Chatgpt4j AGI 74

.env.example

@@ -21,6 +21,12 @@ OPENAI_TEMPERATURE=0.0
 # TABLE_NAME can be used instead
 RESULTS_STORE_NAME=baby-agi-test-table
 
+# Weaviate config
+# Uncomment and fill these to switch from local ChromaDB to Weaviate
+# WEAVIATE_USE_EMBEDDED=true
+# WEAVIATE_URL=
+# WEAVIATE_API_KEY=
+
 # Pinecone config
 # Uncomment and fill these to switch from local ChromaDB to Pinecone
 # PINECONE_API_KEY=

README.md

@@ -1,26 +1,26 @@
 # Translations:
 
-[<img title="عربي" alt="عربي" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/sa.svg" width="22">](docs/README-ar.md)
-[<img title="Français" alt="Français" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/fr.svg" width="22">](docs/README-fr.md)
-[<img title="Polski" alt="Polski" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/pl.svg" width="22">](docs/README-pl.md)
-[<img title="Portuguese" alt="Portuguese" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/br.svg" width="22">](docs/README-pt-br.md)
-[<img title="Romanian" alt="Romanian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ro.svg" width="22">](docs/README-ro.md)
-[<img title="Russian" alt="Russian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ru.svg" width="22">](docs/README-ru.md)
-[<img title="Slovenian" alt="Slovenian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/si.svg" width="22">](docs/README-si.md)
-[<img title="Spanish" alt="Spanish" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/es.svg" width="22">](docs/README-es.md)
-[<img title="Turkish" alt="Turkish" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/tr.svg" width="22">](docs/README-tr.md)
-[<img title="Ukrainian" alt="Ukrainian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ua.svg" width="22">](docs/README-ua.md)
-[<img title="简体中文" alt="Simplified Chinese" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/cn.svg" width="22">](docs/README-cn.md)
-[<img title="繁體中文 (Traditional Chinese)" alt="繁體中文 (Traditional Chinese)" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/tw.svg" width="22">](docs/README-zh-tw.md)
-[<img title="日本語" alt="日本語" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/jp.svg" width="22">](docs/README-ja.md)
-[<img title="한국어" alt="한국어" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/kr.svg" width="22">](docs/README-ko.md)
-[<img title="Magyar" alt="Magyar" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/hu.svg" width="22">](docs/README-hu.md)
-[<img title="فارسی" alt="فارسی" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ir.svg" width="22">](docs/README-fa.md)
-[<img title="German" alt="German" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/de.svg" width="22">](docs/README-de.md)
+[<img title="عربي" alt="عربي" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/sa.svg" width="30">](docs/README-ar.md)
+[<img title="Français" alt="Français" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/fr.svg" width="30">](docs/README-fr.md)
+[<img title="Polski" alt="Polski" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/pl.svg" width="30">](docs/README-pl.md)
+[<img title="Portuguese" alt="Portuguese" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/br.svg" width="30">](docs/README-pt-br.md)
+[<img title="Romanian" alt="Romanian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ro.svg" width="30">](docs/README-ro.md)
+[<img title="Russian" alt="Russian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ru.svg" width="30">](docs/README-ru.md)
+[<img title="Slovenian" alt="Slovenian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/si.svg" width="30">](docs/README-si.md)
+[<img title="Spanish" alt="Spanish" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/es.svg" width="30">](docs/README-es.md)
+[<img title="Turkish" alt="Turkish" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/tr.svg" width="30">](docs/README-tr.md)
+[<img title="Ukrainian" alt="Ukrainian" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ua.svg" width="30">](docs/README-ua.md)
+[<img title="简体中文" alt="Simplified Chinese" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/cn.svg" width="30">](docs/README-cn.md)
+[<img title="繁體中文 (Traditional Chinese)" alt="繁體中文 (Traditional Chinese)" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/tw.svg" width="30">](docs/README-zh-tw.md)
+[<img title="日本語" alt="日本語" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/jp.svg" width="30">](docs/README-ja.md)
+[<img title="한국어" alt="한국어" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/kr.svg" width="30">](docs/README-ko.md)
+[<img title="Magyar" alt="Magyar" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/hu.svg" width="30">](docs/README-hu.md)
+[<img title="فارسی" alt="فارسی" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/ir.svg" width="30">](docs/README-fa.md)
+[<img title="German" alt="German" src="https://cdn.staticaly.com/gh/hjnilsson/country-flags/master/svg/de.svg" width="30">](docs/README-de.md)
 
 # Objective
 
-This Python script is an example of an AI-powered task management system. The system uses OpenAI and Chroma to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma to store and retrieve task results for context. This is a pared-down version of the original [Task-Driven Autonomous Agent](https://twitter.com/yoheinakajima/status/1640934493489070080?s=20) (Mar 28, 2023).
+This Python script is an example of an AI-powered task management system. The system uses OpenAI and vector databases such as Chroma or Weaviate to create, prioritize, and execute tasks. The main idea behind this system is that it creates tasks based on the result of previous tasks and a predefined objective. The script then uses OpenAI's natural language processing (NLP) capabilities to create new tasks based on the objective, and Chroma/Weaviate to store and retrieve task results for context. This is a pared-down version of the original [Task-Driven Autonomous Agent](https://twitter.com/yoheinakajima/status/1640934493489070080?s=20) (Mar 28, 2023).
 
 This README will cover the following:
 
@@ -38,19 +38,19 @@ The script works by running an infinite loop that does the following steps:
 
 1. Pulls the first task from the task list.
 2. Sends the task to the execution agent, which uses OpenAI's API to complete the task based on the context.
-3. Enriches the result and stores it in [Chroma](https://docs.trychroma.com).
+3. Enriches the result and stores it in [Chroma](https://docs.trychroma.com)/[Weaviate](https://weaviate.io/).
 4. Creates new tasks and reprioritizes the task list based on the objective and the result of the previous task.
    </br>
 
-![image](https://user-images.githubusercontent.com/6764957/232961802-e4c4c17d-b520-4db1-827c-a218a1647c73.png)
+![image](https://user-images.githubusercontent.com/21254008/235015461-543a897f-70cc-4b63-941a-2ae3c9172b11.png)
 
 The `execution_agent()` function is where the OpenAI API is used. It takes two parameters: the objective and the task. It then sends a prompt to OpenAI's API, which returns the result of the task. The prompt consists of a description of the AI system's task, the objective, and the task itself. The result is then returned as a string.
 
 The `task_creation_agent()` function is where OpenAI's API is used to create new tasks based on the objective and the result of the previous task. The function takes four parameters: the objective, the result of the previous task, the task description, and the current task list. It then sends a prompt to OpenAI's API, which returns a list of new tasks as strings. The function then returns the new tasks as a list of dictionaries, where each dictionary contains the name of the task.
 
 The `prioritization_agent()` function is where OpenAI's API is used to reprioritize the task list. The function takes one parameter, the ID of the current task. It sends a prompt to OpenAI's API, which returns the reprioritized task list as a numbered list.
 
-Finally, the script uses Chroma to store and retrieve task results for context. The script creates a Chroma collection based on the table name specified in the TABLE_NAME variable. Chroma is then used to store the results of the task in the collection, along with the task name and any additional metadata.
+Finally, the script uses Chroma/Weaviate to store and retrieve task results for context. The script creates a Chroma/Weaviate collection based on the table name specified in the TABLE_NAME variable. Chroma/Weaviate is then used to store the results of the task in the collection, along with the task name and any additional metadata.
 
 # How to Use<a name="how-to-use"></a>
 

babyagi.py

@@ -1,4 +1,8 @@
 #!/usr/bin/env python3
+from dotenv import load_dotenv
+# Load default environment variables (.env)
+load_dotenv()
+
 import os
 import time
 import logging
@@ -10,15 +14,12 @@
 import tiktoken as tiktoken
 from chromadb.utils.embedding_functions import OpenAIEmbeddingFunction
 from chromadb.api.types import Documents, EmbeddingFunction, Embeddings
-from dotenv import load_dotenv
 import re
 
 # default opt out of chromadb telemetry.
 from chromadb.config import Settings
 client = chromadb.Client(Settings(anonymized_telemetry=False))
 
-# Load default environment variables (.env)
-load_dotenv()
 
 # Engine configuration
 
@@ -237,20 +238,33 @@ def query(self, query: str, top_results_num: int) -> List[dict]:
 
 
 # Initialize results storage
-results_storage = DefaultResultsStorage()
-PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "")
-if PINECONE_API_KEY:
-    if can_import("extensions.pinecone_storage"):
+def try_weaviate():
+    WEAVIATE_URL = os.getenv("WEAVIATE_URL", "")
+    WEAVIATE_USE_EMBEDDED = os.getenv("WEAVIATE_USE_EMBEDDED", "False").lower() == "true"
+    if (WEAVIATE_URL or WEAVIATE_USE_EMBEDDED) and can_import("extensions.weaviate_storage"):
+        WEAVIATE_API_KEY = os.getenv("WEAVIATE_API_KEY", "")
+        from extensions.weaviate_storage import WeaviateResultsStorage
+        print("\nUsing results storage: " + "\033[93m\033[1m" + "Weaviate" + "\033[0m\033[0m")
+        return WeaviateResultsStorage(OPENAI_API_KEY, WEAVIATE_URL, WEAVIATE_API_KEY, WEAVIATE_USE_EMBEDDED, LLM_MODEL, LLAMA_MODEL_PATH, RESULTS_STORE_NAME, OBJECTIVE)
+    return None
+
+def try_pinecone():
+    PINECONE_API_KEY = os.getenv("PINECONE_API_KEY", "")
+    if PINECONE_API_KEY and can_import("extensions.pinecone_storage"):
         PINECONE_ENVIRONMENT = os.getenv("PINECONE_ENVIRONMENT", "")
         assert (
             PINECONE_ENVIRONMENT
         ), "\033[91m\033[1m" + "PINECONE_ENVIRONMENT environment variable is missing from .env" + "\033[0m\033[0m"
         from extensions.pinecone_storage import PineconeResultsStorage
+        print("\nUsing results storage: " + "\033[93m\033[1m" + "Pinecone" + "\033[0m\033[0m")
+        return PineconeResultsStorage(OPENAI_API_KEY, PINECONE_API_KEY, PINECONE_ENVIRONMENT, LLM_MODEL, LLAMA_MODEL_PATH, RESULTS_STORE_NAME, OBJECTIVE)
+    return None
 
-        results_storage = PineconeResultsStorage(OPENAI_API_KEY, PINECONE_API_KEY, PINECONE_ENVIRONMENT, LLM_MODEL,
-                                                 LLAMA_MODEL_PATH, RESULTS_STORE_NAME, OBJECTIVE)
-        print("\nReplacing results storage: " + "\033[93m\033[1m" + "Pinecone" + "\033[0m\033[0m")
+def use_chroma():
+    print("\nUsing results storage: " + "\033[93m\033[1m" + "Chroma (Default)" + "\033[0m\033[0m")
+    return DefaultResultsStorage()
 
+results_storage = try_weaviate() or try_pinecone() or use_chroma()
 
 # Task storage supporting only a single instance of BabyAGI
 class SingleTaskListStorage: