菜单
Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools.
默认使用会先检查来源的 Prompt；你也可以切换为直接命令，或下载本地副本。
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基于 SOC 职业分类
| name | document-chat-interface |
| description | Build chat interfaces for querying documents using natural language. Extract information from PDFs, GitHub repositories, emails, and other sources. Use when creating interactive document Q&A systems, knowledge base chatbots, email search interfaces, or document exploration tools. |
Build intelligent chat interfaces that allow users to query and interact with documents using natural language, transforming static documents into interactive knowledge sources.
A document chat interface combines three capabilities:
Document Source
↓
Document Processor
├→ Extract text
├→ Process content
└→ Generate embeddings
↓
Vector Database
↓
Chat Interface ← User Question
├→ Retrieve relevant content
├→ Maintain conversation history
└→ Generate response
See examples/document_processors.py for implementations:
See examples/text_processor.py for implementations:
See examples/conversation_manager.py for implementations:
def format_response_with_citations(response: str, sources: List[Dict]) -> str:
"""Add source citations to response"""
formatted = response + "\n\n**Sources:**\n"
for i, source in enumerate(sources, 1):
formatted += f"[{i}] Page {source['page']} of {source['source']}\n"
if 'excerpt' in source:
formatted += f" \"{source['excerpt'][:100]}...\"\n"
return formatted
def generate_follow_up_questions(context: str, response: str) -> List[str]:
"""Suggest follow-up questions to user"""
prompt = f"""
Based on this Q&A, generate 3 relevant follow-up questions:
Context: {context[:500]}
Response: {response[:500]}
"""
follow_ups = llm.generate(prompt)
return follow_ups
def handle_query_failure(question: str, error: Exception) -> str:
"""Handle when no relevant documents found"""
if isinstance(error, NoRelevantDocuments):
return (
"I couldn't find information about that in the documents. "
"Try asking about different topics like: "
+ ", ".join(get_main_topics())
)
elif isinstance(error, ContextTooLarge):
return (
"The answer requires too much context. "
"Can you be more specific about what you'd like to know?"
)
else:
return f"I encountered an issue: {str(error)[:100]}"
from langchain.document_loaders import PDFLoader
from langchain.text_splitter import CharacterTextSplitter
from langchain.embeddings import OpenAIEmbeddings
from langchain.vectorstores import Chroma
from langchain.chat_models import ChatOpenAI
from langchain.chains import ConversationalRetrievalChain
# Load document
loader = PDFLoader("document.pdf")
documents = loader.load()
# Split into chunks
splitter = CharacterTextSplitter(chunk_size=1000)
chunks = splitter.split_documents(documents)
# Create embeddings
embeddings = OpenAIEmbeddings()
vectorstore = Chroma.from_documents(chunks, embeddings)
# Create chat chain
llm = ChatOpenAI(model="gpt-4")
qa = ConversationalRetrievalChain.from_llm(
llm=llm,
retriever=vectorstore.as_retriever(),
return_source_documents=True
)
# Chat interface
chat_history = []
while True:
question = input("You: ")
result = qa({"question": question, "chat_history": chat_history})
print(f"Assistant: {result['answer']}")
chat_history.append((question, result['answer']))
from llama_index import GPTVectorStoreIndex, SimpleDirectoryReader, ChatMemoryBuffer
from llama_index.llms import ChatMessage, MessageRole
# Load documents
documents = SimpleDirectoryReader("./docs").load_data()
# Create index
index = GPTVectorStoreIndex.from_documents(documents)
# Create chat engine with memory
chat_engine = index.as_chat_engine(
memory=ChatMemoryBuffer.from_defaults(token_limit=3900),
llm="gpt-4"
)
# Chat loop
while True:
question = input("You: ")
response = chat_engine.chat(question)
print(f"Assistant: {response}")
from sentence_transformers import SentenceTransformer
import faiss
import numpy as np
# Load and embed documents
model = SentenceTransformer('all-MiniLM-L6-v2')
documents = load_documents("document.pdf")
embeddings = model.encode(documents)
# Create FAISS index
dimension = embeddings.shape[1]
index = faiss.IndexFlatL2(dimension)
index.add(np.array(embeddings).astype('float32'))
# Chat function
def chat(question):
# Embed question
q_embedding = model.encode(question)
# Retrieve documents
k = 5
distances, indices = index.search(
np.array([q_embedding]).astype('float32'), k
)
# Get relevant documents
context = " ".join([documents[i] for i in indices[0]])
# Generate response
response = llm.generate(
f"Context: {context}\nQuestion: {question}\nAnswer:"
)
return response
Solutions:
Solutions:
Solutions:
Solutions:
def compare_documents(question: str, documents: List[str]):
"""Analyze and compare across multiple documents"""
results = []
for doc in documents:
response = query_document(doc, question)
results.append({
"document": doc.name,
"answer": response
})
# Compare and synthesize
comparison = llm.generate(
f"Compare these answers: {results}"
)
return comparison
class DocumentExplorer:
def __init__(self, documents):
self.documents = documents
def browse_by_topic(self, topic):
"""Find documents by topic"""
pass
def get_related_documents(self, doc_id):
"""Find similar documents"""
pass
def get_key_terms(self, document):
"""Extract key terms and concepts"""
pass