auth_credentials(cfg)
+
+        url = f'{cfg.weaviate_protocol}://{cfg.weaviate_host}:{cfg.weaviate_port}'
+
+        if cfg.use_weaviate_embedded:
+            self.client = Client(embedded_options=EmbeddedOptions(
+                hostname=cfg.weaviate_host,
+                port=int(cfg.weaviate_port),
+                persistence_data_path=cfg.weaviate_embedded_path
+            ))
+
+            print(f"Weaviate Embedded running on: {url} with persistence path: {cfg.weaviate_embedded_path}")
+        else:
+            self.client = Client(url, auth_client_secret=auth_credentials)
+
+        self.index = cfg.memory_index
+        self._create_schema()
+
+    def _create_schema(self):
+        schema = default_schema(self.index)
+        if not self.client.schema.contains(schema):
+            self.client.schema.create_class(schema)
+
+    def _build_auth_credentials(self, cfg):
+        if cfg.weaviate_username and cfg.weaviate_password:
+            return weaviate.AuthClientPassword(cfg.weaviate_username, cfg.weaviate_password)
+        if cfg.weaviate_api_key:
+            return weaviate.AuthApiKey(api_key=cfg.weaviate_api_key)
+        else:
+            return None
+
+    def add(self, data):
+        vector = get_ada_embedding(data)
+
+        doc_uuid = generate_uuid5(data, self.index)
+        data_object = {
+            'raw_text': data
+        }
+
+        with self.client.batch as batch:
+            batch.add_data_object(
+                uuid=doc_uuid,
+                data_object=data_object,
+                class_name=self.index,
+                vector=vector
+            )
+
+        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(self.index, ['raw_text']) \
+                          .with_near_vector({'vector': query_embedding, 'certainty': 0.7}) \
+                          .with_limit(num_relevant)  \
+                          .do()
+
+            if len(results['data']['Get'][self.index]) > 0:
+                return [str(item['raw_text']) for item in results['data']['Get'][self.index]]
+            else:
+                return []
+
+        except Exception as err:
+            print(f'Unexpected error {err=}, {type(err)=}')
+            return []
+
+    def get_stats(self):
+        result = self.client.query.aggregate(self.index) \
+                     .with_meta_count() \
+                     .do()
+        class_data = result['data']['Aggregate'][self.index]
+
+        return class_data[0]['meta'] if class_data else {}

requirements.txt

@@ -27,4 +27,4 @@ isort
 gitpython==3.1.31
 pytest
 pytest-mock
-tweepy
+tweepy