“LangChain Essentials: From Theory to Deployment in AI Projects”#

2.1 Prompts#

Prompts are the core instructions sent to the LLM. A robust prompt leads to higher-quality output. LangChain offers:

• Prompt Templates: Support parameterized prompts using placeholders.
• Prompt Engineering: Fine-tune your instructions to guide the LLM.

Example usage (in pseudo-code, though a version in Python is similar):

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from langchain import PromptTemplate
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template_text = "Translate the following English text to French:\n\n{english_text}"
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prompt = PromptTemplate(input_variables=["english_text"], template=template_text)
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output_prompt = prompt.format(english_text="Hello, how are you?")
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print(output_prompt)

This yields a formatted string that could be sent to an LLM:

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Translate the following English text to French:
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Hello, how are you?

2.2 Models#

LangChain integrates seamlessly with multiple LLM providers. You can use:

• OpenAI: GPT-3.5, GPT-4, and beyond.
• Anthropic: Models like Claude andClaude Instant.
• Google: PaLM (available through Vertex AI).
• Hugging Face Inference Endpoints: Community and specialized models.

Code sample for using an OpenAI model:

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from langchain.llms import OpenAI
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llm = OpenAI(model_name="text-davinci-003", temperature=0.7)
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result = llm("Write a short poem about the sea.")
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print(result)

2.3 Chains#

In LangChain, a “Chain” is a curated sequence of steps. For instance, a Q&A chain might involve:

1. Generating a question from the user’s prompt.
2. Retrieving relevant data from a knowledge base.
3. Providing a final answer.

With LangChain, you can piece these together in a structured way. A simple chain might look like:

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from langchain.chains import LLMChain
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from langchain import PromptTemplate
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prompt_text = "Question: {question}\nAnswer:"
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prompt = PromptTemplate(template=prompt_text, input_variables=["question"])
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llm_chain = LLMChain(prompt=prompt, llm=llm)
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user_question = "What is the capital of France?"
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chain_output = llm_chain.run(question=user_question)
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print(chain_output)

2.4 Memory#

Memory modules store conversation history or specialized knowledge. Popular memory modules include:

1. Buffer Memory: Stores entire conversation transcripts, useful for chatbots.
2. Summarized Memory: Maintains compressed conversation context as it grows.
3. Knowledge Graph Memory: Extracts entities and relations into a graph.

Example of adding basic buffer memory:

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from langchain.memory import ConversationBufferMemory
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from langchain.chains import ConversationChain
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from langchain.llms import OpenAI
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memory = ConversationBufferMemory()
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conversation_chain = ConversationChain(llm=OpenAI(), memory=memory)
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conversation_chain.predict(input="Hello, who are you?")
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conversation_chain.predict(input="What did I just say?")
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conversation_chain.predict(input="How does a memory buffer work in LangChain?")

The memory keeps track of everything said so far, enabling context continuity.

2.5 Agents & Tools#

Agents allow LLMs to perform more complex, multi-step reasoning. They can decide which “Tool” to use based on the conversation.

Tools are external utilities, such as:

• Web Search or Database Query
• Math Calculation
• Custom APIs for domain-specific tasks

Here’s a conceptual snippet for an agent that uses a search tool:

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from langchain.agents import Tool, AgentExecutor
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from langchain.agents.agent import LLMAgent
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from langchain.llms import OpenAI
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def search_web(query):
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    # Implement a search call
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    return "Search results for: " + query
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search_tool = Tool(
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    name="search_web",
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    func=search_web,
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    description="Search the web for relevant data"
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)
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agent = LLMAgent(tools=[search_tool], llm=OpenAI())
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executor = AgentExecutor(agent=agent, tools=[search_tool])
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response = executor.run("Find the latest news about artificial intelligence.")
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print(response)

With Agents and Tools, LangChain goes beyond static prompts, enabling dynamic and context-driven interactions.

3.1 Installation#

LangChain can be installed directly from PyPI:

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pip install langchain

You also need to install a specific LLM provider’s library if required, for example:

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pip install openai

Additionally, if you are using advanced functionalities like retrieval or knowledge embedding, you might need:

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pip install faiss-cpu

3.2 Minimal “Hello World” Example#

Below is a minimal code snippet to test your installation:

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from langchain.llms import OpenAI
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llm = OpenAI(temperature=0.9)
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response = llm("Hello, LangChain!")
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print(response)

• OpenAI API Key: Make sure your OPENAI_API_KEY environment variable is set or you have specified it in your code.
• Response Quality: By setting temperature=0.9, we encourage more creative answers.

4.1 Prompt Templates and Basic Chains#

Let’s start by creating a Q&A chain.

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from langchain.llms import OpenAI
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from langchain.chains import LLMChain
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from langchain import PromptTemplate
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# Create a prompt template
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qa_template = PromptTemplate(
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    input_variables=["question"],
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    template="You are a helpful assistant. Answer the following question:\n\n{question}\n\nAnswer:"
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)
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# Initialize an LLM
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llm = OpenAI(model_name="text-davinci-003")
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# Build the chain
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qa_chain = LLMChain(llm=llm, prompt=qa_template)
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# Run the chain
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result = qa_chain.run({"question": "What is the capital of Italy?"})
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print("Answer:", result)

4.2 Adding Memory for Context Persistence#

If we want a multi-turn Q&A system that remembers previous questions and answers, we add memory:

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from langchain.chains import ConversationChain
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from langchain.memory import ConversationBufferMemory
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memory = ConversationBufferMemory()
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conversation_chain = ConversationChain(llm=OpenAI(), memory=memory)
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conversation_chain.predict(input="Hello, I'll be asking you some questions about geography.")
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conversation_chain.predict(input="What's the highest mountain in the world?")
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conversation_chain.predict(input="Just to confirm, which mountain did I ask about?")

The chain has knowledge of previous interactions, allowing it to reference them.

4.3 Introducing Tools for Enhanced Interactivity#

A Q&A chain might be even more powerful if it could search external data. We can integrate a search tool:

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from langchain.agents import load_tools, initialize_agent, AgentType
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from langchain.llms import OpenAI
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import os
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os.environ["SERPAPI_API_KEY"] = "YOUR_SERP_API_KEY"
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llm = OpenAI(temperature=0)
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tools = load_tools(["serpapi"])  # Web search tool
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agent_chain = initialize_agent(tools, llm, agent=AgentType.ZERO_SHOT_REACT_DESCRIPTION, verbose=True)
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result = agent_chain.run("What is the current population of Japan?")
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print("Search-based answer:", result)

Here the agent uses SerpAPI to query information from the web, enhancing the accuracy of Q&A results.

5.1 Retrieval-Augmented Generation (RAG)#

RAG is a method where the LLM looks up relevant context from a knowledge source (like a vector database) before generating an answer. This drastically improves factual accuracy and reduces hallucinations.

The RAG process typically involves:

1. Indexing: Embedding documents into a vector store (e.g., FAISS, Pinecone, Chroma).
2. Retrieval: Finding the most relevant chunks of text for a query.
3. Answer Generation: Using these chunks as context in the LLM prompt.

Conceptual code snippet:

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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.chains import RetrievalQA
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from langchain.llms import OpenAI
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# Prepare embeddings and vector store
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embeddings = OpenAIEmbeddings()
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docs = ["Document text 1", "Document text 2", "Document text 3"]
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db = FAISS.from_texts(docs, embeddings)
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# Initialize retrieval
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retriever = db.as_retriever(search_kwargs={"k": 2})
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# Build a RetrievalQA chain
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qa_chain = RetrievalQA.from_chain_type(
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    llm=OpenAI(),
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    chain_type="stuff",  # 'map_reduce' or 'refine' are also popular
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    retriever=retriever
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)
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answer = qa_chain.run("Question about Doc 1 or 2...")
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print("RAG Answer:", answer)

5.2 Customizing Prompt Engineering#

LangChain’s template system supports advanced Jinja2 constructions, conditional logic, or multi-part prompts. For instance:

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template_str = """
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{% if tone == 'formal' %}
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You are a highly professional assistant.
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{% else %}
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You are a casual friend.
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{% endif %}
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Answer the query below:
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{{ query }}
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"""
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prompt = PromptTemplate.from_template(template_str)
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formatted_prompt = prompt.format(tone="formal", query="How do I tie a tie?")

This approach allows you to dynamically change prompts based on application context.

5.3 Advanced Memory Modules#

Memory can do more than just store entire transcripts. For very long-running conversations or knowledge bases, advanced memory strategies come into play:

• ConversationBufferWindowMemory: Only keep the last “N” turns.
• EntityMemory: Track and store specific data about entities mentioned.
• VectorStoreMemory: Store conversation data in a vector database for retrieval.

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from langchain.memory import ConversationBufferWindowMemory
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from langchain.chains import ConversationChain
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window_memory = ConversationBufferWindowMemory(k=3)  # Only remember last 3 exchanges
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conversation_chain = ConversationChain(llm=OpenAI(), memory=window_memory, verbose=True)
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conversation_chain.predict(input="We will talk about many topics. Keep track carefully.")
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# ...

5.4 Integrating Vector Stores#

For more sophisticated use cases, you can combine vector storage with your chain. This is essential when dealing with large document sets:

1. Split large texts into chunks.
2. Embed each chunk.
3. Insert embeddings into your vector database.
4. Use MIPS (Maximum Inner Product Search) or approximate nearest neighbor queries to find relevant contexts.

LangChain has built-in connectors to vector DBs like FAISS, Pinecone, Weaviate, Milvus, and more. Once you set up a vector store, you can pass a retriever to your chain to provide relevant context at query time.

6.1 Performance Optimization#

• Prompt Caching: Stores prompt-response pairs to reduce repeated calls.
• Batching: Send multiple queries at once if the API supports it.
• Autoscaling: Use container orchestrators or serverless setups for elasticity.
• Model Selection: Choose “faster” or “cheaper” LLMs for less critical tasks.

6.2 Logging and Monitoring#

Observability in production systems is key. You can log:

• Prompt & Response content (redact sensitive info).
• Latency for each exchange.
• Errors and Retry attempts from your chain.

Integration with tools like Prometheus, Datadog, or New Relic can give real-time insights into usage patterns and anomaly detection.

6.3 Security and Access Control#

• API Key Management: Store keys in secure vaults like AWS Secrets Manager or HashiCorp Vault.
• Rate Limiting: Protect your system from overuse.
• Data Encryption: Especially for any PII (Personally Identifiable Information).

When your chain deals with user data (like conversation transcripts or private documents), ensure compliance with relevant data regulations (e.g., GDPR, HIPAA).

6.4 Serverless vs. Containerized Deployments#

Both approaches can be valid:

• Serverless (AWS Lambda, GCP Cloud Functions):
  • Quick to scale, cost-effective, stateless.
  • Great for short-running tasks or event-driven flows.
• Containerized (Docker, Kubernetes):
  • More control, easier local dev environment replication.
  • Better for longer-running or specialized tasks.

Choose the approach that best meets your performance, cost, and operational overhead requirements.

7.1 Text Summarization Pipeline#

1. Goal: Summarize large texts into concise highlights.
2. Setup:
   • Split documents into smaller chunks for better LLM handling.
   • Use a summarization chain or a map-reduce chain.
3. Implementation:

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from langchain.text_splitter import RecursiveCharacterTextSplitter
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from langchain.chains.summarize import load_summarize_chain
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from langchain.llms import OpenAI
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large_text = """(Your large text content here)"""
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text_splitter = RecursiveCharacterTextSplitter(chunk_size=1000, chunk_overlap=100)
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docs = text_splitter.create_documents([large_text])
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summarize_chain = load_summarize_chain(OpenAI(), chain_type="map_reduce")
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summary = summarize_chain.run(docs)
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print("Summary:", summary)

4. Output: A short summary capturing the key points from the extensive text.

7.2 Document Retrieval and Chatbot#

1. Goal: Build a chatbot capable of referencing long documents.
2. Setup:
   • Embed each document chunk in a vector database.
   • Use a conversational chain with RAG.
3. Implementation:

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from langchain.chains import ConversationalRetrievalChain
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from langchain.embeddings.openai import OpenAIEmbeddings
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from langchain.vectorstores import FAISS
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from langchain.llms import OpenAI
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from langchain.memory import ConversationBufferMemory
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# Suppose docs_to_load is a list of text documents
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embeddings = OpenAIEmbeddings()
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db = FAISS.from_texts(docs_to_load, embeddings)
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retriever = db.as_retriever()
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conversation_memory = ConversationBufferMemory()
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chat_chain = ConversationalRetrievalChain.from_llm(
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    llm=OpenAI(),
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    retriever=retriever,
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    memory=conversation_memory
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)
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user_query = "What does the second document say about climate change?"
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response = chat_chain.run(user_query)
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print("Chatbot Response:", response)