Dev

.github/workflows/main.yml

@@ -6,27 +6,47 @@ on:
       - master
 
 jobs:
-  docker:
-    runs-on: ubuntu-latest
-
+  push_to_registries:
+    name: Push Docker image to multiple registries
+    runs-on: self-hosted
+    permissions:
+      packages: write
+      contents: read
     steps:
-    - name: Checkout code
-      uses: actions/checkout@v2
-
-    - name: Set up Docker Buildx
-      uses: docker/setup-buildx-action@v1
-
-    - name: Login to GitHub Container Registry
-      uses: docker/login-action@v1 
-      with:
-        registry: ghcr.io
-        username: ${{ github.repository_owner }}
-        password: ${{ secrets.GHCR_PAT }}
-
-    - name: Build and push
-      uses: docker/build-push-action@v2
-      with:
-        context: .
-        file: ./Eve/Dockerfile
-        push: true
-        tags: latest
+      - name: Check out the repo
+        uses: actions/checkout@v3
+
+      - name: Log in to Docker Hub
+        uses: docker/login-action@v2
+        with:
+          username: ${{ secrets.DOCKER_USERNAME }}
+          password: ${{ secrets.DOCKER_TOKEN }}
+
+      - name: Log in to the Container registry
+        uses: docker/login-action@v2
+        with:
+          registry: ghcr.io
+          username: ${{ github.actor }}
+          password: ${{ secrets.GITHUB_TOKEN }}
+
+      - name: Extract metadata (tags, labels) for Docker
+        id: meta
+        uses: docker/metadata-action@v4
+        with:
+          images: |
+            webgrip/cogniswarm
+          tags: |
+            type=ref,event=branch
+            type=ref,event=pr
+            type=semver,pattern={{version}}
+            type=semver,pattern={{major}}.{{minor}}
+
+      - name: Build and push Docker images
+        uses: docker/build-push-action@v4
+        with:
+          context: .
+          file: ./Eve/Dockerfile
+          push: ${{ github.event_name != 'pull_request' }}
+          tags: ${{ steps.meta.outputs.tags }}
+          labels: ${{ steps.meta.outputs.labels }}
+          no-cache: true

Eve/Dockerfile

@@ -15,21 +15,32 @@ RUN rm -rf /var/cache/apk/*
 ENV VIRTUAL_ENV=/opt/venv
 RUN python3 -m venv $VIRTUAL_ENV
 ENV PATH="$VIRTUAL_ENV/bin:$PATH"
+WORKDIR /app
+
+COPY Eve/requirements-compiled.txt .
+RUN pip3 install --upgrade pip
+RUN pip3 install --no-cache-dir -r requirements-compiled.txt
+
+FROM builder AS prebuilt
+
+COPY Eve/requirements-prebuilt.txt .
+RUN pip3 install --upgrade pip
+RUN pip3 install --no-cache-dir -r requirements-prebuilt.txt
 
-COPY Eve/requirements.txt .
+FROM prebuilt AS extra
 
+COPY Eve/requirements-extra.txt .
 RUN pip3 install --upgrade pip
-RUN pip3 install --no-cache-dir -r requirements.txt
+RUN pip3 install --no-cache-dir -r requirements-extra.txt
 
-FROM builder AS final
+FROM extra AS final
 
-COPY --from=builder /opt/venv /opt/venv
+COPY --from=extra /opt/venv /opt/venv
 COPY --from=builder /usr/include/xlocale.h /usr/include/xlocale.h
 ENV PATH=/opt/venv/bin:$PATH
 
 WORKDIR /app
 
 COPY ./Eve ./app
 
-#CMD [ "/venv/bin/python", "main.py" ]
 ENTRYPOINT ["tail", "-f", "/dev/null"]

Eve/agents/AutonomousAgent.py

@@ -1,79 +1,32 @@
+from functools import partial
+from json import tool
+import os
 import math
 from array import array
 from langchain.llms.base import BaseLLM
-
-from typing import Sequence
-
-from .GenerativeAgent import GenerativeAgent
-
+from langchain.prompts import load_prompt
 from langchain.vectorstores import Weaviate
 from langchain.retrievers import TimeWeightedVectorStoreRetriever
 from langchain.embeddings import OpenAIEmbeddings
 from langchain.schema import BaseRetriever
-from langchain import PromptTemplate
-from langchain.tools.base import BaseTool
-from langchain.agents import ZeroShotAgent
-from langchain.prompts import load_prompt
-
-
-import os
-
-import weaviate 
-
-
-WEAVIATE_HOST = os.getenv("WEAVIATE_HOST", "")
-WEAVIATE_VECTORIZER = os.getenv("WEAVIATE_VECTORIZER", "")
-
-def relevance_score_fn(score: float) -> float:
-    """Return a similarity score on a scale [0, 1]."""
-    # This will differ depending on a few things:
-    # - the distance / similarity metric used by the VectorStore
-    # - the scale of your embeddings (OpenAI's are unit norm. Many others are not!)
-    # This function converts the euclidean norm of normalized embeddings
-    # (0 is most similar, sqrt(2) most dissimilar)
-    # to a similarity function (0 to 1)
-    return 1.0 - score / math.sqrt(2)
+import weaviate
+from .GenerativeAgent import GenerativeAgent
 
+# Helper function to create a default memory retriever
 def create_new_memory_retriever_default():
-    """Create a new vector store retriever unique to the agent."""
-
-    client = weaviate.Client(
-        url=WEAVIATE_HOST,
-        additional_headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY")},
-        # auth_client_secret: Optional[AuthCredentials] = None,
-        # timeout_config: Union[Tuple[Real, Real], Real] = (10, 60),
-        # proxies: Union[dict, str, None] = None,
-        # trust_env: bool = False,
-        # additional_headers: Optional[dict] = None,
-        # startup_period: Optional[int] = 5,
-        # embedded_options=[],
-    )
-
-    embeddings_model = OpenAIEmbeddings(
-        #deployment="your-embeddings-deployment-name",
-        model="text-embedding-ada-002"
-    )
-
+    client = weaviate.Client(url=os.getenv("WEAVIATE_HOST"), additional_headers={"X-OpenAI-Api-Key": os.getenv("OPENAI_API_KEY")})
+    embeddings_model = OpenAIEmbeddings(model="text-embedding-ada-002")
     vectorstore = Weaviate(client, "Paragraph", "content", embedding=embeddings_model, relevance_score_fn=relevance_score_fn)
-
     return TimeWeightedVectorStoreRetriever(vectorstore=vectorstore, other_score_keys=["importance"], k=15)
 
-class AutonomousAgent():
+# Relevance score function
+def relevance_score_fn(score: float) -> float:
+    return 1.0 - score / math.sqrt(2)
 
-    def make(
-        self,
-        name: str,
-        age: int,
-        traits: str,
-        status: str,
-        llm: BaseLLM,
-        daily_summaries: array,
-        reflection_threshold: int = 8,
-        memory_retriever: BaseRetriever = create_new_memory_retriever_default(),
-        verbose: bool = False
-    )->GenerativeAgent:
+class AutonomousAgent:
 
-        return GenerativeAgent( # TODO current_plan
+    def make_agent(self, name: str, age: int, traits: str, status: str, llm: BaseLLM, daily_summaries: array, reflection_threshold: int = 8, memory_retriever: BaseRetriever = create_new_memory_retriever_default(), verbose: bool = False):
+        return GenerativeAgent(
             name=name,
             age=age,
             traits=traits,
@@ -82,50 +35,27 @@ def make(
             memory_retriever=memory_retriever,
             llm=llm,
             daily_summaries=daily_summaries,
-            verbose=verbose,
+            verbose=verbose
         )
 
-    def getPrompt(generativeAgent: GenerativeAgent, objective, operating_system, tool_names, tools_summary)->PromptTemplate:
+    def get_prompt(self, generative_agent: GenerativeAgent, tools):
 
-        prompt = load_prompt("prompts/ryan.json")
-        prompt.partial(agent_summary=generativeAgent.get_summary(True))
-        prompt.format(
-            task = objective,
-            objective = objective,
-            agent_name = "Ryan",
-            operating_system = operating_system,
-            tool_names = tool_names,
-            tools_summary = tools_summary,
-            agent_summary = generativeAgent.get_summary(True)
-        )
 
-        return prompt
-
-        #return prompt.format(adjective="funny")
-
 
-        #if input_variables is None:
-           # input_variables = ["input", "agent_scratchpad"]
-        #return PromptTemplate(template=template, input_variables=input_variables)
 
 
 
-        #ZeroShotAgent.create_prompt(
-        #    tools=tools,
-        #    prefix=template,
-        #    suffix="",
-        #    input_variables=["objective", "task", "context", "agent_scratchpad"],
-        #)
-
-         #template="""You are {name}, an instance of an autonomous AGI agent, running on {operating_system}. This is a recent summary of you: {agent_summary}. You have been given a single task: {task}, based on the overarching objective: {objective}. The tools I can use are: {tools}. Think smart.\n{agent_scratchpad}""", 
-         #   input_variables=["operating_system", "tools", "objective", "task", "agent_scratchpad"],
-         #   partial_variables={"agent_summary": generativeAgent.get_summary(), "agent_name": generativeAgent.name},
-         #   tools=tools,
-         # #  prefix="You are an AI who performs one task based on the following objective: {objective}. Take into account these previously completed tasks: {context}.",
-         #  # suffix="Question: {task}",
-
-
 
-        return prompt
-
-
+        #prompt = load_prompt("prompts/ryan.json")
+        #prompt.partial()
+        return load_prompt("prompts/ryan.json")
+
+    #.format(
+    #        task=objective,
+    #        objective=objective,
+    #        agent_name=generative_agent.name,
+    #        operating_system=operating_system,
+    #        tool_names=tool_names,
+    #        tools_summary=tools_summary,
+    #        agent_summary=generative_agent.get_summary(True)
+    #    )

Eve/helloworld.md

@@ -0,0 +1,25 @@
+total 36
+-rwxrwxrwx    1 root     root          1462 Apr 27 05:30 Dockerfile
+-rwxrwxrwx    1 root     root          8032 Apr 20 17:25 Eve.pyproj
+-rwxrwxrwx    1 root     root           227 Apr 15 16:07 Eve.pyproj.user
+drwxr-xr-x    1 root     root          4096 Apr 27 00:11 __pycache__
+drwxrwxrwx    1 root     root          4096 Apr 28 01:27 agents
+drwxrwxrwx    1 root     root          4096 Apr 28 01:27 chains
+drwxrwxrwx    1 root     root          4096 Apr 28 01:27 characters
+drwxrwxrwx    1 root     root          4096 Apr 28 01:27 data
+drwxrwxrwx    1 root     root          4096 Apr 26 06:40 env
+drwxrwxrwx    1 root     root          4096 Apr 28 01:26 experiments
+-rw-r--r--    1 root     root             0 Apr 28 20:04 helloworld.md
+-rwxrwxrwx    1 root     root          9830 Apr 28 20:01 main.py
+drwxrwxrwx    1 root     root          4096 Apr 28 01:26 old
+drwxrwxrwx    1 root     root          4096 Apr 28 19:08 output_parsers
+drwxrwxrwx    1 root     root          4096 Apr 28 01:58 prompts
+drwxrwxrwx    1 root     root          4096 Apr 28 01:26 protos
+-rwxrwxrwx    1 root     root           142 Apr 27 00:53 requirements-compiled.txt
+-rwxrwxrwx    1 root     root            36 Apr 27 12:54 requirements-extra.txt
+-rwxrwxrwx    1 root     root            99 Apr 26 23:51 requirements-prebuilt.txt
+drwxrwxrwx    1 root     root          4096 Apr 17 06:45 retrievers
+drwxrwxrwx    1 root     root          4096 Apr 28 01:26 toddleragi
+-rwxrwxrwx    1 root     root          4131 Apr 25 16:03 tools.py
+drwxrwxrwx    1 root     root          4096 Apr 28 01:26 vectorstores
+-rwxrwxrwx    1 root     root          1180 Apr 17 07:18 weaviate_schema.py