Retrievalqawithsourceschain (2024)

1. RetrievalQAWithSourcesChain - LangChain

langchain.chains.qa_with_sources.retrieval .RetrievalQAWithSourcesChain¶ ... Question-answering with sources over an index. Create a new model by parsing and ...
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[docs]class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain): """Question-answering with sources over an index.""" retriever: BaseRetriever = Field ...
[docs]class RetrievalQAWithSourcesChain(BaseQAWithSourcesChain): """Question-answering with sources over an index.""" retriever: BaseRetriever = Field(exclude=True) """Index to connect to.""" reduce_k_below_max_tokens: bool = False """Reduce the number of results to return from store based on tokens limit""" max_tokens_limit: int = 3375 """Restrict the docs to return from store based on tokens, enforced only for StuffDocumentChain and if reduce_k_below_max_tokens is to true""" def _reduce_tokens_below_limit(self, docs: List[Document]) -> List[Document]: num_docs = len(docs) if self.reduce_k_below_max_tokens and isinstance( self.combine_documents_chain, StuffDocumentsChain ): tokens = [ self.combine_documents_chain.llm_chain._get_num_tokens(doc.page_content) for doc in docs ] token_count = sum(tokens[:num_docs]) while token_count > self.max_tokens_limit: num_docs -= 1 token_count -= tokens[num_docs] return docs[:num_docs] def _get_docs( self, inputs: Dict[str, Any], *, run_manager: CallbackManagerForChainRun ) -> List[Document]: question = inputs[self.question_key] docs = self.retriever.invoke( question, config={"callbacks": run_manager.get_child()} ) return self._reduce_tokens_below_limit(docs) async def _aget_docs(...

26 okt 2023 · RetrievalQAWithSourcesChain retrieves documents and provides citations. from langchain.chains import RetrievalQAWithSourcesChain user_input ...
Learn how to create a chatbot to streamline your research process

19 mei 2023 · I am trying to develop an interactive chatbot based on a knowledge base. What I have done for now is that i constructed a Faiss vector based ...
I am trying to develop an interactive chatbot based on a knowledge base. What I have done for now is that i constructed a Faiss vector based data from the text files I scraped on a website. Next, using langchain ChatOpenAI and RetrievalQAWithSourcesChain, i have built a simple chatbot with memory using langchain prompt tools (SystemMessagePromptTemplate, HumanMessagePromptTemplate and ChatPromptTemplate). def process_query(query, messages, vector_store, llm): messages.append(HumanMessagePro...

9 feb 2024 · How to use the vectorstore with langchain create_retrieval_chain or RetrievalQAWithSourcesChain · Search, No Filter · vector-database · koushik ...
How to use the vectorstore as a retriever to the langchain retrieval chains. It seems to give me a error with ValueError: The argument order for query() has changed; please use keyword arguments instead of positional arguments. Example: index.query(vector=[0.1, 0.2, 0.3], top_k=10, namespace='my_namespace') The same thing also persists with similarity_search. Even after giving the keyword arguments, the same error shows up.

3 okt 2023 · Hello, i have a problem, after a few messages with my chat i have an errot: error_code=context_length_exceeded error_message=“This model's ...
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Mariana Raschilla
Hello, i have a problem, after a few messages with my chat i have an errot: error_code=context_length_exceeded error_message=“This model’s maximum context length is 8192 tokens. However, your messages resulted in 9066 tokens. Please reduce the length of the messages.” error_param=messages error_type=invalid_request_error message=‘OpenAI API error received’ stream_error=False my main Chain looks like this: chain = RetrievalQAWithSourcesChain.from_chain_type( llm=llm, chain_ty...

13 mrt 2024 · RetrievalQAWithSourcesChain import os os.environ["HUGGINGFACEHUB_API_TOKEN ... Step 4: Creating RAG Object. chain = RetrievalQAWithSourcesChain.
RAG is additional context or knowledge nugget for the LLM. Suppose you want to ask LLM what is the current hot-new in the last month, then…

21 jul 2023 · The combination of LangChain's RetrievalQAWithSourcesChain and GPT-3 is excellent for enhancing the transparency of Question Answering. As ...
Guide to developing an informative QA bot with displayed sources used

RetrievalQAWithSourcesChain; ConversationalRetrievalChain. We begin by initializing a Vertex AI LLM and a LangChain 'retriever' to fetch documents from our ...
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25 apr 2024 · initialize a RetrievalQAWithSourcesChain, which is inherited from the BaseCombineDocumentsChain instance; embedding model => 3.9; Neo4jVector ...
A guide to building LLM applications with the Neo4j GenAI Stack on LangChain, from initializing the database to building RAG strategies.

content_pasteCopied! import os from dotenv import load_dotenv from langchain.chains.qa_with_sources.retrieval import RetrievalQAWithSourcesChain from ...

4 apr 2024 · RetrievalQAWithSourcesChain — automates loading context and retrieving additional data from an external knowledge base. Now, let's address each ...
Learn how to improve user engagement by building a context-aware chatbot powered by ChatGPT and LangChain in our expert guide.