mirror of
https://github.com/hwchase17/langchain
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454 lines
17 KiB
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454 lines
17 KiB
Plaintext
# QA and Chat over Documents
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Chat and Question-Answering (QA) over `data` are popular LLM use-cases.
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`data` can include many things, including:
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* `Unstructured data` (e.g., PDFs)
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* `Structured data` (e.g., SQL)
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* `Code` (e.g., Python)
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LangChain supports Chat and QA on various `data` types:
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* See [here](https://python.langchain.com/docs/use_cases/code/) and [here](https://twitter.com/cristobal_dev/status/1675745314592915456?s=20) for `Code`
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* See [here](https://python.langchain.com/docs/use_cases/tabular) for `Structured data`
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Below we will review Chat and QA on `Unstructured data`.
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`Unstructured data` can be loaded from many sources.
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Use the [LangChain integration hub](https://integrations.langchain.com/) to browse the full set of loaders.
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Each loader returns data as a LangChain [`Document`](https://docs.langchain.com/docs/components/schema/document).
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`Documents` are turned into a Chat or QA app following the general steps below:
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* `Splitting`: [Text splitters](https://python.langchain.com/docs/modules/data_connection/document_transformers/) break `Documents` into splits of specified size
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* `Storage`: Storage (e.g., often a [vectorstore](https://python.langchain.com/docs/modules/data_connection/vectorstores/)) will house [and often embed](https://www.pinecone.io/learn/vector-embeddings/) the splits
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* `Retrieval`: The app retrieves splits from storage (e.g., often [with similar embeddings](https://www.pinecone.io/learn/k-nearest-neighbor/) to the input question)
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* `Output`: An [LLM](https://python.langchain.com/docs/modules/model_io/models/llms/) produces an answer using a prompt that includes the question and the retrieved splits
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## Quickstart
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The above pipeline can be wrapped with a `VectorstoreIndexCreator`.
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In particular:
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* Specify a `Document` loader
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* The `splitting`, `storage`, `retrieval`, and `output` generation stages are wrapped
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Let's load this [blog post](https://lilianweng.github.io/posts/2023-06-23-agent/) on agents as an example `Document`.
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We have a QA app in a few lines of code.
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Set enviorment varaibles and get packages:
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```python
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pip install openai
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pip install chromadb
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export OPENAI_API_KEY="..."
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```
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Run:
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```python
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from langchain.document_loaders import WebBaseLoader
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from langchain.indexes import VectorstoreIndexCreator
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# Document loader
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loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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# Index that wraps above steps
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index = VectorstoreIndexCreator().from_loaders([loader])
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# Question-answering
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question = "What is Task Decomposition?"
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index.query(question)
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```
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' Task decomposition is a technique used to break down complex tasks into smaller and simpler steps. It can be done using LLM with simple prompting, task-specific instructions, or human inputs. Tree of Thoughts (Yao et al. 2023) is an example of a task decomposition technique that explores multiple reasoning possibilities at each step and generates multiple thoughts per step, creating a tree structure.'
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Of course, some users do not want this level of abstraction.
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Below, we will discuss each stage in more detail.
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## 1. Loading, Splitting, Storage
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### 1.1 Getting started
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Specify a `Document` loader.
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```python
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# Document loader
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from langchain.document_loaders import WebBaseLoader
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loader = WebBaseLoader("https://lilianweng.github.io/posts/2023-06-23-agent/")
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data = loader.load()
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```
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Split the `Document` into chunks for embedding and vector storage.
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```python
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# Split
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from langchain.text_splitter import RecursiveCharacterTextSplitter
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text_splitter = RecursiveCharacterTextSplitter(chunk_size = 500, chunk_overlap = 0)
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all_splits = text_splitter.split_documents(data)
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```
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Embed and store the splits in a vector database ([Chroma](https://python.langchain.com/docs/modules/data_connection/vectorstores/integrations/chroma)).
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```python
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# Store
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from langchain.vectorstores import Chroma
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from langchain.embeddings import OpenAIEmbeddings
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vectorstore = Chroma.from_documents(documents=all_splits,embedding=OpenAIEmbeddings())
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```
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Here are the three pieces together:
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### 1.2 Going Deeper
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#### 1.2.1 Integrations
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`Document Loaders`
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* Browse the > 120 data loader integrations [here](https://integrations.langchain.com/).
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* See further documentation on loaders [here](https://python.langchain.com/docs/modules/data_connection/document_loaders/).
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`Document Transformers`
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* All can ingest loaded `Documents` and process them (e.g., split).
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* See further documentation on transformers [here](https://python.langchain.com/docs/modules/data_connection/document_transformers/).
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`Vectorstores`
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* Browse the > 35 vectorstores integrations [here](https://integrations.langchain.com/).
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* See further documentation on vectorstores [here](https://python.langchain.com/docs/modules/data_connection/vectorstores/).
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#### 1.2.2 Retaining metadata
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`Context-aware splitters` keep the location ("context") of each split in the origional `Document`:
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* [Markdown files](https://python.langchain.com/docs/use_cases/question_answering/document-context-aware-QA)
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* [Code (py or js)](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/source_code)
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* [Documents](https://python.langchain.com/docs/modules/data_connection/document_loaders/integrations/grobid)
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## 2. Retrieval
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### 2.1 Getting started
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Retrieve [relevant splits](https://www.pinecone.io/learn/what-is-similarity-search/) for any question using `similarity_search`.
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```python
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question = "What are the approaches to Task Decomposition?"
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docs = vectorstore.similarity_search(question)
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len(docs)
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```
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4
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### 2.2 Going Deeper
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#### 2.2.1 Retrieval
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Vectorstores are commonly used for retrieval.
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But, they are not the only option.
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For example, SVMs (see thread [here](https://twitter.com/karpathy/status/1647025230546886658?s=20)) can also be used.
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LangChain [has many retrievers](https://python.langchain.com/docs/modules/data_connection/retrievers/) including, but not limited to, vectorstores.
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All retrievers implement some common methods, such as `get_relevant_documents()`.
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```python
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from langchain.retrievers import SVMRetriever
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svm_retriever = SVMRetriever.from_documents(all_splits,OpenAIEmbeddings())
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docs_svm=svm_retriever.get_relevant_documents(question)
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len(docs)
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```
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4
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#### 2.2.2 Advanced retrieval
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Improve on `similarity_search`:
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* `MultiQueryRetriever` [generates variants of the input question](https://python.langchain.com/docs/modules/data_connection/retrievers/how_to/MultiQueryRetriever) to improve retrieval.
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* `Max marginal relevance` selects for [relevance and diversity](https://www.cs.cmu.edu/~jgc/publication/The_Use_MMR_Diversity_Based_LTMIR_1998.pdf) among the retrieved documents.
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* Documents can be filtered during retrieval using [`metadata` filters](https://python.langchain.com/docs/use_cases/question_answering/document-context-aware-QA).
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```python
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# MultiQueryRetriever
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import logging
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from langchain.chat_models import ChatOpenAI
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from langchain.retrievers.multi_query import MultiQueryRetriever
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logging.basicConfig()
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logging.getLogger('langchain.retrievers.multi_query').setLevel(logging.INFO)
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retriever_from_llm = MultiQueryRetriever.from_llm(retriever=vectorstore.as_retriever(),
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llm=ChatOpenAI(temperature=0))
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unique_docs = retriever_from_llm.get_relevant_documents(query=question)
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len(unique_docs)
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```
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INFO:langchain.retrievers.multi_query:Generated queries: ['1. How can Task Decomposition be approached?', '2. What are the different methods for Task Decomposition?', '3. What are the various approaches to decomposing tasks?']
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5
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## 3. QA
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### 3.1 Getting started
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Distill the retrieved documents into an answer using an LLM (e.g., `gpt-3.5-turbo`) with `RetrievalQA` chain.
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```python
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from langchain.chat_models import ChatOpenAI
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
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from langchain.chains import RetrievalQA
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qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever())
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qa_chain({"query": question})
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```
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{'query': 'What are the approaches to Task Decomposition?',
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'result': 'The approaches to task decomposition include:\n\n1. Simple prompting: This approach involves using simple prompts or questions to guide the agent in breaking down a task into smaller subgoals. For example, the agent can be prompted with "Steps for XYZ" and asked to list the subgoals for achieving XYZ.\n\n2. Task-specific instructions: In this approach, task-specific instructions are provided to the agent to guide the decomposition process. For example, if the task is to write a novel, the agent can be instructed to "Write a story outline" as a subgoal.\n\n3. Human inputs: This approach involves incorporating human inputs in the task decomposition process. Humans can provide guidance, feedback, and suggestions to help the agent break down complex tasks into manageable subgoals.\n\nThese approaches aim to enable efficient handling of complex tasks by breaking them down into smaller, more manageable parts.'}
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### 3.2 Going Deeper
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#### 3.2.1 Integrations
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`LLMs`
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* Browse the > 55 LLM integrations [here](https://integrations.langchain.com/).
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* See further documentation on LLMs [here](https://python.langchain.com/docs/modules/model_io/models/).
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#### 3.2.2 Running LLMs locally
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The popularity of [PrivateGPT](https://github.com/imartinez/privateGPT) and [GPT4All](https://github.com/nomic-ai/gpt4all) underscore the importance of running LLMs locally.
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LangChain has integrations with many open source LLMs that can be run locally.
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Using `GPT4All` is as simple as [downloading the binary]((https://python.langchain.com/docs/modules/model_io/models/llms/integrations/gpt4all)) and then:
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```python
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from langchain.llms import GPT4All
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from langchain.chains import RetrievalQA
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llm = GPT4All(model="/Users/rlm/Desktop/Code/gpt4all/models/nous-hermes-13b.ggmlv3.q4_0.bin",max_tokens=2048)
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qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever())
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```
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```python
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qa_chain({"query": question})
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```
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{'query': 'What are the approaches to Task Decomposition?',
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'result': ' There are three main approaches to task decomposition: (1) using language models like GPT-3 for simple prompting such as "Steps for XYZ.\\n1.", (2) using task-specific instructions, and (3) with human inputs.'}
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#### 3.2.2 Customizing the prompt
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The prompt in `RetrievalQA` chain can be easily customized.
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```python
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# Build prompt
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from langchain.prompts import PromptTemplate
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template = """Use the following pieces of context to answer the question at the end.
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If you don't know the answer, just say that you don't know, don't try to make up an answer.
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Use three sentences maximum and keep the answer as concise as possible.
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Always say "thanks for asking!" at the end of the answer.
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{context}
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Question: {question}
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Helpful Answer:"""
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QA_CHAIN_PROMPT = PromptTemplate(input_variables=["context", "question"],template=template,)
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# Run chain
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from langchain.chains import RetrievalQA
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llm = ChatOpenAI(model_name="gpt-3.5-turbo", temperature=0)
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qa_chain = RetrievalQA.from_chain_type(llm,
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retriever=vectorstore.as_retriever(),
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chain_type_kwargs={"prompt": QA_CHAIN_PROMPT})
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result = qa_chain({"query": question})
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result["result"]
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```
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'The approaches to Task Decomposition are (1) using simple prompting by LLM, (2) using task-specific instructions, and (3) with human inputs. Thanks for asking!'
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#### 3.2.3 Returning source documents
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The full set of retrieved documents used for answer distillation can be returned using `return_source_documents=True`.
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```python
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from langchain.chains import RetrievalQA
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qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever(),
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return_source_documents=True)
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result = qa_chain({"query": question})
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print(len(result['source_documents']))
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result['source_documents'][0]
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```
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4
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Document(page_content='Task decomposition can be done (1) by LLM with simple prompting like "Steps for XYZ.\\n1.", "What are the subgoals for achieving XYZ?", (2) by using task-specific instructions; e.g. "Write a story outline." for writing a novel, or (3) with human inputs.', metadata={'source': 'https://lilianweng.github.io/posts/2023-06-23-agent/', 'title': "LLM Powered Autonomous Agents | Lil'Log", 'description': 'Building agents with LLM (large language model) as its core controller is a cool concept. Several proof-of-concepts demos, such as AutoGPT, GPT-Engineer and BabyAGI, serve as inspiring examples. The potentiality of LLM extends beyond generating well-written copies, stories, essays and programs; it can be framed as a powerful general problem solver.\nAgent System Overview In a LLM-powered autonomous agent system, LLM functions as the agent’s brain, complemented by several key components:', 'language': 'en'})
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#### 3.2.4 Citations
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Answer citations can be returned using `RetrievalQAWithSourcesChain`.
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```python
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from langchain.chains import RetrievalQAWithSourcesChain
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qa_chain = RetrievalQAWithSourcesChain.from_chain_type(llm,retriever=vectorstore.as_retriever())
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result = qa_chain({"question": question})
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result
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```
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{'question': 'What are the approaches to Task Decomposition?',
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'answer': 'The approaches to Task Decomposition include (1) using LLM with simple prompting, (2) using task-specific instructions, and (3) incorporating human inputs.\n',
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'sources': 'https://lilianweng.github.io/posts/2023-06-23-agent/'}
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#### 3.2.5 Customizing retrieved docs in the LLM prompt
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Retrieved documents can be fed to an LLM for answer distillation in a few different ways.
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`stuff`, `refine`, `map-reduce`, and `map-rerank` chains for passing documents to an LLM prompt are well summarized [here](https://python.langchain.com/docs/modules/chains/document/).
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`stuff` is commonly used because it simply "stuffs" all retrieved documents into the prompt.
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The [load_qa_chain](https://python.langchain.com/docs/modules/chains/additional/question_answering.html) is an easy way to pass documents to an LLM using these various approaches (e.g., see `chain_type`).
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```python
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from langchain.chains.question_answering import load_qa_chain
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chain = load_qa_chain(llm, chain_type="stuff")
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chain({"input_documents": unique_docs, "question": question},return_only_outputs=True)
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```
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{'output_text': 'The approaches to task decomposition include (1) using simple prompting to break down tasks into subgoals, (2) providing task-specific instructions to guide the decomposition process, and (3) incorporating human inputs for task decomposition.'}
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We can also pass the `chain_type` to `RetrievalQA`.
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```python
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qa_chain = RetrievalQA.from_chain_type(llm,retriever=vectorstore.as_retriever(),
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chain_type="stuff")
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result = qa_chain({"query": question})
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```
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In summary, the user can choose the desired level of abstraction for QA:
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## 4. Chat
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### 4.1 Getting started
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To keep chat history, first specify a `Memory buffer` to track the conversation inputs / outputs.
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```python
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from langchain.memory import ConversationBufferMemory
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memory = ConversationBufferMemory(memory_key="chat_history", return_messages=True)
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```
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The `ConversationalRetrievalChain` uses chat in the `Memory buffer`.
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```python
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from langchain.chains import ConversationalRetrievalChain
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retriever=vectorstore.as_retriever()
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chat = ConversationalRetrievalChain.from_llm(llm,retriever=retriever,memory=memory)
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```
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```python
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result = chat({"question": "What are some of the main ideas in self-reflection?"})
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result['answer']
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```
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"Some of the main ideas in self-reflection include:\n1. Iterative improvement: Self-reflection allows autonomous agents to improve by refining past action decisions and correcting mistakes.\n2. Trial and error: Self-reflection is crucial in real-world tasks where trial and error are inevitable.\n3. Two-shot examples: Self-reflection is created by showing pairs of failed trajectories and ideal reflections for guiding future changes in the plan.\n4. Working memory: Reflections are added to the agent's working memory, up to three, to be used as context for querying.\n5. Performance evaluation: Self-reflection involves continuously reviewing and analyzing actions, self-criticizing behavior, and reflecting on past decisions and strategies to refine approaches.\n6. Efficiency: Self-reflection encourages being smart and efficient, aiming to complete tasks in the least number of steps."
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The `Memory buffer` has context to resolve `"it"` ("self-reflection") in the below question.
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```python
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result = chat({"question": "How does the Reflexion paper handle it?"})
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result['answer']
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```
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"The Reflexion paper handles self-reflection by showing two-shot examples to the Learning Language Model (LLM). Each example consists of a failed trajectory and an ideal reflection that guides future changes in the agent's plan. These reflections are then added to the agent's working memory, up to a maximum of three, to be used as context for querying the LLM. This allows the agent to iteratively improve its reasoning skills by refining past action decisions and correcting previous mistakes."
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### 4.2 Going deeper
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The [documentation](https://python.langchain.com/docs/modules/chains/popular/chat_vector_db) on `ConversationalRetrievalChain` offers a few extensions, such as streaming and source documents.
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