Search error: Expecting value: line 1 column 1 (char 0)

[cmilanes93]

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Example Code

import os
from azure.core.credentials import AzureKeyCredential
from azure.search.documents.indexes import SearchIndexClient
from azure.search.documents.indexes.models import (
    SearchIndex,
    SimpleField,
    SearchableField,
    SearchField,
    SearchFieldDataType,
    VectorSearch,
    VectorSearchProfile,
    HnswAlgorithmConfiguration,
)
from azure.search.documents import SearchClient
from langchain_community.vectorstores.azuresearch import AzureSearch
from langchain_openai import AzureOpenAIEmbeddings

# Configuración de Azure OpenAI
azure_endpoint = "#####"
azure_openai_api_key = "#####"
azure_openai_api_version = "2024-08-01-preview"
azure_deployment = "####"

# Configuración de Azure AI Search
vector_store_address = "######"
vector_store_password = "######"
index_name = "langchain-vector-demo"

# Configurar el entorno
os.environ["AZURE_OPENAI_API_KEY"] = azure_openai_api_key
os.environ["AZURE_OPENAI_ENDPOINT"] = azure_endpoint
os.environ["AZURE_OPENAI_API_VERSION"] = azure_openai_api_version

# Inicializar embeddings de Azure OpenAI
embeddings = AzureOpenAIEmbeddings(
    azure_deployment=azure_deployment,
    openai_api_version=azure_openai_api_version,
    azure_endpoint=azure_endpoint,
    api_key=azure_openai_api_key,
)

# Crear el índice en Azure AI Search
def create_index():
    credential = AzureKeyCredential(vector_store_password)
    index_client = SearchIndexClient(endpoint=vector_store_address, credential=credential)

    fields = [
        SimpleField(
            name="id",
            type=SearchFieldDataType.String,
            key=True,
            filterable=True,
        ),
        SearchableField(
            name="content",  # LangChain espera este nombre
            type=SearchFieldDataType.String,
            searchable=True,
        ),
        SearchField(
            name="content_vector",
            type=SearchFieldDataType.Collection(SearchFieldDataType.Single),
            searchable=True,
            vector_search_dimensions=1536,
            vector_search_profile_name="myHnswProfile",
        ),
        SearchableField(
            name="metadata",
            type=SearchFieldDataType.String,
            searchable=True,
        ),
    ]

    vector_search = VectorSearch(
        profiles=[
            VectorSearchProfile(
                name="myHnswProfile",
                algorithm_configuration_name="hnsw-config",
            )
        ],
        algorithms=[HnswAlgorithmConfiguration(name="hnsw-config")],
    )

    new_index = SearchIndex(
        name=index_name,
        fields=fields,
        vector_search=vector_search,
    )

    # Crear o actualizar el índice
    index_client.create_or_update_index(new_index)
    print(f"Índice '{index_name}' creado o actualizado exitosamente.")

create_index()

# Subir documentos al índice
def upload_documents():
    search_client = SearchClient(
        endpoint=vector_store_address,
        index_name=index_name,
        credential=AzureKeyCredential(vector_store_password),
    )

    documents = [
        {
            "id": "1",
            "content": "Axel acompaña a su tío el científico a un viaje al centro de la tierra",
            "metadata": "Libro de aventuras",
            "content_vector": embeddings.embed_query(
                "Axel acompaña a su tío el científico a un viaje al centro de la tierra"
            ),
        }
    ]

    try:
        result = search_client.upload_documents(documents=documents)
        print("Documentos subidos exitosamente:", result)
    except Exception as e:
        print("Error al subir documentos:", e)

upload_documents()

# Crear instancia de vector store
vector_store = AzureSearch(
    azure_search_endpoint=vector_store_address,
    azure_search_key=vector_store_password,
    index_name=index_name,
    embedding_function=embeddings.embed_query,
)

# Realizar búsqueda vectorial
def search_with_top_k(query, k):
    try:
        print(f"Realizando búsqueda vectorial con Top K = {k}...")
        results = vector_store.similarity_search(query=query, k=k)
        if not results:
            print("No se encontraron resultados relevantes.")
        else:
            for i, result in enumerate(results, start=1):
                print(f"Resultado {i}:")
                print(f"  ID: {result.metadata.get('id', 'Sin ID')}")
                print(f"  Texto: {result.page_content}")
                print(f"  Metadata: {result.metadata}")
                print()
        return results
    except Exception as e:
        print("Error en la búsqueda:", e)

# Probar búsqueda
query_text = "viaje al centro de la tierra"
top_k = 1
search_results = search_with_top_k(query=query_text, k=top_k)

Description

Im trying to do this:
Embbed a document
Upload it to AI Search
And then make a retrieval using Top K
But im getting this error:
Search error: Expecting value: line 1 column 1 (char 0)

System Info

python 3.11.9

[cmilanes93]

It seems that "content" is empty but i dont know why