mirror of
https://github.com/hwchase17/langchain
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ed789be8f4
- chat models, messages - documents - agentaction/finish - baseretriever,document - stroutputparser - more messages - basemessage - format_document - baseoutputparser --------- Co-authored-by: Bagatur <baskaryan@gmail.com>
93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
import os
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from typing import List, Optional
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from langchain.chains.query_constructor.schema import AttributeInfo
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from langchain.retrievers import SelfQueryRetriever
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from langchain_community.embeddings import OpenAIEmbeddings
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from langchain_community.llms import BaseLLM
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from langchain_community.llms.openai import OpenAI
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from langchain_community.vectorstores.qdrant import Qdrant
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from langchain_core.documents import Document, StrOutputParser
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from langchain_core.embeddings import Embeddings
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from langchain_core.pydantic_v1 import BaseModel
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from langchain_core.runnables import RunnableParallel, RunnablePassthrough
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from qdrant_client import QdrantClient
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from self_query_qdrant import defaults, helper, prompts
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class Query(BaseModel):
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__root__: str
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def create_chain(
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llm: Optional[BaseLLM] = None,
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embeddings: Optional[Embeddings] = None,
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document_contents: str = defaults.DEFAULT_DOCUMENT_CONTENTS,
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metadata_field_info: List[AttributeInfo] = defaults.DEFAULT_METADATA_FIELD_INFO,
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collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
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):
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"""
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Create a chain that can be used to query a Qdrant vector store with a self-querying
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capability. By default, this chain will use the OpenAI LLM and OpenAIEmbeddings, and
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work with the default document contents and metadata field info. You can override
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these defaults by passing in your own values.
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:param llm: an LLM to use for generating text
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:param embeddings: an Embeddings to use for generating queries
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:param document_contents: a description of the document set
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:param metadata_field_info: list of metadata attributes
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:param collection_name: name of the Qdrant collection to use
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:return:
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"""
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llm = llm or OpenAI()
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embeddings = embeddings or OpenAIEmbeddings()
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# Set up a vector store to store your vectors and metadata
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client = QdrantClient(
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url=os.environ.get("QDRANT_URL", "http://localhost:6333"),
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api_key=os.environ.get("QDRANT_API_KEY"),
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)
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vectorstore = Qdrant(
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client=client,
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collection_name=collection_name,
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embeddings=embeddings,
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)
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# Set up a retriever to query your vector store with self-querying capabilities
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retriever = SelfQueryRetriever.from_llm(
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llm, vectorstore, document_contents, metadata_field_info, verbose=True
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)
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context = RunnableParallel(
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context=retriever | helper.combine_documents,
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query=RunnablePassthrough(),
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)
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pipeline = context | prompts.LLM_CONTEXT_PROMPT | llm | StrOutputParser()
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return pipeline.with_types(input_type=Query)
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def initialize(
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embeddings: Optional[Embeddings] = None,
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collection_name: str = defaults.DEFAULT_COLLECTION_NAME,
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documents: List[Document] = defaults.DEFAULT_DOCUMENTS,
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):
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"""
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Initialize a vector store with a set of documents. By default, the documents will be
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compatible with the default metadata field info. You can override these defaults by
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passing in your own values.
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:param embeddings: an Embeddings to use for generating queries
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:param collection_name: name of the Qdrant collection to use
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:param documents: a list of documents to initialize the vector store with
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:return:
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"""
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embeddings = embeddings or OpenAIEmbeddings()
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# Set up a vector store to store your vectors and metadata
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Qdrant.from_documents(
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documents, embedding=embeddings, collection_name=collection_name
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)
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# Create the default chain
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chain = create_chain()
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