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import os
from typing import Dict, List, Any
import uuid
from langchain.embeddings import OpenAIEmbeddings
from chromadb import Client as ChromaClient
from flows.base_flows import AtomicFlow
class ChromaDBFlow(AtomicFlow):
def __init__(self, **kwargs):
super().__init__(**kwargs)
self.client = ChromaClient()
self.collection = self.client.get_or_create_collection(name=self.flow_config["name"])
def get_input_keys(self) -> List[str]:
return self.flow_config["input_keys"]
def get_output_keys(self) -> List[str]:
return self.flow_config["output_keys"]
def run(self, input_data: Dict[str, Any]) -> Dict[str, Any]:
api_information = self._get_from_state("api_information")
if api_information.backend_used == "openai":
embeddings = OpenAIEmbeddings(openai_api_key=api_information.api_key)
else:
# ToDo: Add support for Azure
embeddings = OpenAIEmbeddings(openai_api_key=os.getenv("OPENAI_API_KEY"))
response = {}
operation = input_data["operation"]
if operation not in ["write", "read"]:
raise ValueError(f"Operation '{operation}' not supported")
content = input_data["content"]
if operation == "read":
if not isinstance(content, str):
raise ValueError(f"content(query) must be a string during read, got {type(content)}: {content}")
if content == "":
response["retrieved"] = [[""]]
return response
query = content
query_result = self.collection.query(
query_embeddings=embeddings.embed_query(query),
n_results=self.flow_config["n_results"]
)
response["retrieved"] = [doc for doc in query_result["documents"]]
elif operation == "write":
if content != "":
if not isinstance(content, list):
content = [content]
documents = content
self.collection.add(
ids=[str(uuid.uuid4()) for _ in range(len(documents))],
embeddings=embeddings.embed_documents(documents),
documents=documents
)
response["retrieved"] = ""
return response
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