Select Indexed for FAISS

[kwunhang]

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Example Code

if index:
    vector_store = FAISS.from_documents([doc], embeddings, distance_strategy="METRIC_INNER_PRODUCT", index = index)
else:
    vector_store = FAISS.from_documents([doc], embeddings, distance_strategy="METRIC_INNER_PRODUCT")

Description

I am using the FAISS of Langchain library. I found there are multiple indexes is support in faiss.
https://github.com/facebookresearch/faiss/wiki/Faiss-indexes
And I also found there is a "index" parameter in FAISS init function in langchain library. Now my code is using the function "FAISS.from_documents" and I want to self define the index for it. But I meet a error:
FAISS.init() got multiple values for argument 'index'
May I please ask is there any other way is suggested if I want to defined the index in such case?

System Info

FAISS.init() got multiple values for argument 'index'

[dosubot[bot]]

@kwunhang Hello there! I'm a bot designed to help you solve bugs, answer questions, and become a contributor. Feel free to ask me anything while waiting for a human maintainer.

To define a custom index for the FAISS vector store using the FAISS.from_documents function without encountering the error FAISS.__init__() got multiple values for argument 'index', you should use the FAISS.__from method directly. This method allows you to specify a custom index without causing the error.

Here is an example of how you can do this:

from langchain_community.embeddings.openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
import faiss

# Define your custom index
dimension = 128  # Example dimension, set this to the dimension of your embeddings
custom_index = faiss.IndexFlatL2(dimension)

# Create embeddings
embeddings = OpenAIEmbeddings()
texts = ["FAISS is an important library", "LangChain supports FAISS"]
embedded_texts = embeddings.embed_documents(texts)

# Use the __from method to create the FAISS vector store with the custom index
faiss_store = FAISS.__from(
    texts=texts,
    embeddings=embedded_texts,
    embedding=embeddings,
    index=custom_index  # Pass your custom index here
)

# Now you can use faiss_store as needed

By using the __from method directly, you can pass your custom index without encountering the error ^[1].

To continue talking to Dosu, mention @dosu.

[kwunhang]

@dosu I think it would not working. Since in the "__from" function the index is defined.
Here is the source code I found.
@classmethod def __from( cls, texts: Iterable[str], embeddings: List[List[float]], embedding: Embeddings, metadatas: Optional[Iterable[dict]] = None, ids: Optional[List[str]] = None, normalize_L2: bool = False, distance_strategy: DistanceStrategy = DistanceStrategy.EUCLIDEAN_DISTANCE, **kwargs: Any, ) -> FAISS: faiss = dependable_faiss_import() if distance_strategy == DistanceStrategy.MAX_INNER_PRODUCT: index = faiss.IndexFlatIP(len(embeddings[0])) else: # Default to L2, currently other metric types not initialized. index = faiss.IndexFlatL2(len(embeddings[0])) docstore = kwargs.pop("docstore", InMemoryDocstore()) index_to_docstore_id = kwargs.pop("index_to_docstore_id", {}) vecstore = cls( embedding, index, docstore, index_to_docstore_id, normalize_L2=normalize_L2, distance_strategy=distance_strategy, **kwargs, ) vecstore.__add(texts, embeddings, metadatas=metadatas, ids=ids) return vecstore
The index is defined in __from function, and I think passing the index into "__from" function would not work and would still face the same error.

[dosubot[bot]]

You're correct that the __from method defines the index internally, which would indeed cause the same error if you try to pass a custom index. Instead, you should directly use the FAISS class constructor to avoid this issue. Here’s how you can do it:

1. Define your custom FAISS index.
2. Create embeddings.
3. Initialize the FAISS vector store with your custom index.

Here’s an example:

from langchain_openai import OpenAIEmbeddings
from langchain_community.vectorstores import FAISS
from langchain_community.docstore.in_memory import InMemoryDocstore
import faiss

# Define your custom FAISS index
dimension = 128  # Example dimension, set this to the dimension of your embeddings
custom_index = faiss.IndexFlatL2(dimension)

# Create embeddings
embeddings = OpenAIEmbeddings()
texts = ["FAISS is an important library", "LangChain supports FAISS"]
embedded_texts = embeddings.embed_documents(texts)

# Create the FAISS vector store with the custom index
faiss_store = FAISS(
    embedding_function=embeddings,
    index=custom_index,
    docstore=InMemoryDocstore(),
    index_to_docstore_id={},
)

# Add the texts to the FAISS vector store
faiss_store.add_texts(texts)

This approach ensures that you can use your custom index without encountering the error FAISS.__init__() got multiple values for argument 'index' ^[1][2].

To continue talking to Dosu, mention @dosu.

[kwunhang]

@dosu
I found that some function would be fail if I self define index in using faiss under Langchain. I am now trying Milvus. What if I want to self define the index for Milvus database? Is there any restriction or problem?

[dosubot[bot]]

When self-defining an index for the Milvus database under LangChain, you should be aware of the following restrictions or problems:

1. Metric Type and Data Normalization:

   • If you are using L2 or IP metrics, it is highly suggested to normalize your data.

2. Index Parameters:

   • The default index parameters are set to HNSW/AUTOINDEX depending on the service. If you provide custom index parameters, ensure they are compatible with Milvus.

3. Search Parameters:

   • The search parameters default to the index's default. If you provide custom search parameters, ensure they match the index type.

4. Primary Key Field:

   • The primary key field must be compatible with Milvus. If auto_id is set to False, you need to provide text IDs that are strings less than 65535 bytes.

5. Field Names:

   • The primary key field, text field, and vector field must be named appropriately (pk, text, and vector respectively) for compatibility.

6. Index Creation:

   • If the default index parameters do not work, especially on Zilliz Cloud, the system will fall back to using AUTOINDEX.

7. Connection Arguments:

   • Ensure that the connection arguments are correctly set up. This includes specifying the host, port, user, password, and other necessary parameters for connecting to the Milvus instance.

8. Collection Properties:

   • If you set collection properties, they will override existing properties. Ensure that these properties are correctly configured to avoid conflicts.

9. Metadata Field:

   • If you specify a metadata field, the document's metadata will be stored as JSON. Ensure that the metadata schema is compatible with Milvus.

10. Timeouts and Batch Sizes:

    • Be mindful of the timeout settings and batch sizes when inserting data to avoid performance issues.

These points highlight the key restrictions and potential issues you may encounter when defining an index for Milvus in LangChain ^[1][2].

To continue talking to Dosu, mention @dosu.

[thiswillbeyourgithub]

If you are still interested in custom langchain faiss index, I made a custom backend enabling both transparent compression (when saving to file) as well as binary embeddings (smaller, faster, usually negligible loss) for my personal rag library called wdoc (for now it's in the dev branch, in wdoc/utils/customs/binary_faiss_vectorstore.py).