1. RetrievalQAWithSourcesChain - LangChain
langchain.chains.qa_with_sources.retrieval .RetrievalQAWithSourcesChain¶ ... Question-answering with sources over an index. Create a new model by parsing and ...
Bases: BaseQAWithSourcesChain
2. Source code for langchain.chains.qa_with_sources ...
[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(...
3. RetrievalQAWithSourcesChain Hallucination - Prompting
May 19, 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...
4. How to use the vectorstore with langchain create_retrieval_chain or ...
Feb 9, 2024 · 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 ...
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.
5. Context length error with RetrievalQAWithSourcesChain - API
Oct 3, 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 ...
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...
6. Building a Question Answering Chatbot over Documents with ...
The RetrievalQAWithSourcesChain not only retrieves relevant documents but also tracks their sources. ... chain = RetrievalQAWithSourcesChain.from_chain_type(llm= ...
Introduction
7. Very insightful post. I'm currently using langchain and the ... - Ahmed Besbes
Jun 1, 2023 · Very insightful post. I'm currently using langchain and the RetrievalQAWithSourcesChain class specifically to build an agent that crawls ...
I'm currently using langchain and the RetrievalQAWithSourcesChain class specifically to build an agent that crawls Reddit, loads posts into…
8. Build a Transparent QA Bot with LangChain and GPT-3
Jul 21, 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
9. Creating a web research chatbot using LangChain and OpenAI
Oct 26, 2023 · RetrievalQAWithSourcesChain retrieves documents and provides citations. ... user_input = “How do LLM Powered Autonomous Agents work?” qa_chain = ...
Learn how to create a chatbot to streamline your research process
10. Questions about the new streaming feature - 🗣️ LLMs and AI - Streamlit
Jul 5, 2023 · I could get the new streaming feature to work together with a LangChain RetrievalQAWithSourcesChain chain. To achieve this, I used the new ...
I could get the new streaming feature to work together with a LangChain RetrievalQAWithSourcesChain chain. To achieve this, I used the new StreamlitCallbackHandler (read here: Streamlit | 🦜️🔗 Langchain) which is apparently only working correctly for agents. LLM llm = OpenAI(client=OpenAI, streaming=True, callbacks=[StreamlitCallbackHandler(message_placeholder)]) chain chain = RetrievalQAWithSourcesChain.from_chain_type(llm=llm, chain_type='stuff', retriever=docsearch.as_retriever()) get ans...
11. RetrievalQAWithSourcesChain - LangChain中文网
RetrievalQAWithSourcesChain · from langchain. · with open("../.. · = Chroma. · Running Chroma using direct local API. · langchain.chains import ...
LangChain中文站，助力大语言模型LLM应用开发、chatGPT应用开发。
12. StErMi on X: "Ok, if someone with some advanced knowledge of ...
Jun 11, 2023 · JS is wiling to babysit me to port "RetrievalQAWithSourcesChain" python to JS, it would be outstanding. In general, I would like to ...
Something went wrong, but don’t fret — let’s give it another shot.
13. LangChain - Pinecone Docs
... RetrievalQAWithSourcesChain qa_with_sources = RetrievalQAWithSourcesChain.from_chain_type( llm=llm, chain_type="stuff", retriever=vectorstore.as_retriever ...
Using LangChain and Pineone to add knowledge to LLMs
14. Building LLM Application for Document Question Answering ...
Aug 20, 2023 · chain = RetrievalQAWithSourcesChain.from_chain_type( ChatOpenAI(temperature=0, openai_api_key=os.environ["OPENAI_API_KEY"]), chain_type ...
What is Chainlit ?
15. LangChain: Question Answering Agent over Docs | by Marcello Politi
Jun 4, 2023 · ... answer questions chain = RetrievalQAWithSourcesChain .from_chain_type( llm = OpenAI(temperature=0), chain_type="stuff", retriever=retriever ...
Learn about embeddings and agents to build a QA application
16. Using Neo4j and Langchain for Knowledge Graph Creation
Mar 29, 2024 · chain = RetrievalQAWithSourcesChain.from_chain_type( OpenAI(temperature=0), chain_type="stuff", retriever=retriever ) # Ask a question
The Power and Potential of Knowledge Graphs in AI and Data Management
17. Question Condensing Networks for Answer Selection in Community ...
Answer selection is an important subtask of community question answering (CQA). In a real-world CQA forum, a question is often represented as two parts: a ...
Implemented in one code library.
