episodic memory for chatbot

This commit is contained in:
Aarushi 2024-09-11 09:26:29 +01:00
parent 45c238747f
commit 4bae652a3c
6 changed files with 145 additions and 87 deletions

View File

@ -16,7 +16,7 @@ anthropic = "^0.25.1"
beautifulsoup4 = "^4.12.2"
boto3 = "^1.33.6"
charset-normalizer = "^3.1.0"
chromadb = "^0.4.10"
chromadb = "^0.5.5"
click = "*"
colorama = "^0.4.6"
demjson3 = "^3.0.0"

View File

@ -0,0 +1,102 @@
import uuid
from typing import Any, List, Optional
import chromadb
from chromadb.config import Settings
from pydantic import Field
from autogpt_server.data.block import Block, BlockCategory, BlockOutput, BlockSchema
from autogpt_server.data.model import BlockSecret, SchemaField, SecretField
from autogpt_server.blocks.llm import AIStructuredResponseGeneratorBlock, LlmModel
class EpisodicMemory:
def __init__(self):
self.client = chromadb.Client(Settings(is_persistent=True))
try:
self.collection = self.client.get_collection("episodic_memory")
except ValueError:
self.collection = self.client.create_collection("episodic_memory")
def add_memory(self, content: str, metadata: Optional[dict] = None) -> str:
memory_id = str(uuid.uuid4())
self.collection.add(
documents=[content],
metadatas=[metadata or {}],
ids=[memory_id]
)
return memory_id
def query_memory(self, query: str, n_results: int = 5) -> List[dict]:
results = self.collection.query(
query_texts=[query],
n_results=n_results
)
return [
{"id": id, "content": doc, "metadata": meta}
for id, doc, meta in zip(results['ids'][0], results['documents'][0], results['metadatas'][0])
]
class MemoryAugmentedAIBlock(Block):
class Input(BlockSchema):
prompt: str = SchemaField(description="The main prompt for the AI")
memory_query: str = SchemaField(description="Query to retrieve relevant memories")
model: LlmModel = LlmModel.GPT4_TURBO
api_key: BlockSecret = SecretField(value="")
sys_prompt: str = ""
expected_format: dict[str, str] = Field(default_factory=dict)
retry: int = 3
n_memories: int = SchemaField(default=3, description="Number of memories to retrieve")
class Output(BlockSchema):
response: dict[str, str] = SchemaField(description="The AI's response")
error: str = SchemaField(description="Error message if the operation failed")
def __init__(self):
super().__init__(
id="2c3c15d1-387a-4124-a087-39999f2f3b09",
description="This block augments AI responses with episodic memory.",
categories={BlockCategory.AI, BlockCategory.MEMORY},
input_schema=MemoryAugmentedAIBlock.Input,
output_schema=MemoryAugmentedAIBlock.Output
)
self.memory = EpisodicMemory()
self.ai_block = AIStructuredResponseGeneratorBlock()
def run(self, input_data: Input) -> BlockOutput:
try:
# Retrieve relevant memories
memories = self.memory.query_memory(input_data.memory_query, input_data.n_memories)
# Format memories for inclusion in the prompt
formatted_memories = "\n".join([f"Memory {i + 1}: {m['content']}" for i, m in enumerate(memories)])
# Augment the prompt with memories
augmented_prompt = f"""Relevant memories:
{formatted_memories}
Using the above memories if they are relevant, please respond to the following prompt:
{input_data.prompt}"""
# Prepare input for the AI block
ai_input = AIStructuredResponseGeneratorBlock.Input(
prompt=augmented_prompt,
expected_format=input_data.expected_format,
model=input_data.model,
api_key=input_data.api_key,
sys_prompt=input_data.sys_prompt,
retry=input_data.retry
)
# Run the AI block
for output_name, output_data in self.ai_block.run(ai_input):
if output_name == "response":
# Add the response to memory
self.memory.add_memory(str(output_data), {"prompt": input_data.prompt})
yield "response", output_data
elif output_name == "error":
yield "error", output_data
except Exception as e:
yield "error", str(e)

View File

@ -36,6 +36,7 @@ class BlockCategory(Enum):
INPUT = "Block that interacts with input of the graph."
OUTPUT = "Block that interacts with output of the graph."
LOGIC = "Programming logic to control the flow of your agent"
MEMORY = "Block to store information."
def dict(self) -> dict[str, str]:
return {"category": self.name, "description": self.value}

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@ -25,7 +25,7 @@ requests = "*"
sentry-sdk = "^1.40.4"
[package.extras]
benchmark = ["agbenchmark @ file:///home/reinier/code/agpt/AutoGPT/benchmark"]
benchmark = ["agbenchmark"]
[package.source]
type = "directory"
@ -345,7 +345,7 @@ anthropic = "^0.25.1"
beautifulsoup4 = "^4.12.2"
boto3 = "^1.33.6"
charset-normalizer = "^3.1.0"
chromadb = "^0.4.10"
chromadb = "^0.5.5"
click = "*"
colorama = "^0.4.6"
demjson3 = "^3.0.0"
@ -386,7 +386,7 @@ watchdog = "4.0.0"
webdriver-manager = "^4.0.1"
[package.extras]
benchmark = ["agbenchmark @ file:///home/reinier/code/agpt/AutoGPT/benchmark"]
benchmark = ["agbenchmark @ file:///Users/aarushi/autogpt/AutoGPT/benchmark"]
[package.source]
type = "directory"
@ -836,36 +836,40 @@ files = [
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name = "chroma-hnswlib"
version = "0.7.3"
version = "0.7.6"
description = "Chromas fork of hnswlib"
optional = false
python-versions = "*"
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[package.dependencies]
@ -873,25 +877,26 @@ numpy = "*"
[[package]]
name = "chromadb"
version = "0.4.24"
version = "0.5.5"
description = "Chroma."
optional = false
python-versions = ">=3.8"
files = [
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]
[package.dependencies]
bcrypt = ">=4.0.1"
build = ">=1.0.3"
chroma-hnswlib = "0.7.3"
chroma-hnswlib = "0.7.6"
fastapi = ">=0.95.2"
grpcio = ">=1.58.0"
httpx = ">=0.27.0"
importlib-resources = "*"
kubernetes = ">=28.1.0"
mmh3 = ">=4.0.1"
numpy = ">=1.22.5"
numpy = ">=1.22.5,<2.0.0"
onnxruntime = ">=1.14.1"
opentelemetry-api = ">=1.2.0"
opentelemetry-exporter-otlp-proto-grpc = ">=1.2.0"
@ -900,11 +905,9 @@ opentelemetry-sdk = ">=1.2.0"
orjson = ">=3.9.12"
overrides = ">=7.3.1"
posthog = ">=2.4.0"
pulsar-client = ">=3.1.0"
pydantic = ">=1.9"
pypika = ">=0.48.9"
PyYAML = ">=6.0.0"
requests = ">=2.28"
tenacity = ">=8.2.3"
tokenizers = ">=0.13.2"
tqdm = ">=4.65.0"
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@ -3914,53 +3915,6 @@ files = [
[package.extras]
test = ["enum34", "ipaddress", "mock", "pywin32", "wmi"]
[[package]]
name = "pulsar-client"
version = "3.5.0"
description = "Apache Pulsar Python client library"
optional = false
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[package.dependencies]
certifi = "*"
[package.extras]
all = ["apache-bookkeeper-client (>=4.16.1)", "fastavro (>=1.9.2)", "grpcio (>=1.60.0)", "prometheus-client", "protobuf (>=3.6.1,<=3.20.3)", "ratelimit"]
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[[package]]
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@ -6454,4 +6408,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
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lock-version = "2.0"
python-versions = "^3.10"
content-hash = "0ecd19c5cdf414368aa81b83ae76ba6db34b1bfc8f32a482d1222d6b839792da"
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View File

@ -48,6 +48,7 @@ uvicorn = { extras = ["standard"], version = "^0.30.1" }
websockets = "^12.0"
youtube-transcript-api = "^0.6.2"
chromadb = "^0.5.5"
[tool.poetry.group.dev.dependencies]
poethepoet = "^0.26.1"
httpx = "^0.27.0"