qdrant vector store

[Haashimi]

how i can query exists qdrant collection db

[dosubot[bot]]

Hey @Haashimi! 👋 I'm here to help you with any bugs, questions, or contributions while we wait for a human maintainer. I'm a bot designed to assist with all things code-related.

To query an existing Qdrant collection database in a Qdrant vector store, you can use the query method of the QdrantVectorStore class. Here is an example of how to perform a query:

from qdrant_client import QdrantClient
from llama_index.legacy.vector_stores.qdrant import QdrantVectorStore
from llama_index.legacy.vector_stores.types import VectorStoreQuery, VectorStoreQueryMode

# Initialize the Qdrant client
client = QdrantClient(url="your_qdrant_url", api_key="your_api_key")

# Initialize the QdrantVectorStore
qdrant_store = QdrantVectorStore(
    collection_name="your_collection_name",
    client=client
)

# Create a query
query = VectorStoreQuery(
    query_embedding=[0.1, 0.2, 0.3],  # Example embedding
    similarity_top_k=5,  # Number of top similar nodes to retrieve
    mode=VectorStoreQueryMode.DEFAULT  # Query mode
)

# Perform the query
result = qdrant_store.query(query)

# Process the result
print(result)

This code initializes the Qdrant client and the QdrantVectorStore, creates a query with the desired parameters, and performs the query to retrieve the top k most similar nodes. The query method supports different query modes, including hybrid and sparse searches, and allows for the use of custom filters ^[1][2].

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[dosubot[bot]]

To use the as_chat_engine() method, you can call it on an instance of the VectorStoreIndexWithCitationsChat class. This method allows you to create a chat engine with a specified chat mode and optional language model (LLM). By default, it uses the COHERE_CITATIONS_CONTEXT chat mode.

Here is an example of how to use the as_chat_engine() method:

from llama_index.packs.cohere_citation_chat.citations_context_chat_engine import VectorStoreIndexWithCitationsChat, ChatModeCitations

# Create an instance of VectorStoreIndexWithCitationsChat
vector_store_index = VectorStoreIndexWithCitationsChat()

# Use the as_chat_engine method to create a chat engine
chat_engine = vector_store_index.as_chat_engine(
    chat_mode=ChatModeCitations.COHERE_CITATIONS_CONTEXT
)

# Now you can use the chat_engine for your chat operations
response = chat_engine.chat("Tell me a joke.")
print(response)

In this example, the as_chat_engine method is called with the default COHERE_CITATIONS_CONTEXT chat mode. You can also specify a different chat mode and provide additional keyword arguments if needed.

Additionally, if you want to stream the response, you can use the stream_chat method:

streaming_response = chat_engine.stream_chat("Tell me a joke.")
for token in streaming_response.response_gen:
    print(token, end="")

This allows you to handle responses in a streaming fashion, which can be useful for real-time applications.

For more advanced configurations, you can directly build and configure a chat engine from an index in one line of code:

chat_engine = index.as_chat_engine(chat_mode="condense_question", verbose=True)

You can access different chat engines by specifying the chat_mode as a keyword argument. Here are some available chat modes:

• best
• condense_question
• context
• condense_plus_context
• simple
• react
• openai

For more granular control, you can use the low-level composition API to explicitly construct a ChatEngine object instead of calling index.as_chat_engine(...).

Using Vectara Chat

Vectara also supports a simple chat mode. In this mode, the chat history is maintained by Vectara, so you don't have to worry about it. To use it, simply call as_chat_engine.

(Chat mode always uses Vectara's summarization, so you don't have to explicitly specify summary_enabled=True like before)

ce = index.as_chat_engine()

questions = [
    "What are the risks of AI?",
    "What should we do to prevent bad actors from using AI?",
    "What are the benefits?",
]

for q in questions:
    print(f"Question: {q}\n")
    response = ce.chat(q).response
    print(f"Response: {response}\n")

Of course, streaming works as well with Chat:

ce = index.as_chat_engine(streaming=True)

response = ce.stream_chat("Will artificial intelligence rule the government?")
for chunk in response.chat_stream:
    print(chunk.delta or "", end="", flush=True)

Make sure you have the necessary dependencies and environment configurations in place to use the as_chat_engine() method effectively ^[1][2][3][4].

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[Haashimi]

this is my code is it correct or not i want collection exists not create again
if not self.collection_exists('kitchen_db'):
reader = SimpleDirectoryReader(
input_files=[r"E:\voice_assistant_llm\rag\merchent_data.txt"]
)
documents = reader.load_data()
vector_store = QdrantVectorStore(client=self._client, collection_name="kitchen_db")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
self._index = VectorStoreIndex.from_documents(
documents, service_context=self._service_context, storage_context=storage_context
)
print("Knowledgebase created successfully!")
else:
vector_store = QdrantVectorStore(client=self._client, collection_name="kitchen_db")
storage_context = StorageContext.from_defaults(vector_store=vector_store)
self._index = VectorStoreIndex(service_context=self._service_context, storage_context=storage_context)
print("Knowledgebase loaded from existing collection!")

[dosubot[bot]]

Your code for checking if a Qdrant collection exists and not creating it again looks mostly correct. However, you should ensure that the collection_exists method is properly implemented and used. Here is a refined version of your code:

if not self.collection_exists('kitchen_db'):
    reader = SimpleDirectoryReader(
        input_files=[r"E:\voice_assistant_llm\rag\merchent_data.txt"]
    )
    documents = reader.load_data()
    vector_store = QdrantVectorStore(client=self._client, collection_name="kitchen_db")
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    self._index = VectorStoreIndex.from_documents(
        documents, service_context=self._service_context, storage_context=storage_context
    )
    print("Knowledgebase created successfully!")
else:
    vector_store = QdrantVectorStore(client=self._client, collection_name="kitchen_db")
    storage_context = StorageContext.from_defaults(vector_store=vector_store)
    self._index = VectorStoreIndex(service_context=self._service_context, storage_context=storage_context)
    print("Knowledgebase loaded from existing collection!")

Ensure that the collection_exists method is implemented correctly. Here is an example implementation:

def collection_exists(self, collection_name: str) -> bool:
    """Check if a collection exists."""
    from grpc import RpcError
    from qdrant_client.http.exceptions import UnexpectedResponse

    try:
        self._client.get_collection(collection_name)
    except (RpcError, UnexpectedResponse, ValueError):
        return False
    return True

This method checks if the collection exists by attempting to retrieve it and catching any exceptions that indicate it does not exist ^[1][2].

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[Haashimi]

then how i can chat that using chat engine

[dosubot[bot]]

To chat using the chat engine with the existing Qdrant collection, follow these steps:

1. Load the existing Qdrant collection:

   import qdrant_client
   from llama_index import VectorStoreIndex
   from llama_index.embeddings.openai import OpenAIEmbedding
   from llama_index.vector_stores.qdrant import QdrantVectorStore
   
   client = qdrant_client.QdrantClient("http://localhost:6333")
   aclient = qdrant_client.AsyncQdrantClient("http://localhost:6333")
   
   vector_store = QdrantVectorStore(
       collection_name="kitchen_db",
       client=client,
       aclient=aclient,
       fastembed_sparse_model="Qdrant/bm42-all-minilm-l6-v2-attentions",
   )
   
   loaded_index = VectorStoreIndex.from_vector_store(
       vector_store,
       embed_model=OpenAIEmbedding(
           model="text-embedding-3-small", api_key="sk-proj-..."
       ),
   )

2. Initialize the chat engine:

   from llama_index.llms.openai import OpenAI
   
   chat_engine = loaded_index.as_chat_engine(
       chat_mode="condense_plus_context",
       llm=OpenAI(model="gpt-4o", api_key="sk-proj-..."),
   )

3. Chat using the chat engine:

   response = chat_engine.chat("What training hardware was used for Llama2?")
   print(str(response))
   
   response = chat_engine.chat("What is the main idea of Llama2?")
   print(str(response))
   
   response = chat_engine.chat("What was Llama2 evaluated and compared against?")
   print(str(response))

Additionally, if you want to stream responses, you can use the following pattern:

```python
streaming_response = chat_engine.stream_chat("Tell me a joke.")
for token in streaming_response.response_gen:
    print(token, end="")
```

This code will allow you to chat using the chat engine with the existing Qdrant collection and also provides an option to stream responses ^[1][2].

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