mirror of
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100 lines
3.4 KiB
Python
100 lines
3.4 KiB
Python
"""
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RAGNode Module
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"""
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from typing import List, Optional
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from .base_node import BaseNode
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from qdrant_client import QdrantClient
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from qdrant_client.models import PointStruct, VectorParams, Distance
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class RAGNode(BaseNode):
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"""
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A node responsible for compressing the input tokens and storing the document
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in a vector database for retrieval. Relevant chunks are stored in the state.
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It allows scraping of big documents without exceeding the token limit of the language model.
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Attributes:
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llm_model: An instance of a language model client, configured for generating answers.
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verbose (bool): A flag indicating whether to show print statements during execution.
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Args:
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input (str): Boolean expression defining the input keys needed from the state.
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output (List[str]): List of output keys to be updated in the state.
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node_config (dict): Additional configuration for the node.
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node_name (str): The unique identifier name for the node, defaulting to "Parse".
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"""
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def __init__(
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self,
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input: str,
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output: List[str],
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node_config: Optional[dict] = None,
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node_name: str = "RAG",
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):
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super().__init__(node_name, "node", input, output, 2, node_config)
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self.llm_model = node_config["llm_model"]
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self.embedder_model = node_config.get("embedder_model", None)
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self.verbose = (
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False if node_config is None else node_config.get("verbose", False)
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)
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def execute(self, state: dict) -> dict:
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self.logger.info(f"--- Executing {self.node_name} Node ---")
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if self.node_config.get("client_type") in ["memory", None]:
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client = QdrantClient(":memory:")
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elif self.node_config.get("client_type") == "local_db":
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client = QdrantClient(path="path/to/db")
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elif self.node_config.get("client_type") == "image":
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client = QdrantClient(url="http://localhost:6333")
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else:
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raise ValueError("client_type provided not correct")
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docs = [elem.get("summary") for elem in state.get("docs")]
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ids = [i for i in range(1, len(state.get("docs"))+1)]
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if state.get("embeddings"):
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import openai
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openai_client = openai.Client()
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files = state.get("documents")
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array_of_embeddings = []
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i=0
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for file in files:
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embeddings = openai_client.embeddings.create(input=file,
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model=state.get("embeddings").get("model"))
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i+=1
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points = PointStruct(
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id=i,
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vector=embeddings,
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payload={"text": file},
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)
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array_of_embeddings.append(points)
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collection_name = "collection"
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client.create_collection(
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collection_name,
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vectors_config=VectorParams(
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size=1536,
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distance=Distance.COSINE,
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),
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)
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client.upsert(collection_name, points)
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state["vectorial_db"] = client
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return state
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client.add(
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collection_name="vectorial_collection",
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documents=docs,
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ids=ids
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)
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state["vectorial_db"] = client
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return state
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